Data processing method and device, storage medium and electronic device

Abstract

The present application discloses a data processing method and device, storage medium and electronic device, the data processing method includes: obtaining multiple read sequences to be analyzed from spatial transcriptome data; comparing multiple read sequences with reference sequences in a preset database to obtain comparison information of multiple read sequences and V(D)J genes; parsing the comparison information to obtain multiple clonotypes corresponding to the multiple read sequences; in the case of obtaining the abundance information of multiple clonotypes corresponding to each position in the spatial transcriptome data, analyzing and processing each position according to the abundance information of multiple clonotypes and multiple clonotypes to obtain the target clonotype and quantitative information of the target clonotype at each position. The problem that there is no immune group library analysis scheme supporting the spatial transcriptome sequencing technology in the prior art is solved.

Description

Technical Field

[0001] The present application relates to the field of immunotherapy technology, and in particular to a data processing method and device, a storage medium and an electronic device. Background Art

[0002] The immune repertoire refers to the sum of all functionally diverse T cells and B cells in an individual's circulation system at any given time. Antigen-specific T cells and B cells act as the main effector cells that mediate immune responses, and their specificity is mainly determined by the complementarity determining regions of the T cell receptor (TCR) and B cell receptor (BCR). The complementarity determining region sequences are generated by the recombination of V(D)J germline gene segments and bind to specific antigens. Therefore, accurate identification of the immune repertoire is crucial for a deeper understanding of T cell development, immune responses, and disease mechanisms.

[0003] Single-cell immune repertoire sequencing is a technology used to study the immune repertoire of individual immune cells in the immune system. Traditional bulk RNA-seq immune repertoire technology usually performs population sequencing on a large number of immune cells, masking the heterogeneity between individual cells, while the single-cell immune repertoire is able to sequence each cell individually, revealing the individual differences and cellular heterogeneity of its immune repertoire, making the systematic characterization of the immune repertoire possible. However, when single-cell immune repertoire technology dissociates tissues into single-cell suspensions, it will cause the loss of information on tissue and cell space, making it impossible to accurately identify the relationship between immune repertoire status, clone identification, and tissue localization, which limits the study of the distribution of T cell and B cell clonal types in organs and the interaction between different cell types.

[0004] Currently, spatial transcriptomics is a groundbreaking molecular analysis method that is gradually maturing and receiving widespread attention. This technology can measure the activity of all genes in tissue samples and draw a map of the location of expression, solving the problem of loss of information on the spatial location of conventional transcriptome tissues and cells. However, there is currently no immune repertoire analysis solution that matches spatial transcriptome sequencing technology. How to improve the accurate identification of the immune repertoire and the immune repertoire analysis solution that integrates spatial information are issues that need to be urgently addressed in the current field of immunotherapy.

[0005] In view of the problem that there is no immune repertoire analysis scheme compatible with spatial transcriptome sequencing technology in the existing technology, no effective solution has been proposed yet. Summary of the invention

[0006] The embodiments of the present application provide a data processing method and system, a storage medium, and an electronic device to at least solve the problem that there is no immune repertoire analysis solution compatible with spatial transcriptome sequencing technology in the prior art.

[0007] According to an embodiment of the embodiments of the present application, a data processing method is provided, comprising: obtaining a plurality of read sequences to be analyzed from spatial transcriptome data; comparing the plurality of read sequences with reference sequences in a preset database to obtain comparison information of each read sequence with a V(D)J gene, wherein the preset database contains a plurality of data dictionaries, the data dictionaries are used to indicate the association relationship between the reference sequence and the target V(D)J gene, and each read sequence corresponds to a position information indicating the specific position of each read sequence in the spatial transcriptome data; parsing the comparison information to obtain a clonotype corresponding to each read sequence; when the abundance information of the plurality of clonotypes corresponding to each position in the spatial transcriptome data is obtained, analyzing and processing the sequencing data for each position according to the abundance information of the plurality of clonotypes and the plurality of clonotypes to obtain a target clonotype at each position and quantitative information of the target clonotype.

[0008] In an exemplary embodiment, before comparing the multiple read sequences with reference sequences in a preset database to obtain comparison information between the multiple read sequences and V(D)J genes, the method further includes: downloading target species sequences corresponding to all TCR / BCR molecular germline genes of a specific species from a target professional database; performing kmer analysis on the target species sequences to obtain a target data dictionary corresponding to the target species sequences; and sending the target data dictionary to a preset database for storage.

[0009] In an exemplary embodiment, kmer analysis is performed on the target species sequence to obtain a target data dictionary corresponding to the target species sequence, including: determining the number of short sequences of length K corresponding to the target species sequence, wherein K is a positive integer; determining a target key in the target data dictionary according to the number of sequences; when the target key is determined, storing germline gene position information corresponding to the short sequence of length K as a target value of the target data dictionary, wherein the target key corresponds to the target value one-to-one; and determining the target data dictionary corresponding to the target species sequence based on the target key and the target value.

[0010] In an exemplary embodiment, the multiple read sequences are compared with reference sequences in a preset database to obtain comparison information of the multiple read sequences and V(D)J genes, including: decomposing each of the multiple read sequences into multiple short read sequences, wherein the length of the short read sequence is K, and K is a positive integer; identifying the target positions corresponding to the multiple short read sequences on the reference sequence; when the multiple target positions corresponding to the multiple short read sequences are determined, determining the starting position and the ending position of each read sequence on the reference sequence according to the multiple target positions; counting the data of the starting position and the ending position, and determining the sub-comparison information of each read sequence and the V(D)J gene based on the data counting result; and summarizing the multiple sub-comparison information corresponding to the multiple read sequences to obtain the comparison information.

[0011] In an exemplary embodiment, data counting is performed on the starting position and the ending position, and the comparison information of each read sequence and the V(D)J gene is determined based on the data counting result, including: determining the number of data counts corresponding to the starting position and the ending position at different positions on the reference sequence; obtaining the position information corresponding to the target position with the largest number of times on the reference sequence; determining the comparison result of each read sequence with the reference sequence according to the position information; and when the V(D)J gene corresponding to the comparison result is obtained, determining the sub-comparison information according to the V(D)J gene and the comparison result.

[0012] In an exemplary embodiment, when the abundance information of multiple clonotypes corresponding to each position in the spatial transcriptome data is obtained, each position is analyzed and processed according to the abundance information of the multiple clonotypes and the multiple clonotypes to obtain a target clonotype at each position and quantitative information of the target clonotype, including: searching for a first clonotype with the highest abundance among the multiple clonotypes; defining the first clonotype as the target clonotype corresponding to the multiple read sequences; determining a target complementary determining region sequence corresponding to the target clonotype as reference data; calculating sequence similarity information between other complementary determining region sequences corresponding to other clonotypes of the multiple clonotypes except the target clonotype and the reference data; and accumulating the abundance corresponding to the other clonotypes to the target clonotype according to the sequence similarity information to obtain the quantitative information of the target clonotype.

[0013] In an exemplary embodiment, accumulating the abundances corresponding to the other clonotypes to the target clonotype according to the sequence similarity information to obtain quantitative information of the target clonotype includes: determining, according to the sequence similarity information, a plurality of target other complementary determining region sequences having a similarity greater than a preset similarity to the reference data; obtaining a plurality of target abundances corresponding to the plurality of target other complementary determining region sequences; accumulating the plurality of target abundances and a first abundance corresponding to the first clonotype to obtain a first clonotype with a second abundance; recalculating an actual abundance and a frequency of the first clonotype with the second abundance; and determining the quantitative information of the target clonotype according to the actual abundance and the frequency.

[0014] According to another embodiment of the embodiment of the present application, a data processing device is further provided, including: an acquisition module, used to acquire multiple read sequences to be analyzed from spatial transcriptome data; a comparison module, used to compare the multiple read sequences with reference sequences in a preset database to obtain comparison information between the multiple read sequences and V(D)J genes, wherein the preset database has multiple data dictionaries, and the data dictionaries are used to indicate the association relationship between the reference sequence and the target V(D)J gene; a parsing module, used to parse the comparison information to obtain multiple clonotypes corresponding to the multiple read sequences; and an analysis module, used to, when the abundance information of the multiple clonotypes corresponding to each position in the spatial transcriptome data is obtained, analyze and process each position according to the abundance information of the multiple clonotypes and the multiple clonotypes to obtain a target clonotype at each position and quantitative information of the target clonotype.

[0015] According to another aspect of the embodiments of the present application, a computer-readable storage medium is provided, in which a computer program is stored, wherein the computer program is configured to execute the above-mentioned data processing method when running.

[0016] According to another aspect of an embodiment of the present application, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the data processing method through the computer program.

[0017] According to another embodiment of the present application, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.

[0018] In an embodiment of the present application, a plurality of read sequences to be analyzed are obtained from spatial transcriptome data; the plurality of read sequences are compared with reference sequences in a preset database to obtain comparison information of the plurality of read sequences and V(D)J genes, wherein the preset database has a plurality of data dictionaries, and the data dictionary is used to indicate the association relationship between the reference sequence and the target V(D)J gene; the comparison information is parsed to obtain a plurality of clonotypes corresponding to the plurality of read sequences; when the abundance information of the plurality of clonotypes corresponding to each position in the spatial transcriptome data is obtained, each position is analyzed and processed according to the abundance information of the plurality of clonotypes and the plurality of clonotypes to obtain the target at each position. The quantitative information of clonotype and target clonotype is obtained by adopting the above technical scheme, which at least solves the problem that there is no immune repertoire analysis scheme matching the spatial transcriptome sequencing technology in the prior art, so that the comparison relationship between multiple read sequences and germline gene reference sequences can be accurately and quickly determined from the complex sequencing data to be analyzed, and finally the V(D)J germline genes from which the multiple read sequences are derived are accurately parsed, and the optimal clonotype clonotype under each bin is screened and quantitatively analyzed by clustering and abundance accumulation methods, so that on the basis of this information, a more detailed feature analysis of the immune repertoire data is performed, thereby improving the analysis efficiency of the data in the immune repertoire. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0020] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0021] Figure 1 It is a hardware structure block diagram of a device terminal of a data processing method according to an embodiment of the present application;

[0022] Figure 2 is a flowchart of an optional data processing method according to an embodiment of the present application;

[0023] Figure 3 is an example diagram of an optional spatial immune repertoire data processing system according to an embodiment of the present application;

[0024] Figure 4 This is an example flow chart of an optional V(D)J alignment strategy based on the integration of multiple steps such as kmer, alignment and voting according to an embodiment of the present application;

[0025] Figure 5 This is an optional optimal clonotype identification and quantitative analysis flow chart under a single bin according to an embodiment of the present application;

[0026] Figure 6 This is a schematic diagram showing the CDR3 length distribution statistics of the top ten V genes obtained optionally according to an embodiment of the present application;

[0027] Figure 7 is a schematic diagram showing an optional display of the abundance statistics of the top ten clonal types obtained according to an embodiment of the present application;

[0028] Figure 8 This is a schematic diagram showing an optional statistical situation of V and J gene frequencies obtained according to an embodiment of the present application;

[0029] Fig. 9 is a schematic diagram showing an optional V and J pairing statistics obtained according to an embodiment of the present application;

[0030] Fig.10 This is a schematic diagram showing the spatial distribution of the top ten V genes, J genes, V-J pairs and CDR3s obtained in accordance with an optional embodiment of the present application;

[0031] Fig.11 It is a structural block diagram of an optional data processing device according to an embodiment of the present application. DETAILED DESCRIPTION

[0032] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0033] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0034] The method embodiments provided in the embodiments of the present application can be executed in a device terminal, a mobile terminal or a similar computing device. Taking running on a device terminal as an example, Figure 1 1 is a hardware structure block diagram of a device terminal of a data processing method according to an embodiment of the present application. Figure 1 As shown, the device terminal may include one or more ( Figure 1 Only one is shown in the figure) processor 102 (processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data. In an exemplary embodiment, the above-mentioned device terminal may also include a transmission device 106 for communication functions and an input and output device 108. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above-mentioned device terminal. Figure 1 More or fewer components as shown, or with Figure 1 Equivalent functions or comparisons shown Figure 1 A different configuration with more features is shown.

[0035] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the data processing method in the embodiment of the present application. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, to implement the above method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the device terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0036] The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the device terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0037] In this embodiment, a data processing method is provided, which is applied to a device terminal. Figure 2 : is a flowchart of an optional data processing method according to an embodiment of the present application, the process includes the following steps:

[0038] Step S202, obtaining a plurality of read sequences to be analyzed from the spatial transcriptome data;

[0039] Step S204, comparing the multiple read sequences with reference sequences in a preset database to obtain comparison information between each read sequence and the V(D)J gene, wherein the preset database contains multiple data dictionaries, and the data dictionaries are used to indicate the association relationship between the reference sequence and the target V(D)J gene;

[0040] It should be noted that each read sequence determined from the spatial transcriptome data mentioned above corresponds to position information, that is, each read sequence is connected to a position sequence, and the position sequence corresponds to a specific position to form a mapping relationship, and then the position sequence can be used to quickly locate the position of the read sequence in the overall sequence corresponding to the spatial transcriptome data.

[0041] Optionally, the above-mentioned reference sequence may include a reference genome, and the reference genome may be composed of multiple subsequences. This application does not impose too many restrictions on this.

[0042] In an exemplary embodiment, before comparing the multiple read sequences with reference sequences in a preset database to obtain comparison information between the multiple read sequences and V(D)J genes, the method further includes: downloading target species sequences corresponding to all TCR / BCR molecular germline genes of a specific species from a target professional database; performing kmer analysis on the target species sequences to obtain a target data dictionary corresponding to the target species sequences; and sending the target data dictionary to a preset database for storage.

[0043] In an exemplary embodiment, kmer analysis is performed on the target species sequence to obtain a target data dictionary corresponding to the target species sequence, including: determining the number of short sequences of length K corresponding to the target species sequence, wherein K is a positive integer; determining a target key in the target data dictionary according to the number of sequences; when the target key is determined, storing germline gene position information corresponding to the short sequence of length K as a target value of the target data dictionary, wherein the target key corresponds to the target value one-to-one; and determining the target data dictionary corresponding to the target species sequence based on the target key and the target value.

[0044] In an exemplary embodiment, the multiple read sequences are compared with reference sequences in a preset database to obtain comparison information of the multiple read sequences and V(D)J genes, including: decomposing each of the multiple read sequences into multiple short read sequences, wherein the length of the short read sequence is K; identifying the target positions corresponding to the multiple short read sequences on the reference sequence; in the case of determining the multiple target positions corresponding to the multiple short read sequences, determining the starting position and the ending position of each read sequence on the reference sequence according to the multiple target positions; counting the data of the starting position and the ending position, and determining the sub-comparison information of each read sequence and the V(D)J gene based on the data counting result; and summarizing the multiple sub-comparison information corresponding to the multiple read sequences to obtain the comparison information.

[0045] It should be noted that since the second-generation sequencing technology cannot obtain full-length transcripts, and the reads generated during the sequencing process (equivalent to the read sequence in the above embodiment) may have errors, the result obtained by a complete alignment is poor, and many reads that can actually match the germline genome due to sequencing errors are often lost. Therefore, the present application constructs a reference sequence database (equivalent to the preset database in the above embodiment) by downloading all TCR / BCR molecular germline gene reference sequences of a specific species (such as human / mouse) from the professional database IMGT, thereby performing kmer analysis on the sequences in the reference sequence database and the reads of the sample FASTQ data (equivalent to the sequencing data to be analyzed in the above embodiment), respectively, to achieve rapid access to the position of the kmer of the sample reads (equivalent to the short sequence of length K in the above embodiment) on the germline gene reference sequence, and determine the common kmer of the read and the reference sequence, and infer the start and end positions of the read on the reference sequence based on the common kmer. After determining the position information of all reads, the position information is counted to determine the final position information of the reference sequence on each read alignment.

[0046] Optionally, in practical applications, the kmer of the reference genome (equivalent to the reference sequence in the above embodiment) is stored as the key of the dictionary, and the germline gene position information corresponding to the kmer is stored as the value of the dictionary, so as to quickly access the position of the kmer of the sample reads on the germline gene reference sequence. Subsequently, all positions of the kmer of each read on the reference genome are traversed, and the starting and ending positions of the read on the reference genome are inferred through the shared kmer of the read and the reference genome, and counted. Ultimately, the alignment result with the most counts is voted as the position information of the reference genome on the read alignment.

[0047] Step S206, parsing the alignment information to obtain multiple clonotypes corresponding to the multiple read sequences, optionally, parsing the alignment information through an application to obtain a complementary determining region sequence of the reference sequence aligned with each read sequence and a clonotype corresponding to each read sequence;

[0048] It is understood that the immune repertoire refers to the sum of all functionally diverse T cells and B cells in the individual's circulation system at any given time. Antigen-specific T cells and B cells act as the main effector cells that mediate immune responses, and their specificity is mainly determined by the complementary determining region (CDR3) of the T cell receptor (TCR) and the B cell receptor (BCR). The complementary determining region (CDR3) sequence is generated by the recombination of V(D)J germline gene segments and binds to specific antigens. Therefore, after the alignment is completed, the complementary determining region sequence corresponding to each read sequence can be obtained based on the determined alignment information containing the V(D)J gene.

[0049] Step S208, when the abundance information of multiple clonotypes corresponding to each position in the spatial transcriptome data is obtained, each position is analyzed and processed according to the abundance information of the multiple clonotypes and the multiple clonotypes to obtain a target clonotype at each position and quantitative information of the target clonotype.

[0050] In an embodiment of the present application, a plurality of read sequences to be analyzed are obtained from spatial transcriptome data; the plurality of read sequences are compared with reference sequences in a preset database to obtain comparison information of the plurality of read sequences and V(D)J genes, wherein the preset database has a plurality of data dictionaries, and the data dictionary is used to indicate the association relationship between the reference sequence and the target V(D)J gene; the comparison information is parsed to obtain a plurality of clonotypes corresponding to the plurality of read sequences; when the abundance information of the plurality of clonotypes corresponding to each position in the spatial transcriptome data is obtained, each position is analyzed and processed according to the abundance information of the plurality of clonotypes and the plurality of clonotypes to obtain the target at each position. The quantitative information of clonotype and target clonotype is obtained by adopting the above technical scheme, which at least solves the problem that there is no immune repertoire analysis scheme matching the spatial transcriptome sequencing technology in the prior art, so that the comparison relationship between multiple read sequences and germline gene reference sequences can be accurately and quickly determined from the complex sequencing data to be analyzed, and finally the V(D)J germline genes from which the multiple read sequences are derived are accurately parsed, and the optimal clonotype clonotype under each bin is screened and quantitatively analyzed by clustering and abundance accumulation methods, so that on the basis of this information, a more detailed feature analysis of the immune repertoire data is performed, thereby improving the analysis efficiency of the data in the immune repertoire.

[0051] It should be noted that the clonotype can be defined by at least one of the complementary determining region sequences (CDR3 amino acid sequence, CDR3 nucleotide sequence) and V(D)J gene sequences.

[0052] In an exemplary embodiment, the multiple read sequences are compared with reference sequences in a preset database to obtain comparison information of the multiple read sequences and V(D)J genes, including: decomposing each of the multiple read sequences into multiple short read sequences, wherein the length of the short read sequence is K, and K is a positive integer; identifying the target positions corresponding to the multiple short read sequences on the reference sequence; when the multiple target positions corresponding to the multiple short read sequences are determined, determining the starting position and the ending position of each read sequence on the reference sequence according to the multiple target positions; counting the data of the starting position and the ending position, and determining the sub-comparison information of each read sequence and the V(D)J gene based on the data counting result; and summarizing the multiple sub-comparison information corresponding to the multiple read sequences to obtain the comparison information.

[0053] In an exemplary embodiment, data counting is performed on the starting position and the ending position, and the comparison information of each read sequence and the V(D)J gene is determined based on the data counting result, including: determining the number of data counts corresponding to the starting position and the ending position at different positions on the reference sequence; obtaining the position information corresponding to the target position with the largest number of times on the reference sequence; determining the comparison result of each read sequence with the reference sequence according to the position information; and when the V(D)J gene corresponding to the comparison result is obtained, determining the sub-comparison information according to the V(D)J gene and the comparison result.

[0054] It can be understood that in order to ensure that the determined comparison result is more accurate, after determining the starting position and the ending position corresponding to each of the multiple read sequences on the reference sequence, the target starting position and the target ending position that appear the most times in the multiple read sequences are counted, and then the gene information corresponding to the reference sequence corresponding to the target starting position and the target ending position is determined as the gene information on the comparison of the multiple read sequences, so that according to the above gene information, clonotypes can be assembled based on the sequences obtained by the comparison, so that the original T cell or B cell receptor library sequencing data can be quickly and accurately analyzed.

[0055] In summary, through the above implementation, by utilizing a set of strategies based on kmer, alignment and voting, the alignment relationship between reads and germline gene reference sequences can be accurately and quickly determined from complex data, and finally the V(D)J germline genes from which the reads are derived can be accurately parsed.

[0056] In an exemplary embodiment, when the abundance information of multiple clonotypes corresponding to each position in the spatial transcriptome data is obtained, each position is analyzed and processed according to the abundance information of the multiple clonotypes and the multiple clonotypes to obtain a target clonotype at each position and quantitative information of the target clonotype, including: searching for a first clonotype with the highest abundance among the multiple clonotypes; defining the first clonotype as the target clonotype corresponding to the multiple read sequences; determining a target complementary determining region sequence corresponding to the target clonotype as reference data; calculating sequence similarity information between other complementary determining region sequences corresponding to other clonotypes of the multiple clonotypes except the target clonotype and the reference data; and accumulating the abundance corresponding to the other clonotypes to the target clonotype according to the sequence similarity information to obtain the quantitative information of the target clonotype.

[0057] In an exemplary embodiment, accumulating the abundances corresponding to the other clonotypes to the target clonotype according to the sequence similarity information to obtain quantitative information of the target clonotype includes: determining, according to the sequence similarity information, a plurality of target other complementary determining region sequences having a similarity greater than a preset similarity to the reference data; obtaining a plurality of target abundances corresponding to the plurality of target other complementary determining region sequences; accumulating the plurality of target abundances and a first abundance corresponding to the first clonotype to obtain a first clonotype with a second abundance; recalculating an actual abundance and a frequency of the first clonotype with the second abundance; and determining the quantitative information of the target clonotype according to the actual abundance and the frequency.

[0058] In order to better understand the above scheme, the application of the technology is now explained. It should be noted that the effective clonotype screening under a single bin. The resolution of Stereo-seq technology reaches nanometers. Bin1 refers to the smallest unit that divides the spatial position of tissues or cells. Its nanopore radius is 250 nanometers. When the bin size is set to 20, the side length of the bin20 unit is about 10 microns, which can just cover the TCR / BCR with a radius of 5um. At the bin1 level, theoretically, bins derived from the same cell should only contain one clonotype information, but due to multiple PCR amplifications during spatial immune repertoire sequencing and the cell drift effect caused by the cell infiltration chip, a bin may actually detect multiple clonotype information. However, in practical applications, the detection is more accurate, and it is necessary to effectively determine the unique clonotype under a single bin and its quantitative information.

[0059] As an optional implementation, first, all clonotypes and their abundance information under each bin are counted, and the clonotype with the highest abundance is defined as the optimal clonotype for each bin; then, the CDR3 sequence of the optimal clonotype is used as a reference to calculate the sequence similarity between the CDR3 of the remaining clonotypes under the bin and the CDR3 of the optimal clonotype, and the abundance of clonotypes with highly similar sequence consistency is added to the optimal clonotype, and finally the unique clonotype, abundance and frequency information under each bin can be obtained. The sequence similarity score calculation uses the Smith-Waterman (SW) algorithm to perform local alignment, and the scoring matrix is ​​the BLOSUM45 substitution matrix.

[0060] Obviously, the embodiments described above are only some embodiments of the present application, not all embodiments. In order to better understand the above method, the above process is described below in conjunction with the embodiments, but it is not intended to limit the technical solutions of the embodiments of the present application, specifically:

[0061] In the related technology, the identification software of conventional immune repertoires mainly includes TRUST4 and MIXCR. TRUST4 is used to reconstruct the immune receptor repertoire of T cells and B cells from single-cell transcriptome data, and is compatible with SMART-seq and 5'10x Genomics platforms. The main technical scheme of TRUST4 is as follows: first, the candidate reads in the original sequencing data are screened according to whether there is a matching part between the reads and the V(D)J gene region; then TRUST4 clusters the reads with similar somatic hypermutations to the same contig sequence, and by adding four base weights to each position of the contig sequence, the match between the reads and the contig can tolerate a certain degree of mutation. If the reads match any existing contig sequence, the reads derive the contig sequence, and if they cannot match, a new contig sequence is created; finally, TRUST4 compares the assembled contigs sequence with the IMGT database to identify the V(D)J gene corresponding to the contig sequence. MIXCR is a general software that can be applied to any type of sequencing data. Its principle is to align sequencing reads to the reference V(D)J genes of T cell or B cell receptors, and use the sequences obtained in the previous step to assemble clonotypes, which can quickly and accurately analyze the original T cell or B cell receptor library sequencing data. These software are widely used in large pharmaceutical companies in research and development projects such as vaccine development, antibody discovery and cancer immunotherapy. However, for the data types generated by spatial immune repertoire technology, existing software platforms cannot directly achieve accurate spatial immune repertoire feature analysis.

[0062] That is, because spatial transcriptome sequencing technology was applied later than conventional transcriptome sequencing technology, no analysis solution for spatial transcriptome immune repertoire sequencing data has been found. The existing software TRUST4 and MIXCR are designed for single-cell transcriptome and bulk transcriptome data, and do not provide spatial immune repertoire feature analysis solutions for cell spatial location information.

[0063] In response to the above problems, the optional embodiment of the present application provides a method and system for processing spatial immune group library data. The above method is specifically as follows: through in-depth research on the characteristics of spatial transcriptome data, a set of dedicated analysis schemes that meet the spatial transcriptome immune group library sequencing data are designed, and a strategy based on the integration of multiple steps such as kmer, alignment and voting is used to accurately and quickly determine the alignment relationship between reads and germline gene reference sequences from complex data, and finally accurately parse out the V (D) J germline genes from which the reads are derived. Further, based on the above-mentioned determination results, the optimal clonotype under each bin is screened and quantitatively analyzed by clustering and abundance accumulation methods, so that on the basis of this information, a more detailed feature analysis of the immune group library data is performed.

[0064] Through the above method, V(D)J gene identification and effective clonotype screening under a single bin were achieved, thereby enabling more detailed feature analysis of the immune repertoire data.

[0065] Optional, Figure 3 This is an example diagram of an optional spatial immune repertoire data processing system according to an embodiment of the present application; through strategies such as V(D)J gene identification, CDR3 sequence inference and bin screening, the clonotypes of TCR / BCR molecules in spatial positions and the effective clonotype information under a single bin are gradually inferred, specifically including the following three parts:

[0066] Part 1: Data preprocessing; specifically, download all TCR / BCR molecular germline gene reference sequences of specific species (such as human / mouse) from the professional database IMGT to build a reference sequence database.

[0067] Part 2: Alignment; Specifically, kmer analysis is performed on the sequences in the reference sequence database and the reads of the sample FASTQ data. The kmer of the reference sequence is stored as the key of the dictionary, and the germline gene position information corresponding to the kmer is stored as the value of the dictionary, so as to quickly access the position of the kmer of the sample reads on the germline gene reference sequence. Subsequently, all positions of the kmer of each read on the reference sequence are traversed, and the start and end positions of the read on the reference sequence are inferred through the shared kmer of the read and the reference sequence, and counted. Finally, the alignment result with the most counts is voted as the position information of the reference sequence on the read alignment.

[0068] Optional, Figure 4 This is an example flow chart of an optional V(D)J alignment strategy based on the integration of multiple steps such as kmer, alignment and voting according to an embodiment of the present application.

[0069] Part III: Clonotype determination and feature analysis. Specifically, all clonotypes and their abundance information under each bin are counted, and the clonotype with the highest abundance is defined as the optimal clonotype for each bin; then, the CDR3 sequence of the optimal clonotype is used as a reference to calculate the sequence similarity between the CDR3 of the remaining clonotypes under the bin and the CDR3 of the optimal clonotype, and the abundance of clonotypes with highly similar sequence consistency is added to the optimal clonotype, and finally the unique clonotype, abundance and frequency information under each bin can be obtained. The sequence similarity score calculation uses the Smith-Waterman (SW) algorithm to perform local alignment, and the scoring matrix is ​​the BLOSUM45 substitution matrix. Optional, Figure 5 The present invention is an optional optimal clonotype identification and quantitative analysis flow chart under a single bin according to an embodiment of the present application.

[0070] In summary, through the above optional embodiments, the low accuracy of spatial transcriptome sequencing data and the molecular structure characteristics of TCR / BCR are fully taken into account, and a set of spatial transcriptome TCR / BCR immune library data analysis solutions are developed specifically. Through kmer analysis, multiple alignments and voting multi-step analysis strategies, the alignment accuracy of reads and V and J genes is greatly improved. At the same time, combined with the Smith-Waterman local alignment algorithm, the unique clonotype under a single bin and its quantitative information can be effectively determined.

[0071] As an optional implementation method, the above solution is illustrated by example, specifically:

[0072] Using Stereo-seq, a spatial transcriptome sequencing technology independently developed by BGI, high-throughput transcriptome analysis was performed on tissue sections to obtain spatial immune repertoire TCR / BCR sequencing data, and the above-mentioned technical solution of this application was used to implement specific data analysis tasks. The specific analysis process and results are as follows:

[0073] Step 1. The number of clean reads that passed the initial quality control was 11738663. Use MIXTB to align these clean reads with the germline gene reference sequence of a professional database (such as IMGT) to obtain the alignment information of each read with the V and J genes. Among them, 7948 reads failed to be aligned due to the loss of the V gene, 3880436 reads failed to be aligned due to the loss of the J gene, and 528992 reads failed to be aligned due to the loss of both the V and J genes. Finally, a total of 7321287 reads successfully identified the paired V and J genes. Optionally, Table 1 below is a schematic table of MIXTB alignment results statistics.

[0074] Table 1

[0075]

[0076] Step 2: Based on the start and end position information of the reference sequence aligned with the reads, a self-written script was used to infer the CDR3 sequence of each read aligned with the reference sequence, and a total of 5,668,972 reads were obtained that could successfully infer the CDR3 sequence.

[0077] It should be noted that in order to make the data presentation clearer, different types of data are visualized through the visualization function corresponding to the target application after the inference is completed.

[0078] Optional, Figure 6 It is a schematic diagram showing the CDR3 length distribution statistics of the top ten V genes obtained optionally according to an embodiment of the present application. Figure 7 This is a schematic diagram showing an optional display of the top ten clonal abundance statistics obtained according to an embodiment of the present application. Figure 8 This is a schematic diagram showing an optional statistical situation of V and J gene frequencies obtained according to an embodiment of the present application; Fig. 9 This is a schematic diagram showing an optional V and J pairing statistics obtained according to an embodiment of the present application.

[0079] Step 3. Use the statistical function in the target application to obtain all clonotypes and their abundance information under each bin, and complete the clonotype redundancy removal at the bin1 level. A total of 556,452 bins correspond to 984,044 clonotypes. Subsequently, the clonotype with the highest clonotype abundance under each bin is defined as the optimal clonotype, and the CDR3 sequences of the remaining clonotypes under the bin are compared with the CDR3 sequences of the optimal clonotype using the Smith-Waterman local alignment algorithm. The scoring matrix is ​​the BLOSUM45 substitution matrix, and the gap penalty is set to -10 to focus on matching sequences with basically no insertions or deletions. The abundance of the remaining clonotypes with a sequence similarity greater than 0.9 with the CDR3 sequences of the optimal clonotype is added to the optimal clonotype, and the abundance and frequency of the optimal clonotype after the abundance accumulation are recalculated, and finally 556,452 bins containing only one clonotype are obtained. Optional, Fig.10 This is a schematic diagram showing the spatial distribution of the top ten V genes, J genes, VJ pairs and CDR3s in abundance, which is optionally obtained according to an embodiment of the present application.

[0080] Through the above steps, the analysis strategy based on kmer, alignment and voting is integrated, and the algorithm based on kmer, alignment and voting is integrated to identify the immune repertoire V(D)J, which can improve the accuracy of immune repertoire identification. At the same time, through the screening of effective clonal types under a single bin based on CDR3 sequence similarity and Clonotype abundance, the TCR / BCR molecular sequence information analysis scheme based on spatiotemporal immune repertoire is improved, which is helpful for the subsequent cell bin division. However, for TCR / BCR sequence molecules with high similarity, the response is faster.

[0081] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, disk, CD), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods of each embodiment of the present application.

[0082] In the present embodiment, a data processing device is also provided, and the data processing device is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware of a predetermined function. Although the device described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware is also possible and conceived.

[0083] Fig.11 is a structural block diagram of an optional data processing device according to an embodiment of the present application, such as Fig.11 As shown, including:

[0084] An acquisition module 1102 is used to acquire a plurality of read sequences to be analyzed from the spatial transcriptome data;

[0085] A comparison module 1104 is used to compare the multiple read sequences with reference sequences in a preset database to obtain comparison information of the multiple read sequences and V(D)J genes, wherein the preset database has multiple data dictionaries, and the data dictionaries are used to indicate the association relationship between the reference sequence and the target V(D)J gene;

[0086] An analysis module 1106 is used to analyze the comparison information to obtain multiple clonal types corresponding to the multiple read sequences;

[0087] The analysis module 1108 is used to, when the abundance information of multiple clonotypes corresponding to each position in the spatial transcriptome data is obtained, analyze and process each position according to the abundance information of the multiple clonotypes and the multiple clonotypes to obtain a target clonotype at each position and quantitative information of the target clonotype.

[0088] Through the above device, multiple read sequences to be analyzed are obtained from the spatial transcriptome data; the multiple read sequences are compared with the reference sequences in the preset database to obtain the comparison information of the multiple read sequences and the V(D)J genes, wherein the preset database has multiple data dictionaries, and the data dictionary is used to indicate the association relationship between the reference sequence and the target V(D)J gene; the comparison information is parsed to obtain multiple clonotypes corresponding to the multiple read sequences; when the abundance information of the multiple clonotypes corresponding to each position in the spatial transcriptome data is obtained, each position is analyzed and processed according to the abundance information of the multiple clonotypes and the multiple clonotypes to obtain the target clonotype of each position. The quantitative information of the clonotype and the target clonotype is obtained by adopting the above technical scheme, which at least solves the problem that there is no immune repertoire analysis scheme matching the spatial transcriptome sequencing technology in the prior art, so that the comparison relationship between multiple read sequences and germline gene reference sequences can be accurately and quickly determined from the complex sequencing data to be analyzed, and finally the V(D)J germline genes from which the multiple read sequences are derived are accurately parsed, and the optimal clonotype clonotype under each bin is screened and quantitatively analyzed by clustering and abundance accumulation methods, so that on the basis of this information, a more detailed feature analysis of the immune repertoire data is performed, thereby improving the analysis efficiency of the data in the immune repertoire.

[0089] In an exemplary embodiment, the above-mentioned device also includes: a download module, which is used to compare the multiple read sequences with the reference sequences in a preset database. Before obtaining the comparison information of the multiple read sequences and the V(D)J genes, the above-mentioned method also includes: downloading the target species sequence corresponding to all TCR / BCR molecular germline genes of a specific species from the target professional database; performing kmer analysis on the target species sequence to obtain a target data dictionary corresponding to the target species sequence; and sending the target data dictionary to the preset database for storage.

[0090] In an exemplary embodiment, the above-mentioned download module is also used to determine the number of short sequences of length K corresponding to the target species sequence, wherein K is a positive integer; determine the target key in the target data dictionary according to the number of sequences; when the target key is determined, store the germline gene position information corresponding to the short sequence of length K as the target value of the target data dictionary, wherein the target key corresponds to the target value one-to-one; and determine the target data dictionary corresponding to the target species sequence based on the target key and the target value.

[0091] In an exemplary embodiment, the above-mentioned comparison module is also used to decompose each of the multiple read sequences into multiple short read sequences, wherein the length of the short read sequence is K, and K is a positive integer; identify the target positions corresponding to the multiple short read sequences on the reference sequence; when determining the multiple target positions corresponding to the multiple short read sequences, determine the starting position and the ending position of each read sequence on the reference sequence according to the multiple target positions; count the data of the starting position and the ending position, and determine the sub-comparison information of each read sequence with the V(D)J gene based on the data counting result; and summarize the multiple sub-comparison information corresponding to the multiple read sequences to obtain the comparison information.

[0092] In an exemplary embodiment, the above-mentioned comparison module is also used to determine the number of data counts corresponding to the starting position and the ending position at different positions on the reference sequence; obtain the position information corresponding to the target position with the largest number of times on the reference sequence; determine the comparison result of each read sequence with the reference sequence according to the position information; when the V(D)J gene corresponding to the comparison result is obtained, determine the sub-comparison information according to the V(D)J gene and the comparison result.

[0093] In an exemplary embodiment, the above-mentioned analysis module is further used to find the first clonotype with the highest abundance among the multiple clonotypes; define the first clonotype as the target clonotype corresponding to the multiple read sequences; determine the target complementary determining region sequence corresponding to the target clonotype as reference data; calculate sequence similarity information between other complementary determining region sequences corresponding to other clonotypes of the multiple clonotypes except the target clonotype and the reference data; and add the abundance corresponding to the other clonotypes to the target clonotype according to the sequence similarity information to obtain quantitative information of the target clonotype.

[0094] In an exemplary embodiment, the above-mentioned analysis module is further used to determine, according to the sequence similarity information, a plurality of target other complementary determining region sequences having a similarity with the reference data greater than a preset similarity; obtain a plurality of target abundances corresponding to the plurality of target other complementary determining region sequences; accumulate the plurality of target abundances and the first abundance corresponding to the first clonotype to obtain a first clonotype with a second abundance; recalculate the actual abundance and frequency of the first clonotype with the second abundance; and determine a processing result of the sequencing data according to the actual abundance and the frequency.

[0095] In the embodiments of the present application, the term "module" or "unit" refers to a computer program or a part of a computer program that has a predetermined function and works together with other related parts to achieve a predetermined goal, and can be implemented in whole or in part by using software, hardware (such as processing circuits or memories), or a combination thereof. Similarly, a processor (or multiple processors or memories) can be used to implement one or more modules or units. In addition, each module or unit can be part of an overall module or unit that includes the function of the module or unit.

[0096] It should be noted that, for the above-mentioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should know that the present invention is not limited by the described action sequence, because according to the present invention, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present invention.

[0097] An embodiment of the present application further provides a storage medium, which includes a stored program, wherein the program executes any of the above methods when it is run.

[0098] Optionally, in this embodiment, the storage medium may be configured to store program codes for executing the following steps:

[0099] S1. Obtain multiple read sequences to be analyzed from spatial transcriptome data;

[0100] S2, comparing the multiple read sequences with reference sequences in a preset database to obtain comparison information of the multiple read sequences and V(D)J genes, wherein the preset database contains multiple data dictionaries, and the data dictionaries are used to indicate the association relationship between the reference sequence and the target V(D)J gene;

[0101] S3, parsing the comparison information to obtain multiple clonal types corresponding to the multiple read sequences;

[0102] S4. When the abundance information of multiple clonotypes corresponding to each position in the spatial transcriptome data is obtained, each position is analyzed and processed according to the abundance information of the multiple clonotypes and the multiple clonotypes to obtain a target clonotype at each position and quantitative information of the target clonotype.

[0103] An embodiment of the present application further provides an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0104] Optionally, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0105] Optionally, in this embodiment, the processor may be configured to perform the following steps through a computer program:

[0106] S1. Obtain multiple read sequences to be analyzed from spatial transcriptome data;

[0107] S2, comparing the multiple read sequences with reference sequences in a preset database to obtain comparison information of the multiple read sequences and V(D)J genes, wherein the preset database contains multiple data dictionaries, and the data dictionaries are used to indicate the association relationship between the reference sequence and the target V(D)J gene;

[0108] S3, parsing the comparison information to obtain multiple clonal types corresponding to the multiple read sequences;

[0109] S4. When the abundance information of multiple clonotypes corresponding to each position in the spatial transcriptome data is obtained, each position is analyzed and processed according to the abundance information of the multiple clonotypes and the multiple clonotypes to obtain a target clonotype at each position and quantitative information of the target clonotype.

[0110] Optionally, in this embodiment, the above-mentioned storage medium may include but is not limited to: a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk, and other media that can store program codes.

[0111] An embodiment of the present application further provides a computer program product, including a computer program, and when the computer program is executed by a processor, the steps in any one of the above method embodiments are performed.

[0112] An embodiment of the present application further provides another computer program product, including a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.

[0113] Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments and optional implementation modes, and this embodiment will not be described in detail here.

[0114] Obviously, those skilled in the art should understand that the above modules or steps of the present application can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, and optionally, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order from that herein, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present application is not limited to any specific combination of hardware and software.

[0115] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the principles of the present application shall be included in the protection scope of the present application.

Claims

1. A data processing method, It is characterized in that include: Obtain multiple read sequences to be analyzed from spatial transcriptome data; Comparing the plurality of read sequences with reference sequences in a preset database to obtain comparison information of the plurality of read sequences and V(D)J genes, wherein the preset database contains a plurality of data dictionaries, and the data dictionaries are used to indicate the association relationship between the reference sequence and the target V(D)J gene; Parsing the comparison information to obtain multiple clonal types corresponding to the multiple read sequences; When the abundance information of multiple clonotypes corresponding to each position in the spatial transcriptome data is obtained, the multiple clonotypes are analyzed and processed for each position according to the abundance information of the multiple clonotypes to obtain a target clonotype at each position and quantitative information of the target clonotype; Wherein, when the abundance information of multiple clonotypes corresponding to each position in the spatial transcriptome data is obtained, the multiple clonotypes are analyzed and processed for each position according to the abundance information of the multiple clonotypes to obtain the target clonotype at each position and the quantitative information of the target clonotype, including: Finding the first clonotype with the highest abundance among the plurality of clonotypes; defining the first clonotype as a target clonotype corresponding to the plurality of read sequences; determining a target complementary determining region sequence corresponding to the target clonotype as reference data; Calculating sequence similarity information between other complementary determining region sequences corresponding to other clonotypes of the plurality of clonotypes except the target clonotype and the reference data; According to the similarity information, the abundances corresponding to other complementary determining region sequences having a similarity with the reference data greater than a preset similarity are added to the abundance of the target clonotype to obtain quantitative information of the target clonotype.

2. The data processing method according to claim 1, It is characterized in that Before comparing the plurality of read sequences with reference sequences in a preset database to obtain comparison information of the plurality of read sequences with V(D)J genes, the method further comprises: Download the target species sequences corresponding to all TCR / BCR molecular germline genes of a specific species from the target professional database; Performing kmer analysis on the target species sequence to obtain a target data dictionary corresponding to the target species sequence; The target data dictionary is sent to a preset database for storage.

3. The data processing method according to claim 2, It is characterized in that Perform kmer analysis on the target species sequence to obtain a target data dictionary corresponding to the target species sequence, including: Determine the number of short sequences of length K corresponding to the target species sequence, where K is a positive integer; Determining a target key in the target data dictionary according to the number of sequences; When the target key is determined, the germline gene position information corresponding to the short sequence of length K is stored as a target value of the target data dictionary, wherein the target key corresponds to the target value one by one; A target data dictionary corresponding to the target species sequence is determined based on the target key and the target value.

4. The data processing method according to claim 1, It is characterized in that Comparing the plurality of read sequences with reference sequences in a preset database to obtain comparison information of the plurality of read sequences with V(D)J genes, including: Decomposing each of the plurality of read sequences into a plurality of short read sequences, wherein the length of the short read sequence is K; Identify the target positions corresponding to the multiple short read sequences on the reference sequence; In the case of determining a plurality of target positions corresponding to the plurality of short read sequences, determining a starting position and an ending position of each read sequence on the reference sequence according to the plurality of target positions; Counting data for the starting position and the ending position, and determining sub-alignment information of each read sequence and the V(D)J gene based on the data counting result; A plurality of sub-alignment information corresponding to the plurality of read sequences is aggregated to obtain the alignment information.

5. The data processing method according to claim 4, It is characterized in that Counting data for the starting position and the ending position, and determining sub-alignment information of each read sequence and the V(D)J gene based on the data counting result, including: Determine the number of data counts corresponding to the starting position and the ending position at different positions on the reference sequence; The position information corresponding to the target position with the largest number of acquisitions on the reference sequence; Determine a comparison result between each read sequence and the reference sequence according to the position information; When the V(D)J gene corresponding to the alignment result is obtained, sub-alignment information is determined based on the V(D)J gene and the alignment result.

6. The data processing method according to claim 1, It is characterized in that According to the similarity information, the abundances corresponding to other complementary determining region sequences having a similarity with the reference data greater than a preset similarity are added to the abundance of the target clonotype to obtain quantitative information of the target clonotype, including: Determine, based on the sequence similarity information, a plurality of target other complementary determining region sequences having a similarity with the reference data greater than a preset similarity; Obtaining a plurality of target abundances corresponding to the plurality of target other complementary determining region sequences; Accumulating the plurality of target abundances and a first abundance corresponding to the first clonotype to obtain a first clonotype with a second abundance; recalculating the actual abundance and frequency of the first clonotype having the second abundance; Quantitative information of the target clonotype is determined according to the actual abundance and the frequency.

7. A data processing device, It is characterized in that include: An acquisition module, used for acquiring multiple read sequences to be analyzed from the spatial transcriptome data; A comparison module, used to compare the multiple read sequences with reference sequences in a preset database to obtain comparison information of the multiple read sequences and V(D)J genes, wherein the preset database contains multiple data dictionaries, and the data dictionaries are used to indicate the association relationship between the reference sequence and the target V(D)J gene; A parsing module, configured to parse the comparison information to obtain a plurality of clonal types corresponding to the plurality of read sequences; An analysis module, configured to, when the abundance information of a plurality of clonotypes corresponding to each position in the spatial transcriptome data is obtained, analyze and process each position according to the abundance information of the plurality of clonotypes, and obtain a target clonotype at each position and quantitative information of the target clonotype; The analysis module is further used to find a first clonotype with the highest abundance among the multiple clonotypes; define the first clonotype as a target clonotype corresponding to the multiple read sequences; determine a target complementary determining region sequence corresponding to the target clonotype as reference data; calculate sequence similarity information between other complementary determining region sequences corresponding to other clonotypes of the multiple clonotypes except the target clonotype and the reference data; and add the abundances corresponding to other complementary determining region sequences having a similarity with the reference data greater than a preset similarity to the abundance of the target clonotype according to the similarity information, to obtain quantitative information of the target clonotype.

8. A computer-readable storage medium, It is characterized in that The computer-readable storage medium includes a stored program, wherein the program executes the method described in any one of claims 1 to 6 when executed.

9. An electronic device comprising a memory and a processor, It is characterized in that A computer program is stored in the memory, and the processor is configured to execute the method according to any one of claims 1 to 6 through the computer program.

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