Immune receptor barcode error correction

Abstract

The invention relates to immune receptor barcode error correction. Disclosed herein are methods and systems for determining the presence of a target. In some embodiments, the method includes: folding an estimated sequence of the targets; folding a molecular marker sequence associated with the estimated sequence of the target; and estimating the appearance of the target, where after folding the appearance of the estimated sequence of the target and folding the appearance of a noise molecular marker sequence, the estimated appearance of the target correlates with the appearance of a molecular marker sequence associated with the estimated sequence of the target in the sequencing data.

Description

[0001] This application is a divisional application of an application filed on September 24, 2018, with application number 201880068906.3 and invention name “Immune Receptor Barcode Error Correction”. CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to U.S. Provisional Application No. 62 / 562,978, filed on September 25, 2017. The contents of the related application are expressly incorporated herein by reference in their entirety. Background of the Invention Technical Field

[0004] The present disclosure relates generally to the field of molecular barcoding, and more particularly to the use of molecular tags to correct substitution errors and non-substitution errors. Background Art

[0006] Methods and techniques such as barcoding (including random barcoding) are useful for cell analysis, particularly for deciphering gene expression profiles to determine the state of cells using, for example, reverse transcription, polymerase chain reaction (PCR) amplification, and next-generation sequencing (NGS). However, these methods and techniques can introduce errors, such as substitution errors (e.g., substitution errors involving one or more base substitutions) and non-substitution errors (e.g., primer crossing errors and PCR mosaic errors), which, if not corrected, can lead to overestimation of molecular counts. Therefore, methods and techniques that can correct for various errors are needed to obtain accurate molecular counts. Summary of the Invention

[0007] Disclosed herein include methods for determining the presence of a target. In some embodiments, the method comprises: (a) barcoding (e.g., randomly barcoding) a plurality of targets using a plurality of barcodes (e.g., random barcodes) to create a plurality of barcoded targets (e.g., randomly barcoded targets), wherein each of the plurality of barcodes comprises a cellular marker and a molecular marker, wherein the molecular markers of at least two of the plurality of barcodes comprise different molecular marker sequences, and wherein at least two of the plurality of barcodes comprise cellular markers having the same cellular marker sequence; (b) obtaining sequencing data for the barcoded targets; and (c) for at least one of the plurality of targets: (i) identifying a putative sequence of the target in the sequencing data; (ii) counting occurrences of the molecular marker sequence associated with the putative sequence of the target in the sequencing data identified in (i); (iii) identifying clusters of putative sequences of the target; (iv) collapsing the obtained sequencing data using the clusters of putative sequences of the target identified in (iii); (v) Identifying clusters of the molecular marker sequences associated with putative sequences of the target; (vi) collapsing the sequencing data using the clusters of the molecular marker sequences identified in (v); (vii) identifying clusters of combined sequences, wherein each combined sequence comprises a sequence from the sequence of the target and an associated molecular marker sequence from the molecular marker sequence; (viii) collapsing the sequencing data using the clusters of combined sequences identified in (vii); (ix) identifying one or more putative sequences of the target corresponding to one or more chimeric sequences of the target, wherein the occurrence of the one or more putative sequences of the target corresponding to the one or more chimeric sequences of the target is less than the occurrence of the remaining one or more putative sequences of the target that do not correspond to the one or more chimeric sequences of the target; (x) removing from the sequencing data the one or more putative sequences of the target corresponding to the one or more chimeric sequences of the target identified in (ix); and (xi) estimating the occurrence of the target, wherein the sequencing data is collapsed in (iv), (vi), and (viii) and in (x). The estimated occurrence of the target is related to the number of molecular marker sequences counted in (ii) after removing the one or more putative sequences of the target corresponding to the one or more chimeric sequences of the target.

[0008] In some embodiments, the multiple targets include targets across the entire transcriptome of the cell. The multiple targets may include genes. The genes may include variable sequences encoding immune receptors, such as variable (V) regions, diversity (D) regions, joining (J) regions, or any combination thereof. The genes may encode T cell receptors. The deduced sequences of the targets may differ from each other by at least one nucleotide.

[0009] In some embodiments, identifying clusters of putative sequences for the target comprises identifying clusters of putative sequences for the target using directed contiguity. The putative sequences of the target within a cluster may be within a first predetermined directed contiguity threshold of each other. The first directed contiguity threshold may be a Hamming distance, wherein the Hamming distance is one. The putative sequences of the target within the cluster may include one or more parent sequences and one or more subsequences of the one or more parent sequences, and wherein the occurrence of the parent sequences is greater than or equal to a first predetermined directed contiguity occurrence threshold. The first predetermined directed contiguity occurrence threshold may be twice the occurrence of the subsequence that is less than one.

[0010] In some embodiments, collapsing the sequencing data obtained in (b) using the cluster of putative sequences of the target identified in (iii) comprises attributing an occurrence of a subsequence in the one or more subsequences to a parent sequence of the subsequence.

[0011] In some embodiments, identifying a cluster of molecular marker sequences associated with the putative sequence of the target includes identifying a cluster of molecular marker sequences associated with the putative sequence of the target using directional adjacency. The molecular marker sequences of the target within the cluster may be within a second predetermined directional adjacency threshold of each other. The second directional adjacency threshold may be a Hamming distance, which is one. The putative molecular marker sequences of the target within the cluster may include one or more parent molecular marker sequences and one or more child molecular marker sequences of the one or more parent molecular marker sequences, and wherein the occurrence of the parent molecular marker sequences is greater than or equal to a second predetermined directional adjacency occurrence threshold. The second predetermined directional adjacency occurrence threshold may be twice the occurrence of the child molecular marker sequence, which is less than one.

[0012] In some embodiments, collapsing the sequencing data using the cluster of molecular marker sequences associated with the sequence of the target identified in (v) comprises attributing the occurrence of a sub-molecular marker sequence in the one or more sub-molecular marker sequences to a parent molecular marker of the sub-molecular marker sequence.

[0013] In some embodiments, identifying the cluster of combinatorial sequences comprises identifying the cluster of combinatorial sequences using directional adjacency. The combinatorial sequences within a cluster may be within a third predetermined directional adjacency threshold of each other. The third directional adjacency threshold may be a Hamming distance, wherein the Hamming distance is one. The combinatorial sequences within the cluster may include one or more parent combinatorial sequences and one or more subcombination sequences of the one or more parent combinatorial sequences, and wherein the occurrence of the parent combinatorial sequences is greater than or equal to a third predetermined directional adjacency occurrence threshold. The third predetermined directional adjacency occurrence threshold may be twice the occurrence of the subcombination sequence that is less than one.

[0014] In some embodiments, collapsing the sequencing data using the clusters of combined sequences identified in (vii) comprises attributing the occurrence of a subcombined sequence in the one or more subcombined sequences to a parental combined sequence of the subcombined sequence.

[0015] In some embodiments, identifying one or more putative sequences of the target corresponding to one or more chimeric sequences of the target: identifying a putative sequence of the target associated with a molecular marker sequence in the plurality of molecular sequences; identifying a putative sequence in the putative sequence of the target associated with the molecular marker sequence, wherein the occurrence of the molecular marker sequence is less than a chimeric occurrence threshold corresponding to a chimeric sequence in the one or more chimeric sequences of the target. The value of the chimeric occurrence threshold can be an occurrence of the putative sequence in the putative sequence of the target associated with the molecular marker sequence, wherein the occurrence is greater than the occurrence of any other sequence in the putative sequence of the target.

[0016] In some embodiments, the method further comprises: adjusting the sequencing data after folding the sequencing data in (iv), (vi), and (viii) and removing the one or more putative sequences of the target corresponding to the one or more chimeric sequences of the target in (x). Adjusting the sequencing data after folding the sequencing data in (iv), (vi), and (viii) and removing the one or more putative sequences of the target corresponding to the one or more chimeric sequences of the target in (x) may comprise: thresholding the molecular marker sequences associated with the putative sequences of the target after folding the sequencing data in (iv), (vi), and (viii) and removing the one or more putative sequences of the target corresponding to the one or more chimeric sequences of the target in (x) to determine signal molecular marker sequences and noise molecular marker sequences associated with the sequences of the target counted in the sequencing data in (b). Thresholding the molecular marker sequences associated with the putative sequences of the target may comprise performing a statistical analysis on the molecular marker sequences of the target. Performing the statistical analysis may include fitting the molecular signature sequences associated with the putative sequences of the target and their occurrences to two negative binomial distributions; determining the occurrence n of a signal molecular signature sequence using the two negative binomial distributions; and removing the noise molecular signature sequences from the sequencing data obtained in (b) after folding the sequencing data in (iv), (vi), and (viii) and removing the one or more putative sequences of the target corresponding to the one or more chimeric sequences of the target in (x), wherein the noise molecular signature sequences include molecular signature sequences whose occurrences are less than the occurrences of the nth most abundant molecular signature, and wherein the signal molecular signature sequences include molecular signature sequences whose occurrences are greater than or equal to the occurrences of the nth most abundant molecular signature. The two negative binomial distributions may include a first negative binomial distribution corresponding to the signal molecular signature sequence and a second negative binomial distribution corresponding to the noise molecular signature sequence.

[0017] Disclosed herein are methods for determining the occurrence of a target. In some embodiments, the method comprises: (a) receiving sequencing data for a plurality of targets, wherein the sequencing data comprises putative sequences of targets from the plurality of targets and occurrences of molecular marker sequences associated with the sequences of the targets in the sequencing data; (b) folding the putative sequences of the targets; (c) folding the molecular marker sequences associated with the putative sequences of the targets; and (d) estimating the occurrence of the target, wherein after folding the occurrences of the putative sequences of the targets in (b) and folding the occurrences of noise molecular marker sequences determined in (c), the estimated occurrence of the target is correlated with the occurrences of the molecular marker sequences associated with the putative sequences of the targets in the sequencing data.

[0018] In some embodiments, the method comprises: identifying a sequence of the target in the sequencing data; and counting occurrences of molecular marker sequences associated with the sequence of the target in the sequencing data.

[0019] In some embodiments, the method includes collapsing clusters of combinatorial sequences, wherein each combinatorial sequence includes a sequence from the target sequence and an associated molecular marker sequence from the molecular marker sequence, wherein, after collapsing the occurrences of the combinatorial sequences, the estimated occurrences of the target are correlated with the occurrences of the molecular marker sequence associated with the sequence of the target in the sequencing data. Collapsing the clusters of combinatorial sequences may include collapsing the clusters of combinatorial sequences using directed contiguity. Collapsing the clusters of combinatorial sequences using directed contiguity may include identifying the clusters of combinatorial sequences using directed contiguity; and collapsing the sequencing data using the identified clusters of combinatorial sequences. Collapsing the putative sequences of the target includes collapsing the putative sequences of the target using directed contiguity. Collapsing the putative sequences of the target using directed contiguity may include identifying the clusters of putative sequences of the target using directed contiguity; and collapsing the sequencing data using the identified clusters of putative sequences of the target. In some embodiments, folding the molecular marker sequences associated with the putative sequence of the target comprises folding the molecular marker sequences associated with the putative sequence of the target using directed contiguity. Folding the molecular marker sequences associated with the putative sequence of the target using directed contiguity can comprise identifying clusters of the molecular marker sequences associated with the putative sequence of the target using directed contiguity; and folding the sequencing data using the identified clusters of the molecular marker sequences associated with the putative sequence of the target.

[0020] In some embodiments, the method comprises: identifying one or more putative sequences of the target corresponding to one or more chimeric sequences of the target, wherein the occurrence of the one or more putative sequences of the target corresponding to the one or more chimeric sequences of the target is less than the occurrence of the remaining one or more putative sequences of the target that do not correspond to the one or more chimeric sequences of the target; and removing the one or more putative sequences of the target corresponding to the one or more chimeric sequences of the target from the sequencing data. Identifying the one or more putative sequences of the target corresponding to the one or more chimeric sequences of the target can comprise: identifying a putative sequence of the target associated with a molecular marker sequence in the plurality of molecular sequences; identifying a putative sequence in the putative sequence of the target associated with the one molecular marker sequence, wherein the occurrence of the one molecular marker sequence is less than a chimeric occurrence threshold corresponding to the chimeric sequence in the one or more chimeric sequences of the target. The value of the chimeric occurrence threshold can be the occurrence of the putative sequence in the putative sequence of the target associated with a molecular marker sequence, wherein the occurrence is greater than the occurrence of any other sequence in the putative sequence of the target.

[0021] In some embodiments, the method includes determining a sequencing state of the target in the sequencing data; and determining the presence of a noise molecular marker sequence associated with an estimated sequence of the target in the sequencing data, wherein the estimated presence of the target is correlated with the presence of the molecular marker sequence associated with the estimated sequence of the target in the sequencing data, and the sequencing data is adjusted for the presence of the noise molecular marker sequence. The sequencing state of the target in the sequencing data can be saturated sequencing, under-sequencing, or over-sequencing.

[0022] In some embodiments, the insufficient sequencing state can be determined by a target having a depth less than a predetermined insufficient sequencing threshold, and wherein the depth of the target includes an average, minimum, or maximum depth of the molecular marker sequences associated with the putative sequence of the target in the sequencing data. The insufficient sequencing threshold can be about four. The insufficient sequencing threshold can be independent of the number of molecular marker sequences. If the sequencing state of the target in the sequencing data is the insufficient sequencing state, the number of noise molecular marker sequences determined can be zero.

[0023] In some embodiments, the saturated sequencing state is determined by the number of the molecular marker sequences associated with the putative sequence of the target being greater than a saturation threshold. If the molecular marker sequences in the molecular marker sequences associated with the putative sequence of the target have sequences selected from approximately 6561 molecular marker sequences, the saturation threshold may be approximately 6557. If the molecular marker sequences in the molecular marker sequences associated with the putative sequence of the target have sequences selected from approximately 65536 molecular marker sequences, the predetermined saturation threshold may be approximately 65532. If the sequencing state of the target in the sequencing data is the saturated sequencing state, the number of noise molecular marker sequences determined may be zero.

[0024] In some embodiments, the oversequencing state is determined by targets having a depth greater than a predetermined oversequencing threshold, wherein the depth of the target comprises an average, minimum, or maximum depth of the molecular marker sequences associated with the putative sequence of the target in the sequencing data. If the molecular marker sequences in the molecular marker sequences associated with the putative sequence of the target have sequences selected from approximately 6561 molecular marker sequences, then the oversequencing threshold can be approximately 250. In some embodiments, the method can include: if the sequencing state of the target in the sequencing data is a saturated sequencing state or an oversequencing state: subsampling the number of molecular marker sequences associated with the sequence of the target in the sequencing data to approximately the predetermined oversequencing threshold.

[0025] In some embodiments, determining the occurrence of a noise molecular marker sequence associated with the putative sequence of the target in the sequencing data comprises: if a negative binomial distribution fitting condition is satisfied, fitting a signal negative binomial distribution to the occurrence of the molecular marker sequence associated with the sequence of the target in the sequencing data (serving as a signal molecular marker sequence); fitting a noise negative binomial distribution to the occurrence of the molecular marker sequence associated with the sequence of the target in the sequencing data (serving as a noise molecular marker sequence); and determining the occurrence of the noise molecular marker sequence using the signal negative binomial distribution and the noise negative binomial distribution. In some embodiments, the negative binomial distribution fitting condition may include: the sequencing state of the target in the sequencing data is not the undersequenced state or the oversequenced state. Using the signal negative binomial distribution and the noise negative binomial distribution to determine the number of noise molecular marker sequences may include: for each of the molecular marker sequences associated with the putative sequence of the target in the sequencing data: determining the signal probability of the molecular marker sequence in the signal negative binomial distribution; determining the noise probability of the molecular marker sequence in the noise negative binomial distribution; and if the signal probability is less than the noise probability, determining the molecular marker sequence as a noise molecular marker.

[0026] In some embodiments, determining the occurrence of a noise molecular marker sequence associated with the sequence of the target in the sequencing data includes: if the sequencing state of the target in the sequencing data is not the under-sequencing state or the over-sequencing state and the occurrence of the molecular marker sequence associated with the sequence of the target in the sequencing data is less than a pseudo-point threshold, then, before determining the occurrence of the noise molecular marker sequence associated with the sequence of the target in the sequencing data, adding a pseudo-point to the occurrence of the molecular marker sequence associated with the sequence of the target in the sequencing data. The pseudo-point threshold can be ten. Determining the occurrence of a noise molecular marker sequence associated with the sequence of the target in the sequencing data can include: if the sequencing state of the target in the sequencing data is not the under-sequencing state or the over-sequencing state and the occurrence of the molecular marker sequence associated with the sequence of the target in the sequencing data is not less than a pseudo-point threshold, then, when determining the occurrence of the noise molecular marker sequence associated with the sequence of the target in the sequencing data, removing non-unique molecular marker sequences.

[0027] In some embodiments, receiving sequencing data for the plurality of targets comprises: barcoding (e.g., randomly barcoding) the plurality of targets using a plurality of barcodes (e.g., random barcodes) to create a plurality of barcoded targets (e.g., randomly barcoded targets), wherein each of the plurality of barcodes comprises a cellular marker and a molecular marker, wherein the molecular markers of at least two of the plurality of barcodes comprise different molecular marker sequences, and wherein at least two of the plurality of barcodes comprise cellular markers having the same cellular marker sequence; and obtaining sequencing data for the barcoded targets. Barcoding (e.g., randomly barcoding) the plurality of targets in the plurality of cells using the plurality of barcodes to create a plurality of barcoded targets for cells in the plurality of cells may include barcoding (e.g., randomly barcoding) the plurality of targets using a plurality of barcodes of a particle to create a plurality of barcoded targets, wherein the particle includes a subset of the plurality of barcodes, wherein each of the subset of barcodes includes the same cellular marker sequence and has at least 100 different molecular marker sequences.

[0028] In some embodiments, the particles are beads. The beads can be selected from the group consisting of streptavidin beads, agarose beads, magnetic beads, conjugated beads, protein A conjugated beads, protein G conjugated beads, protein A / G conjugated beads, protein L conjugated beads, oligo(dT) conjugated beads, silica beads, silica-like beads, anti-biotin microbeads, anti-fluorescent dye microbeads, and any combination thereof. The particles can comprise a material selected from the group consisting of polydimethylsiloxane (PDMS), polystyrene, glass, polypropylene, agarose, gelatin, hydrogel, paramagnetic material, ceramic, plastic, glass, methylstyrene, acrylic polymer, titanium, latex, sepharose, cellulose, nylon, silicone, and any combination thereof. The barcode (e.g., random barcode) of the particle comprises a molecular marker having at least 1,000, 10,000, or any combination thereof, of different molecular marker sequences.

[0029] In some embodiments, the molecular marker of the barcode (e.g., a random barcode) comprises a random sequence. The particle may comprise at least 10,000 barcodes. Barcoding (e.g., randomly barcoding) the plurality of targets using the plurality of barcodes (e.g., random barcodes) to create a plurality of barcoded targets (e.g., randomly barcoded targets) may comprise: (i) contacting copies of the targets with target binding regions of the barcodes; and (ii) reverse transcribing the plurality of targets using the plurality of barcodes to create a plurality of reverse transcribed targets. In some embodiments, the method comprises: prior to obtaining sequencing data for the plurality of barcoded targets, amplifying the barcoded targets to generate a plurality of barcoded targets (e.g., amplified randomly barcoded targets). Amplifying the barcoded targets to generate a plurality of randomly barcoded targets may comprise: amplifying the barcoded targets by polymerase chain reaction (PCR).

[0030] In some embodiments, a computer system for determining the presence of a target is disclosed. The computer system may include: a hardware processor; and non-transitory memory having instructions stored thereon, the instructions causing the processor to perform any of the methods described above when executed by the hardware processor. A computer-readable medium is disclosed. In some embodiments, the computer-readable medium includes executable code for performing any of the methods described above. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 Non-limiting exemplary random barcodes are shown.

[0032] Figure 2 A non-limiting exemplary workflow for random barcoding and digital counting is shown.

[0033] Figure 3 is a schematic diagram illustrating a non-limiting exemplary process for generating an indexed library of randomly barcoded targets from a plurality of targets.

[0034] Figure 4 is a schematic diagram showing non-limiting exemplary distributions of molecular labeling errors, sample labeling errors, and true molecular labeling signals.

[0035] Figure 5 is a flow chart illustrating a non-limiting exemplary embodiment of using molecular markers to correct PCR and sequencing errors based on directed contiguity.

[0036] Figure 6 is a flow chart illustrating a non-limiting exemplary embodiment of correcting PCR and sequencing errors based on recursive substitution error correction and distribution-based error correction.

[0037] Figure 7 is a schematic diagram illustrating a non-limiting exemplary embodiment of immune receptor barcode correction based on recursive substitution error correction.

[0038] Figure 8 is a flow chart illustrating a non-limiting exemplary embodiment of correcting errors in nucleotide sequences and molecular markers using recursive substitution error correction, and correcting errors in sequencing data due to PCR chimeras.

[0039] Figure 9 Schematic diagram of one possible source of immune receptor chimeras.

[0040] Figure 10 Non-limiting exemplary apparatus suitable for use in the methods of the present disclosure are shown.

[0041] Figure 11 A non-limiting exemplary architecture of a computer system that can be used in conjunction with embodiments of the present disclosure is presented.

[0042] Figure 12 A non-limiting exemplary architecture illustrating a network having multiple computer systems suitable for use in the methods of the present disclosure is presented.

[0043] Figure 13 A non-limiting exemplary architecture of a multi-processor computer system using a shared virtual address storage space according to the methods of the present disclosure is presented.

[0044] Figure 14 is an exemplary diagram of theoretical calculations for unique molecular signatures used as input molecules for augmentation.

[0045] Figure 15 is an exemplary graph showing molecular marker coverage of each molecular marker for a microplate of a highly expressed gene - ATCB, in which different distributions are observed between false molecular markers and true molecular markers.

[0046] Figure 16 Figure 2 is an exemplary graph showing the molecular marker coverage fit of two negative binomial distributions for each molecular marker on a microplate of the highly expressed gene ATCB. The fit of the two negative binomial distributions demonstrates that molecular marker errors with low molecular marker depth and true molecular markers with high molecular marker depth can be statistically distinguished. x The axis is the molecular depth.

[0047] Figure 17 Molecular marker correction is shown, where pairwise Hamming distances of 1 are over-represented. After molecular marker correction, molecular markers that are a Hamming distance apart are clustered and collapsed to the same parental molecular marker.

[0048] Figure 18A plot of corrected molecular marker numbers versus corrected read coverage numbers is shown.

[0049] Figure 19 A schematic diagram showing an example of recursive substitution error correction.

[0050] Figures 20A to 20C Exemplary results of correcting for PCR and sequencing errors based on two negative binomial distributions of CD69 are shown.

[0051] Figures 21A to 21C Exemplary results of correcting for PCR and sequencing errors based on two negative binomial distributions of CD3E are shown.

[0052] Figures 22A to 22J A non-limiting exemplary validation using two negative binomial distribution corrected data sets is shown.

[0053] Figures 23A to 23D Precision analysis of 86 genes from 96-well pooled Jurkat and breast cancer (BrCa) single cells is shown. TM Example t-stochastic neighbor embedding (t-SNE) visualization of the targeted assay.

[0054] Figures 24A to 24B is a non-limiting exemplary graph showing differential expression analysis between cell clusters of genes with >0 ML in two selected clusters as calculated by DBScan and determined by gene marker levels in each cluster.

[0055] Figures 25A to 25D BD Precise image showing mixed Jurkat and breast cancer (T47D) single cells from a 96-well plate (with 86 genes examined). TM Non-limiting exemplary plots of t-Stochastic Neighbor Embedding (t-SNE) visualization of targeted assays.

[0056] Figures 26A to 26B is in any error correction step ( Figure 26A The original ML shown in ), before RSEC and DBEC correction ( Figure 26B After the adjusted ML shown in Figures 25A to 25D Non-limiting exemplary heatmap showing differential gene expression of molecular marker counts between different cell clusters identified in .

[0057] Figures 27A to 27B A table demonstrating a non-limiting example of immune receptor barcode error correction using recursive substitution error correction is shown.

[0058] Figure 28 is a histogram showing non-limiting exemplary results of immune receptor barcode error correction. DETAILED DESCRIPTION

[0059] In the following detailed description, reference is made to the accompanying drawings, which form a part of this document. In the accompanying drawings, similar symbols generally identify similar parts unless the context indicates otherwise. The illustrative embodiments described in the specific embodiments, drawings, and claims are not meant to be limiting. Other embodiments may be utilized, and other changes may be made, without departing from the spirit or scope of the subject matter presented herein. It is readily understood that aspects of the present disclosure, as generally described herein and illustrated in the drawings, can be arranged, substituted, combined, separated, and designed in a variety of different configurations, all of which are expressly contemplated herein and form a part of the present disclosure.

[0060] All patents, published patent applications, other publications, and sequences from GenBank, and other databases mentioned herein with respect to the relevant art are incorporated by reference in their entirety.

[0061] Quantifying small amounts of nucleic acids, such as messenger ribonucleotide (mRNA) molecules, is clinically important for determining, for example, which genes are expressed in cells at different developmental stages or under different environmental conditions. However, determining the absolute number of nucleic acid molecules, such as mRNA molecules, is also very challenging, especially when the number of molecules is very small. One method for determining the absolute number of molecules in a sample is the digital polymerase chain reaction (PCR). Ideally, PCR produces identical copies of the molecule in each cycle. However, PCR can have the disadvantage that each molecule is replicated with a random probability, and this probability varies depending on the PCR cycle and the gene sequence, which leads to amplification bias and inaccurate gene expression measurements. Random barcodes with unique molecular labels, also called molecular indices (MIs) or universal molecular indices (UMIs), can be used to count the number of molecules and correct for amplification bias. Methods such as Precise TM The random barcoding assay (Cellular Research, Inc. (Palo Alto, CA)) can correct for biases induced by PCR and library preparation steps by labeling mRNA during reverse transcription (RT) using molecular markers (MLs).

[0062] Precise TM The assay can utilize a non-depleting pool of a large number (e.g., 6561 to 65536) of random barcodes, unique molecular tags on poly(T) oligonucleotides, to hybridize with all poly(A)-mRNA in the sample during the RT step. In addition to the molecular tags, a randomly barcoded sample tag (also known as a sample index (SI)) can be used to identify the Precise TMEach well of the microplate. The random barcode may include a universal PCR priming site. During RT, the target gene molecule reacts randomly with the random barcode. Each target molecule can hybridize with the random barcode, thereby generating a randomly barcoded complementary ribonucleotide (cDNA) molecule. After labeling, the randomly barcoded cDNA molecules from the microwells of the microplate can be pooled into a single tube for PCR amplification and sequencing. The raw sequencing data can be analyzed to generate the number of reads, the number of random barcodes with unique molecular labels, and the number of mRNA molecules based on Poisson correction or two negative binomial distribution correction methods.

[0063] In addition to bias correction, molecular markers can also provide a better understanding of the statistical quality of results by revealing the starting number of cDNA molecules present in an observed sequencing read. For example, a large number of reads can indicate a statistically accurate answer, but if the reads are derived from only a small number of starting mRNA molecules, the measurement accuracy may be compromised.

[0064] Although amplification bias induced by PCR and library preparation steps can be corrected by, for example, molecular markers, quantification of the absolute number of molecules remains challenging due to several other factors. First, the estimation of the number of mRNA molecules can be limited by the overall diversity of the molecular markers. In random barcoding, mRNA molecules can react arbitrarily with the available random barcodes. Therefore, each mRNA molecule can hybridize with a random barcode; however, its molecular marker may not necessarily be unique for any given gene. When the number of mRNA molecules is small relative to the number of random barcodes, each mRNA molecule may hybridize with a random barcode with a unique molecular marker, and the number of counted molecules can be equal to the number of counted molecular markers.

[0065] As the number of mRNA molecules increases, it becomes increasingly likely that multiple mRNA molecules will hybridize to a random barcode with the same molecular signature. Therefore, counting using unique molecular signatures can underestimate the number of molecules. In some cases, the number of mRNA molecules can be estimated based on a Poisson correction or a correction based on two negative binomial distributions for the total number of unique molecular signatures observed. However, in the extreme case where the entire set of 6561 random barcodes is observed, a Poisson correction or a correction based on two negative binomial distributions is no longer possible. For example, regardless of whether the starting mRNA molecules are 65,000 or 100,000, a maximum of 6561 saturated random barcodes are expected in any case.

[0066] Second, PCR errors (i.e., mistakes that occur during PCR amplification) can introduce artificial random barcodes and arbitrarily inflate marker counts. Third, PCR amplification bias and inefficient PCR can generate low-copy barcode molecules that are indistinguishable from errors. Fourth, sequencing errors (inaccurate calling of random barcode sequences) can introduce artificial random barcodes and inflate marker counts. Furthermore, sequencing depth can be important, especially when sequencing is too shallow to detect all randomly barcoded mRNAs present in a sample library.

[0067] When performing immune receptor sequencing and analysis, substitution errors, primer crossing errors, and PCR chimera errors may occur. For example, such errors may occur when determining the occurrence or copy number of mRNA molecules encoding immune receptors (such as T cell receptors). Immune receptors are highly diverse and closely related genes. Therefore, when compared with other genes, the possibility of such errors may be higher when performing immune receptor sequencing and analysis. Such errors often lead to an overestimation of the diversity of the immune repertoire. The method for mitigating these errors is referred to herein as immune receptor barcode error correction. In some embodiments, immune receptor barcode error correction utilizes recursive substitution error correction to correct substitution errors in molecular markers and nucleotide sequences (e.g., substitution errors in the complementarity determining region 3 (CDR3)). For a given sample marker or cell marker, many different CDR3s can be associated with the same molecular marker sequence, resulting in an overestimation of immune receptor diversity. The method can correct for PCR chimeras that cross before molecular markers and sample labeling, and then identify and remove erroneous molecular markers through distribution-based error correction.

[0068] Disclosed herein include methods for determining the presence of a target. In some embodiments, the method comprises: (a) barcoding (e.g., randomly barcoding) a plurality of targets using a plurality of barcodes (e.g., random barcodes) to create a plurality of barcoded targets (e.g., randomly barcoded targets), wherein each of the plurality of barcodes comprises a cellular marker and a molecular marker, wherein the molecular markers of at least two of the plurality of barcodes comprise different molecular marker sequences, and wherein at least two of the plurality of barcodes comprise cellular markers having the same cellular marker sequence; (b) obtaining sequencing data for the barcoded targets; and (c) for at least one of the plurality of targets: (i) identifying a putative sequence of the target in the sequencing data; (ii) counting occurrences of the molecular marker sequence associated with the putative sequence of the target in the sequencing data identified in (i); (iii) identifying clusters of putative sequences of the target; (iv) collapsing the obtained sequencing data using the clusters of putative sequences of the target identified in (iii); (v) Identifying clusters of the molecular marker sequences associated with putative sequences of the target; (vi) collapsing the sequencing data using the clusters of the molecular marker sequences identified in (v); (vii) identifying clusters of combined sequences, wherein each combined sequence comprises a sequence from the sequence of the target and an associated molecular marker sequence from the molecular marker sequence; (viii) collapsing the sequencing data using the clusters of combined sequences identified in (vii); (ix) identifying one or more putative sequences of the target corresponding to one or more chimeric sequences of the target, wherein the occurrence of the one or more putative sequences of the target corresponding to the one or more chimeric sequences of the target is less than the occurrence of the remaining one or more putative sequences of the target that do not correspond to the one or more chimeric sequences of the target; (x) removing from the sequencing data the one or more putative sequences of the target corresponding to the one or more chimeric sequences of the target identified in (ix); and (xi) estimating the occurrence of the target, wherein the sequencing data is collapsed in (iv), (vi), and (viii) and in (x). The estimated occurrence of the target is related to the number of molecular marker sequences counted in (ii) after removing the one or more putative sequences of the target corresponding to the one or more chimeric sequences of the target.

[0069] Disclosed herein are methods for determining the occurrence of a target. In some embodiments, the method comprises: (a) receiving sequencing data for a plurality of targets, wherein the sequencing data comprises putative sequences of targets from the plurality of targets and occurrences of molecular marker sequences associated with the sequences of the targets in the sequencing data; (b) folding the putative sequences of the targets; (c) folding the molecular marker sequences associated with the putative sequences of the targets; and (d) estimating the occurrence of the target, wherein after folding the occurrences of the putative sequences of the targets in (b) and folding the occurrences of noise molecular marker sequences determined in (c), the estimated occurrence of the target is correlated with the occurrences of the molecular marker sequences associated with the putative sequences of the targets in the sequencing data.

[0070] A computer system for determining the presence of a target is disclosed. A non-transitory computer-readable medium containing executable code that, when executed, causes one or more computing devices to determine the presence of a target is disclosed. definition

[0071] Unless otherwise defined, the technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which this disclosure belongs. See, for example, Singleton et al., Dictionary of Microbiology and Molecular Biology, 2nd ed., J. Wiley & Sons, (New York, NY 1994); Sambrook et al., Molecular Cloning, A Laboratory Manual, Cold Springs Harbor Press (Cold Springs Harbor, NY 1989). For the purposes of this disclosure, the following terms are defined as follows.

[0072] As used herein, the term "adapter" may refer to a sequence that facilitates the amplification or sequencing of associated nucleic acids. The associated nucleic acids may include target nucleic acids. The associated nucleic acids may include one or more of a spatial marker, a target marker, a sample marker, an index marker, or a barcode sequence (e.g., a molecular marker). The adaptor may be linear. The adaptor may be a pre-adenylated adaptor. The adaptor may be double-stranded or single-stranded. One or more adaptors may be located at the 5' or 3' end of the nucleic acid. When the adaptor includes known sequences at both the 5' and 3' ends, the known sequences may be the same or different sequences. The adaptors located at the 5' and / or 3' ends of the polynucleotide are capable of hybridizing to one or more oligonucleotides immobilized on a surface. In some embodiments, the adaptor may include a universal sequence. A universal sequence may be a region of nucleotide sequence shared by two or more nucleic acid molecules. Two or more nucleic acid molecules may also have regions of different sequences. Thus, for example, a 5' adaptor may include the same and / or universal nucleic acid sequence, and a 3' adaptor may include the same and / or universal sequence. A universal sequence that may be present in different members of a plurality of nucleic acid molecules may allow the use of a single universal primer that is complementary to the universal sequence to replicate or amplify a plurality of different sequences. Similarly, at least one, two (e.g., a pair) or more universal sequences that may be present in different members of a collection of nucleic acid molecules may allow the use of at least one, two (e.g., a pair) or more single universal primers that are complementary to the universal sequence to replicate or amplify a plurality of different sequences. Therefore, universal primers include sequences that can hybridize with such universal sequences. Molecules carrying target nucleic acid sequences may be modified to attach universal adapters (e.g., non-target nucleic acid sequences) to one or both ends of different target nucleic acid sequences. One or more universal primers attached to a target nucleic acid may provide sites for universal primer hybridization. One or more universal primers attached to a target nucleic acid may be identical or different from each other.

[0073] As used herein, the term "associated" or "associated with..." may mean that two or more species can be identified as co-localized at a certain point in time. Association may mean that two or more species are or were in similar containers. Association can be an informatics association. For example, digital information about two or more species can be stored and can be used to determine that one or more of the species are co-localized at a certain point in time. Association can also be a physical association. In some embodiments, two or more associated species are "connected," "attached," or "immobilized" to each other or to a common solid or semi-solid surface. Association can refer to a covalent or non-covalent means for attaching a label to a solid or semi-solid support (e.g., a bead). Association can be a covalent bond between a target and a label. Association can include hybridization between two molecules (e.g., a target molecule and a label).

[0074] As used herein, the term "complementarity" can refer to the ability of two nucleotides to accurately match. For example, if the nucleotides at a given position of a nucleic acid can hydrogen bond with the nucleotides of another nucleic acid, the two nucleic acids are considered to be complementary to each other at the position. The complementarity between two single-stranded nucleic acid molecules can be "partial," wherein only some of the nucleotides bind, or when there is complete complementarity between the single-stranded molecules, this complementarity can be complete. If a first nucleotide sequence is complementary to a second nucleotide sequence, the first nucleotide sequence can be considered to be the "complement" of the second sequence. If the first nucleotide sequence is complementary to a sequence opposite to the second sequence (i.e., the nucleotide order is reversed), the first nucleotide sequence can be considered to be the "reverse complement" of the second sequence. As used herein, the terms "complement," "complementary," and "reverse complement" can be used interchangeably. It can be understood from this disclosure that if a molecule can hybridize with another molecule, it can be the complement of the hybridized molecule.

[0075] As used herein, the term "digital counting" can refer to a method for estimating the number of target molecules in a sample. Digital counting can include the step of determining the number of unique markers that have been associated with the target in the sample. This method, which can be stochastic in nature, transforms the problem of counting molecules from one of locating and identifying identical molecules to a series of yes / no digital questions about detecting a set of predefined markers.

[0076] As used herein, the term (one or more) "markers" can refer to a nucleic acid code associated with a target in a sample. The marker can be, for example, a nucleic acid marker. The marker can be a fully or partially amplifiable marker. The marker can be a fully or partially sequenceable marker. The marker can be a portion of a natural nucleic acid that can be identified as being distinguishable. The marker can be a known sequence. The marker can include a junction of a nucleic acid sequence, such as a junction of a natural and a non-natural sequence. As used herein, the term "marker" can be used interchangeably with the term "index," "tag," or "marker-tag." A marker can convey information. For example, in various embodiments, a marker can be used to determine the identity of a sample, the source of a sample, the identity of a cell, and / or a target.

[0077] As used herein, the term "non-depleting reservoir" can refer to a pool of barcodes (e.g., random barcodes) composed of many different markers. A non-depleting reservoir can include a large number of different barcodes such that when the non-depleting reservoir is associated with a pool of targets, each target can be associated with a unique barcode. The uniqueness of each labeled target molecule can be determined by the statistics of random selection and depends on the number of copies of the same target molecule in the set compared to the diverse markers. The size of the resulting set of labeled target molecules can be determined by the stochastic nature of the barcoding process, and analysis of the number of detected barcodes then allows calculation of the number of target molecules present in the original set or sample. When the ratio of the number of copies of the target molecule present to the number of unique barcodes is low, the labeled target molecule is highly unique (i.e., the probability of labeling more than one target molecule with a given marker is very low).

[0078] As used herein, the term "nucleic acid" refers to a polynucleotide sequence, or a fragment thereof. A nucleic acid may include nucleotides. A nucleic acid may be exogenous or endogenous to a cell. A nucleic acid may be present in a cell-free environment. A nucleic acid may be a gene or a fragment thereof. A nucleic acid may be DNA. A nucleic acid may be RNA. A nucleic acid may include one or more analogs (e.g., altered backbones, sugars, or nucleobases). Some non-limiting examples of analogs include: 5-bromouracil, peptide nucleic acids, exogenous nucleic acids, morpholinos, locked nucleic acids, diol nucleic acids, threose nucleic acids, dideoxynucleotides, cordycepin, 7-deaza-GTP, fluorophores (e.g., rhodamine or sugar-linked fluorescein), thiol-containing nucleotides, biotin-linked nucleotides, fluorescent analogs, CpG islands, methyl-7-guanosine, methylated nucleotides, inosine, thiouridine, pseudouridine, dihydrouridine, quercetin, and wyosine. "Nucleic acid," "polynucleotide," "target polynucleotide," and "target nucleic acid" are used interchangeably.

[0079] Nucleic acids can include one or more modifications (e.g., base modifications, backbone modifications) to provide new or enhanced characteristics (e.g., improved stability). Nucleic acids can include nucleic acid affinity tags. Nucleosides can be base-sugar combinations. The base portion of a nucleoside can be a heterocyclic base. The two most common classes of such heterocyclic bases are purines and pyrimidines. Nucleotides can further include a phosphate group covalently linked to the sugar portion of the nucleoside. For those nucleosides that include a pentofuranosyl sugar, the phosphate group can be linked to the 2', 3', or 5' hydroxyl moiety of the sugar. In forming nucleic acids, phosphate groups can covalently link adjacent nucleosides to form a linear polymer compound. The ends of this linear polymer compound can, in turn, be further joined to form a cyclic compound; however, linear compounds are generally suitable. Furthermore, linear compounds can have internal nucleotide base complementarity and can therefore fold in a manner that produces fully or partially double-stranded compounds. In nucleic acids, the phosphate groups are often referred to as forming the internucleoside backbone of the nucleic acid. The linkage or backbone can be a 3' to 5' phosphodiester bond.

[0080] Nucleic acids can include modified backbones and / or modified internucleoside bonds. Nucleic acids can include a polynucleotide backbone formed by short-chain alkyl or cycloalkyl internucleoside bonds, mixed heteroatoms, and alkyl or cycloalkyl internucleoside bonds or one or more short-chain heteroatomic or heterocyclic internucleoside bonds. Nucleic acids can include nucleic acid mimics. Nucleic acids can include morpholino backbone structures. Nucleic acids can include morpholino units (i.e., morpholino nucleic acids) having heterocyclic bases attached to the morpholino ring.

[0081] Nucleic acids may also include modifications or substitutions of nucleobases (often referred to simply as "bases"). As used herein, "unmodified" or "natural" nucleobases may include purine bases, such as adenine (A) and guanine (G), and pyrimidine bases, such as thymine (T), cytosine (C), and uracil (U). Modified nucleobases may include other synthetic as well as natural nucleobases.

[0082] As used herein, the term "sample" may refer to a composition that includes a target. Suitable samples for analysis by the disclosed methods, devices, and systems include cells, tissues, organs, or organisms.

[0083] As used herein, the term "sampling device" or "device" may refer to a device that can take a portion of a sample and / or place the portion on a substrate. The sampling device may refer to, for example, a fluorescence activated cell sorter (FACS) machine, a cell sorter, a biopsy needle, a biopsy device, a tissue sectioning device, a microfluidic device, a cascade, and / or an ultramicrotome.

[0084] As used herein, the term "solid support" can refer to a discrete solid or semi-solid surface to which a plurality of barcodes (e.g., random barcodes) can be attached. A solid support can include any type of solid, porous, or hollow sphere, ball, socket, cylinder, or other similar configuration composed of plastic, ceramic, metal, or polymeric material (e.g., hydrogel) to which nucleic acids can be immobilized (e.g., covalently or non-covalently). A solid support can include discrete particles that can be spherical (e.g., microspheres) or have a non-spherical or irregular shape, such as a cube, rectangle, cone, cylinder, conical, elliptical, or disc. The shape of a bead can be non-spherical. A plurality of solid supports spaced apart in an array can not include a substrate. A solid support can be used interchangeably with the term "bead."

[0085] A solid support may be referred to as a "substrate." A substrate may be a solid support. A substrate may refer to a continuous solid or semisolid surface on which the methods of the present disclosure may be performed. For example, a substrate may refer to an array, a cassette, a chip, a device, and a slide.

[0086] As used herein, the term "spatial marker" may refer to a marker that may be associated with a location in space.

[0087] As used herein, the term "random barcode" can refer to a polynucleotide sequence comprising a tag of the present disclosure. A random barcode can be a polynucleotide sequence that can be used for random barcoding. A random barcode can be used to quantify a target in a sample. A random barcode can be used to control errors that may occur after a tag is associated with a target. For example, a random barcode can be used to assess amplification or sequencing errors. A random barcode associated with a target can be referred to as a random barcode-target or a random barcode-tag-target.

[0088] As used herein, the term "gene-specific random barcode" may refer to a polynucleotide sequence comprising a tag and a gene-specific target binding region. A random barcode may be a polynucleotide sequence that can be used for random barcoding. A random barcode can be used to quantify a target in a sample. A random barcode can be used to control errors that may occur after a tag is associated with a target. For example, a random barcode can be used to assess amplification or sequencing errors. A random barcode associated with a target can be referred to as a random barcode-target or a random barcode-tag-target.

[0089] As used herein, the term "stochastic barcoding" can refer to random labeling (e.g., barcoding) of nucleic acids. Stochastic barcoding can utilize a recursive Poisson strategy to correlate and quantify labels associated with a target. As used herein, the term "stochastic barcoding" can be used interchangeably with "random labeling."

[0090] As used herein, the term "target" can refer to a composition that can be associated with a barcode (e.g., a random barcode). Exemplary suitable targets for analysis by the disclosed methods, devices, and systems include oligonucleotides, DNA, RNA, mRNA, microRNA, tRNA, and the like. The target can be single-stranded or double-stranded. In some embodiments, the target can be a protein, peptide, or polypeptide. In some embodiments, the target is a lipid. As used herein, "target" can be used interchangeably with "species."

[0091] As used herein, the term "reverse transcriptase" can refer to a group of enzymes having reverse transcriptase activity (i.e., catalyzing the synthesis of DNA from an RNA template). Typically, such enzymes include, but are not limited to, retroviral reverse transcriptases, retrotransposon reverse transcriptases, retroplasmid reverse transcriptases, retrotranscriptases, bacterial reverse transcriptases, type II intron-derived reverse transcriptases, and mutants, variants, or derivatives thereof. Non-retroviral reverse transcriptases include non-LTR retrotransposon reverse transcriptases, retroplasmid reverse transcriptases, retrotranscriptases, and type II intron reverse transcriptases. Examples of type II intron reverse transcriptases include Lactococcus lactis L1.LtrB intron reverse transcriptase, Thermosynechoccuse longatus TeI4c intron reverse transcriptase, or Geobacillus stearothermophilus GsI-IIC intron reverse transcriptase. Other classes of reverse transcriptases can include many types of non-retroviral reverse transcriptases (ie, retrovirons, group II introns, and diversity-generating retroelements, etc.).

[0092] The terms "universal adapter primer," "universal primer adapter," or "universal adapter sequence" are used interchangeably to refer to a nucleotide sequence that can be used to hybridize with a barcode (e.g., a random barcode) to generate a gene-specific barcode. The universal adapter sequence can, for example, be a known sequence that is common to all barcodes used in the methods disclosed herein. For example, when multiple targets are labeled using the methods disclosed herein, each target-specific sequence can be linked to the same universal adapter sequence. In some embodiments, more than one universal adapter sequence can be used in the methods disclosed herein. For example, when multiple targets are labeled using the methods disclosed herein, at least two target-specific sequences are linked to different universal adapter sequences. The universal adapter primer and its complement can be included in two oligonucleotides, one of which includes a target-specific sequence and the other includes a barcode. For example, the universal adapter sequence can be part of an oligonucleotide that includes a target-specific sequence to generate a nucleotide sequence that is complementary to the target nucleic acid. A second oligonucleotide that includes a barcode and a complementary sequence to the universal adapter sequence can hybridize to the nucleotide sequence and generate a target-specific barcode (e.g., a target-specific random barcode). In some embodiments, the universal adapter primer has a different sequence than the universal PCR primer used in the methods of the disclosure.

[0093] Disclosed herein are methods and systems for detecting and / or correcting errors that occur during PCR and / or sequencing. The types of errors can vary, for example, including but not limited to substitution errors (one or more bases) and non-substitution errors. Among substitution errors, single-base substitution errors occur much more frequently than those where one base is different. The methods and systems can be used, for example, to provide accurate counting of molecular targets through random barcoding. barcode

[0094] Barcoding (such as random barcoding) has been described, for example, in US 20150299784, WO 2015031691, and Fu et al., Proc Natl Acad Sci USA 2011 May 31; 108(22): 9026-31, the contents of which are incorporated herein in their entirety. In some embodiments, the barcodes disclosed herein can be random barcodes, which can be polynucleotide sequences that can be used to stochastically label (e.g., barcode, tag) a target. A barcode can be referred to as a stochastic barcode if the ratio of the number of different barcode sequences of a stochastic barcode to the number of occurrences of any target to be labeled is at or about 1:1, 2:1, 3:1, 4:1, 5:1, 6:1, 7:1, 8:1, 9:1, 10:1, 11:1, 12:1, 13:1, 14:1, 15:1, 16:1, 17:1, 18:1, 19:1, 20:1, 30:1, 40:1, 50:1, 60:1, 70:1, 80:1, 90:1, 100:1, or a number or range between any two of these values. : 1, 40: 1, 50: 1, 60: 1, 70: 1, 80: 1, 90: 1, or 100: 1. The barcode sequence of the random barcode can be called a molecular marker.

[0095] A barcode (e.g., a random barcode) can include one or more markers. Exemplary markers can include a universal marker, a cell marker, a barcode sequence (e.g., a molecular marker), a sample marker, a plate marker, a spatial marker, and / or a pre-spatial marker. Figure 1An exemplary barcode 104 with spatial markers is shown. The barcode 104 may include a 5' amine that can link the barcode to a solid support 105. The barcode may include universal markers, dimensional markers, spatial markers, cellular markers, and / or molecular markers. The order of the different markers (including but not limited to universal markers, dimensional markers, spatial markers, cellular markers, and molecular markers) in the barcode may be varied. For example, Figure 1 In the figure, the universal marker can be a 5'-end marker, and the molecular marker can be a 3'-end marker. The spatial marker, dimensional marker, and cell marker can be in any order. In certain embodiments, the universal marker, spatial marker, dimensional marker, cell marker, and molecular marker are in any order. The barcode can include a target binding region. The target binding region can interact with the target (e.g., target nucleic acid, RNA, mRNA, DNA) in the sample. For example, the target binding region can include an oligo (dT) sequence that can interact with the poly (A) tail of the mRNA. In some cases, the markers of the barcode (e.g., universal marker, dimensional marker, spatial marker, cell marker, and barcode sequence) can be separated by 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 or more nucleotides.

[0096] Mark (for example, cell marker) can comprise a group of unique nucleic acid subsequences of defined length, for example, each seven nucleotides (equivalent to the number of bits used in some Hamming error correction codes), which can be designed to provide error correction capabilities. The error correction subsequence group comprising seven nucleotide sequences can be designed so that any paired combination of the sequences in the group exhibits a defined "genetic distance" (or number of mismatched bases), for example, a group of error correction subsequences can be designed to exhibit a genetic distance of three nucleotides. In this case, the review of the error correction sequence in the sequence data set of the target nucleic acid molecule through labeling (described more fully below) can allow detection or correction of amplification or sequencing errors. In certain embodiments, the length of the nucleic acid subsequence for generating the error correction code can vary, for example, they can be, or about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 30, 31, 40, 50, or a nucleotide length of a number or range between any two of these values. In certain embodiments, nucleic acid subsequences of other lengths can be used to generate the error correction code.

[0097] The barcode (e.g., a random barcode) can include a target binding region. The target binding region can interact with a target in the sample. The target can be or include ribonucleic acid (RNA), messenger RNA (mRNA), microRNA, small interfering RNA (siRNA), RNA degradation products, RNAs each containing a poly(A) tail, or any combination thereof. In some embodiments, the multiple targets can include deoxyribonucleic acid (DNA).

[0098] In some embodiments, the target binding region may include an oligo (dT) sequence that can interact with the poly (A) tail of the mRNA. One or more markers of the barcode (e.g., a universal marker, a dimensional marker, a spatial marker, a cellular marker, and a barcode sequence (e.g., a molecular marker)) can be separated from another or two of the remaining markers of the barcode by a spacer. The spacer can be, for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 or more nucleotides. In some embodiments, no markers of the barcode are separated by spacers.

[0099] General Markers A barcode may include one or more universal markers. In some embodiments, the one or more universal markers may be identical for all barcodes in a barcode set (attached to a given solid support). In some embodiments, the one or more universal markers may be identical for all barcodes attached to a plurality of beads. In some embodiments, the universal marker may include a nucleic acid sequence capable of hybridizing with a sequencing primer. A sequencing primer may be used to sequence a barcode including a universal marker. A sequencing primer (e.g., a universal sequencing primer) may include a sequencing primer associated with a high-throughput sequencing platform. In some embodiments, the universal marker may include a nucleic acid sequence capable of hybridizing with a PCR primer. In some embodiments, the universal marker may include a nucleic acid sequence capable of hybridizing with both a sequencing primer and a PCR primer. The nucleic acid sequence of a universal marker capable of hybridizing with a sequencing or PCR primer may be referred to as a primer binding site. The universal marker may include a sequence that can be used to initiate transcription of a barcode. The universal marker may include a sequence that can be used to extend a barcode or a region within a barcode. The universal tag can be at or about 1, 2, 3, 4, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50 nucleotides in length, or a number or range of nucleotides between any two of these values. For example, the universal tag can include at least about 10 nucleotides. The universal tag can be at least, or at most, 1, 2, 3, 4, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 100, 200, or 300 nucleotides in length. In some embodiments, a cleavable linker or modified nucleotide can be part of the universal tag sequence to enable the barcode to be cleaved from the support.

[0100] Dimension Tagging A barcode may include one or more dimensional markers. In some embodiments, a dimensional marker may include a nucleic acid sequence that provides information about the dimension in which the labeling (e.g., random labeling) occurred. For example, a dimensional marker may provide information about the time at which a target was barcoded. A dimensional marker may be associated with the time of barcoding (e.g., random barcoding) in a sample. A dimensional marker may be activated at the time of labeling. Different dimensional markers may be activated at different times. The dimensional markers provide information about the order in which targets, target groups, and / or samples were barcoded. For example, a cell population may be barcoded during the G0 phase of the cell cycle. During the G1 phase of the cell cycle, the cells may be pulsed again with a barcode (e.g., a random barcode). During the S phase of the cell cycle, the cells may be pulsed again with a barcode, and so on. The barcode at each pulse (e.g., each phase of the cell cycle) may include a different dimensional marker. In this way, the dimensional markers provide information about which targets were labeled at which phase of the cell cycle. Dimensional markers can interrogate many different biological phases. Exemplary biological events may include, but are not limited to, cell cycle, transcription (e.g., transcription initiation), and transcript degradation. In another example, a sample (e.g., a cell, a cell population) may be labeled before and / or after treatment with a drug and / or therapy. Changes in the copy number of different targets may indicate the sample's response to the drug and / or therapy.

[0101] The dimension marker can be activatable. The activatable dimension marker can be activated at a specific time point. The activatable marker can be, for example, constitutively activated (for example, not closed). The activatable dimension marker can be, for example, reversibly activated (for example, the activatable dimension marker can be turned on and off). The dimension marker can be, for example, reversibly activated at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 times or more. The dimension marker can be reversibly activated, for example, at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 times or more. In some embodiments, the dimension marker can be activated by fluorescence, light, chemical events (for example, cutting, connection of another molecule, addition of modification (for example, pegylation, sumoylation, acetylation, methylation, deacetylation, demethylation), photochemical events (for example, light locking) and introduction of non-natural nucleotides.

[0102] In some embodiments, the dimensional marking can be the same for all barcodes (e.g., random barcodes) attached to a given solid support (e.g., a bead), but different for different solid supports (e.g., beads). In some embodiments, at least 60%, 70%, 80%, 85%, 90%, 95%, 97%, 99%, or 100% of the barcodes on the same solid support can include the same dimensional marking. In some embodiments, at least 60% of the barcodes on the same solid support can include the same dimensional marking. In some embodiments, at least 95% of the barcodes on the same solid support can include the same dimensional marking.

[0103] Multiple solid supports (e.g., beads) can represent up to 10 6 More unique dimension marker sequences. The length of the dimension marker can be at or about 1, 2, 3, 4, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50 nucleotides, or a number or range of nucleotides between any two of these values. The length of the dimension marker can be at least or at most 1, 2, 3, 4, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 100, 200, or 300 nucleotides. The dimension marker can include a nucleotide between about 5 to about 200. The dimension marker can include a nucleotide between about 10 to about 150. The dimension marker can include a nucleotide with a length between about 20 to about 125.

[0104] Space Marker The barcode may include one or more spatial markers. In some embodiments, the spatial marker may include a nucleic acid sequence that provides information about the spatial orientation of the target molecule associated with the barcode. The spatial marker may be associated with a coordinate in the sample. The coordinates may be fixed coordinates. For example, the coordinates may be fixed with reference to a substrate. The spatial marker may be referenced to a two-dimensional or three-dimensional grid. The coordinates may be fixed with reference to a landmark. Landmarks are identifiable in space. Landmarks may be structures that can be imaged. Landmarks may be biological structures, such as anatomical landmarks. Landmarks may be cellular landmarks, such as organelles. Landmarks may be non-natural landmarks, such as structures with identifiable markers (such as color codes, barcodes, magnetism, fluorescence, radioactivity, or unique size or shape). Spatial markers may be associated with physical partitions (e.g., wells, containers, or droplets). In some embodiments, multiple spatial markers are used together to encode one or more locations in space.

[0105] The spatial markers can be the same for all bar codes attached to a given solid support (e.g., a bead), but different for different solid supports (e.g., a bead). In some embodiments, the percentage of bar codes on the same solid support that include the same spatial marker can be at or about 60%, 70%, 80%, 85%, 90%, 95%, 97%, 99%, 100%, or a number or range between any two of these values. In some embodiments, the percentage of bar codes on the same solid support that include the same spatial marker can be at least, or at most, 60%, 70%, 80%, 85%, 90%, 95%, 97%, 99%, or 100%. In some embodiments, at least 60% of the bar codes on the same solid support can include the same spatial marker. In some embodiments, at least 95% of the bar codes on the same solid support can include the same spatial marker.

[0106] Multiple solid supports (e.g., beads) can represent up to 10 6 The length of spatial marker can be or about 1, 2, 3, 4, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50 nucleotides or the nucleotides of the numeral or scope between any two of these values. The length of spatial marker can be at least or at most 1, 2, 3, 4, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 100, 200 or 300 nucleotides. Spatial marker can be included in the nucleotide between about 5 to about 200. Spatial marker can be included in the nucleotide between about 10 to about 150. Spatial marker can comprise the nucleotide of length between about 20 to about 125.

[0107] Cell labeling A barcode (e.g., a random barcode) may include one or more cell markers. In some embodiments, a cell marker may include a nucleic acid sequence that provides information for determining which target nucleic acid originates from which cell. In some embodiments, the cell marker is the same for all barcodes attached to a given solid support (e.g., a bead), but is different for different solid supports (e.g., a bead). In some embodiments, the percentage of barcodes on the same solid support that include the same cell marker is at or about 60%, 70%, 80%, 85%, 90%, 95%, 97%, 99%, 100%, or a number or range between any two of these values. In some embodiments, the percentage of barcodes on the same solid support that include the same cell marker is at or about 60%, 70%, 80%, 85%, 90%, 95%, 97%, 99%, or 100%. For example, at least 60% of the barcodes on the same solid support may include the same cell marker. As another example, at least 95% of the barcodes on the same solid support may include the same cell marker.

[0108] Multiple solid supports (e.g., beads) can represent up to 10 6 The length of cell marker can be at or about 1, 2, 3, 4, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50 nucleotides or a number or range of nucleotides between any two of these values. The length of cell marker can be at least or at most 1, 2, 3, 4, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 100, 200 or 300 nucleotides. For example, cell marker can include about 5 to about 200 nucleotides. As another example, cell marker can include about 10 to about 150 nucleotides. Also as another example, cell marker can include a length of about 20 to about 125 nucleotides.

[0109] Barcode sequence The barcode can include one or more barcode sequences. In some embodiments, the barcode sequence can include a nucleic acid sequence that provides identification information for a particular type of target nucleic acid species that hybridizes to the barcode. The barcode sequence can include a nucleic acid sequence that provides a counter (e.g., provides a rough approximation) of a particular occurrence of a target nucleic acid species that hybridizes to the barcode (e.g., a target binding region).

[0110] In some embodiments, a set of different barcode sequences are attached to a given solid support (e.g., a bead). In some embodiments, there can be, or about 10 2 , 10 3 , 10 4, 10 5 , 10 6 , 10 7 , 10 8 , 10 9 or a number or range of unique molecular marker sequences between any two of these values. For example, the plurality of barcodes may include about 6561 barcode sequences having different sequences. As another example, the plurality of barcodes may include about 65536 barcode sequences having different sequences. In some embodiments, there may be at least, or at most, 10 2 , 10 3 , 10 4 , 10 5 , 10 6 , 10 7 , 10 8 , or 10 9 A unique molecular marker sequence can be attached to a given solid support (e.g., a bead).

[0111] In various embodiments, the length of the barcode can vary. For example, the barcode can be at or about 1, 2, 3, 4, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50 nucleotides in length, or a number or range between any two of these values. As another example, the barcode can be at least, or at most, 1, 2, 3, 4, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 100, 200, or 300 nucleotides in length.

[0112] Molecular markers A barcode (e.g., a random barcode) can include one or more molecular markers. The molecular markers can include barcode sequences. In some embodiments, the molecular markers can include nucleic acid sequences that provide identification information for a specific type of target nucleic acid species that hybridizes to the barcode. The molecular markers can include nucleic acid sequences that provide a counter for a specific occurrence of a target nucleic acid species that hybridizes to the barcode (e.g., a target binding region).

[0113] In some embodiments, a set of different molecular markers are attached to a given solid support (e.g., a bead). In some embodiments, there can be, or about 10 2 , 10 3 、 10 4 , 10 5 , 10 6 , 10 7 , 10 8 , 10 9or a number or range of unique molecular marker sequences between any two of these values. For example, a plurality of barcodes may include about 6561 molecular markers with different sequences. As another example, a plurality of barcodes may include about 65536 molecular markers with different sequences. In some embodiments, there may be at least, or at most, 10 2 , 10 3 , 10 4 , 10 5 , 10 6 , 10 7 , 10 8 , or 10 9 A barcode with a unique molecular marker sequence can be attached to a given solid support (e.g., a bead).

[0114] For stochastic barcoding using multiple stochastic barcodes, the ratio of the number of different molecular marker sequences to the number of occurrences of any target can be at or about 1:1, 2:1, 3:1, 4:1, 5:1, 6:1, 7:1, 8:1, 9:1, 10:1, 11:1, 12:1, 13:1, 14:1, 15:1, 16:1, 17:1, 18:1, 19:1, 20:1, 30:1, 40:1, 50:1, 60:1, 70:1, 80:1, 90:1, 100:1, or a number or range between any two of these values. The target can be an mRNA species that includes mRNA molecules having identical or nearly identical sequences. In some embodiments, the ratio of the number of different molecular marker sequences to the number of occurrences of any target is at least, or at most, 1:1, 2:1, 3:1, 4:1, 5:1, 6:1, 7:1, 8:1, 9:1, 10:1, 11:1, 12:1, 13:1, 14:1, 15:1, 16:1, 17:1, 18:1, 19:1, 20:1, 30:1, 40:1, 50:1, 60:1, 70:1, 80:1, 90:1, or 100:1.

[0115] The length of a molecular marker can be or is about 1, 2, 3, 4, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50 nucleotides, or a number or range between any two of these values. The length of a molecular marker can be at least, or at most, 1, 2, 3, 4, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 100, 200, or 300 nucleotides.

[0116] target binding region The barcode can include one or more target binding regions, such as capture probes. In some embodiments, the target binding region can hybridize with the target of interest. In some embodiments, the target binding region can include a nucleic acid sequence that specifically hybridizes with the target (e.g., a target nucleic acid, a target molecule, such as a cellular nucleic acid to be analyzed) (e.g., hybridizes with a specific gene sequence). In some embodiments, the target binding region can include a nucleic acid sequence that can attach (e.g., hybridize) to a specific position of a specific target nucleic acid. In some embodiments, the target binding region can include a nucleic acid sequence that can specifically hybridize with a restriction enzyme site overhang (e.g., an EcoRI sticky end overhang). The barcode can then be attached to any nucleic acid molecule that includes a sequence complementary to the restriction site overhang.

[0117] In certain embodiments, the target binding region may include a non-specific target nucleic acid sequence. The non-specific target nucleic acid sequence may refer to a sequence that is independent of the specific sequence of the target nucleic acid and can be combined with multiple target nucleic acids. For example, the target binding region may include a random polymer sequence or an oligomer (dT) sequence hybridized with the poly (A) tail on the mRNA molecule. The random polymer sequence may be, for example, a random dimer, trimer, tetramer, pentamer, hexamer, heptamer, octamer, nonamer, decamer or a higher polymer sequence of any length. In certain embodiments, for all barcodes attached to a given bead, the target binding region is identical. In certain embodiments, for a plurality of barcodes attached to a given bead, the target binding region may include two or more different target binding sequences. The length of the target binding region may be or is about 5, 10, 15, 20, 25, 30, 35, 40, 45, 50 nucleotides or a number or range of nucleotides between any two of these values. The target binding region can be up to about 5, 10, 15, 20, 25, 30, 35, 40, 45, 50 or more nucleotides in length.

[0118] In some embodiments, the target binding region can include an oligo (dT) that can hybridize to an mRNA that includes a polyadenylated end. The target binding region can be gene specific. For example, the target binding region can be configured to hybridize to a specific region of the target. The length of the target binding region can be or is about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30 nucleotides, or a number or range of nucleotides between any two of these values. The target binding region can be at least, or at most, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, or 30 nucleotides in length. The target binding region can be about 5 to 30 nucleotides in length. When a barcode includes a gene-specific target binding region, the barcode can be referred to as a gene-specific barcode.

[0119] Directional characteristics Random barcodes (e.g., random barcodes) can include one or more directional properties that can be used to orient (e.g., align) the barcodes. The barcodes can include portions for isoelectric focusing. Different barcodes can include different isoelectric focusing points. When these barcodes are introduced into a sample, the sample can undergo isoelectric focusing to facilitate orienting the barcodes in a known manner. In this way, the directional properties can be used to develop a known map of the barcodes in the sample. Exemplary directional properties can include electrophoretic mobility (e.g., based on the size of the barcode), isoelectric point, spin, conductivity, and / or self-assembly. For example, a barcode can have a directional property of self-assembly and, when activated, can self-assemble into a specific orientation (e.g., a nucleic acid nanostructure).

[0120] Affinity characteristics Barcodes (e.g., random barcodes) can include one or more affinity properties. For example, spatial labels can include affinity properties. Affinity properties can include chemical and / or biological moieties that can promote binding of the barcode to another entity (e.g., a cell receptor). For example, affinity properties can include antibodies, e.g., antibodies specific for a particular moiety (e.g., a receptor) on a sample. In some embodiments, antibodies can guide the barcode to a specific cell type or molecule. Targets at and / or near a specific cell type or molecule can be labeled (e.g., randomly labeled). In some embodiments, in addition to the spatially labeled nucleotide sequence, affinity properties can provide spatial information because the antibodies can guide the barcode to a specific location. The antibody can be a therapeutic antibody, such as a monoclonal antibody or a polyclonal antibody. The antibody can be humanized or chimeric. The antibody can be a naked antibody or a fusion antibody.

[0121] Antibodies can be full-length (i.e., naturally occurring or produced by normal immunoglobulin gene segment recombination processes) immunoglobulin molecules (e.g., IgG antibodies) or immunologically active (i.e., specifically binding) portions of immunoglobulin molecules (such as antibody fragments).

[0122] An antibody fragment can be, for example, a portion of an antibody, such as F(ab')2, Fab', Fab, Fv, sFv, etc. In some embodiments, an antibody fragment can bind to the same antigen recognized by a full-length antibody. Antibody fragments can include isolated fragments consisting of the variable regions of an antibody, such as an "Fv" fragment consisting of the variable regions of a heavy chain and a light chain, and a recombinant single-chain polypeptide molecule ("scFv protein") in which the light chain and heavy chain variable regions are connected by a peptide linker. Exemplary antibodies can include, but are not limited to, cancer cell antibodies, viral antibodies, antibodies that bind to cell surface receptors (CD8, CD34, CD45), and therapeutic antibodies.

[0123] Universal adapter primer Barcodes can include one or more universal adapter primers. For example, gene-specific barcodes (such as gene-specific random barcodes) can include universal adapter primers. A universal adapter primer can refer to a nucleotide sequence that is common to all barcodes. A universal adapter primer can be used to construct a gene-specific barcode. The length of a universal adapter primer can be or is about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30 nucleotides, or a number or range of nucleotides between any two of these values. The universal adapter primer can be at least, or at most, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, or 30 nucleotides in length. The universal adapter primer can be 5 to 30 nucleotides in length.

[0124] Solid support In some embodiments, the barcodes disclosed herein (such as random barcodes) can be associated with a solid support. For example, the solid support can be a synthetic particle. In some embodiments, some or all barcode sequences (such as molecular markers of a random barcode (e.g., a first barcode sequence)) of a plurality of barcodes on a solid support (e.g., a first plurality of barcodes) can differ by at least one nucleotide. The cell markers of the barcodes on the same solid support can be identical. The cell markers of the barcodes on different solid supports can differ by at least one nucleotide. For example, a first cell marker of a first plurality of barcodes on a first solid support can have the same sequence, and a second cell marker of a second plurality of barcodes on a second solid support can have the same sequence. The first cell marker of the first plurality of barcodes on the first solid support and the second cell marker of the second plurality of barcodes on the second solid support can differ by at least one nucleotide. The cell marker can be, for example, approximately 5 to 20 nucleotides in length. The barcode sequence can be, for example, approximately 5 to 20 nucleotides in length. The synthetic particle can be, for example, a bead.

[0125] The beads can be, for example, silica beads, controlled pore glass beads, magnetic beads, Dynabeads, cross-linked dextran / agarose beads, beaded cellulose, polystyrene beads, or any combination thereof. The beads can include materials such as polydimethylsiloxane (PDMS), polystyrene, glass, polypropylene, agarose, gelatin, hydrogels, paramagnetic substances, ceramics, plastics, glass, methylstyrene, acrylic polymers, titanium, latex, agarose gel, cellulose, nylon, silicone, or any combination thereof.

[0126] In some embodiments, the beads can be polymer microspheres (e.g., deformable beads or gel beads) functionalized with barcodes or random barcodes (such as gel beads from 10X Genomics (San Francisco, CA)). In some implementations, the gel beads can include a polymer-based gel. For example, the gel beads can be produced by encapsulating one or more polymer precursors in a droplet. The gel beads can be produced after exposing the polymer precursors to an accelerator (e.g., tetramethylethylenediamine (TEMED)).

[0127] In some embodiments, the particles can be degradable. For example, the polymer microspheres can dissolve, melt, or degrade under desired conditions. The desired conditions can include environmental conditions. The desired conditions can cause the polymer microspheres to dissolve, melt, or degrade in a controlled manner. The gel beads can dissolve, melt, or degrade due to chemical stimulation, physical stimulation, biological stimulation, thermal stimulation, magnetic stimulation, electrical stimulation, light stimulation, or any combination thereof.

[0128] Analytes and / or reagents (such as oligonucleotide barcodes) can, for example, be coupled / immobilized to the inner surface of the gel beads (the accessible interior of the oligonucleotide barcode and / or the material used to generate the oligonucleotide barcode) and / or the outer surface of the gel beads or any other microcapsules described herein. The coupling / immobilization can be via any form of chemical bond (e.g., covalent bond, ionic bond) or physical phenomenon (e.g., van der Waals forces, dipole-dipole interactions, etc.). In some embodiments, the coupling / immobilization of the reagent to the gel beads or any other microcapsules described herein can be reversible, for example, via an unstable moiety (e.g., via a chemical crosslinker, including the chemical crosslinkers described herein). Upon application of a stimulus, the unstable moiety can be cleaved and release the immobilized reagent. In some embodiments, the unstable moiety is a disulfide bond. For example, where the oligonucleotide barcode is immobilized to the gel beads via a disulfide bond, exposing the disulfide bond to a reducing agent can cleave the disulfide bond and release the oligonucleotide barcode from the beads. The labile moiety can be included as part of a gel bead or microcapsule, as part of a chemical linker that connects a reagent or analyte to a gel bead or microcapsule, and / or as part of a reagent or analyte. In some embodiments, at least one barcode of the plurality of barcodes can be immobilized on a particle, partially immobilized on a particle, encapsulated in a particle, partially encapsulated in a particle, or any combination thereof.

[0129] In some embodiments, gel beads can include a wide variety of different polymers, including but not limited to: polymers, thermosensitive polymers, photosensitive polymers, magnetic polymers, pH sensitive polymers, salt sensitive polymers, chemical sensitive polymers, polyelectrolytes, polysaccharides, peptides, proteins and / or plastics. The polymer may include, but is not limited to, the following materials: poly(N-isopropylacrylamide) (PNIPAAm), poly(styrenesulfonate) (PSS), poly(allylamine) (PAAm), poly(acrylic acid) (PAA), poly(ethyleneimine) (PEI), poly(bisallyldimethyl-ammonium chloride) (PDADMAC), poly(pyrrole) (PPy), poly(vinylpyrrolidone) (PVPON), poly(vinylpyridine) (PVP), poly(methyl methacrylate) (PMAA), poly(methyl methacrylate) (PMMA), polystyrene (PS), poly(tetrahydrofuran) (PTHF), poly(o-phthalaldehyde) (PTHF), poly(hexylviologen) (PHV), poly(L-lysine) (PLL), poly(L-arginine) (PARG), poly(lactic acid-polyglycolic acid) (PLGA).

[0130] A variety of chemical stimuli can be used to trigger the destruction, dissolution, or degradation of beads. Examples of these chemical changes can include, but are not limited to, pH-mediated changes in the bead wall, chemical cleavage of cross-links to disintegrate the bead wall, triggered disaggregation of the bead wall, and bead wall conversion reactions. Batch changes can also be used to trigger bead destruction.

[0131] Batch or physical changes in microcapsules by various stimuli also offer numerous advantages in designing capsules to release agents. Batch or physical changes occur on a macroscale, where bead rupture is the result of mechanical-physical forces induced by the stimulus. These processes may include, but are not limited to, pressure-induced rupture, bead wall melting, or changes in bead wall porosity.

[0132] Biostimulation can also be used to trigger the destruction, dissolution, or degradation of beads. Generally, biotriggers are similar to chemical triggers, but many examples use biomolecules, or molecules commonly found in living systems, such as enzymes, peptides, sugars, fatty acids, nucleic acids, and the like. For example, beads may include polymers with peptide crosslinks that are susceptible to cleavage by specific proteases. More specifically, one example may include microcapsules containing GFLGK peptide crosslinks. Upon addition of a biotrigger (such as the protease cathepsin B), the peptide crosslinks in the shell pores are cleaved and the contents of the beads are released. In other cases, the protease can be heat-activated. In another example, the beads include a shell wall containing cellulose. Addition of the hydrolase chitosan serves as a biotrigger for cleavage of cellulose bonds, depolymerization of the shell wall, and release of the internal contents.

[0133] It is also possible to induce beads to release their contents upon application of a thermal stimulus. Changes in temperature can lead to various changes in the beads. Thermal changes can cause the beads to melt, leading to the disintegration of the bead wall. In other cases, heat can increase the internal pressure of components within the beads, causing them to rupture or explode. In still other cases, heat can cause the beads to enter a shrunken, dehydrated state. Heat can also act on thermosensitive polymers within the bead wall, causing the beads to break down.

[0134] Including magnetic nanoparticles in the bead wall of the microcapsule can allow for triggered rupture of the beads and for directing the beads into arrays. The devices disclosed herein can include magnetic beads for either purpose. In one example, Fe3O4 nanoparticles are incorporated into beads containing polyelectrolytes to trigger rupture in the presence of an oscillating magnetic field stimulus.

[0135] Beads can also be destroyed, dissolved, or degraded as a result of electrical stimulation. Similar to the magnetic particles described in the previous section, electrosensitive beads can allow for triggered rupture of beads, as well as other functions such as alignment in an electric field, conductivity, or redox reactions. In one example, beads containing electrosensitive materials align in an electric field, allowing for controlled release of an internal agent. In other examples, the electric field can induce redox reactions within the bead wall itself, which can increase porosity.

[0136] Beads can also be disrupted using light stimulation. Many light triggers are possible and can include systems using various molecules, such as nanoparticles and chromophores that absorb photons within a specific wavelength range. For example, metal oxide coatings can be used as capsule triggers. UV irradiation of polyelectrolyte capsules coated with SiO2 can lead to the disintegration of the bead wall. In yet another example, photoswitchable materials, such as azobenzene groups, can be incorporated into the bead wall. Upon application of UV or visible light, chemicals such as these undergo reversible cis-trans isomerization upon absorption of photons. In this regard, the incorporation of photon switching results in bead walls that can disintegrate or become more porous upon application of a light trigger.

[0137] For example, in Figure 2 In the non-limiting example of barcoding (e.g., random barcoding) shown in FIG, after cells (such as single cells) are introduced into a plurality of microwells of a microwell array at block 208, beads can be introduced into the plurality of microwells of the microwell array at block 212. Each microwell can include a bead. The beads can include a plurality of barcodes. The barcodes can include a 5' amine region attached to the beads. The barcodes can include a universal tag, a barcode sequence (e.g., a molecular tag), a target binding region, or any combination thereof.

[0138] The barcodes disclosed herein can be associated (e.g., attached) to a solid support (e.g., a bead). Each barcode associated with a solid support can include a barcode sequence selected from the group consisting of at least 100 or 1000 barcode sequences having unique sequences. In some embodiments, different barcodes associated with a solid support can include barcodes having different sequences. In some embodiments, a percentage of the barcodes associated with a solid support include the same cell marker. For example, the percentage can be, or is about, 60%, 70%, 80%, 85%, 90%, 95%, 97%, 99%, 100%, or a number or range between any two of these values. As another example, the percentage can be at least, or at most, 60%, 70%, 80%, 85%, 90%, 95%, 97%, 99%, or 100%. In some embodiments, the barcodes associated with a solid support can have the same cell marker. The barcodes associated with different solid supports can have different cell markers selected from the group consisting of at least 100 or 1000 cell markers having unique sequences.

[0139] The barcodes disclosed herein can be associated with (e.g., attached to) a solid support (e.g., a bead). In some embodiments, a solid support comprising multiple synthetic particles associated with multiple barcodes can be used to barcode multiple targets in a sample. In some embodiments, the solid support can include multiple synthetic particles associated with multiple barcodes. The spatial labels of the multiple barcodes on different solid supports can differ by at least one nucleotide. The solid support can, for example, include multiple barcodes in two or three dimensions. The synthetic particles can be beads. The beads can be silica beads, controlled pore glass beads, magnetic beads, Dynabeads, cross-linked dextran / agarose beads, beaded cellulose, polystyrene beads, or any combination thereof. The solid support can include a polymer, a matrix, a hydrogel, a pin array device, an antibody, or any combination thereof. In some embodiments, the solid support can be free-floating. In some embodiments, the solid support can be embedded in a semi-solid or solid array. The barcodes can be unassociated with the solid support. The barcodes can be individual nucleotides. The barcodes can be associated with a substrate.

[0140] As used herein, the terms "tethered," "attached," and "fixed" are used interchangeably and may refer to covalent or non-covalent means for attaching a barcode to a solid support. Any of a variety of different solid supports can be used as a solid support for attaching pre-synthesized barcodes or for in situ solid phase synthesis of barcodes.

[0141] In some embodiments, the solid support is a bead. The bead can include one or more types of solid, porous, or hollow spheres, balls, sockets, cylinders, or other similar configurations, to which nucleic acids can be immobilized (e.g., covalently or non-covalently). The bead can be composed, for example, of plastic, ceramic, metal, polymeric material, or any combination thereof. The bead can be, or include, spherical (e.g., microspheres) or discrete particles having a non-spherical or irregular shape, such as a cube, rectangle, cone, cylinder, conical, elliptical, or disc. In some embodiments, the bead can be non-spherical in shape.

[0142] Beads can comprise a variety of materials, including but not limited to paramagnetic materials (e.g., magnesium, molybdenum, lithium, and tantalum), superparamagnetic materials (e.g., ferrite (Fe3O4; magnetite) nanoparticles), ferromagnetic materials (e.g., iron, nickel, cobalt, some alloys thereof, and some rare earth metal compounds), ceramics, plastics, glass, polystyrene, silica, methylstyrene, acrylic polymers, titanium, latex, cross-linked agarose, agarose, hydrogels, polymers, cellulose, nylon, or any combination thereof.

[0143] In some embodiments, the beads (e.g., beads to which a label is attached) are hydrogel beads. In some embodiments, the hydrogel beads are dissolvable. In some embodiments, the beads comprise a hydrogel.

[0144] Some embodiments disclosed herein include one or more particles (e.g., beads). Each of the particles may include multiple oligonucleotides (e.g., barcodes). Each of the multiple oligonucleotides may include a barcode sequence (e.g., a molecular marker sequence), a cell marker, and a target binding region (e.g., an oligo (dT) sequence, a gene-specific sequence, a random polymer, or a combination thereof). The cell marker sequence of each of the multiple oligonucleotides may be the same. The cell marker sequences of the oligonucleotides on different particles may be different, so that the oligonucleotides on different particles can be identified. In different implementations, the number of different cell marker sequences may be different. In some embodiments, the number of cell marker sequences can be, can be about, can be at least, or can be at most 10, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, 20000, 30000, 40000, 50000, 60000, 70000, 80000, 90000, 10 ... 6 , 10 7 , 10 8 , 10 9, a number or range between any two of these values, or more. In some embodiments, no more than or about no more than 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, or more of the plurality of particles comprise an oligonucleotide having the same cellular sequence.

[0145] The plurality of oligonucleotides on each particle can include different barcode sequences (e.g., molecular markers). In some embodiments, the number of barcode sequences can be, can be about, can be at least, or can be at most 10, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, 20000, 30000, 40000, 50000, 60000, 70000, 80000, 90000, 10 ...0, 1000000, 1000000, 1000000, 1000000, 1000000, 1000000, 1000000, 1000000, 1000000 6 , 10 7 , 10 8 , 10 9 , a number or range between any two of these values. As another example, in a single particle, at least 100, 500, 1000, 5000, 10,000, 15,000, 20,000, 50,000, a number or range between any two of these values, or more of the plurality of oligonucleotides comprise a different barcode sequence. Some embodiments provide a plurality of particles comprising a barcode. In some embodiments, the ratio of the occurrence (or copies or number) of the target to be labeled and the different barcode sequences can be at least 1:1, 1:2, 1:3, 1:4, 1:5, 1:6, 1:7, 1:8, 1:9, 1:10, 1:11, 1:12, 1:13, 1:14, 1:15, 1:16, 1:17, 1:18, 1:19, 1:20, 1:30, 1:40, 1:50, 1:60, 1:70, 1:80, 1:90, or more. In some embodiments, each of the plurality of oligonucleotides further comprises a sample label, a universal label, or both. The particles can be, for example, nanoparticles or microparticles.

[0146] Barcoding method The present disclosure provides methods for estimating the number of different targets at different locations in a bodily sample (e.g., a tissue, organ, tumor, or cell). The methods may include placing a barcode (e.g., a random barcode) near the sample, lysing the sample, associating different targets with the barcodes, amplifying the targets, and / or digitally counting the targets. The methods may further include analyzing and / or visualizing information obtained from the spatial markers on the barcodes. In some embodiments, the methods include visualizing multiple targets in the sample. Mapping the multiple targets onto a map of the sample may include generating a two-dimensional map or a three-dimensional map of the sample. The two-dimensional map and the three-dimensional map may be generated before or after barcoding the multiple targets in the sample (e.g., random barcoding). Visualizing the multiple targets in the sample includes mapping the multiple targets onto a map of the sample. Mapping the multiple targets onto a map of the sample may include generating a two-dimensional map or a three-dimensional map of the sample. The two-dimensional map and the three-dimensional map may be generated before or after barcoding the multiple targets in the sample. In some embodiments, the two-dimensional map and the three-dimensional map may be generated before or after lysing the sample. Lysing the sample before or after generating the two-dimensional map or the three-dimensional map can include heating the sample, contacting the sample with a detergent, changing the pH of the sample, or any combination thereof.

[0147] In some embodiments, barcoding the plurality of targets comprises hybridizing the plurality of barcodes to the plurality of targets to create barcoded targets (e.g., randomly barcoded targets). Barcoding the plurality of targets can comprise generating an indexed library of barcoded targets. Generating the indexed library of barcoded targets can be performed using a solid support comprising a plurality of barcodes (e.g., random barcodes).

[0148] Bring the sample and barcode into contact The present disclosure provides methods for contacting a sample (e.g., cells) with a substrate of the present disclosure. A sample comprising, for example, cells, an organ, or a tissue slice can be contacted with a barcode (e.g., a random barcode). For example, the cells can be contacted by gravity flow, whereby the cells can be sedimented and a monolayer of cells can be formed. The sample can be a thin tissue section. The thin section can be placed on a substrate. The sample can be one-dimensional (e.g., forming a flat surface). The sample (e.g., cells) can be applied to a substrate, for example, by growing / culturing the cells on a substrate.

[0149] When the barcode is in proximity to the target, the target can hybridize to the barcode. The barcodes can be contacted at a non-exhaustive ratio such that each different target can be associated with a different barcode of the present disclosure. To ensure effective association between the target and the barcode, the target and the barcode can be cross-linked.

[0150] Cell lysis After distribution of the cells and barcodes, the cells can be lysed to release the target molecule. Cell lysis can be accomplished by any of a variety of means, such as chemical or biochemical means, by osmotic shock, or by thermal, mechanical, or optical lysis. Cells can be lysed by adding a cell lysis buffer comprising a detergent (e.g., SDS, lithium dodecyl sulfate, Triton X-100, Tween-20, or NP-40), an organic solvent (e.g., methanol or acetone), or a digestive enzyme (e.g., proteinase K, pepsin, or trypsin), or any combination thereof. To increase the association of the target and barcode, the diffusion rate of the target molecule can be altered by, for example, lowering the temperature of the lysate and / or increasing the viscosity of the lysate.

[0151] In some embodiments, the cracking can be performed by mechanical cracking, thermal cracking, optical cracking, and / or chemical cracking. The cracked cells can include at least about 100,000, 200,000, 300,000, 400,000, 500,000, 600,000, or 700,000 target nucleic acid molecules, or more. The cracked cells can include at most about 100,000, 200,000, 300,000, 400,000, 500,000, 600,000, or 700,000 target nucleic acid molecules, or more.

[0152] Attaching barcodes to target nucleic acid molecules After cell lysis and nucleic acid molecules are released, the nucleic acid molecules can be randomly associated with the barcodes of the co-located solid supports. Association can include hybridization of the target recognition region of the barcode with the complementary portion of the target nucleic acid molecule (e.g., the oligo (dT) of the barcode can interact with the poly (A) tail of the target). Assay conditions for hybridization can be selected (e.g., buffer pH, ionic strength, temperature, etc.) to promote the formation of specific stable hybrids. In some embodiments, the nucleic acid molecules released from the lysed cells can be associated with multiple probes on the substrate (e.g., hybridized with probes on the substrate). When the probe includes oligo (dT), the mRNA molecule can be hybridized with the probe and reverse transcribed. The oligo (dT) portion of the oligonucleotide can be used as a primer for the first chain synthesis of the cDNA molecule. For example, Figure 2 In the non-limiting example of barcoding described in (at block 216), mRNA molecules can be hybridized to barcodes on beads. For example, single-stranded nucleotide fragments can be hybridized to target binding regions of the barcodes.

[0153] Attachment can further include ligating the target recognition region of the barcode to a portion of the target nucleic acid molecule. For example, the target binding region can include a nucleic acid sequence capable of specifically hybridizing to a restriction site overhang (e.g., an EcoRI sticky end overhang). The assay procedure can further include treating the target nucleic acid with a restriction enzyme (e.g., EcoRI) to generate a restriction site overhang. The barcode can then be ligated to any nucleic acid molecule that includes a sequence complementary to the restriction site overhang. A ligase (e.g., T4 DNA ligase) can be used to join the two fragments.

[0154] For example, in Figure 2 In the non-limiting example of barcoding described above (at block 220), labeled targets (e.g., target-barcode molecules) from multiple cells (or multiple samples) can then be pooled, for example, into a tube. The labeled targets can be pooled, for example, by recovering barcodes and / or beads to which target-barcode molecules are attached.

[0155] Retrieval of a solid support-based collection of attached target-barcode molecules can be achieved by using magnetic beads and an externally applied magnetic field. Once the target-barcode molecules have been pooled, all further processing can be performed in a single reaction vessel. Further processing can include, for example, reverse transcription reactions, amplification reactions, cleavage reactions, dissociation reactions, and / or nucleic acid extension reactions. Further processing reactions can be performed within the microwells, i.e., without first pooling labeled target nucleic acid molecules from multiple cells.

[0156] Reverse transcription The present disclosure provides methods for generating target-barcode conjugates using reverse transcription (in Figure 2 224). The target-barcode conjugate can include a barcode and a complementary sequence to all or part of a target nucleic acid (i.e., a barcoded cDNA molecule, such as a randomly barcoded cDNA molecule). Reverse transcription of the associated RNA molecule can occur by adding a reverse transcription primer together with a reverse transcriptase. The reverse transcription primer can be an oligo dT primer, a random hexanucleotide primer, or a target-specific oligonucleotide primer. The oligo (dT) primer can be, or can be, about 12 to 18 nucleotides in length and binds to the endogenous poly (A) tail at the 3' end of mammalian mRNA. The random hexanucleotide primer can bind to the mRNA at multiple complementary sites. The target-specific oligonucleotide primer typically selectively primes the mRNA of interest.

[0157] In certain embodiments, the reverse transcription of the labeled RNA molecule can be performed by adding a reverse transcription primer. In certain embodiments, the reverse transcription primer is an oligo (dT) primer, a random hexanucleotide primer, or a target-specific oligonucleotide primer. Typically, the oligo (dT) primer has a length of 12 to 18 nucleotides and is bound to an endogenous poly (A) tail at the 3' end of mammalian mRNA. Random hexanucleotide primers can be bound to mRNA at multiple complementary sites. Target-specific oligonucleotide primers typically selectively trigger the mRNA of interest.

[0158] Reverse transcription can occur repeatedly to produce a plurality of labeled cDNA molecules. The methods disclosed herein may comprise performing at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 reverse transcription reactions. The methods may comprise performing at least about 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100 reverse transcription reactions.

[0159] Amplification One or more nucleic acid amplification reactions can be performed (e.g., Figure 2 228) to produce multiple copies of the labeled target nucleic acid molecule. Amplification can be performed in a multiplexed manner, wherein multiple target nucleic acid sequences are amplified simultaneously. The amplification reaction can be used to add sequencing adapters to the nucleic acid molecules. The amplification reaction can include amplifying at least a portion of the sample label (if present). The amplification reaction can include amplifying at least a portion of a cell label and / or a barcode sequence (e.g., a molecular marker). The amplification reaction can include amplifying at least a portion of a sample label, a cell label, a spatial label, a barcode sequence (e.g., a molecular marker), a target nucleic acid, or a combination thereof. The amplification reaction can include amplifying 0.5%, 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 97%, 100%, or a number or range between any two of these values ​​of the multiple nucleic acids. The method can further include performing one or more cDNA synthesis reactions to generate one or more cDNA copies of a target-barcode molecule comprising a sample marker, a cell marker, a spatial marker, and / or a barcode sequence (eg, a molecular marker).

[0160] In some embodiments, amplification can be performed using polymerase chain reaction (PCR). As used herein, PCR may refer to a reaction for amplifying a specific DNA sequence in vitro by simultaneous primer extension of complementary strands of DNA. As used herein, PCR may include derivatives of the reaction, including but not limited to RT-PCR, real-time PCR, nested PCR, quantitative PCR, multiplex PCR, digital PCR, and assembly PCR.

[0161] Amplification of labeled nucleic acids can include non-PCR-based methods. Examples of non-PCR-based methods include, but are not limited to, multiple displacement amplification (MDA), transcription-mediated amplification (TMA), nucleic acid sequence-based amplification (NASBA), strand displacement amplification (SDA), real-time SDA, rolling circle amplification, or circle-to-circle amplification. Other non-PCR-based amplification methods include DNA-dependent RNA polymerase-driven RNA transcription amplification or RNA-guided DNA synthesis and transcription multiple cycles to amplify DNA or RNA targets, ligase chain reaction (LCR), and Qβ replicase (Qβ) methods, the use of palindromic probes, strand displacement amplification, oligonucleotide-driven amplification using restriction endonucleases, amplification methods in which primers hybridize to nucleic acid sequences and the resulting duplexes are cleaved prior to extension reactions and amplification, strand displacement amplification using nucleic acid polymerases lacking 5' exonuclease activity, rolling circle amplification, and branch extension amplification (RAM). In some embodiments, amplification does not produce circularized transcripts.

[0162] In some embodiments, the methods disclosed herein further comprise performing a polymerase chain reaction on a labeled nucleic acid (e.g., labeled RNA, labeled DNA, labeled cDNA) to produce a labeled amplicon (e.g., a randomly labeled amplicon). The labeled amplicon may be a double-stranded molecule. The double-stranded molecule may comprise a double-stranded RNA molecule, a double-stranded DNA molecule, or an RNA molecule hybridized with a DNA molecule. One or both strands of the double-stranded molecule may comprise a sample marker, a spatial marker, a cell marker, and / or a barcode sequence (e.g., a molecular marker). The labeled amplicon may be a single-stranded molecule. The single-stranded molecule may comprise DNA, RNA, or a combination thereof. The nucleic acids disclosed herein may comprise synthetic or altered nucleic acids.

[0163] Amplification can include the use of one or more non-natural nucleotides. Non-natural nucleotides can include photolabile or triggerable nucleotides. Examples of non-natural nucleotides can include, but are not limited to, peptide nucleic acids (PNA), morpholino and locked nucleic acids (LNA), as well as glycol nucleic acids (GNA) and threose nucleic acids (TNA). Non-natural nucleotides can be added to one or more cycles of the amplification reaction. The addition of non-natural nucleotides can also be used to identify products at specific cycles or time points in the amplification reaction.

[0164] The one or more primers may include a universal primer. The universal primer may anneal to a universal primer binding site. The one or more custom primers may anneal to a first sample marker, a second sample marker, a spatial marker, a cellular marker, a barcode sequence (e.g., a molecular marker), a target, or any combination thereof. The one or more primers may include a universal primer and a custom primer. The custom primers may be designed to amplify one or more targets. The target may include a subset of the total nucleic acid in one or more samples. The target may include a subset of the total labeled targets in one or more samples. The one or more primers may include at least 96 or more custom primers. The one or more primers may include at least 960 or more custom primers. The one or more primers may include at least 9600 or more custom primers. The one or more custom primers may anneal to two or more different labeled nucleic acids. The two or more different labeled nucleic acids may correspond to one or more genes.

[0165] Any amplification scheme can be used in the methods disclosed herein. For example, in one scheme, the first round of PCR can use gene-specific primers and primers to the universal Illumina sequencing primer 1 sequence to amplify molecules attached to the beads. The second round of PCR can use nested gene-specific primers flanking the Illumina sequencing primer 2 sequence and primers to the universal Illumina sequencing primer 1 sequence to amplify the first PCR product. The third round of PCR adds P5 and P7 and a sample index to enter the PCR product into the Illumina sequencing library. Sequencing using 150 bp x 2 sequencing can reveal cell markers and barcode sequences (e.g., molecular markers) on read 1, genes on read 2, and sample indexes on the index 1 read.

[0166] In some embodiments, nucleic acids can be removed from a substrate using chemical cleavage. For example, chemical groups or modified bases present in nucleic acids can be used to facilitate their removal from a solid support. For example, enzymes can be used to remove nucleic acids from a substrate. For example, nucleic acids can be removed from a substrate by restriction endonuclease digestion. For example, nucleic acids containing dUTP or ddUTP can be treated with uracil-d-glycosylase (UDG) to remove them from a substrate. For example, enzymes for nucleotide excision (e.g., base excision repair enzymes (e.g., apurinic / apyrimidinic (AP) endonucleases)) can be used to remove nucleic acids from a substrate. In some embodiments, nucleic acids can be removed from a substrate using photocleavable groups and light. In some embodiments, nucleic acids can be removed from a substrate using cleavable linkers. For example, cleavable linkers can include at least one of the following: biotin / avidin, biotin / streptavidin, biotin / neutravidin, Ig protein A, a photolabile linker, an acid- or base-labile linker group, or an aptamer.

[0167] When the probe is gene-specific, the molecule can be hybridized to the probe and reverse transcribed and / or amplified. In some embodiments, after the nucleic acid has been synthesized (e.g., reverse transcribed), it can be amplified. Amplification can be performed in a multiplex manner, wherein multiple target nucleic acid sequences are amplified simultaneously. Amplification can add sequencing adapters to the nucleic acid.

[0168] In some embodiments, amplification can be performed on a substrate using, for example, bridging amplification. cDNA can be tailed with homopolymers to produce compatible ends for bridging amplification using oligo (dT) probes on a substrate. In bridging amplification, a primer complementary to the 3' end of the template nucleic acid can be the first primer of each pair of primers covalently attached to a solid particle. When a sample containing template nucleic acid is contacted with a particle and subjected to a single thermal cycle, the template molecule can be annealed to the first primer, and the first primer is extended forward by adding nucleotides to form a duplex molecule, which is composed of the template molecule and the newly formed DNA chain complementary to the template. In the heating step of the next cycle, the duplex molecule can be denatured, releasing the template molecule from the particle, and the complementary DNA chain is attached to the particle by the first primer. In the annealing phase of the subsequent annealing and extension steps, the complementary chain can hybridize with the second primer, which is complementary to the fragment of the complementary chain at the position removed from the first primer. The hybridization can cause the complementary chain to form a bridge between the first and second primers fixed to the first primer by a covalent bond, and form the second primer by hybridization. During the extension phase, by adding nucleotides to the same reaction mixture, the second primer can be extended in the opposite direction, thereby converting the bridge into a double-stranded bridge. The next cycle then begins, and the double-stranded bridge can be denatured to produce two single-stranded nucleic acid molecules, one end of each of which is attached to the particle surface via the first and second primers, respectively, and the other end of each single-stranded nucleic acid molecule is unattached. During the annealing and extension steps of the second cycle, each strand can hybridize with another previously unused complementary primer on the same particle to form a new single-stranded bridge. The two previously unused primers that are now hybridized are extended to convert the two new bridges into double-stranded bridges.

[0169] Amplification of the labeled nucleic acid can include PCR-based methods or non-PCR-based methods. Amplification of the labeled nucleic acid can include indexed amplification of the labeled nucleic acid. Amplification of the labeled nucleic acid can include linear amplification of the labeled nucleic acid. Amplification can be performed by polymerase chain reaction (PCR). PCR can refer to a reaction for amplifying a specific DNA sequence in vitro by simultaneous primer extension of complementary strands of DNA. PCR can encompass derivative forms of the reaction, including but not limited to RT-PCR, real-time PCR, nested PCR, quantitative PCR, multiplex PCR, digital PCR, inhibition PCR, semi-inhibition PCR, and assembly PCR.

[0170] In some embodiments, the amplification of the labeled nucleic acid includes a non-PCR-based method. Examples of non-PCR-based methods include, but are not limited to, multiple displacement amplification (MDA), transcription-mediated amplification (TMA), nucleic acid sequence-based amplification (NASBA), strand displacement amplification (SDA), real-time SDA, rolling circle amplification, or circle-to-circleamplification. Other non-PCR-based amplification methods include DNA-dependent RNA polymerase-driven RNA transcription amplification or RNA-guided DNA synthesis and transcription multiple cycles to amplify DNA or RNA targets, ligase chain reaction (LCR), Qβ replicase (Qβ), the use of palindromic probes, strand displacement amplification, oligonucleotide-driven amplification using restriction endonucleases, amplification methods in which primers are hybridized to nucleic acid sequences and the resulting duplex is cleaved prior to extension reaction and amplification, strand displacement amplification using a nucleic acid polymerase lacking 5' exonuclease activity, rolling circle amplification, and branch extension amplification (RAM).

[0171] In some embodiments, the methods disclosed herein further comprise performing a nested polymerase chain reaction on the amplified amplicon (e.g., target). The amplicon may be a double-stranded molecule. The double-stranded molecule may comprise a double-stranded RNA molecule, a double-stranded DNA molecule, or an RNA molecule hybridized with a DNA molecule. One or both strands of the double-stranded molecule may comprise a sample tag or a molecular identifier marker. Alternatively, the amplicon may be a single-stranded molecule. The single-stranded molecule may comprise DNA, RNA, or a combination thereof. The nucleic acids of the present invention may comprise synthetic or altered nucleic acids.

[0172] In some embodiments, the method comprises repeatedly amplifying the labeled nucleic acid to produce a plurality of amplicons. The methods disclosed herein may comprise performing at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 amplification reactions. Alternatively, the method comprises performing at least about 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100 amplification reactions.

[0173] Amplification can further include adding one or more control nucleic acids to one or more samples comprising multiple nucleic acids. Amplification can further include adding one or more control nucleic acids to multiple nucleic acids. The control nucleic acids can include a control label.

[0174] Amplification can include the use of one or more non-natural nucleotides. Non-natural nucleotides can include photolabile and / or triggerable nucleotides. Examples of non-natural nucleotides include, but are not limited to, peptide nucleic acids (PNA), morpholino and locked nucleic acids (LNA), as well as glycol nucleic acids (GNA) and threose nucleic acids (TNA). Non-natural nucleotides can be added to one or more cycles of the amplification reaction. The addition of non-natural nucleotides can also be used to identify products at specific cycles or time points in the amplification reaction.

[0175] Carrying out one or more amplification reactions can include using one or more primers. One or more primers can include one or more oligonucleotides. One or more oligonucleotides can include at least about 7 to 9 nucleotides. One or more oligonucleotides can include less than 12 to 15 nucleotides. One or more primers can anneal to at least a portion of a plurality of labeled nucleic acids. One or more primers can anneal to the 3' end and / or 5' end of a plurality of labeled nucleic acids. One or more primers can anneal to an internal region of a plurality of labeled nucleic acids. The internal region can be at least about 50, 100, 150, 200, 220, 230, 240, 250, 260, 270, 280, 290, 300, 310, 320, 330, 340, 350, 360, 370, 380, 390, 400, 410, 420, 430, 440, 450, 460, 470, 480, 490, 500, 510, 520, 530, 540, 550, 560, 570, 580, 590, 600, 650, 700, 750, 800, 850, 900 or 1000 nucleotides from the 3' end of the multiple labeled nucleic acids. The one or more primers can include a set of fixed primers. The one or more primers can include at least one or more custom primers. One or more primers may include at least one or more control primers. One or more primers may include at least one or more housekeeping gene primers. One or more primers may include universal primers. The universal primer may anneal to a universal primer binding site. One or more custom primers may anneal to a first sample tag, a second sample tag, a molecular identifier marker, a nucleic acid, or a product thereof. One or more primers may include universal primers and custom primers. Custom primers may be designed to amplify one or more target nucleic acids. The target nucleic acid may include a subset of the total nucleic acid in one or more samples. In some embodiments, the primers are probes attached to an array of the present disclosure.

[0176] In some embodiments, barcoding (e.g., randomly barcoding) a plurality of targets in a sample further comprises generating an index library of barcoded targets (e.g., randomly barcoded targets) or an index library of barcoded fragments of the targets. The barcode sequences of different barcodes (e.g., molecular tags of different random barcodes) can be different from each other. Generating an index library of barcoded targets comprises generating a plurality of index polynucleotides from a plurality of targets in the sample. For example, for an index library of barcoded targets comprising a first index target and a second index target, the tag region of the first index polynucleotide can differ from the tag region of the second index polynucleotide by, by about, by at least, or by at most 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50 nucleotides, or a number or range of nucleotide differences between any two of these values. In some embodiments, generating an indexed library of barcoded targets comprises contacting a plurality of targets (e.g., mRNA molecules) with a plurality of oligonucleotides comprising a poly(T) region and a tag region; and performing first-strand synthesis using a reverse transcriptase to generate single-stranded labeled cDNA molecules (each comprising a cDNA region and a tag region), wherein the plurality of targets comprises at least two mRNA molecules of different sequences, and the plurality of oligonucleotides comprises at least two oligonucleotides of different sequences. Generating an indexed library of barcoded targets may further comprise amplifying the single-stranded labeled cDNA molecules to generate double-stranded labeled cDNA molecules; and performing nested PCR on the double-stranded labeled cDNA molecules to generate labeled amplicons. In some embodiments, the method may comprise generating adapter-tagged amplicons.

[0177] Barcoding (e.g., random barcoding) can include the use of nucleic acid barcodes or tags to label individual nucleic acid (e.g., DNA or RNA) molecules. In some embodiments, this involves adding DNA barcodes or tags to cDNA molecules as they are generated from mRNA. Nested PCR can be performed to minimize PCR amplification bias. Adapters can be added for sequencing using, for example, next generation sequencing (NGS). For example, in Figure 2 At block 232 of the method, the sequencing results can be used to determine the sequence of cellular markers, molecular markers, and nucleotide fragments of one or more copies of the target.

[0178] Figure 3is a schematic diagram illustrating a non-limiting exemplary process for generating an indexed library of barcoded targets (e.g., randomly barcoded targets), such as an indexed library of barcoded mRNA or fragments thereof. As shown in step 1, the reverse transcription process can encode each mRNA molecule with a unique molecular marker, cellular marker, and universal PCR site. Specifically, RNA molecule 302 can be reverse transcribed to produce labeled cDNA molecules 304 (including cDNA region 306) by hybridizing (e.g., randomly hybridizing) a set of barcodes (e.g., random barcodes) 310 to a poly(A) tail region 308 of RNA molecule 302. Each of barcodes 310 can include a target binding region, such as a poly(dT) region 312, a tag region 314 (e.g., a barcode sequence or molecule), and a universal PCR region 316.

[0179] In some embodiments, the cell marker may include 3 to 20 nucleotides. In some embodiments, the molecular marker may include 3 to 20 nucleotides. In some embodiments, each of the plurality of random barcodes further includes one or more of a universal marker and a cell marker, wherein the universal marker is the same for the plurality of random barcodes on the solid support and the cell marker is the same for the plurality of random barcodes on the solid support. In some embodiments, the universal marker may include 3 to 20 nucleotides. In some embodiments, the cell marker includes 3 to 20 nucleotides.

[0180] In some embodiments, the marker region 314 may include a barcode sequence or molecular marker 318 and a cell marker 320. In some embodiments, the marker region 314 may include one or more of a universal marker, a dimensional marker, and a cell marker. The length of the barcode sequence or molecular marker 318 may be, may be about, may be at least, or may be at most 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100 nucleotides, or a number or range of nucleotides between any of these values. The length of the cell marker 320 may be, may be about, may be at least, or may be at most 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100 nucleotides, or a number or range of nucleotides between any of these values. The length of the universal tag can be, can be about, can be at least, or can be at most 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100 nucleotides, or a number or range of nucleotides between any of these values. The universal tag can be the same for multiple random barcodes on the solid support, and the cell tags are the same for multiple random barcodes on the solid support. The length of the dimensional tag can be, can be about, can be at least, or can be at most 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100 nucleotides, or a number or range of nucleotides between any of these values.

[0181] In some embodiments, the marker region 314 can include, include about, include at least, or include at most 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000 different markers, or a number or range of different markers between any of these values, such as barcode sequences or molecular markers 318 and cellular markers 320. Each marker can be, can be about, can be at least, or can be at most 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100 nucleotides in length, or a number or range of nucleotides between any of these values. A set of barcodes or random barcodes 310 may contain, contain about, contain at least, or may be at most 10, 20, 40, 50, 70, 80, 90, 10 2 , 10 3 , 10 4 , 10 5 , 10 6 , 10 7, 10 8 , 10 9 , 10 10 , 10 11 , 10 12 , 10 13 , 10 14 , 10 15 , 10 20 The barcodes or random barcodes 310 may be a single barcode or random barcode 310, or a number or range of barcodes or random barcodes 310 between any of these values. The group of barcodes or random barcodes 310 may, for example, each contain a unique tag region 314. The labeled cDNA molecules 304 may be purified to remove excess barcodes or random barcodes 310. Purification may include Ampure bead purification.

[0182] As shown in step 2, the products from the reverse transcription process in step 1 can be pooled into 1 tube and PCR amplified using the first PCR primer pool and the first universal PCR primer. Pooling is possible because of the unique tag region 314. In particular, the labeled cDNA molecules 304 can be amplified to produce nested PCR labeled amplicons 322. Amplification can include multiplex PCR amplification. Amplification can include multiplex PCR amplification using 96 multiplex primers in a single reaction volume. In some embodiments, the multiplex PCR amplification can utilize, utilize about, utilize at least, or utilize at most 10, 20, 40, 50, 70, 80, 90, 10, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 80 2 , 10 3 , 10 4 , 10 5 , 10 6 , 10 7 , 10 8 , 10 9 , 10 10 , 10 11 , 10 12 , 10 13 , 10 14 , 10 15 , 10 20 304 , or a number or range of multiple primers between any of these values. Amplification can include using a first PCR primer pool 324 including custom primers 326A-C targeting a specific gene and a universal primer 328. Custom primers 326 can hybridize to a region within the cDNA portion 306' of the labeled cDNA molecule 304. Universal primer 328 can hybridize to the universal PCR region 316 of the labeled cDNA molecule 304.

[0183] like Figure 3As shown in step 3 of FIG, the products from the PCR amplification in step 2 can be amplified using a nested PCR primer pool and a second universal PCR primer. Nested PCR can minimize PCR amplification bias. In particular, nested PCR-labeled amplicon 322 can be further amplified by nested PCR. Nested PCR can include multiplex PCR using a nested PCR primer pool 330 of nested PCR primers 332a-c and a second universal PCR primer 328′ in a single reaction volume. Nested PCR primer pool 328 can contain, contain about, contain at least, or contain at most 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000 different nested PCR primers 330, or a number or range of different nested PCR primers 330 between any of these values. Nested PCR primer 332 can contain an adapter 334 and hybridize to a region within cDNA portion 306″ of labeled amplicon 322. Universal primer 328′ can contain an adapter 336 and hybridize to universal PCR region 316 of labeled amplicon 322. Thus, step 3 produces adapter-tagged amplicon 338. In some embodiments, nested PCR primer 332 and second universal PCR primer 328′ may not contain adapters 334 and 336. Instead, adapters 334 and 336 may be ligated to the product of the nested PCR to generate adapter-tagged amplicon 338.

[0184] As shown in step 4, the PCR products from step 3 can be PCR amplified using library amplification primers for sequencing. In particular, adapters 334 and 336 can be used to perform one or more additional assays on adapter-tagged amplicon 338. Adapters 334 and 336 can hybridize with primers 340 and 342. One or more primers 340 and 342 can be PCR amplification primers. One or more primers 340 and 342 can be sequencing primers. One or more adapters 334 and 336 can be used for further amplification of adapter-tagged amplicon 338. One or more adapters 334 and 336 can be used to sequence adapter-tagged amplicon 338. Primer 342 can contain a plate index 344 so that amplicons generated using the same set of barcodes or random barcodes 310 can be sequenced in a single round of sequencing reactions using next-generation sequencing (NGS).

[0185] Sequencing data errors The methods disclosed herein can be used to identify and / or correct sequencing data errors, such as errors that occur in methods for counting one or more target nucleic acids. In some embodiments, errors may include or be deletions of one or more nucleotides, substitutions of one or more nucleotides, and additions of one or more nucleotides, or any combination thereof. Errors may be present on other markers on a molecular marker (ML), a sample marker (SL), or a barcode (e.g., a random barcode). In some embodiments, sequencing data errors may include or be errors introduced by PCR, errors introduced by sequencing, reverse transcription (RT) primer contamination errors, or any combination thereof. Errors introduced by PCR may include or be the result of PCR amplification errors, PCR amplification bias, insufficient PCR amplification, or any combination thereof. Errors introduced by sequencing may include or be the result of inaccurate base calls, insufficient sequencing, or any combination thereof. RT primer contamination errors may be errors caused by reverse transcription primers that enter PCR.

[0186] As used herein, the term "coverage" or "sequencing depth" may refer to the number of reads of a barcoded target with a specific ML and a specific SL in sequencing data. For example, a barcoded target may be sequenced multiple times. Thus, a barcoded target with a specific ML and SL may be observed multiple times. As another example, a cell may contain multiple copies of a target (e.g., multiple copies of an mRNA molecule of a gene). Multiple copies of a target may be barcoded. During PCR amplification (e.g., Figure 2 After the sequencing is complete (block 228 in Figure 2), there may be multiple copies of the barcoded target with a specific ML and SL. During sequencing, some or all of the multiple copies of the barcoded target with a specific ML and SL may be sequenced. The number of reads of the barcoded target with the same ML and SL observed in the sequencing data can be referred to as "coverage" or "sequencing depth."

[0187] In some embodiments, sequencing data errors can be identified and / or corrected. For example, copies of a target from a cell can be barcoded with different MLs and the same SL. A barcoded target with an ML can have multiple reads in the sequencing data. A barcoded target with a different ML can have only a few reads (e.g., a single read). Compared to a barcoded target with a different ML, the former barcoded target is more likely to have a true ML (or real ML or signal ML). The latter barcoded target may include an erroneous ML (or false ML or noise ML). This may be because the two MLs can be expected to have similar coverage or sequencing depth. The latter barcoded target having only a few reads may be an artifact or error generated during sequencing or PCR.

[0188] As another example, barcodes introduced into PCR (e.g., random barcodes) can lead to RT primer contamination errors. In some embodiments, after reverse transcription of mRNA molecules into cDNA molecules (e.g., in Figure 2 At block 224 of the present invention, barcodes that are not incorporated into cDNA molecules can be removed by, for example, Ampure bead purification. The removal method (e.g., Ampure bead purification) may not completely remove barcodes that are not extended by reverse transcription to be incorporated into barcoded cDNA molecules (e.g., randomly barcoded cDNA molecules). For example, 15%, 10%, 9%, 8%, 7%, 6%, 5%, 4%, 3%, 2%, 1%, 0.5%, 0.1%, or a range between any of these two values, of the barcodes that are not extended by reverse transcription to be incorporated into barcoded cDNA molecules cannot be removed by Ampure bead purification. These unremoved barcodes may result in the presence of barcodes during amplification of cDNA molecules (e.g., during Figure 2 Barcodes can be highly similar between samples. For example, the sample labels of the barcodes can be identical for all samples. Consequently, PCR crossover can occur because these unremoved barcodes can hybridize with other nucleic acid molecules from the same sample (e.g., SL regions of barcoded mRNA molecules, such as randomly barcoded mRNA molecules) during PCR and can result in sequencing data errors known as SL errors.

[0189] True ML, false ML, and SL errors may have different distributions. Figure 4 is a schematic diagram showing non-limiting exemplary distributions of molecular labeling errors, sample labeling errors, and true molecular labeling signals. Figure 4 As described in

[15] , false ML may be more likely to have lower ML coverage because false ML may be the result of PCR or sequencing errors. For example, false ML may be the result of mostly sequencing errors and some PCR errors. SL errors may be more likely to have lower ML coverage because SL errors may primarily come from barcodes entering the PCR (e.g., random barcodes).

[0190] Correction of PCR and sequencing errors based on directed neighbor joining Disclosed herein are methods for correcting PCR or sequencing errors. In some embodiments, the methods include: (a) receiving sequencing data for a barcoded target (e.g., a randomly barcoded target). The barcoded targets can be obtained by barcoding (e.g., randomly barcoding) a plurality of targets using a plurality of barcodes (e.g., random barcodes) to create a plurality of barcoded targets (e.g., randomly barcoded targets), wherein each of the plurality of barcodes comprises a molecular marker. In some embodiments, the method includes: (b) for one or more of the plurality of targets: (i) counting the number of molecular markers having distinct sequences associated with the target in the sequencing data; (ii) identifying clusters of molecular markers for the target using directed contiguity; (iii) collapsing the sequencing data received in (b) using the clusters of molecular markers for the target identified in (ii); and (iv) estimating the number of targets, wherein, after collapsing the sequencing data in (ii), the estimated number of targets is correlated with the number of molecular markers having distinct sequences associated with the target in the sequencing data counted in (i). The plurality of targets may include targets across the entire transcriptome of the cell. In some embodiments, the method further includes: (c) barcoding (e.g., randomly barcoding) the plurality of targets using the plurality of barcodes to create the plurality of barcoded targets; and (d) sequencing the barcoded targets to generate sequencing data for the received randomly barcoded targets.

[0191] Figure 5 is a flow chart illustrating a non-limiting exemplary embodiment 500 of using molecular markers to correct PCR and sequencing errors based on directed contiguity. Correcting PCR and sequencing errors using molecular markers based on directed contiguity can be referred to as recursive substitution error correction (RSEC). Method 500 begins at block 504 after receiving sequencing data for a plurality of barcoded targets (e.g., randomly barcoded targets). In some embodiments, method 500 further includes randomly barcoding the plurality of targets using a plurality of barcodes (e.g., random barcodes) to create a plurality of barcoded targets (e.g., randomly barcoded targets), wherein each of the plurality of barcodes comprises a molecular marker. In some embodiments, method 500 further includes sequencing the plurality of barcoded targets to obtain sequencing data.

[0192] At block 508, for one or more of the plurality of targets, the number of molecular markers having different sequences associated with the target in the sequencing data can be counted. At block 512, clusters of molecular markers for the targets can be identified using directional adjacency. The molecular markers for the targets in a cluster can be within a predetermined directional adjacency threshold of each other. The directional adjacency threshold can vary. In some embodiments, the Hamming distance for the predetermined directional adjacency threshold can be, is about, is at least, or is at most one or two.

[0193] In certain embodiments, the molecular marker of the target in the cluster can comprise the sub-molecule marker of one or more parental molecule markers and described one or more parental molecule markers.The occurrence of parental molecule marker can be greater than or equal to predetermined directional adjacency threshold value.In certain embodiments, predetermined directional adjacency threshold value can be, be approximately, at least or at most twice of the occurrence of sub-molecule marker.In certain embodiments, predetermined directional adjacency threshold value can be or be approximately 1.5 times, 2 times, 3 times, 4 times, 5 times, 6 times, 7 times, 8 times, 9 times, 10 times of the occurrence of sub-molecule marker or numeral or the scope between any two of these values.In certain embodiments, predetermined directional adjacency threshold value can be at least or at most 1.5 times, 2 times, 3 times, 4 times, 5 times, 6 times, 7 times, 8 times, 9 times or 10 times of the occurrence of sub-molecule marker.

[0194] In block 520, the sequencing data is folded using the clusters of target molecular markers. Folding the sequencing data may include attributing the occurrence of daughter molecular markers to parent molecular markers. At block 532, the number of targets may be estimated to generate an output after folding the sequencing data. The method ends at block 536 at 500.

[0195] In some embodiments, the method further comprises: determining the sequencing depth of the target. If the sequencing depth of the target is higher than a predetermined sequencing depth threshold, estimating the number of targets comprises adjusting the sequencing data counted in (i). The predetermined sequencing depth threshold may be between 15 and 20. Adjusting the sequencing data counted in (i) comprises: defining a threshold for the molecular markers of the target to determine true molecular markers and false molecular markers associated with the target in the sequencing data obtained in (b). Defining a threshold for the molecular markers of the target comprises performing a statistical analysis on the molecular markers of the target. Performing the statistical analysis comprises: fitting the molecular markers of the target and the distribution of their occurrence to two distributions, such as two negative binomial distributions; and using the two negative binomial distributions to determine the number of true molecular markers. n ; and removing false molecular markers from the sequencing data obtained in (b), wherein the false molecular markers include those having a sequence less than the first n The molecular markers having the occurrence of the most abundant molecular markers, and wherein the true molecular markers include those having the occurrence of the most abundant molecular markers greater than or equal to then The molecular markers that appear in the most abundant molecular markers.

[0196] Correction of PCR and sequencing errors based on directed neighbor joining and distribution-based error correction Disclosed herein are methods for correcting PCR or sequencing errors. The methods can be used to determine the number of targets. In some embodiments, the methods include: (a) receiving sequencing data for barcoded targets (e.g., randomly barcoded targets). The barcoded targets can be obtained by barcoding (e.g., randomly barcoding) the plurality of targets using a plurality of barcodes (e.g., random barcodes) to create a plurality of barcoded targets (e.g., randomly barcoded targets), wherein each of the plurality of barcodes includes a molecular marker. In some embodiments, the methods include (b) for one or more of the plurality of targets: (i) counting the number of molecular markers having distinct sequences associated with the target in the sequencing data; (ii) determining the number of noise molecular markers having distinct sequences associated with the target in the sequencing data; and (iii) estimating the number of targets, wherein the estimated number of targets is related to the number of molecular markers having distinct sequences associated with the target in the sequencing data counted in (i), the number of molecular markers being adjusted based on the number of noise molecular markers determined in (ii). In some embodiments, the method includes determining a sequencing status of a target in the sequencing data. In some embodiments, the method further includes: (c) barcoding (e.g., randomly barcoding) the plurality of targets using a plurality of barcodes to create a plurality of barcoded targets; and (d) sequencing the barcoded targets to generate sequencing data for the received barcoded targets.

[0197] Figure 6 6 is a flow chart illustrating a non-limiting exemplary embodiment 600 of correcting PCR and sequencing errors based on recursive substitution error correction and distribution-based error correction. Method 600 begins at block 604 after receiving sequencing data for a plurality of barcoded targets (e.g., randomly barcoded targets). In some embodiments, method 600 further includes barcoding (e.g., randomly barcoding) the plurality of targets using a plurality of barcodes (e.g., random barcodes) to create a plurality of barcoded targets, wherein each of the plurality of barcodes comprises a molecular marker. In some embodiments, method 600 further includes sequencing the plurality of barcoded targets to obtain sequencing data.

[0198] At block 608, for one or more of the plurality of targets, the number of molecular markers having different sequences associated with the target in the sequencing data can be counted. At decision block 612, a determination can be made as to whether the sequencing data has a saturated sequencing state. For example, if a target has a number of molecular markers having different sequences that is greater than 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, 20000, 30000, 40000, 50000, 60000, 70000, 80000, 90000, 100000, or a number or range between any two of these, then the target is considered to have a saturated sequencing state. As another example, a target can be considered to have a saturated sequencing state if the number of molecular markers with different sequences that it has is greater than 50%, 60%, 70%, 80%, 90%, 95%, 99%, 99.9%, or a number or range between any two of these of molecular markers with barcodes with different sequences (e.g., random barcodes).

[0199] In certain embodiments, saturated sequencing state can be determined by the target of the molecular marker having the quantity greater than predetermined saturation threshold, and described molecular marker has different sequence.In different implementations, predetermined saturation threshold can be different.For example, predetermined saturation threshold can be or be about 1000, 2000, 3000, 4000, 5000, 6000, 6557, 7000, 8000, 9000, 10000, 20000, 30000, 40000, 50000, 60000, 65532, 70000, 80000, 90000, 100000 or the numeral or scope between any two of these values. As another example, the predetermined saturation threshold may be at least, or at most, 1000, 2000, 3000, 4000, 5000, 6000, 6557, 7000, 8000, 9000, 10000, 20000, 30000, 40000, 50000, 60000, 65532, 70000, 80000, 90000, or 100000.

[0200] In some embodiments, the saturated sequencing state may depend on the number of molecular markers with different sequences of a barcode (e.g., a random barcode). For example, if a barcode includes approximately 6561 molecular markers with different sequences, the predetermined saturation threshold may be approximately 6557. As another example, if a barcode (e.g., a random barcode) includes approximately 65536 molecular markers with different sequences, the predetermined saturation threshold may be approximately 65532. In some embodiments, the saturated sequencing state may not depend on the number of molecular markers with different sequences of a barcode.

[0201] If the sequencing data does not have a saturated sequencing state at decision block 612, the method 600 proceeds to block 616, where the molecular marker counts can be adjusted based on the directional adjacency. In some embodiments, adjusting the molecular marker counts based on the directional adjacency can refer to Figure 5 For example, adjusting molecular marker counts based on directed contiguity can include identifying clusters of molecular markers for a target using directed contiguity; collapsing sequencing data using the identified clusters of molecular markers for the target; and estimating a number of targets, wherein after collapsing the sequencing data, the estimated number of targets is associated with a number of molecular markers having distinct sequences associated with the targets in the counted sequencing data.

[0202] At block 620, the sequencing state of the target in the sequencing data can be determined. The sequencing state of the target in the sequencing data can include or be insufficient sequencing. At decision block 624, it can be determined whether the sequencing state of the target in the sequencing data is an insufficient sequencing state. For example, if the depth of the target (e.g., average, minimum, or maximum depth) is less than or less than about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, or a number or range between any two of these, then it can be considered that the target has an insufficient sequencing state. As another example, if the depth of the target is less than at least or at most 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, or 100, then it can be considered that the target has an insufficient sequencing state.

[0203] In some embodiments, an under-sequencing state can be determined by targets having a depth (e.g., average, minimum, or maximum depth) less than a predetermined under-sequencing threshold. In different implementations, the under-sequencing threshold can be different. For example, the under-sequencing threshold can be, or be about, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, or a number or range between any two of these values. As another example, the under-sequencing threshold can be at least, or at most, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, or 100.

[0204] In some embodiments, the under-sequencing status can depend on the number of molecular markers with different sequences of a barcode (e.g., a random barcode). For example, if the barcode includes, or about, 1000, 2000, 3000, 4000, 5000, 6000, 6561, 7000, 8000, 9000, 10000, 20000, 30000, 40000, 50000, 60000, 65532, 70000, 80000, 90000, 100000 molecular markers with different sequences, or a number or range between any two of these values, the under-sequencing threshold can be 10 (or another threshold number). As another example, if the barcode comprises at least, or at most, 1000, 2000, 3000, 4000, 5000, 6000, 6561, 7000, 8000, 9000, 10000, 20000, 30000, 40000, 50000, 60000, 65532, 70000, 80000, 90000, or 100000, the under-sequencing threshold can be 10 (or another threshold number). In some embodiments, the under-sequencing status can be independent of the number of molecular markers with different sequences of a barcode (e.g., a random barcode).

[0205] At decision block 624, if the sequencing status of the target in the sequencing data is not an under-sequencing state, method 600 can proceed to block 628 to filter the molecular marker counts. Filtering the molecular marker counts can include determining, at decision block 632, the number of molecular markers with different sequences associated with the target in the sequencing data that is less than a pseudo-point threshold. In different implementations, the pseudo-point threshold can be different. For example, if a barcode (e.g., a random barcode) includes approximately 6561 molecular markers with different sequences, the pseudo-point threshold can be, or be, approximately 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, or a number or range between any two of these values. As another example, if the barcode (e.g., a random barcode) includes approximately 6561 molecular markers with different sequences, the pseudopoint sequencing threshold can be at least, or at most, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, or 100.

[0206] At decision block 632, if the number of molecular markers with different sequences associated with the target in the sequencing data is less than a pseudo-point threshold, method 600 may optionally proceed to block 636, where pseudo-points may be added to the number of molecular markers with different sequences associated with the target in the sequencing data before determining the number of noise molecular markers with different sequences associated with the target in the sequencing data. In different implementations, pseudo-points may have different molecular marker counts. For example, the molecular marker count of a pseudo-point may be, or be approximately, 0.0001, 0.001, 0.01, 0.1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, or a number or range between any two of these values. As another example, the molecular marker count of a pseudopoint can be at least, or at most, 0.0001, 0.001, 0.01, 0.1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, or 100.

[0207] At decision block 632, if the number of molecular markers with different sequences associated with the target in the sequencing data is not less than the pseudo-point threshold, non-unique molecular markers can be removed at block 640. At block 644, non-unique molecular markers can be removed by determining the number of noise molecular markers with different sequences associated with the target in the sequencing data. Non-unique molecular markers may include molecular markers with different sequences associated with the target in the sequencing data that are greater than a predetermined recycling molecular marker threshold. In different implementations, the recycling molecular marker threshold may be different. For example, if a barcode (e.g., a random barcode) includes approximately 6561 molecular markers with different sequences, the recycling molecular marker threshold may be, or be, approximately 100, 200, 300, 400, 500, 600, 650, 700, 900, 1000, 2000, or a number or range between any two of these values. As another example, if a barcode (e.g., a random barcode) includes approximately 6561 molecular markers with different sequences, the recycled molecular marker threshold can be at least, or at most, 100, 200, 300, 400, 500, 600, 650, 700, 900, 1000, or 2000.

[0208] In some embodiments, removing non-unique molecular markers comprises: determining a theoretical number of non-unique molecular markers for the number of molecular markers having different sequences associated with the target in the sequencing data. Removing non-unique molecular markers may comprise removing molecular markers whose occurrence is greater than the nth most abundant molecular marker among molecular markers having different sequences associated with the target in the sequencing data. The number n can be the theoretical number of non-unique molecular markers.

[0209] At block 644, a distribution-based error correction method may be used to adjust the molecular marker counts. The distribution-based error correction method may include determining the number of noise molecular markers having different sequences associated with the target in the sequencing data. Determining the number of noise molecular markers may include fitting two negative binomial distributions to the number of molecular markers having different sequences associated with the target in the sequencing data. For example, determining the number of noise molecular markers may include fitting a signal negative binomial distribution (one of the two negative binomial distributions) to the number of molecular markers having different sequences associated with the target in the counted sequencing data, wherein the signal negative binomial distribution corresponds to the number of molecular markers having different sequences associated with the target in the counted sequencing data that are signal molecular markers. Determining the number of noise molecular markers may include fitting a noise negative binomial distribution (the other of the two negative binomial distributions) to the number of molecular markers having different sequences associated with the target in the counted sequencing data, wherein the noise negative binomial distribution corresponds to the number of molecular markers having different sequences associated with the target in the counted sequencing data that are noise molecular markers. Determining the number of noise molecular markers may include using the fitted signal negative binomial distribution and the fitted noise negative binomial distribution to determine the number of noise molecular markers.

[0210] In some embodiments, determining the number of noise molecular markers using the fitted signal negative binomial distribution and the fitted noise negative binomial distribution includes, for each of the different sequences associated with the target in the sequencing data: determining the signal probability of the different sequence in the signal negative binomial distribution. And the noise probability of the different sequence in the noise negative binomial distribution can be determined. Furthermore, if the signal probability is less than the noise probability, the different sequence can be determined to be a noise molecular marker. In some embodiments, if fewer than two peaks are found (because two peaks are required to determine the signal negative binomial distribution and the noise negative binomial distribution), adjusting the molecular marker counts at block 644 can include removing singletons (e.g., single base substitutions).

[0211] At block 648, the number of targets can be estimated after proximity-based and distribution-based error correction to generate an output. At decision block 612, if the sequencing state of the target in the sequencing data is a saturated sequencing state, then method 600 can proceed to block 648 to generate an output without adjusting the molecular markers based on directional proximity and distribution-based error correction. For example, the number of noise molecular markers determined can be zero.

[0212] At decision block 624, if the sequencing state of the target in the sequencing data is an under-sequencing state, the method 600 can proceed to block 648 to generate an output without adjusting the molecular markers based on distribution-based error correction. For example, the number of noise molecular markers determined can be zero. The method 600 ends at block 652.

[0213] Immune receptor barcoding error correction When performing sequencing and analysis (such as immune receptor sequencing and analysis), substitution errors, primer crossing errors, and PCR chimera errors may occur. For example, errors may occur when determining the occurrence or copy number of RNA molecules encoding immune receptors (such as T cell receptors). Immune receptors include highly diverse and closely related genes. Therefore, when compared with other genes, the possibility of errors in sequencing data is higher when performing immune receptor sequencing and analysis. Such errors often lead to an overestimation of the diversity of the immune repertoire. The method for mitigating these errors is referred to herein as immune receptor barcode error correction. In some embodiments, immune receptor barcode error correction utilizes recursive substitution error correction to correct substitution errors in molecular markers and nucleotide sequences (e.g., substitution errors in complementary determining region 3 (CDR3)). For a given sample marker or cell marker, many different CDR3s can be associated with the same molecular marker sequence, resulting in an overestimation of immune receptor diversity. The correction method disclosed herein can correct PCR chimeras that cross between molecular markers and sample markers, and then identify and remove erroneous molecular markers.

[0214] Disclosed herein include methods for determining the presence of a target. In some embodiments, the method comprises: (a) barcoding (e.g., randomly barcoding) a plurality of targets using a plurality of barcodes (e.g., random barcodes) to create a plurality of barcoded targets (e.g., randomly barcoded targets), wherein each of the plurality of barcodes comprises a cellular marker and a molecular marker, wherein the molecular markers of at least two of the plurality of barcodes comprise different molecular marker sequences, and wherein at least two of the plurality of barcodes comprise cellular markers having the same cellular marker sequence; (b) obtaining sequencing data for the barcoded targets; and (c) for at least one of the plurality of targets: (i) identifying a putative sequence of the target in the sequencing data; (ii) counting occurrences of the molecular marker sequence associated with the putative sequence of the target in the sequencing data identified in (i); (iii) identifying clusters of putative sequences of the target; (iv) collapsing the obtained sequencing data using the clusters of putative sequences of the target identified in (iii); (v) Identifying clusters of the molecular marker sequences associated with putative sequences of the target; (vi) collapsing the sequencing data using the clusters of the molecular marker sequences identified in (v); (vii) identifying clusters of combined sequences, wherein each combined sequence comprises a sequence from the sequence of the target and an associated molecular marker sequence from the molecular marker sequence; (viii) collapsing the sequencing data using the clusters of combined sequences identified in (vii); (ix) identifying one or more putative sequences of the target corresponding to one or more chimeric sequences of the target, wherein the occurrence of the one or more putative sequences of the target corresponding to the one or more chimeric sequences of the target is less than the occurrence of the remaining one or more putative sequences of the target that do not correspond to the one or more chimeric sequences of the target; (x) removing from the sequencing data the one or more putative sequences of the target corresponding to the one or more chimeric sequences of the target identified in (ix); and (xi) estimating the occurrence of the target, wherein the sequencing data is collapsed in (iv), (vi), and (viii) and in (x). The estimated occurrence of the target is related to the number of molecular marker sequences counted in (ii) after removing the one or more putative sequences of the target corresponding to the one or more chimeric sequences of the target.

[0215] Disclosed herein are methods for determining the occurrence of a target. In some embodiments, the method comprises: (a) receiving sequencing data for a plurality of targets, wherein the sequencing data comprises putative sequences of targets from the plurality of targets and occurrences of molecular marker sequences associated with the sequences of the targets in the sequencing data; (b) folding the putative sequences of the targets; (c) folding the molecular marker sequences associated with the putative sequences of the targets; and (d) estimating the occurrence of the target, wherein, after folding the occurrences of the putative sequences of the targets in (b) and folding the occurrences of noise molecular marker sequences determined in (c), the estimated occurrence of the target is correlated with the occurrences of the molecular marker sequences associated with the putative sequences of the targets in the sequencing data.

[0216] Complementarity-determining regions (CDRs) are part of the variable chains of immunoglobulins (antibodies) produced by B cells and T-cell receptors (T-cell receptors) produced by T cells. Three CDRs (CDR1, CDR2, and CDR3) are arranged non-contiguously along the amino acid sequence of the variable domain of an antigen receptor or immunoreceptor. Because antigen receptors typically consist of two variable domains (on two different polypeptide chains: the heavy chain and the light chain), a total of six CDRs are commonly involved in antigen contact for each heavy and light chain pair. Because the majority of sequence variation associated with immunoglobulins and T-cell receptors is found in the CDRs, it is not easy to distinguish between variations in the nucleotide sequence encoding the CDRs caused by one or more errors during sequencing and variations in the nucleotide sequence encoding the CDRs. Within the variable domain, CDR1 and CDR2 are found in the variable (V) region of the polypeptide chain, while CDR3 encompasses a portion of the V region as well as the entire diversity (D) and joining (J) regions of the heavy chain. Light chains contain the V and J regions but not the D region. CDR3 is the most variable of the CDRs.

[0217] Figure 7This figure is a schematic diagram illustrating a non-limiting exemplary embodiment of immune receptor barcode correction based on recursive substitution error correction (RSEC, also referred to herein as directed contiguity). A barcode (e.g., a random barcode) comprising a cellular marker and a molecular marker can be used to determine the sequence of an mRNA molecule encoding an immune receptor, or generally to identify a target of interest. The CDR3 comprises a portion of the V region and all of the D and J regions. As shown, substitution errors during sample preparation and sequencing can occur in the D and J regions (indicated by "*"). Although not shown, substitution errors can also occur in the V region. Additionally, sequencing errors can occur in the molecular markers (ML, indicated by "*"). Recursive substitution error correction (RSEC) can be used to correct such errors. For example, RSEC can be used to first adjust the counts of the CDR3 sequence. Subsequently, RSEC can be used to adjust the counts of the molecular markers. Alternatively, by treating each CDR3 sequence and associated molecular marker in the sequencing data as a single sequence, RSEC can be used to simultaneously adjust the counts of the CDR3 sequence and the molecular marker. In some embodiments, RSEC can be used to adjust the counts of the molecular markers first.

[0218] Figure 8 800 is a flow chart illustrating a non-limiting exemplary embodiment 800 for correcting errors in nucleotide sequences and molecular markers (based on recursive substitution error correction) and correcting errors in sequencing data attributed to one or more PCR chimeras. Method 800 begins at block 804 after receiving sequencing data for a plurality of barcoded targets (e.g., randomly barcoded targets). In some embodiments, method 800 further includes barcoding (e.g., randomly barcoding) the plurality of targets using a plurality of barcodes (e.g., random barcodes) to create a plurality of barcoded targets, wherein each of the plurality of barcodes includes a molecular marker and / or a cellular marker. In some embodiments, method 800 further includes sequencing the plurality of barcoded targets to obtain sequencing data. The plurality of targets may include targets of the entire transcriptome, genes (e.g., genes encoding immune receptors such as T cell receptors), variable sequences (e.g., variable (V) regions, diversity (D) regions, joining (J) regions encoding immune receptors), or any combination thereof.

[0219] At block 808, method 800 may include, for one or more of the plurality of targets, determining (e.g., counting) a number of molecular markers having different sequences associated with the target in the sequencing data. In some embodiments, counting the number of molecular markers having different sequences associated with the target in the sequencing data may include identifying a putative sequence (e.g., an immune receptor sequence, such as a CDR3 sequence) of the target in the sequencing data; and counting occurrences of molecular marker sequences associated with the identified putative sequence of the target in the sequencing data.

[0220] The putative sequences of the targets (e.g., CDR3) can differ from each other by one or more nucleotides. The sequences are putative in the sense that only one sequence is the true or correct sequence (e.g., there is only one correct CDR3 sequence per cell). The putative sequences of the targets can differ from each other by at least one nucleotide.

[0221] At block 812, method 800 may include adjusting the counts of the putative nucleotide sequence of the target of interest based on recursive substitution error correction (also referred to herein as directed contiguity). In some embodiments, adjusting the nucleotide sequence counts based on RSEC may be similar to adjusting the counts of the putative nucleotide sequence of the target of interest based on a reference sequence. Figure 5 The described directed adjacency adjustment of molecular marker counts. For example, adjusting nucleotide sequence counts based on directed adjacency may include: identifying a putative sequence of the target in the sequencing data; counting the occurrences of molecular marker sequences associated with the identified putative sequence of the target in the sequencing data; identifying clusters of putative sequences of the target; and collapsing the obtained sequencing data using the identified clusters of putative sequences of the target. Identifying clusters of putative sequences of the target may include identifying clusters of putative sequences of the target using RSEC. Collapsing the obtained sequencing data using the identified clusters of putative sequences of the target may include attributing the occurrences of subsequences in the one or more subsequences to the parent sequence of the subsequence.

[0222] In some embodiments, the putative sequences of the targets within a cluster can be within a first predetermined directional contiguous threshold of each other. In different embodiments, the first predetermined directional contiguous threshold can be different. In some embodiments, the first directional contiguous threshold can be a Hamming distance, or about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or a number or range between any two of these values. In some embodiments, the first directional contiguous threshold can be a Hamming distance, at least or at most 1, 2, 3, 4, 5, 6, 7, 8, 9 or 10. The putative sequences of the targets within a cluster can include one or more parent sequences and one or more subsequences of the one or more parent sequences. The number of occurrences of the parent sequence can be greater than or equal to the first predetermined directional contiguous occurrence threshold. In different embodiments, the first predetermined directional contiguous occurrence threshold can be different. In some embodiments, the first predetermined directional contiguous occurrence threshold can be twice the number of occurrences of the subsequence that is less than one. In some embodiments, the first predetermined directional contiguous occurrence threshold can be, or about, 1.5 times, 2 times, 3 times, 4 times, 5 times, 6 times, 7 times, 8 times, 9 times, 10 times the number of occurrences of the subsequence, or a number or range between any two of these values. In some embodiments, the first predetermined directional contiguous occurrence threshold can be at least or at most 1.5 times, 2 times, 3 times, 4 times, 5 times, 6 times, 7 times, 8 times, 9 times, or 10 times the number of occurrences of the subsequence.

[0223] At block 816, method 800 may include adjusting the counts of molecular markers based on RSEC. In some embodiments, adjusting the counts of molecular markers based on directed contiguity may refer to Figure 5Description. For example, adjusting the molecular marker counts based on directional adjacency can include: identifying a cluster of the molecular marker sequences associated with the putative sequence of the target; and folding the sequencing data using the identified cluster of molecular marker sequences. Identifying the cluster of the molecular marker sequences associated with the putative sequence of the target can include identifying the cluster of the molecular marker sequences associated with the putative sequence of the target using directional adjacency. The molecular marker sequences of the targets within a cluster can be within a second predetermined directional adjacency threshold of each other. In different embodiments, the second predetermined directional adjacency threshold can be different. In some embodiments, the second directional adjacency threshold can be a Hamming distance, or about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or a number or range between any two of these values. In some embodiments, the second directional adjacency threshold can be a Hamming distance of at least or at most 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10. The putative molecular marker sequences of the targets within a cluster can include one or more parent molecular marker sequences and one or more child molecular marker sequences of the one or more parent molecular marker sequences. In some embodiments, the occurrence of a parental molecular marker sequence can be greater than or equal to a second predetermined directional adjacent occurrence threshold value. In various embodiments, the second predetermined directional adjacent occurrence threshold value can be different. In some embodiments, the second predetermined directional adjacent occurrence threshold value is twice the occurrence of a sub-molecule marker sequence that is less than one. In some embodiments, the second predetermined directional adjacent occurrence threshold value can be or about 1.5 times, 2 times, 3 times, 4 times, 5 times, 6 times, 7 times, 8 times, 9 times, 10 times the occurrence of a sub-sequence, or a number or range between any two of these values. In some embodiments, the second predetermined directional adjacent occurrence threshold value can be at least or at most 1.5 times, 2 times, 3 times, 4 times, 5 times, 6 times, 7 times, 8 times, 9 times, or 10 times the occurrence of a sub-sequence. Folding the sequencing data using the cluster of the molecular marker sequences associated with the sequence of the target identified can include attributing the occurrence of the sub-molecule marker sequences in the one or more sub-molecule marker sequences to the parental molecular markers of the sub-molecule marker sequences.

[0224] At box 820, method 800 may include optionally adjusting the counts of nucleotide sequences and molecular markers simultaneously based on RSEC. Adjusting the counts of nucleotide sequences and molecular markers simultaneously based on directional adjacency may include identifying clusters of combined sequences, wherein each combined sequence includes a sequence in the sequence of the target and an associated molecular marker sequence in the molecular marker sequence; and folding the sequencing data using the clusters of the identified combined sequences. Identifying clusters of combined sequences may include using directional adjacency to identify clusters of combined sequences. The combined sequences within a cluster may be within a third predetermined directional adjacency threshold of each other. In some embodiments, the third directional adjacency threshold may be a Hamming distance, or about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or a number or range between any two of these values. In some embodiments, the third directional adjacency threshold may be a Hamming distance, at least or at most 1, 2, 3, 4, 5, 6, 7, 8, 9 or 10. In some embodiments, the combination sequence in the cluster can include one or more parental combination sequences and one or more subcombination sequences of the one or more parental combination sequences, and wherein the occurrence of the parental combination sequence is greater than or equal to the 3rd predetermined orientation adjacent to occur threshold value. In different embodiments, the 3rd predetermined orientation adjacent to occur threshold value can be different. In certain embodiments, the 3rd predetermined orientation adjacent to occur threshold value is twice the occurrence of a submolecule marker sequence less than one. In certain embodiments, the 3rd predetermined orientation adjacent to occur threshold value can be or about 1.5 times, 2 times, 3 times, 4 times, 5 times, 6 times, 7 times, 8 times, 9 times, 10 times or numeral or scope between any two of these values ​​of the occurrence of subsequence. In certain embodiments, the 3rd predetermined orientation adjacent to occur threshold value can be at least or at most 1.5 times, 2 times, 3 times, 4 times, 5 times, 6 times, 7 times, 8 times, 9 times or 10 times of the occurrence of subsequence. The cluster of the combination sequence identified in use (vii) can be folded to the sequencing data and can include: the occurrence of the subcombination sequence in the one or more subcombination sequences is attributed to the parental combination sequence of the subcombination sequence.

[0225] At block 824, the sequencing state of the target in the sequencing data can be optionally determined. The sequencing state of the target in the sequencing data can include or be insufficient sequencing. At decision block 828, it can be optionally determined whether the sequencing state of the target in the sequencing data is an insufficient sequencing state. For example, if the depth of the target (e.g., average, minimum, or maximum depth) is less than or less than about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, or a number or range between any two of these, then it can be considered that the target has an insufficient sequencing state. As another example, if the depth of the target is less than at least or at most 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, or 100, then it can be considered that the target has an insufficient sequencing state.

[0226] In some embodiments, an under-sequencing state can be determined by targets having a depth (e.g., average, minimum, or maximum depth) less than a predetermined under-sequencing threshold. In different implementations, the under-sequencing threshold can be different. For example, the under-sequencing threshold can be, or be about, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, or a number or range between any two of these values. As another example, the under-sequencing threshold can be at least, or at most, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, or 100.

[0227] In some embodiments, the under-sequencing status can depend on the number of molecular markers with different sequences of a barcode (e.g., a random barcode). For example, if the barcode includes, or about, 1000, 2000, 3000, 4000, 5000, 6000, 6561, 7000, 8000, 9000, 10000, 20000, 30000, 40000, 50000, 60000, 65532, 70000, 80000, 90000, 100000 molecular markers with different sequences, or a number or range between any two of these values, the under-sequencing threshold can be 10 (or another threshold number). As another example, if the barcode comprises at least, or at most, 1000, 2000, 3000, 4000, 5000, 6000, 6561, 7000, 8000, 9000, 10000, 20000, 30000, 40000, 50000, 60000, 65532, 70000, 80000, 90000, or 100000, the under-sequencing threshold can be 10 (or another threshold number). In some embodiments, the under-sequencing status can be independent of the number of molecular markers with different sequences of a barcode (e.g., a random barcode).

[0228] At decision block 828, if the sequencing status of the target in the sequencing data is not an under-sequenced state, method 800 may proceed to decision block 832 to determine whether any singletons (e.g., single base substitutions) remain in the nucleotide sequence and / or molecular markers after the adjustments made at blocks 812, 816, and 820. If at least one singleton remains, method 800 may proceed to block 836, where molecular marker counts corresponding to chimeras may be removed.

[0229] At block 836 , method 800 may include removing molecular marker counts corresponding to mosaicism. Figure 9 Schematic diagram of one possible source of immune receptor mosaicism (or target mosaicism). As shown, many different CDR3 sequences (or putative target sequences) may have or be associated with the same ML (and sample or cell marker), which may lead to an overestimation of TCR diversity. Two or more true CDR3 sequences may be crossed during PCR. For example, after barcoding (e.g., in Figure 2 After block 224 of Figure 9The two (or more) true CDR3 sequences can be two different CDR3 sequences from two different cells that are not the result of PCR crossover. Immune receptor chimeras can be formed during PCR (e.g., Figure 2 In the example of FIG20 , at block 228 , multiple different CDR3 sequences (e.g., two different CDR3 sequences) can have the same ML. For example, ML-1 can be associated with CDR3-1 and a chimera of CDR3-1 and CDR3-2. As another example, ML-2 can be associated with CDR3-2 and a chimera of CDR3-1 and CDR3-2. It may be advantageous to remove molecular marker counts corresponding to chimeras.

[0230] refer to Figure 8Removing molecular marker counts corresponding to chimeras can include identifying one or more putative sequences of the target corresponding to one or more chimeric sequences of the target, wherein the occurrence of the one or more putative sequences of the target corresponding to the one or more chimeric sequences of the target is less than the occurrence of the remaining one or more putative sequences of the target that do not correspond to the one or more chimeric sequences of the target; and removing the identified one or more putative sequences of the target corresponding to the one or more chimeric sequences of the target from the sequencing data. Identifying one or more putative sequences of the target corresponding to the one or more chimeric sequences of the target can include: identifying a putative sequence of the target associated with a molecular marker sequence in the plurality of molecular sequences; and identifying a putative sequence in the putative sequence of the target associated with the one molecular marker sequence, wherein the occurrence of the molecular marker sequence is less than a chimeric occurrence threshold corresponding to the chimeric sequence in the one or more chimeric sequences of the target. In different embodiments, the chimeric occurrence threshold can be different. In some embodiments, the value of the chimeric occurrence threshold can be the occurrence of a putative sequence in the putative sequence of the target associated with a molecular marker sequence, wherein the occurrence is greater than the occurrence of any other sequence in the putative sequence of the target. In some embodiments, the chimeric occurrence threshold can be the occurrence of an inferred sequence in the inferred sequence of the target associated with a molecular marker sequence that is greater than the occurrence of any other sequence in the inferred sequence of the target adjusted for offset (e.g., subtraction). In different embodiments, the offset can be different. In some embodiments, the offset can be, or about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, or a number or range between any two of these values. In some embodiments, the offset can be at least, or at most, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, or 100.

[0231] After the sequencing data is collapsed at blocks 812, 816, and 820, the estimated occurrence of the target after block 824 is correlated with the number of molecular marker sequences counted at block 808. After the sequencing data is collapsed at blocks 812, 816, and 820 and one or more putative sequences of the target corresponding to one or more chimeric sequences of the target are removed at block 836, the estimated occurrence of the target after block 836 is correlated with the number of molecular marker sequences counted at block 808.

[0232] At block 840, pseudopoints can be optionally added to the number of molecular markers with different sequences associated with the target in the sequencing data before determining the number of noise molecular markers with different sequences associated with the target in the sequencing data. For example, if the number of molecular markers with different sequences associated with the target in the sequencing data is less than a pseudopoint threshold, method 800 may include adding pseudopoints to the number of molecular markers with different sequences associated with the target in the sequencing data before determining the number of noise molecular markers with different sequences associated with the target in the sequencing data. In different implementations, the pseudopoint threshold can be different. For example, if a barcode (e.g., a random barcode) includes approximately 6561 molecular markers with different sequences, the pseudopoint threshold can be, or be, approximately 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, or a number or range between any two of these values. As another example, if a barcode (e.g., a random barcode) includes approximately 6561 molecular markers with different sequences, the pseudo-spot sequencing threshold can be at least, or at most, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, or 100. In various embodiments, the added pseudo-spots can have different molecular marker counts. For example, the molecular marker count of a pseudo-spot can be or is about 0.0001, 0.001, 0.01, 0.1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, or a number or range between any two of these values. As another example, the molecular marker count of a pseudopoint can be at least, or at most, 0.0001, 0.001, 0.01, 0.1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, or 100.

[0233] At block 844, a distribution-based error correction method may be used to adjust the molecular marker counts. Performing the distribution-based error correction method may include determining the number of noise molecular markers having different sequences associated with the target in the sequencing data. Determining the number of noise molecular markers may include fitting two distributions (such as two negative binomial distributions) to the number of molecular markers having different sequences associated with the target in the sequencing data. For example, determining the number of noise molecular markers may include fitting a signal negative binomial distribution (one of the two negative binomial distributions) to the number of molecular markers having different sequences associated with the target in the counted sequencing data, wherein the signal negative binomial distribution corresponds to the number of molecular markers having different sequences associated with the target in the counted sequencing data that are signal molecular markers. Determining the number of noise molecular markers may include fitting a noise negative binomial distribution (the other of the two negative binomial distributions) to the number of molecular markers having different sequences associated with the target in the counted sequencing data, wherein the noise negative binomial distribution corresponds to the number of molecular markers having different sequences associated with the target in the counted sequencing data that are noise molecular markers. Determining the number of noise molecule markers may include determining the number of noise molecule markers using a fitted negative binomial distribution for the signal and a fitted negative binomial distribution for the noise.

[0234] In some embodiments, determining the number of noise molecular markers using the fitted signal negative binomial distribution and the fitted noise negative binomial distribution includes, for each of the different sequences associated with the target in the sequencing data: determining the signal probability of the different sequence in the signal negative binomial distribution. And the noise probability of the different sequence in the noise negative binomial distribution can be determined. Furthermore, if the signal probability is less than the noise probability, the different sequence can be determined to be a noise molecular marker. In some embodiments, if fewer than two peaks are found (because two peaks are required to determine the signal negative binomial distribution and the noise negative binomial distribution), adjusting the molecular marker counts at block 644 can include removing singletons (e.g., single base substitutions).

[0235] After collapsing the sequencing data at blocks 812, 816, and 820 and removing the one or more putative sequences of the target corresponding to the one or more chimeric sequences of the target at block 836, performing distribution-based error correction may include thresholding the molecular signature sequences associated with the putative sequences of the target after collapsing the sequencing data at blocks 812, 816, and 820 and removing the one or more putative sequences of the target corresponding to the one or more chimeric sequences of the target at block 836 to determine signal and noise molecular signature sequences associated with the sequences of the target in the sequencing data counted at block 808. Thresholding the molecular signature sequences associated with the putative sequences of the target may include performing a statistical analysis on the molecular signature sequences of the target. Performing the statistical analysis may include fitting the molecular signature sequences associated with the putative sequences of the target and their occurrences to two negative binomial distributions; determining the occurrence n of a signal molecular signature sequence using the two negative binomial distributions; and removing the noise molecular signature sequences from the sequencing data obtained in (b) after folding the sequencing data in (iv), (vi), and (viii) and removing the one or more putative sequences of the target corresponding to the one or more chimeric sequences of the target in (x), wherein the noise molecular signature sequences include molecular signature sequences whose occurrences are less than the occurrences of the nth most abundant molecular signature, and wherein the signal molecular signature sequences include molecular signature sequences whose occurrences are greater than or equal to the occurrences of the nth most abundant molecular signature. The two negative binomial distributions may include a first negative binomial distribution corresponding to the signal molecular signature sequence and a second negative binomial distribution corresponding to the noise molecular signature sequence.

[0236] At frame 848, after the error correction based on adjacent position and based on distribution, the quantity of target can be estimated to produce output.At decision block 828, if the sequencing state of the target in the sequencing data is the insufficient state of sequencing, then method 800 can proceed to frame 848 to produce output, and does not adjust molecular markers based on the error correction based on distribution.For example, the quantity of noise molecule markers can be zero.At decision block 832, if there is no singleton in the sequencing data through adjustment, then method 800 can proceed to frame 848 to produce output, and does not adjust molecular markers based on the error correction based on distribution.Described method 800 ends at frame 852.

[0237] Sequencing In some embodiments, estimating the number of different labeled or barcoded targets (e.g., randomly barcoded targets) can include determining the sequence of the labeled target, spatial marker, molecular marker, sample marker, cell marker, or any product thereof (e.g., labeled amplicon, or labeled cDNA molecule). The amplified target can be subjected to sequencing. Determining the sequence of the barcoded target (e.g., randomly barcoded target) or any product thereof can include performing a sequencing reaction to determine the sequence of at least a portion of the sample marker, spatial marker, cell marker, molecular marker, the sequence of at least a portion of the barcoded target, its complement, its reverse complement, or any combination thereof.

[0238] The sequence of a barcoded target or a randomly barcoded target (e.g., an amplified nucleic acid, a labeled nucleic acid, a cDNA copy of a labeled nucleic acid, etc.) can be determined using a variety of sequencing methods, including, but not limited to, sequencing by hybridization (SBH), sequencing by ligation (SBL), quantitative incremental fluorescent nucleotide addition sequencing (QIFNAS), segmented ligation and fragmentation, fluorescence resonance energy transfer (FRET), molecular beacons, TaqMan reporter probe digestion, pyrosequencing, fluorescence in situ sequencing (FISSEQ), FISSEQ beads, wobble sequencing, multiplex sequencing, polymerized colony (POLONY) sequencing; nanogrid rolling circle sequencing (ROLONY), allele-specific oligo ligation assays (e.g., oligonucleotide ligation assays (OLA), single template molecules using ligated linear probes and rolling circle amplification (RCA) readout, ligated latch probes, and so on. molecule) OLA, or single-template molecule) OLA using linked circular latch probes and rolling circle amplification (RCA) readout, etc.

[0239] In some embodiments, determining the sequence of a barcoded target (e.g., a randomly barcoded target) or any product thereof comprises paired-end sequencing, nanopore sequencing, high-throughput sequencing, shotgun sequencing, dye terminator sequencing, multiplexed DNA sequencing, primer walking, Sanger dideoxy sequencing, Maxim-Gilbert sequencing, pyrosequencing, true single molecule sequencing, or any combination thereof. Alternatively, the sequence of a barcoded target or any product thereof can be determined by electron microscopy analysis or a chemically sensitive field effect transistor (chemFET) array.

[0240] High-throughput sequencing methods can also be used, such as cycle array sequencing using platforms such as the Roche 454, Illumina Solexa, ABI-SOLiD, ION Torrent, Complete Genomics, Pacific Bioscience, Helicos, or Polonator platforms. In some embodiments, sequencing can include MiSeq sequencing. In some embodiments, sequencing can include HiSeq sequencing.

[0241] The barcoded targets (e.g., randomly barcoded targets) can include nucleic acids representing from about 0.01% to about 100% of the genes in the genome of an organism. For example, a target complementary region comprising a plurality of polymers can be used to sequence from about 0.01% to about 100% of the genes in the genome of an organism by capturing genes containing complementary sequences from the sample. In some embodiments, the barcoded targets include nucleic acids representing from about 0.01% to about 100% of the transcripts in the transcriptome of an organism. For example, a target complementary region comprising a poly(T) tail can be used to sequence from about 0.501% to about 100% of the transcripts in the transcriptome of an organism by capturing mRNA from the sample.

[0242] Determining the sequence of the spatial and molecular markers of the plurality of barcodes (e.g., random barcodes) may comprise sequencing 0.00001%, 0.0001%, 0.001%, 0.01%, 0.1%, 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 99%, 100%, or a number or range between any two of these values ​​of the plurality of barcodes. Determining the sequence of the markers (e.g., sample markers, spatial markers, and molecular markers) of the plurality of barcodes may comprise sequencing 1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 100% of the plurality of barcodes. 3 , 104 , 10 5 , 10 6 , 10 7 , 10 8 , 10 9 , 10 10 , 10 11 , 10 12 , 10 13 , 10 14 , 10 15 , 10 16 , 10 17 , 10 18 , 10 19 , 10 20 , 400, 500, 600, 700, 800, 900, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, or a number or range between any two of these values. Sequencing some or all of the plurality of barcodes can include generating a sequence having, having about, having at least, or having a read length of at most 10, 20, 30, 40, 50, 60, 70, 80, 90, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, or a number or range between any two of these values.

[0243] Sequencing can include sequencing at least or about 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, or more nucleotides or base pairs of a barcoded target (e.g., a randomly barcoded target). For example, sequencing can include generating sequencing data by polymerase chain reaction (PCR) amplification of a plurality of barcoded targets, wherein the sequence has a read length of 50, 75, or 100, or more nucleotides. Sequencing can include sequencing at least or about 200, 300, 400, 500, 600, 700, 800, 900, 1,000, or more nucleotides or base pairs of a barcoded target. Sequencing can include sequencing at least or at least about 1500, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, or 10000 or more nucleotides or base pairs of the barcoded target.

[0244] Sequencing can include at least about 200, 300, 400, 500, 600, 700, 800, 900, 1,000 or more sequencing reads per run. In some embodiments, sequencing includes sequencing at least or at least about 1500, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000 or 10000 or more sequencing reads per run. Sequencing can include less than or equal to about 1,600,000,000 sequencing reads per run. Sequencing can include less than or equal to about 200,000,000 reads per run. sample

[0245] In some embodiments, the multiple targets may be included in one or more samples. The sample may include one or more cells, or nucleic acids from one or more cells. The sample may be a single cell or nucleic acids from a single cell. The one or more cells may be one or more cell types. At least one of the one or more cell types may be a brain cell, a heart cell, a cancer cell, a circulating tumor cell, an organ cell, an epithelial cell, a metastatic cell, a benign cell, a primary cell, a circulating cell, or any combination thereof.

[0246] The sample used in the methods of the present disclosure may include one or more cells. A sample may refer to one or more cells. In some embodiments, a plurality of cells may include one or more cell types. At least one of the one or more cell types may be a brain cell, a heart cell, a cancer cell, a circulating tumor cell, an organ cell, an epithelial cell, a metastatic cell, a benign cell, a primary cell, a circulating cell, or any combination thereof. In some embodiments, the cell is a cancer cell removed from a cancerous tissue, such as breast cancer, lung cancer, colon cancer, prostate cancer, ovarian cancer, pancreatic cancer, brain cancer, melanoma, and non-melanoma skin cancer. In some embodiments, the cell is derived from a cancer but collected from a body fluid (e.g., a circulating tumor cell). Non-limiting examples of cancer may include adenoma, adenocarcinoma, squamous cell carcinoma, basal cell carcinoma, small cell carcinoma, large cell undifferentiated carcinoma, chondrosarcoma, and fibrosarcoma. The sample may include tissue, a monolayer of cells, fixed cells, a tissue section, or any combination thereof. The sample may include a biological sample, a clinical sample, an environmental sample, a biological fluid, a tissue or cell from a subject. The sample can be obtained from a human, mammal, dog, rat, mouse, fish, fly, worm, plant, fungus, bacteria, virus, vertebrate, or invertebrate.

[0247] In some embodiments, the cell is a cell that has been infected with a virus and contains a viral oligonucleotide. In some embodiments, the viral infection can be caused by a virus such as a single-stranded (+ strand or "sense") DNA virus (e.g., a parvovirus) or a double-stranded RNA virus (e.g., a reovirus). In some embodiments, the cell is a bacterium. These can include Gram-positive bacteria or Gram-negative bacteria. In some embodiments, the cell is a fungus. In some embodiments, the cell is a protozoan or other parasite.

[0248] As used herein, the term "cell" can refer to one or more cells. In some embodiments, the cells are normal cells, for example, human cells at various stages of development, or human cells from various organs or tissue types. In some embodiments, the cells are non-human cells, for example, other types of mammalian cells (e.g., mouse, rat, pig, dog, cow, or horse). In some embodiments, the cells are other types of animal or plant cells. In other embodiments, the cells can be any prokaryotic or eukaryotic cell.

[0249] Data analysis and display software Targeted spatial resolution data analysis and visualization The present disclosure provides methods for estimating the number and location of targets using barcoding (e.g., random barcoding) and digital counting using spatial labeling. The data obtained from the disclosed methods can be visualized on a map. The information generated using the methods described herein can be used to construct a map of the number and location of targets from a sample. The map can be used to locate the physical location of the target. The map can be used to identify the location of multiple targets. The multiple targets can be the same target type, or the multiple targets can be multiple different targets. For example, a map of the brain can be constructed to display the digital counts and locations of multiple targets.

[0250] A spectrum can be generated from the data from a single sample. A spectrum can be constructed using data from multiple samples to generate a combined spectrum. A spectrum can be constructed using data from tens, hundreds and / or thousands of samples. The spectrum constructed from multiple samples can illustrate the distribution of digital counts of targets associated with a common region of multiple samples. For example, repeated measurements can be displayed on the same spectrum. At least 1, 2, 3, 4, 5, 6, 7, 8, 9 or 10, or more repetitions (e.g., coverage) can be displayed on the same figure. At most 1, 2, 3, 4, 5, 6, 7, 8, 9 or 10, or more repetitions (e.g., coverage) can be displayed on the same figure. Spatial distribution and target number can be represented by various statistics.

[0251] Combining data from multiple samples can increase the positional resolution of the combined atlas. The orientation of multiple samples can be recorded by common landmarks, where the individual position measurements across the samples are at least partially discontinuous. A particular example is to slice a sample using an ultramicrotome on one axis and then slice a second sample along a different path. The combined data set will give the three-dimensional spatial position associated with the digital count of the target. Multiplexing the above methods will allow for high-resolution three-dimensional atlases of digital count statistics.

[0252] In some embodiments of the instrument system, the system will include a computer-readable medium comprising code for providing data analysis for a sequence dataset generated by performing a single-cell, barcoding assay (eg, a random barcoding assay). Examples of data analysis functions that can be provided by the data analysis software include, but are not limited to, (i) algorithms for decoding / demultiplexing sample labels, cell labels, spatial labels, and molecular labels, as well as target sequence data, provided by sequencing a barcode library (e.g., a random barcode library) generated in a run of an assay, (ii) algorithms for determining the number of reads per gene per cell and the number of unique transcript molecules per gene per cell based on the data and creating summary tables, (iii) statistical analysis of sequence data, such as for clustering cells based on gene expression data or for predicting confidence intervals for the number of transcript molecules per gene per cell, (iv) algorithms for identifying rare cell subpopulations, such as using principal component analysis, hierarchical clustering, k-means clustering, self-organizing maps, neural networks, etc., (v) sequence alignment capabilities for aligning genetic sequence data to known reference sequences and detecting mutations, polymorphic markers, and splice variants, and (vi) automated clustering of molecular markers to compensate for amplification or sequencing errors. In some embodiments, commercially available software can be used to perform all or part of the data analysis, for example, SevenBridges (https: / / www.sbgenomics.com / ) software can be used to compile a table of the copy number of one or more genes that appear in each cell in the entire cell collection. In some embodiments, the data analysis software may include an option for outputting the sequencing results in a useful graphical format, for example, a heat map indicating the copy number of one or more genes present in each cell of the cell collection. In some embodiments, the data analysis software may also include an algorithm for extracting biological significance from the sequencing results, for example, by associating the copy number of one or more genes present in each cell of the cell collection with a cell, a rare cell, or a cell derived from a subject with a specific disease or condition. In some embodiments, the data analysis software may also include an algorithm for comparing cell populations across different biological samples.

[0253] In certain embodiments, all data analysis functions can be packaged in a single software package. In certain embodiments, a complete data analysis capability set can include a set of software packages. In certain embodiments, the data analysis software can be an independent package that is independent of the measuring instrument system and is available to the user. In certain embodiments, the software can be network-based and can allow users to share data.

[0254] In certain embodiments, all data analysis functions can be packaged in a single software package. In certain embodiments, a complete data analysis capability set can include a set of software packages. In certain embodiments, the data analysis software can be an independent package that is independent of the measuring instrument system and is available to the user. In certain embodiments, the software can be network-based and can allow users to share data.

[0255] System processor and network Generally, the instrument systems applicable to the present disclosure (e.g. Figure 10 The computer or processor used in the method described above can be further understood as a logic device that can read instructions from the medium 1011 or the network port 1005, which can optionally be connected to the server 1009 with the fixed medium 1012. Figure 10 The computer system (shown in FIG. 100 ) may include a CPU 1001, a disk drive 1003, an optional input device (such as a keyboard 1015 or a mouse 1016), and an optional monitor 1007. Data communication may be achieved by transmitting data over a designated communication medium to a server at a local or remote location. The communication medium may include any means for sending or receiving data. For example, the communication medium may be a network connection, a wireless connection, or an Internet connection. Such a connection may provide communication via the World Wide Web. It is contemplated that data related to the present disclosure may be transmitted over such a network or connection to be received or reviewed by a recipient 1022, such as Figure 10 As described.

[0256] Figure 11 An exemplary embodiment of a first example architecture of a computer system 1100 that can be used in conjunction with example embodiments of the present disclosure is illustrated. Figure 11 As depicted, the exemplary computer system may include a processor 1102 for processing instructions. Non-limiting examples of processors include: Intel Xeon™ processors, AMD Opteron TM Processor: Samsung 32-bit RISC ARM 1176JZ(F)-S v1.0 TMprocessor, ARM Cortex-A8 Samsung S5PC100™ processor, ARM Cortex-A8 Apple A4™ processor, Marvell PXA930™ processor, or functionally equivalent processors. Multiple execution threads can be used for parallel processing. In some embodiments, multiple processors or processors with multiple cores can also be used, whether in a single computer system, in clusters, or distributed across systems via a network including multiple computers, mobile phones, or personal data assistants.

[0257] like Figure 11 As illustrated, a cache 1104 may be connected to or incorporated into processor 1102 to provide high-speed storage for instructions or data that have been recently or frequently used by processor 1102. Processor 1102 is connected to northbridge 1106 via processor bus 1108. Northbridge 1106 is connected to random access memory (RAM) 1110 via memory bus 1112 and manages processor 1102's access to RAM 1110. Northbridge 1106 is also connected to southbridge 1114 via chipset bus 1116. Southbridge 1114, in turn, is connected to peripheral bus 1118. The peripheral bus may be, for example, PCI, PCI-X, PCI Express, or another peripheral bus. The northbridge and southbridge are often referred to as a processor chipset and manage data transfers between the processor, RAM, and peripheral components on peripheral bus 1118. In some alternative architectures, the functionality of the northbridge may be incorporated into the processor, rather than using a separate northbridge chip.

[0258] In some embodiments, the system 1100 may include an accelerator card 1122 attached to the peripheral bus 1118. The accelerator may include a field programmable gate array (FPGA) or other hardware for accelerating certain processes. For example, the accelerator may be used for adaptive data reconstruction or for evaluating algebraic expressions used in extended set processing.

[0259] Software and data are stored in external memory 1124 and can be loaded into RAM 1110 or cache 1104 for use by the processor. System 1100 includes an operating system for managing system resources; non-limiting examples of operating systems include: Linux, Windows TM 、MACOS TM 、BlackBerry OS TM 、iOS TM , and other functionally equivalent operating systems, and application software running on top of the operating systems for managing data storage and optimization according to example embodiments of the present invention.

[0260] In this example, system 1100 further includes network interface cards (NICs) 1120 and 1121 connected to the peripheral bus to provide a network interface for external storage (such as network attached storage (NAS)) and other computer systems that can be used for distributed parallel processing.

[0261] Figure 12 An exemplary diagram is shown illustrating a network 1200 having multiple computer systems 1202a and 1202b, multiple cell phones and personal data assistants 1202c, and network attached storage (NAS) 1204a and 1204b suitable for use with the methods of the present disclosure. In an exemplary embodiment, systems 1212a, 1212b, and 1212c can manage data storage and optimize data access for data stored in the NAS 1214a and 1214b. Mathematical models can be applied to the data and evaluated using distributed parallel processing across the computer systems 1212a and 1212b and the cell phone and personal data assistant system 1212c. The computer systems 1212a and 1212b and the cell phone and personal data assistant system 1212c can also provide parallel processing for adaptive data reconstruction of the data stored in the NAS 1214a and 1214b. Figure 12 This is just one example, and a wide variety of other computer architectures and systems can be used in conjunction with various embodiments of the present invention. For example, blade servers can be used to provide parallel processing. Processor blades can be connected via a backplane to provide parallel processing. Storage can also be attached to the backplane via a separate network interface or as network attached storage (NAS).

[0262] In some example embodiments, the processors may maintain separate memory spaces and transmit data via a network interface, backplane, or other connector for parallel processing by other processors. In other embodiments, some or all processors may use a shared virtual address memory space.

[0263] Figure 13An exemplary block diagram of a multi-processor computer system 1300 using a shared virtual address memory space is shown, according to an example embodiment. The system includes multiple processors 1302a-f that can access a shared memory subsystem 1304. The system incorporates multiple programmable hardware memory algorithm processors (MAPs) 1306a-f within the memory subsystem 1304. Each MAP 1306a-f can include memory 1308a-f and one or more field programmable gate arrays (FPGAs) 1310a-f. The MAPs provide configurable functional units, and specific algorithms or portions of algorithms can be provided to the FPGAs 1310a-f for processing in close coordination with the corresponding processors. For example, the MAPs can be used to evaluate algebraic expressions against data models and, in example embodiments, to perform adaptive data reconstruction. In this example, each MAP is globally accessible to all processors for these purposes. In one configuration, each MAP can use direct memory access (DMA) to access the associated memory 1308a-f, allowing it to execute tasks independently of and asynchronously with the corresponding microprocessor 1302a-f. In this configuration, a MAP can pass results directly to another MAP for pipelining and parallel execution of algorithms.

[0264] The above computer architectures and systems are merely examples, and a wide variety of other computer, mobile phone, and personal data assistant architectures and systems can be used in conjunction with example embodiments, including systems using any combination of general-purpose processors, coprocessors, FPGAs and other programmable logic devices, systems on a chip (SOCs), application-specific integrated circuits (ASICs), and other processing and logic elements. In some embodiments, all or part of the computer system can be implemented in software or hardware. Any type of data storage medium can be used in conjunction with example embodiments, including random access memory, hard drives, flash memory, tape drives, disk arrays, network-attached storage (NAS), and other local or distributed data storage devices and systems.

[0265] In example embodiments, the computer subsystem of the present disclosure may be implemented using software modules executed on any of the above or other computer architectures and systems. In other embodiments, the functionality of the system may be implemented partially or completely in firmware, a programmable logic device (such as a field programmable gate array (FPGA)), a system on a chip (SOC), an application specific integrated circuit (ASIC), or other processing and logic elements. For example, the integrated processor and optimizer may be implemented through hardware acceleration using a hardware accelerator card (such as an accelerator card).

[0266] Examples Certain aspects of the embodiments discussed above are disclosed in further detail in the following examples, which are not intended to limit the scope of the disclosure in any way.

[0267] Example 1 ML coverage of ACTB for each ML in the plate for highly expressed genes This example demonstrates that the different distributions of ML errors derived during sequencing or PCR often have a different distribution than true ML.

[0268] In addition to absolute gene expression counts and PCR bias correction, ML can also provide a better understanding of library preparation procedures and the statistical quality of sequencing data. By examining the number of reads exhibiting the same gene ML (termed ML coverage), sequencing base call errors or PCR errors generated during library preparation can be detected. For example, the ML of a gene represented by multiple reads for a given SL may be a more accurate measurement than the ML of a gene represented by only a single read. While barcodes with high ML coverage exist in the same library, barcodes with low ML coverage are often artifacts or errors generated during the sequencing run or PCR step during library preparation. ML errors derived during sequencing or PCR typically have a different distribution than true ML. Figure 15 is an exemplary graph showing molecular marker coverage of each molecular marker for a microplate of a highly expressed gene - ATCB, in which different distributions are observed between false molecular markers and true molecular markers. Figure 16 Figure 2 is an exemplary graph showing the molecular marker coverage fit of two negative binomial distributions for each molecular marker on a microplate of the highly expressed gene ATCB. The fit of the two negative binomial distributions demonstrates that molecular marker errors with low molecular marker depth and true molecular markers with high molecular marker depth can be statistically distinguished. x The axis is the molecular depth.

[0269] Together, these data demonstrate that ML errors derived during sequencing or PCR often have a different distribution than true ML.

[0270] Example 2 Correction of molecular marker errors caused by PCR or sequencing substitution This example demonstrates a method for correcting molecular markers due to PCR and sequencing substitution errors that can be applied to whole transcriptome assays without the assumption of uniform coverage and without requiring high sequencing coverage for a fully sequenced state.

[0271] Deduplication was performed on the first mapped coordinate and unique molecular identifier (UMI) of each read, and reads were assumed to be identical given the same start coordinate, UML, and strand. After deduplication, the UML with the highest count per cluster was retained (Table 13).

[0272] Molecular markers (MLs) were corrected for bias per gene. For each gene, ML clusters were identified using directed neighbor joining. MLs were clustered if they were within a Hamming distance of 1 and the parental ML count was ≥ 2*(child ML count)-1. All MLs in the same cluster were assumed to originate from the same parental ML, and child ML counts were collapsed to the parental ML. Figure 17 Molecular marker correction is shown, where pairwise Hamming distances of 1 are over-represented. After molecular marker correction, molecular markers that are a Hamming distance apart are clustered and collapsed to the same parental molecular marker. Figure 18 A plot showing the number of ML corrections compared to the number of reads covered. Because all reads are retained, this method can be used to remove single-base PCR or sequencing errors.

[0273] Table 13. After deduplication of molecular markers, only insufficient numbers of unique molecular markers were considered as errors given the entire transcriptome assay.

[0274] Together, these data demonstrate a correction method that can be used to correct or adjust data from whole transcriptome assays because all reads are retained.

[0275] Example 3 Molecular marker counts for high input samples This example describes unique molecular markers used as input molecules for augmentation.

[0276] When used with small sample inputs (e.g., single cells), BD Precise TM Targeted assays may be most suitable to allow for random and unique tagging of mRNAs. As the number of transcripts increases relative to the barcoded pool in high RNA / cell input experiments, the percentage of ML that recycles to tag the same gene increases and is theoretically calculated using a Poisson distribution ( Figure 14 ). In these cases, quantification of gene expression using ML without statistical correction will underestimate the number of molecules originally present without any Poisson correction or correction based on two negative binomial distributions.

[0277] In extremely high-input samples, where the number of mRNAs per gene exceeds the entire set of 6561 barcodes, Poisson correction or correction based on two negative binomial distributions is no longer possible. For example, regardless of 65,000 or 100,000 input molecules, a maximum of 6561 saturated barcodes are expected in any case. Therefore, genes and samples that appear to have high sample input can be altered, potentially underestimating ML counts.

[0278] Together, these data demonstrate the need to adjust raw data when using ML to quantify gene expression.

[0279] Example 4 Recursive Substitution Error Correction (RSEC) This example demonstrates recursive substitution error correction.

[0280] In BD Precise TM Two collaborative approaches can be applied to remove ML errors in the targeted assay analysis pipeline. Briefly, using recursive substitution error correction (RSEC), ML errors originating from substitution errors in sequencing base calls are identified and adjusted to true ML barcodes. Subsequently, distribution-based error correction (DBEC) is used to adjust for ML errors originating from library preparation steps or sequencing base deletion errors.

[0281] The RSEC algorithm can adjust for ML errors originating from PCR or sequencing substitutions. These rare error events have been observed when examining ML coverage. For example, the ML coverage of an erroneous ML can be significantly lower than the true ML in a fully sequenced sample ( Figure 15 ); in the original Molecular Indexing TM In cases where two very similar MLs are used during the (reverse transcription) step, they typically have similar ML coverage and do not need to be eliminated. As sequencing depth increases, more ML errors appear, so RSEC is crucial for adjusting ML counts for highly sequenced barcoded libraries.

[0282] In short, RSEC considers two factors in error correction: 1) the similarity of ML sequences; and 2) their ML coverage. For each target gene, MLs are connected when their ML sequences are within 1 base of each other (Hamming distance = 1). x and y For each connection between Cover(y)>2*cover(x)+1, equation (5) Where y represents "parent ML" and x represents "child ML".

[0283] Based on this assignment, the child MLs can be collapsed to their parental MLs. This process is recursive until there are no more identifiable parental / child MLs for the gene.

[0284] Figure 19Schematic diagram showing an example of the recursive substitution error correction outlined above. Before RSEC correction, the MLs in the original data included nine unique MLs: GTCAAATT, GTCAAAAT, GTCAAAA, TTCAAAA, TTCAGAAA, CTCAAAA, TTCAAACT, TTCAAAAT, and TTCAAACA (SEQ ID NOs: 3-11). By applying RSEC, GTCAAA T T (SEQ ID NO: 3) can be folded into GTCAAA A T (SEQ ID NO: 4), because the two MLs have one nucleotide (underlined) difference, and the ML count of ML GTCAAATT (SEQ ID NO: 3) is lower than that of GTCAAAAT (SEQ ID NO: 4). T (SEQ ID NO: 4) can be folded into ML GTCAAAA A (SEQ ID NO: 5) (differences in the ML sequence are underlined), which has a higher ML count than GTCAAAAT (SEQ ID NO: 4). Similarly, the MLs TTCAGAAA (SEQ ID NO: 7) and CTCAAAA (SEQ ID NO: 8) can be collapsed into the ML TTCAAAAA (SEQ ID NO: 6). The ML TTCAAACT (SEQ ID NO: 9) can be collapsed into the ML TTCAAAAT (SEQ ID NO: 10), which in turn can be collapsed into the ML TTCAAAAA (SEQ ID NO: 6). The ML TTCAAACA (SEQ ID NO: 11) has more than one nucleotide difference compared to all other MLs and therefore cannot be collapsed into any of the other eight MLs. Before RSEC correction, the raw ML count was nine. After RSEC correction, the ML count was two: ML TTCAAAAA (SEQ ID NO: 6) and TTCAAACA (SEQ ID NO: 11).

[0285] Together, these data justify the use of RSEC to correct raw ML counts.

[0286] Example 5 ML Coverage Computing This example describes ML coverage calculations.

[0287] After RSEC, gene ML techniques were calculated for each well to determine if they were suitable for further correction. Genes with low ML coverage (<4 reads per ML) bypassed subsequent correction steps and were reported in the final ML data table and recorded as "low depth" in the bioinformatics pipeline. For genes with extremely high input, where at least 6557 of a possible 6561 barcodes were observed, where determining the number of molecules became challenging due to barcode diversity and the gene was marked as "saturated". Gene MLs that did not meet either of the 2 decision points would proceed to the subsequent DBEC algorithm and be marked as "Ignore (Pass)" in the output log file. Additionally, genes with an average of above 650 ML per well were recorded as "high input" because >5% of these MLs were recycled based on a Poisson distribution ( Figure 15 ).

[0288] In summary, this example describes ML coverage computation.

[0289] Example 6 Distribution-based error correction (DBEC) This example describes distribution-based error correction.

[0290] Unlike RSEC, the DBEC algorithm is a method that distinguishes whether ML is an error or a true signal, regardless of its ML sequence. While RSEC can use both ML sequence and ML coverage information to correct errors, DBEC relies primarily on ML coverage to correct only non-substitution errors. As mentioned earlier, error barcodes typically have low ML coverage, which is different from the ML coverage of true barcodes; this difference in ML coverage can be observed in the ML coverage histogram as a different distribution ( Figure 15 Given this difference, DBEC fits two negative binomial distributions to statistically distinguish ML errors (with lower ML coverage) from true signals with higher ML coverage.

[0291] Removal of recycled ML for best distribution fit For a given gene, the percentage of recycled MLs (i.e., the same ML is used to label 2 or more mRNAs from the same gene) increases as the number of detected MLs increases and can be estimated. Using a Poisson distribution ( λ 非独特 ), estimated from the ML recirculation rate equation (Eq. (6)) for hole i ( n 非独特，i If the estimated recycled ML is greater than the hole i 5% of the total ML for a given gene in the iThe gene in is labeled as “high input.” For these “high input” data, the top ML coverage ML is eliminated from the distribution fitting—but retained for the later counting step—to obtain a better negative binomial distribution.

[0292]

[0293] Equation (6) Equation (7) Add pseudopoints for low-expression genes If the number of unique ML values ​​is less than 10, fitting the distribution is often more challenging due to the sparsity of the data. To alleviate this issue, DBEC adds a dummy point at 1% signal count to aid in distribution fitting, but does not affect the data.

[0294] Parameter estimation In order to fit two negative binomial distributions to separate the error from the signal ML, two sets of starting values ​​for parameter estimation are approximated. The error distribution is assumed to be a negative binomial with mean and dispersion 1.

[0295] Error / signal probability estimation Assume that the signal and error distributions are negative binomial (μ 信号 ,size 信号 ) and negative binomial (μ 错误 ,size 错误 ). To determine the number of signal MLs (in ascending order), the probability that a reading from a given ML is from the signal and error distributions is calculated until equation (8) is satisfied, where all the previous MLs are considered to be error MLs.

[0296]

[0297] Equation (8) In summary, this example shows the calculations used to perform distribution-based error correction.

[0298] Example 7 Correction of PCR and sequencing errors based on DBEC This example demonstrates correction for PCR and sequencing errors based on two negative binomial distributions.

[0299] Figures 20A to 20C Exemplary results of correcting for PCR and sequencing errors based on two negative binomial distributions of CD69 are shown. Figure 20A Two negative binomial distributions of CD69 (D n is a negative binomial distribution of noise, and D sis a signal binomial distribution) in Figure 20B The ML depth histogram in the figure shows the fit of the ML count data. Figure 20B The dashed line shows the Figure 20A The two negative binomial distributions shown determine the separation of the ML signal and the SL error. Figure 20C The vertical lines in show the local maxima of the second derivative as determined based on the cumulative sum plot of the readings. Figures 20A to 20C , Figures 21A to 21C Exemplary results of correcting for PCR and sequencing errors based on two negative binomial distributions of CD3E are shown.

[0300] Together, these data demonstrate that DBEC can be used to correct PCR and sequencing errors.

[0301] Example 8 ML count correction using two negative binomial distributions This example demonstrates ML counts of ten targets using two negative binomial distribution corrections.

[0302] Figures 22A to 22J A non-limiting exemplary validation using two negative binomial distribution corrected datasets is shown. Figures 22A to 22J As shown, the ML counts for the 10 targets were corrected. Figures 22A to 22J The vertical line in each graph shows the separation of the ML signal and SL error of the target determined using the two negative binomial distributions.

[0303] Together, these data validate the ML count correction using two negative binomial distributions.

[0304] Example 9 BD Precise 96-well pooled Jurkat and breast cancer (BrCa) single cells TM t-random neighbor targeting assay Domain Embedding Visualization This example demonstrates a method for correcting PCR and sequencing errors based on recursive substitution error correction and distribution-based error correction for mixed Jurkat and breast cancer (BrCa) single cells.

[0305] Figures 23A to 23D Precision analysis of 86 genes from 96-well pooled Jurkat and breast cancer (BrCa) single cells is shown. TM Example t-stochastic neighbor embedding (t-SNE) visualization of the targeted assay. Figure 23A Shown are the identification of cell clusters using DBScan with the same parameters before and after ML tuning. Figures 23B to 23D Individual marker expression scaled by both color and dot size is shown. Figure 23BShown is PSMB4 (a housekeeping gene), which is present in both cell types, and after ML adjustment, the lack of PSMB4 signal is further highlighted in the "low signal" cluster. Figure 23C CD3E, a lymphocyte marker highlighting Jurkat cell clusters, is shown. Figure 23D CDH1, an epithelial cell marker highlighting BrCa clusters, is shown.

[0306] Together, these data demonstrate that ML conditioning removes ML noise, which allows clear discrimination of gene expression between cell clusters.

[0307] Example 10 Differential expression analysis between cell clusters This example demonstrates a method to correct PCR and sequencing errors based on recursive substitution error correction and distribution-based error correction for low-signal cells and breast cancer (BrCa) cells.

[0308] Figures 24A to 24B is a non-limiting exemplary graph showing differential expression analysis between cell clusters of genes with >0 ML in two selected clusters as calculated by DBScan and determined by gene marker levels in each cluster. Figure 24A 'Low signal' cluster gene expression compared to the remaining cells is shown. Figure 24A The top panel shows the raw ML comparison, which shows that ML noise is generally higher for genes with higher average expression in other cells. Figure 24A The bottom panel shows the reduction in ML noise detected in the “low signal” cluster after ML adjustment using RSEC and DBEC, which allows for clearer discrimination of gene expression between clusters. Figure 24B Shown are 'BrCa' cluster gene expression compared to the remaining cells. Figure 24B The top panel shows that primitive ML in non-BrCa cells also have significant ML counts of BrCa markers such as KRT1, MUC1. Figure 24B The bottom panel shows that the adjusted ML of BrCa markers are highly enriched in BrCa clusters than other cells.

[0309] Together, these data demonstrate that for cells such as low-signal cells and breast cancer cells, PCR and sequencing errors can be corrected based on recursive substitution error correction and distribution-based error correction.

[0310] Example 11 Adjusting molecular marker counts in mixed Jurkat and T47D cells This example demonstrates a method for adjusting molecular marker counts in mixed Jurkat and T47D cells.

[0311] Figures 25A to 25D BD Precise image showing mixed Jurkat and breast cancer (T47D) single cells from a 96-well plate (with 86 genes examined). TM Non-limiting exemplary plots of t-Stochastic Neighbor Embedding (t-SNE) visualization of targeted assays. Figure 25A Shown are the identification of cell clusters using DBScan with the same parameters before and after ML tuning. Figures 25B to 25D Individual marker expression scaled by color and dot size is shown. Figure 25B Shown is the scaling of PSMB4 (a housekeeping gene present in both cell types and after ML adjustment). The lack of PSMB4 signal is further highlighted in the no template control (NTC) cluster. Figure 25C Zoom of CD3E, a lymphocyte marker highlighting Jurkat cell clusters, is shown. Figure 25D Zooms of CDH1, an epithelial cell marker highlighting T47D clusters, are shown.

[0312] Figures 26A to 26B is in any error correction step ( Figure 26A The original ML shown in ), before RSEC and DBEC correction ( Figure 26B After the adjusted ML shown in Figures 25A to 25D Non-limiting exemplary heatmap showing differential gene expression by molecular marker counts between different cell clusters identified in

[15] . Lowly expressed genes are blue, and highly expressed genes are orange. Genes with similar gene expression patterns between these cell types cluster together. Without error correction, NTCs have noise from highly expressed genes such as CD3E and KRT18, which are Jurkat and T47D markers, respectively. Furthermore, error correction revealed distinct gene expression patterns between Jurkat and T47D.

[0313] Together, these data demonstrate that ML conditioning can remove MI noise, which allows for clear discrimination of gene expression between cell clusters.

[0314] Example 12 Immune receptor barcoding error correction using recursive substitution error correction This example demonstrates immune receptor barcode error correction based on recursive substitution error correction.

[0315] Figures 27A to 27BA table showing a non-limiting example of immune receptor barcode error correction based on recursive substitution error correction is shown. Performing immune receptor barcode error correction includes adjusting the counts of a target nucleotide sequence (NS) (e.g., a putative nucleotide sequence of CDR3) by recursive substitution error correction. Multiple clusters of putative CDR3 sequences were identified. One cluster included sequences that differed from the parent nucleotide sequence TGTTGGTGAACGGAGACGGCACTGCCAGTAAACTCACCTTT (SEQ ID NO: 58) by one nucleotide (underlined), such as the subnucleotide sequence TGTTGGTGAACGGAGACGGCACTGCCAGTAAACTCAC T TTT (SEQ ID NO: 59) and C GTGTGGTGAACGGAGACGGCACTGCCAGTAAACTCACCTTT (SEQ ID NO: 77). The nucleotide sequence TGTTGGTGAACGGAGACGGCACTGCCAGTAAACTCACCTTT (SEQ ID NO: 58) is the parent nucleotide sequence because it has the highest raw read count of 294 in this cluster, which is the number of molecular tags with different sequences associated with this nucleotide sequence in the sequencing data. Other clusters include TGTGCTGTCCCACCGAGGAAGCCAAGGAAATCTCATCTTT (SEQ ID NO: 78) and TGTGCTGTCCCACCGAGGAAGCCAAGGAAATCTCATC G TT (SEQ ID NO: 79); TGTGCAGGAGAATCTGGGGATTACCAGAAAGTTACCTTT (SEQ ID NO: 80) and TGTGCAGGAGAATCTGGGG G TTACCAGAAAGTTACCTTT (SEQ ID NO: 81); TGTGCAGCAACCGAGTCCTATGGTCAGAATTTTGTCTTT (SEQ ID NO: 82) and TGTGCAGCAAC A GAGTCCTATGGTCAGAATTTTGTCTTT (SEQ ID NO: 83) and TGCCTCGTGGGGAGCCTTTCTGGTTCTGCAAGGCAACTGACCTTT (SEQ ID NO: 84) and TGCCTCGTGGGGAGCCTTTC CGGTTCTGCAAGGCAACTGACCTTT (SEQ ID NO: 85). Each occurrence of the daughter nucleotide sequence is attributed to the corresponding parental molecular marker sequence (as indicated by the arrow from the column labeled "Raw Reads" to the column labeled "NS Adjusted Reads").

[0316] Performing immune receptor barcode error correction includes adjusting the counts of molecular markers (ML) by recursive substitution error correction. The cluster of molecular marker sequences includes the parent molecular marker sequence AGTGCGAG (SEQ ID NO: 110) and the daughter molecular marker sequence AGTGCG G G and AGTGC N AG (SEQ ID NOs. 111 and 112, respectively), which differ from the parent molecular marker sequence by one nucleotide (underlined). In this example, the molecular marker sequence AGTGCGAG (SEQ ID NO: 110) associated with the CDR3 sequence TGTGTGTGAACGGAGACGGCACTGCCAGTAAACTCACCTTT (SEQ ID NO: 58) has the highest nucleotide sequence adjusted read count for all CDR3 sequences associated with the molecular marker AGTGCGAG (SEQ ID NO: 110). The occurrence of each child nucleotide sequence (child molecular marker sequence AGTGCGAG) is G G and AGTGC N AG (SEQ ID NOs: 111 and 112, respectively) twice and once) were attributed to the corresponding parental molecular marker sequence with the highest nucleotide sequence adjusted read count (319 for the parental molecular marker AGTGCGAG (SEQ ID NO: 110)). Performing immune receptor barcode error correction involves simultaneously adjusting nucleotide sequence and molecular marker counts based on RSEC. In this example, when applying RSEC, molecular marker counts were not adjusted as each nucleotide sequence and corresponding molecular marker were treated as one sequence.

[0317] Multiple chimeras were identified, and the molecular signature counts corresponding to the chimeras were removed. For example, the molecular signature sequence AGTCGAG (SEQ ID NO: 110) was associated with multiple CDR3 nucleotide sequences, such as TGTTGGTGAACGGAGACGGCACTGCCAGTAAACTCACCTTT (SEQ ID NO: 58) and TGTGCGTCCACCGAGGAAGCCAAGGAAATCTCATCTTT (SEQ ID NO: 78). The adjusted occurrence (e.g., the number of occurrences observed in the sequencing data after such adjustment) of the nucleotide sequence TGTGCGTCCACCGAGGAAGCCAAGGAAATCTCATCTTT (SEQ ID NO: 78) was 7, which is lower than the adjusted occurrence (322) of the nucleotide sequence TGTTGGTGAACGGAGACGGCACTGCCAGTAAACTCACCTTT (SEQ ID NO: 58), which was the highest among all nucleotide sequences with the molecular signature sequence AGTCGAG (SEQ ID NO: 110). The putative sequence of the target corresponding to the identified chimeric sequence of the CDR3 was removed (as indicated by the arrow from the column labeled "NS and ML adjusted reads" to the column labeled "Removed chimeras"). After adjustment and removal of chimeras, the CDR3 sequence was determined to be TGTTGGTGAACGGAGACGGCACTGCCAGTAAACTCACCTTT (SEQ ID NO. 58), which is associated with the molecular signature sequence AGTGCGAG (SEQ ID NO. 110) with an adjusted molecular signature count of 322.

[0318] Together, these data demonstrate the use of RSEC to adjust nucleotide sequence counts, molecular marker counts, and both nucleotide sequence and molecular counts, and to remove chimeric CDR3 sequences.

[0319] Example 13 Immune receptor barcode error correction using recursive substitution error correction and distribution-based error correction This example demonstrates immune receptor barcode error correction using recursive substitution error correction and distribution-based error correction.

[0320] A 500-cell sample consisting of 75% healthy peripheral blood mononuclear cells (PBMCs) and 25% Jurkat cells was loaded onto the Rhapsody TM In the box, and including Rhapsody TM The beads are captured in the microwells. TMThe beads are magnetic beads with barcodes attached to their surfaces. Each bead is barcoded with a cell marker (which has the same cell marker sequence) and a molecular marker (which is selected from a group of different molecular sequences). Different beads have barcodes with cell markers having different cell marker sequences. Each bead's barcode has a capture site for capturing TCR mRNA molecules. As described herein, the captured TCR mRNA molecules are barcoded and sequenced. The cell markers and molecular markers are used to determine the cellular and molecular origin of each TCR molecule. The molecular markers are used to determine the number or count of occurrences of different TCR molecules.

[0321] Error correction using immune receptor barcodes (refer to Figure 7 、 Figure 8 、 FIG. 27A to FIG. 27B and Example 12) to correct sequencing data for different TCR molecules. Briefly, counts of putative nucleotide sequences for different TCR genes (e.g., TCRb) are adjusted based on recursive substitution error correction. Counts of molecular markers are adjusted based on recursive substitution error correction. Counts of nucleotide sequences (e.g., TCRb) and molecular markers are adjusted based on recursive substitution error correction. Counts of molecular markers corresponding to chimeras are removed. Subsequently, molecular marker counts are adjusted using distribution-based error correction as described herein. Figure 28 is a histogram showing non-limiting exemplary results of performing immune receptor barcode error correction followed by distribution-based error correction for TCRb. Without error correction, TCR diversity (including TCRb diversity) would be overestimated.

[0322] Together, these data demonstrate the utility of adjusting TCR nucleotide sequence counts, molecular signature counts, and TCR nucleotide sequence and molecule counts with recursive substitution error correction and removal of chimeric TCR sequences, followed by distribution-based error correction to avoid overestimation of TCR diversity.

[0323] In at least some of the previously described embodiments, one or more elements used in one embodiment may be interchangeably used in another embodiment, unless such replacement is technically infeasible. It will be understood by those skilled in the art that various other omissions, additions, and modifications may be made to the above-described methods and structures without departing from the scope of the claimed subject matter. All such modifications and changes are intended to fall within the scope of the subject matter defined by the appended claims.

[0324] With respect to the use of substantially any plural and / or singular terms herein, those skilled in the art can translate from the plural to the singular and / or from the singular to the plural as appropriate to the context and / or application. For the sake of clarity, various singular / plural permutations may be expressly set forth herein. As used in this specification and the appended claims, the singular forms "a," "an," and "the" include plural references unless the context clearly dictates otherwise. Any reference to "or" herein is intended to encompass "and / or" unless otherwise stated.

[0325] Those skilled in the art will understand that, in general, the terms used herein, and particularly in the appended claims (e.g., the bodies of the appended claims), are generally intended as "open-ended" terms (e.g., the term "including" should be interpreted as "including but not limited to," the term "having" should be interpreted as "having at least," the term "includes" should be interpreted as "includes but is not limited to," etc.). Those skilled in the art will further understand that if a specific number of an introduced claim recitation is intended, such intent will be expressly recited in the claim, and in the absence of such recitation no such intent exists. For example, as an aid to understanding, the following appended claims may contain usage of the introductory phrases "at least one" and "one or more" to introduce claim recitations. However, the use of such phrases should not be interpreted as meaning that introducing a claim recitation with the indefinite article "a" or "an" will limit any particular claim containing such introduced claim recitation to embodiments containing only one such recitation, even when the same claim includes the introductory phrases "one or more" or "at least one" and an indefinite article such as "a" or "an" (e.g., "a" and / or "an" should be interpreted as meaning "at least one" or "one or more"); the same applies to the use of definite articles to introduce claim recitations. Furthermore, even if a specific number of introduced claim recitations is explicitly stated, one skilled in the art will recognize that such recitation should be interpreted to mean at least the stated number (e.g., merely reciting "two recitations" without other modifiers means at least two recitations, or two or more recitations). Furthermore, in those cases where a convention similar to “at least one of A, B, and C, etc.” is used, generally such syntactic structure is intended in the sense that one skilled in the art would understand the convention (e.g., “a system having at least one of A, B, and C” would include but is not limited to systems having only A, only B, only C, A and B together, A and C together, B and C together, and / or A, B, and C together, etc.). In those cases where a convention similar to “at least one of A, B, or C, etc.” is used, generally such syntactic structure is intended in the sense that one skilled in the art would understand the convention (e.g., “a system having at least one of A, B, or C” would include but is not limited to systems having only A, only B, only C, A and B together, A and C together, B and C together, and / or A, B, and C together, etc.).Those skilled in the art will further understand that, in practice, any disjunctive word and / or phrase presenting two or more alternative terms, whether in the specification, claims, or drawings, should be understood to include the possibility of including one, either, or both terms. For example, the phrase "A or B" should be understood to include the possibility of "A" or "B" or "A and B."

[0326] In addition, where features or aspects of the disclosure are described in terms of Markush groups, those skilled in the art will recognize that the disclosure is also thereby described in terms of any individual member or subgroup of members of the Markush group.

[0327] As will be understood by those skilled in the art, for any and all purposes, such as in providing a written description, all ranges disclosed herein also include any and all possible subranges and subrange combinations thereof. Any listed range can be easily identified as fully describing and enabling the same range to be decomposed into at least equal halves, thirds, quarters, fifths, tenths, etc. As non-limiting examples, each range discussed herein can be easily decomposed into a lower third, a middle third, and an upper third, etc. As will be understood by those skilled in the art, all language, such as "up to," "at least," "greater than," "less than," etc., includes the stated number and refers to a range that can subsequently be decomposed into subranges as discussed above. Finally, as will be understood by those skilled in the art, a range includes each individual member. Thus, for example, a group having 1-3 items refers to a group having 1, 2, or 3 items. Similarly, a group having 1-5 items refers to a group having 1, 2, 3, 4, or 5 items, etc.

[0328] The present invention also provides the following items: 1. A method for determining the presence of a target, the method comprising: (a) randomly barcoding a plurality of targets using a plurality of random barcodes to create a plurality of randomly barcoded targets, wherein each of the plurality of random barcodes comprises a cellular marker and a molecular marker, wherein the molecular markers of at least two of the plurality of random barcodes comprise different molecular marker sequences, wherein at least two of the plurality of random barcodes comprise cellular markers having the same cellular marker sequence; (b) obtaining sequencing data for the randomly barcoded targets; and (c) for at least one target among the plurality of targets: (i) identifying a putative sequence of the target in the sequencing data; (ii) counting the occurrences of molecular marker sequences associated with the putative sequence of the target in the sequencing data identified in (i); (iii) identifying clusters of putative sequences for the target; (iv) folding the obtained sequencing data using the clusters of putative sequences of the target identified in (iii); (v) identifying clusters of said molecular marker sequences associated with putative sequences of said target; (vi) folding the sequencing data using the clusters of molecular marker sequences identified in (v); (vii) identifying clusters of combinatorial sequences, wherein each combinatorial sequence comprises a sequence in the target sequence and an associated molecular marker sequence in the molecular marker sequence; (viii) collapsing the sequencing data using the clusters of combined sequences identified in (vii); (ix) identifying one or more putative sequences of the target corresponding to one or more chimeric sequences of the target, wherein the occurrence of the one or more putative sequences of the target corresponding to the one or more chimeric sequences of the target is less than the occurrence of the remaining one or more putative sequences of the target that do not correspond to the one or more chimeric sequences of the target; (x) removing from the sequencing data one or more putative sequences of the target corresponding to one or more chimeric sequences of the target identified in (ix); and (xi) estimating the occurrence of the target, wherein the estimated occurrence of the target is related to the number of molecular marker sequences counted in (ii) after folding the sequencing data in (iv), (vi) and (viii) and removing one or more putative sequences of the target corresponding to one or more chimeric sequences of the target in (x).

[0329] 2. The method of item 1, wherein the plurality of targets comprises targets of the entire transcriptome of the cell.

[0330] 3. The method of item 2, wherein the plurality of targets include genes.

[0331] 4. The method of item 3, wherein the gene comprises a variable sequence.

[0332] 5. The method according to item 4, wherein the gene encodes a T cell receptor.

[0333] 6. The method of any one of items 1 to 5, wherein the putative sequences of the targets differ from each other by at least one nucleotide.

[0334] 7. The method of any one of items 1 to 6, wherein identifying clusters of putative sequences of the target comprises identifying clusters of putative sequences of the target using directed contiguity.

[0335] 8. A method as described in item 7, wherein the putative sequences of the targets within the cluster are within a first predetermined directional adjacency threshold of each other.

[0336] 9. The method of item 8, wherein the first directional adjacency threshold is a Hamming distance.

[0337] 10. A method as described in any one of items 8 to 9, wherein the putative sequence of the target within the cluster includes one or more parent sequences and one or more subsequences of the one or more parent sequences, and wherein the occurrence of the parent sequences is greater than or equal to a first predetermined directional adjacent occurrence threshold.

[0338] 11. The method of item 10, wherein the first predetermined directed contiguity occurrence threshold is twice the occurrence of a subsequence that is less than one.

[0339] 12. The method of any one of items 1 to 11, wherein folding the sequencing data obtained in (b) using the clusters of putative sequences of the target identified in (iii) comprises: The occurrence of a subsequence in the one or more subsequences is attributed to the parent sequence of the subsequence.

[0340] 13. The method of any one of items 1 to 12, wherein identifying the cluster of molecular marker sequences associated with the putative sequence of the target comprises identifying the cluster of molecular marker sequences associated with the putative sequence of the target using directed contiguity.

[0341] 14. The method of claim 13, wherein the molecular marker sequences of the targets within the cluster are within a second predetermined directional adjacency threshold of each other.

[0342] 15. The method of item 14, wherein the second directional adjacency threshold is a Hamming distance.

[0343] 16. A method as described in any one of items 14 to 15, wherein the putative molecular marker sequence of the target within the cluster includes one or more parent molecular marker sequences and one or more sub-molecular marker sequences of the one or more parent molecular marker sequences, and wherein the occurrence of the parent molecular marker sequence is greater than or equal to a second predetermined directional adjacent occurrence threshold.

[0344] 17. The method of item 16, wherein the second predetermined directional contiguity occurrence threshold is twice the occurrence of the sub-molecule marker sequence that is less than one.

[0345] 18. The method of any one of items 1 to 17, wherein folding the sequencing data using the cluster of molecular marker sequences associated with the sequence of the target identified in (v) comprises: The occurrence of a daughter molecular signature sequence in the one or more daughter molecular signature sequences is attributed to the parent molecular signature of the daughter molecular signature sequence.

[0346] 19. The method of any one of items 1 to 18, wherein identifying clusters of combinatorial sequences comprises identifying clusters of combinatorial sequences using directed neighbor joining.

[0347] 20. The method of clause 19, wherein the combined sequences within a cluster are within a third predetermined directional adjacency threshold of each other.

[0348] 21. The method of item 20, wherein the third directional adjacency threshold is a Hamming distance.

[0349] 22. A method as described in any one of items 20 to 21, wherein the combination sequences within the cluster include one or more parent combination sequences and one or more sub-combination sequences of the one or more parent combination sequences, and wherein the occurrence of the parent combination sequences is greater than or equal to a third predetermined directional adjacent occurrence threshold.

[0350] 23. The method of clause 22, wherein the third predetermined directed contiguity occurrence threshold is twice the occurrence of a subcombination sequence that is less than one.

[0351] 24. The method of any one of items 1 to 23, wherein collapsing the sequencing data using the clusters of combined sequences identified in (vii) comprises: The occurrence of a subcombination sequence in the one or more subcombination sequences is attributed to a parent combinatorial sequence of the subcombination sequence.

[0352] 25. The method of any one of items 1 to 24, wherein one or more putative sequences of the target corresponding to one or more chimeric sequences of the target are identified: identifying a putative sequence of the target associated with a molecular marker sequence in the plurality of molecular sequences; Putative sequences among the putative sequences of the target that are associated with the one molecular marker sequence, the one molecular marker sequence having an occurrence less than a chimeric occurrence threshold corresponding to a chimeric sequence among the one or more chimeric sequences of the target are identified.

[0353] 26. A method as described in item 25, wherein the value of the chimeric occurrence threshold is the occurrence of a putative sequence in the putative sequence of the target associated with the one molecular marker sequence, and the occurrence is greater than the occurrence of any other sequence in the putative sequence of the target.

[0354] 27. The method according to any one of items 1 to 26, further comprising: The sequencing data are folded in (iv), (vi) and (viii) and adjusted after removing one or more putative sequences of the target corresponding to one or more chimeric sequences of the target in (x).

[0355] 28. The method of item 27, wherein folding the sequencing data in (iv), (vi), and (viii) and adjusting the sequencing data after removing one or more putative sequences of the target corresponding to one or more chimeric sequences of the target in (x) comprises: After folding the sequencing data in (iv), (vi) and (viii) and removing one or more putative sequences of the target corresponding to one or more chimeric sequences of the target in (x), the molecular marker sequences associated with the putative sequences of the target are thresholded to determine signal molecular marker sequences and noise molecular marker sequences associated with the sequences of the target in the sequencing data counted in (b).

[0356] 29. The method of claim 28, wherein thresholding the molecular marker sequence associated with the putative sequence of the target comprises performing a statistical analysis on the molecular marker sequence of the target.

[0357] 30. The method of item 29, wherein performing the statistical analysis comprises: fitting the molecular signature sequences and their occurrences associated with the putative sequence of the target to two negative binomial distributions; The two negative binomial distributions are used to determine the occurrence of signal molecule signature sequences n ;as well as After folding the sequencing data in (iv), (vi) and (viii) and removing one or more putative sequences of the target corresponding to one or more chimeric sequences of the target in (x), the noise molecule marker sequence is removed from the sequencing data obtained in (b), wherein the noise molecule marker sequence includes a sequence whose occurrence is less than n The molecular marker sequence of the most abundant molecular marker, and wherein the signal molecular marker sequence includes a molecular marker sequence whose occurrence is greater than or equal to the nThe molecular marker sequence of the most abundant molecular marker.

[0358] 31. The method of item 30, wherein the two negative binomial distributions include a first negative binomial distribution corresponding to the signal molecule marker sequence and a second negative binomial distribution corresponding to the noise molecule marker sequence.

[0359] 32. A method for determining the presence of a target, the method comprising: (a) receiving sequencing data for a plurality of targets, wherein the sequencing data comprises putative sequences of targets in the plurality of targets and occurrences of molecular marker sequences associated with the sequences of the targets in the sequencing data; (b) folding the putative sequence of the target; (c) folding a molecular marker sequence associated with the putative sequence of the target; and (d) estimating the occurrence of the target, wherein after folding the occurrence of the putative sequence of the target in (b) and folding the occurrence of the noise molecular marker sequence determined in (c), the estimated occurrence of the target is correlated with the occurrence of the molecular marker sequence associated with the putative sequence of the target in the sequencing data.

[0360] 33. The method according to item 32, comprising: identifying the sequence of the target in the sequencing data; and The occurrences of molecular marker sequences associated with the sequence of the target in the sequencing data are counted.

[0361] 34. The method according to any one of items 32 to 33, comprising: Collapsing clusters of combined sequences, wherein each combined sequence includes a sequence in the target sequence and an associated molecular marker sequence in the molecular marker sequence, Wherein, after collapsing the occurrence of the combined sequences, the estimated occurrence of the target is correlated with the occurrence of a molecular marker sequence associated with the sequence of the target in the sequencing data.

[0362] 35. The method of item 34, wherein folding the clusters of combinatorial sequences comprises folding the clusters of combinatorial sequences using directed adjacency.

[0363] 36. The method of item 35, wherein collapsing the cluster of combined sequences using directed neighbor joining comprises: identifying clusters of the combined sequences using directed neighbor joining; and The sequencing data are collapsed using the identified clusters of combined sequences.

[0364] 37. The method of any one of items 32 to 36, wherein folding the putative sequence of the target comprises folding the putative sequence of the target using directed adjacency.

[0365] 38. The method of claim 37, wherein folding the putative sequence of the target using directed contiguity comprises: identifying clusters of putative sequences for the target using directed neighbor joining; and The sequencing data were collapsed using the identified clusters of putative sequences for the targets.

[0366] 39. The method of any one of items 32 to 38, wherein folding the molecular marker sequence associated with the putative sequence of the target comprises folding the molecular marker sequence associated with the putative sequence of the target using directed adjacency.

[0367] 40. The method of item 39, wherein folding the molecular marker sequence associated with the putative sequence of the target using directed contiguity comprises: identifying clusters of the molecular marker sequences associated with the putative sequence of the target using directed neighbor joining; and The sequencing data is collapsed using the identified clusters of the molecular marker sequences associated with the putative sequence of the target.

[0368] 41. The method of any one of items 32 to 40, comprising: identifying one or more putative sequences of the target corresponding to one or more chimeric sequences of the target, wherein the occurrence of the one or more putative sequences of the target corresponding to the one or more chimeric sequences of the target is less than the occurrence of the remaining one or more putative sequences of the target that do not correspond to the one or more chimeric sequences of the target; and One or more putative sequences of the target corresponding to one or more chimeric sequences of the target are removed from the sequencing data.

[0369] 42. The method of item 41, wherein identifying one or more putative sequences of the target corresponding to one or more chimeric sequences of the target comprises: identifying a putative sequence of the target associated with a molecular marker sequence in the plurality of molecular sequences; Putative sequences among the putative sequences of the target that are associated with the one molecular marker sequence, the one molecular marker sequence having an occurrence less than a chimeric occurrence threshold corresponding to a chimeric sequence among the one or more chimeric sequences of the target are identified.

[0370] 43. A method as described in item 42, wherein the value of the chimeric occurrence threshold is the occurrence of a putative sequence in the putative sequence of the target associated with the one molecular marker sequence, and the occurrence is greater than the occurrence of any other sequence in the putative sequence of the target.

[0371] 44. The method according to any one of items 32 to 43, comprising: determining a sequencing status of the target in the sequencing data; and determining the occurrence of a noise molecule marker sequence associated with the putative sequence of the target in the sequencing data, The estimated occurrence of the target is related to the occurrence of the molecular marker sequence associated with the putative sequence of the target in the sequencing data, and the sequencing data is adjusted according to the occurrence of the noise molecular marker sequence.

[0372] 45. The method of item 44, wherein the sequencing status of the target in the sequencing data is saturated sequencing, under-sequencing, or over-sequencing.

[0373] 46. ​​A method as described in item 45, wherein the under-sequencing state is determined by a target having a depth less than a predetermined under-sequencing threshold, and wherein the depth of the target includes the average, minimum, or maximum depth of the molecular marker sequence associated with the putative sequence of the target in the sequencing data.

[0374] 47. The method of any one of items 45 to 46, wherein the under-sequencing threshold is about four.

[0375] 48. A method as described in any one of items 45 to 47, wherein the insufficient sequencing threshold is independent of the number of molecular marker sequences.

[0376] 49. The method of any one of items 45 to 48, wherein if the sequencing status of the target in the sequencing data is the under-sequencing status, the number of noise molecule marker sequences determined is zero.

[0377] 50. The method of any one of items 45 to 49, wherein the saturated sequencing state is determined by the number of the molecular marker sequences associated with the putative sequence of the target being greater than a saturation threshold.

[0378] 51. The method of item 50, wherein if the molecular marker sequence in the molecular marker sequence associated with the putative sequence of the target has a sequence selected from approximately 6561 molecular marker sequences, the saturation threshold is approximately 6557.

[0379] 52. A method as described in any one of items 50 to 51, wherein if the molecular marker sequence in the molecular marker sequence associated with the putative sequence of the target has a sequence selected from approximately 65536 molecular marker sequences, the predetermined saturation threshold is approximately 65532.

[0380] 53. The method of any one of items 45 to 52, wherein if the sequencing state of the target in the sequencing data is the saturated sequencing state, the number of noise molecule marker sequences determined is zero.

[0381] 54. A method as described in any of items 45 to 53, wherein the over-sequencing state is determined by targets having a depth greater than a predetermined over-sequencing threshold, wherein the depth of the target includes the average, minimum, or maximum depth of the molecular marker sequence associated with the putative sequence of the target in the sequencing data.

[0382] 55. The method of item 54, wherein the over-sequencing threshold is about 250 if the molecular marker sequence in the molecular marker sequence associated with the putative sequence of the target has a sequence selected from about 6561 molecular marker sequences.

[0383] 56. The method according to any one of items 54 to 55, further comprising: if the sequencing state of the target in the sequencing data is a saturated sequencing state or an over-sequencing state: The number of molecular marker sequences associated with the sequence of the target in the sequencing data is subsampled to approximately the predetermined oversequencing threshold.

[0384] 57. The method of any one of items 44 to 56, wherein determining the occurrence of a noise molecule signature sequence associated with the putative sequence of the target in the sequencing data comprises: If the negative binomial distribution fitting conditions are met, fitting a signal negative binomial distribution to the occurrences of the molecular marker sequence associated with the sequence of the target in the sequencing data, wherein the signal negative binomial distribution corresponds to the occurrences of the molecular marker sequence associated with the sequence of the target in the sequencing data that is a signal molecular marker sequence; fitting a noisy negative binomial distribution to the occurrences of the molecular marker sequences associated with the sequence of the target in the sequencing data, wherein the noisy negative binomial distribution corresponds to the occurrences of molecular marker sequences associated with the sequence of the target in the sequencing data that are noisy molecular marker sequences; and The signal negative binomial distribution and the noise negative binomial distribution are used to determine the occurrence of the noise molecule signature sequence.

[0385] 58. The method of item 57, wherein the negative binomial distribution fitting condition includes: the sequencing state of the target in the sequencing data is neither the under-sequencing state nor the over-sequencing state.

[0386] 59. The method of any one of items 57 to 58, wherein determining the number of noise molecular marker sequences using the signal negative binomial distribution and the noise negative binomial distribution comprises: For each of the molecular marker sequences associated with the putative sequence of the target in the sequencing data: determining a signal probability of the molecular marker sequence in the negative binomial distribution of the signal; Determining the noise probability of the molecular marker sequence in the noise negative binomial distribution; and If the signal probability is less than the noise probability, the molecular marker sequence is determined to be a noise molecular marker.

[0387] 60. The method of any one of items 44 to 59, wherein determining the occurrence of a noise molecule marker sequence associated with the sequence of the target in the sequencing data comprises: If the sequencing state of the target in the sequencing data is not the under-sequencing state or the over-sequencing state and the occurrence of the molecular marker sequence associated with the sequence of the target in the sequencing data is less than a pseudo-point threshold, then before determining the occurrence of the noise molecular marker sequence associated with the sequence of the target in the sequencing data, a pseudo-point is added to the occurrence of the molecular marker sequence associated with the sequence of the target in the sequencing data.

[0388] 61. The method of item 60, wherein the pseudo-point threshold is ten.

[0389] 62. The method of any one of items 60 to 61, wherein determining the presence of a noise molecule marker sequence associated with the sequence of the target in the sequencing data comprises: If the sequencing state of the target in the sequencing data is not the under-sequencing state or the over-sequencing state and the occurrence of the molecular marker sequence associated with the sequence of the target in the sequencing data is not less than the pseudo-point threshold, then the non-unique molecular marker sequence is removed when determining the occurrence of the noise molecular marker sequence associated with the sequence of the target in the sequencing data.

[0390] 63. The method of any one of items 32 to 62, wherein receiving sequencing data for the plurality of targets comprises: randomly barcoding a plurality of targets using a plurality of random barcodes to create a plurality of randomly barcoded targets, wherein each of the plurality of random barcodes comprises a cellular marker and a molecular marker, wherein the molecular markers of at least two of the plurality of random barcodes comprise different molecular marker sequences, wherein at least two of the plurality of random barcodes comprise cellular markers having the same cellular marker sequence; and Sequencing data is obtained for the randomly barcoded targets.

[0391] 64. The method of clause 63, wherein randomly barcoding a plurality of targets in a plurality of cells using the plurality of random barcodes to create a plurality of randomly barcoded targets for cells in the plurality of cells comprises: randomly barcoding the plurality of targets using the plurality of random barcodes of the particles to create the plurality of randomly barcoded targets, The particle comprises a subset of the plurality of random barcodes, wherein each of the subset of random barcodes comprises the same cell marker sequence and has at least 100 different molecular marker sequences.

[0392] 65. The method of item 64, wherein the particles are beads.

[0393] 66. A method as described in item 65, wherein the beads are selected from the group consisting of: streptavidin beads, agarose beads, magnetic beads, conjugated beads, protein A conjugated beads, protein G conjugated beads, protein A / G conjugated beads, protein L conjugated beads, oligo (dT) conjugated beads, silica beads, silica-like beads, anti-biotin microbeads, anti-fluorescent dye microbeads, and any combination thereof.

[0394] 67. The method of any one of items 63 to 67, wherein the particles comprise a material selected from the group consisting of polydimethylsiloxane (PDMS), polystyrene, glass, polypropylene, agarose, gelatin, hydrogel, paramagnetic substances, ceramics, plastics, glass, methylstyrene, acrylic polymers, titanium, latex, agarose gel, cellulose, nylon, silicone, and any combination thereof.

[0395] 68. The method of any one of items 63 to 67, wherein the random barcode of the particle comprises molecular markers having at least 1000 different molecular marker sequences.

[0396] 69. The method of any one of items 63 to 68, wherein the random barcode of the particle comprises molecular markers having at least 10,000 different molecular marker sequences.

[0397] 70. The method of any one of items 63 to 69, wherein the molecular marker of the random barcode comprises a random sequence.

[0398] 71. The method of any one of items 63 to 70, wherein the particles comprise at least 10,000 random barcodes.

[0399] 72. The method of any one of items 63 to 71, wherein randomly barcoding the plurality of targets using the plurality of random barcodes to create the plurality of randomly barcoded targets comprises: (i) contacting a copy of the target with the target binding region of the barcode; and (ii) reverse transcribing the plurality of targets using the plurality of random barcodes to create a plurality of reverse transcribed targets.

[0400] 73. The method of any one of items 63 to 72, comprising amplifying the randomly barcoded targets to generate a plurality of amplified randomly barcoded targets before obtaining sequencing data for the plurality of randomly barcoded targets.

[0401] 74. The method of item 73, wherein amplifying the randomly barcoded targets to generate a plurality of amplified randomly barcoded targets comprises amplifying the randomly barcoded targets by polymerase chain reaction (PCR).

[0402] 75. A computer system for determining the presence of a target, the computer system comprising: Hardware processor; and Non-volatile memory having instructions stored thereon that, when executed by the hardware processor, cause the processor to perform the method of any one of items 1 to 74.

[0403] 76. A computer-readable medium comprising a software program comprising code for performing the method of any one of items 1 to 74.

[0404] While various aspects and embodiments have been disclosed herein, other aspects and embodiments will be apparent to those skilled in the art. The various aspects and embodiments disclosed herein are for illustrative purposes and are not intended to be limiting, with the true scope and spirit being indicated by the following claims.

Claims

1. A method for determining the presence of a target, the method comprising: (a) randomly barcoding a plurality of targets using a plurality of random barcodes to create a plurality of randomly barcoded targets, wherein each of the plurality of random barcodes comprises a cellular marker and a molecular marker, wherein the molecular markers of at least two of the plurality of random barcodes comprise different molecular marker sequences, wherein at least two of the plurality of random barcodes comprise cellular markers having the same cellular marker sequence; (b) obtaining sequencing data for the randomly barcoded targets; and (c) for at least one target among the plurality of targets: (i) identifying a putative sequence of the target in the sequencing data; (ii) counting the occurrences of molecular marker sequences associated with the putative sequence of the target in the sequencing data identified in (i); (iii) identifying clusters of putative sequences for the target; (iv) folding the obtained sequencing data using the clusters of putative sequences of the target identified in (iii); (v) identifying clusters of said molecular marker sequences associated with putative sequences of said target; (vi) folding the sequencing data using the clusters of molecular marker sequences identified in (v); (vii) identifying clusters of combinatorial sequences, wherein each combinatorial sequence comprises a sequence in the target sequence and an associated molecular marker sequence in the molecular marker sequence; (viii) collapsing the sequencing data using the clusters of combined sequences identified in (vii); (ix) identifying one or more putative sequences of the target corresponding to one or more chimeric sequences of the target, wherein the occurrence of the one or more putative sequences of the target corresponding to the one or more chimeric sequences of the target is less than the occurrence of the remaining one or more putative sequences of the target that do not correspond to the one or more chimeric sequences of the target; (x) removing from the sequencing data one or more putative sequences of the target corresponding to one or more chimeric sequences of the target identified in (ix); and (xi) estimating the occurrence of the target, wherein the estimated occurrence of the target is related to the number of molecular marker sequences counted in (ii) after folding the sequencing data in (iv), (vi) and (viii) and removing one or more putative sequences of the target corresponding to one or more chimeric sequences of the target in (x).

2. The method according to claim 1, wherein The plurality of targets includes targets across the entire transcriptome of the cell.

3. The method according to claim 2, wherein: The plurality of targets includes genes.

4. The method according to claim 3, wherein: The gene includes variable sequences.

5. The method according to claim 4, wherein: The gene encodes the T cell receptor.

6. The method according to any one of claims 1 to 5, wherein The putative sequences of the targets differ from each other by at least one nucleotide.

7. The method according to any one of claims 1 to 6, wherein Identifying clusters of putative sequences for the target comprises identifying clusters of putative sequences for the target using directed contiguity joining.

8. The method of claim 7, wherein: The putative sequences of the targets within a cluster are within a first predetermined directional contiguity threshold of each other.

9. The method of claim 8, wherein: The first directed adjacency threshold is the Hamming distance.

10. The method according to any one of claims 8 to 9, wherein The putative sequence of the target within the cluster includes one or more parent sequences and one or more subsequences of the one or more parent sequences, and wherein the occurrence of the parent sequences is greater than or equal to a first predetermined directed contiguous occurrence threshold.

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