Analysis method and device based on single cell sequencing data and electronic equipment

Abstract

The embodiment of the invention discloses an analysis method and device based on single cell sequencing data and electronic equipment, and belongs to the technical field of bioinformatics and the field of data processing. The method comprises the steps that annotation results based on single-cell sequencing data are obtained, and the annotation results comprise a single-cell transcriptome annotation result and a single-cell VDJ annotation result; values of evaluation parameters are determined according to the annotation result, the evaluation parameters comprise the number of target cells and comparison parameters of the target cells, and the target cells are cells comprising target functional chains or cells with detected target cell receptor sequences; and determining the value of the evaluation index of the single cell sequencing data according to the number of the target cells and the comparison parameters thereof. According to the embodiment of the invention, the quality of the single-cell sequencing data or the library information established based on the single-cell sequencing data can be evaluated more comprehensively.

Description

[0001] This application relates to the fields of bioinformatics and data processing, and more specifically, to an analysis method, apparatus, and electronic device based on single-cell sequencing data. Background Technology

[0002] In the field of single-cell sequencing, existing technologies have limitations in data quality control. Specifically, the quality control processes of existing solutions are basically limited to assessing the general quality of the raw sequencing data itself. This mainly includes: sequencing base quality (such as Q30 score), cell barcode and UMI recognition efficiency, sequence alignment rate, and transcriptome-based general quality control indicators (such as the number of genes detected per cell, UMI count, and mitochondrial gene ratio).

[0003] According to the inventors' research, while these quality control indicators are universal, they are both "general" and "technical." They can only macroscopically indicate whether the sequencing experiment itself was successful, but they cannot detect information biases in library information built based on single-cell sequencing data. In other words, existing technology presents situations where sequencing experiments that can be considered successful based on current standards may still have quality problems with the sequencing data. Summary of the Invention

[0004] This application provides an analysis method, apparatus, and electronic device based on single-cell sequencing data to at least more comprehensively evaluate the quality of single-cell sequencing data.

[0005] According to a first aspect of the embodiments of this application, an analysis method based on single-cell sequencing data is provided, the method comprising: Obtain annotation results based on single-cell sequencing data, including single-cell transcriptome annotation results and single-cell VDJ annotation results; The values ​​of evaluation parameters are determined based on the annotation results. The evaluation parameters include the number of target cells and their comparison parameters. The target cells are cells that include the target functional chain or cells that have detected the target cell receptor sequence. The evaluation index of the single-cell sequencing data is determined based on the number of target cells and their comparison parameters.

[0006] According to a second aspect of the embodiments of this application, a method for adjusting a single-cell immunoassay reagent is provided, the method comprising: Obtain annotation results based on single-cell sequencing data, including single-cell transcriptome annotation results and single-cell VDJ annotation results; The number of target cells identified by both the single-cell VDJ annotation results and the single-cell transcriptome annotation results is determined, and the target cells are cells that include the target functional chain. The number of target cells annotated as conventional adaptive immune cells was determined based on the single-cell transcriptome annotation results. The ratio of the number of conventional adaptive immune cells to the number of target cells is calculated and used as the value of the second evaluation index of the single-cell sequencing data. The second evaluation index is used to evaluate the target cell receptor capture efficiency. If the value of the second evaluation index is lower than the minimum value range of the second evaluation index, the coverage of the target cell receptor primer in the single-cell immunoassay reagent is increased, and the target cell receptor primer corresponds to the target cell.

[0007] According to a third aspect of the embodiments of this application, a single-cell immunoassay reagent obtained based on the method of the second aspect of the embodiments of this application is provided.

[0008] According to a fourth aspect of the embodiments of this application, an analysis device based on single-cell sequencing data is provided, the device comprising: The data acquisition module is used to acquire annotation results based on single-cell sequencing data, including single-cell transcriptome annotation results and single-cell VDJ annotation results. The parameter value determination module is used to determine the value of the evaluation parameter based on the annotation result. The evaluation parameter includes the number of target cells and its comparison parameter. The target cell is a cell that includes a target functional chain or a cell that has detected a target cell receptor sequence. The indicator value determination module is used to determine the value of the evaluation indicator of the single-cell sequencing data based on the number of target cells and their comparison parameters.

[0009] According to a fifth aspect of the embodiments of this application, an electronic device is provided, comprising: Memory, used to store one or more computer instructions; A processor is configured to invoke and execute the computer instructions to implement the method described in the first aspect of the embodiments of this application.

[0010] According to a sixth aspect of the present application, a computer-readable storage medium is provided, storing one or more computer instructions, which, when executed, implement the method described in the first aspect of the present application.

[0011] According to a seventh aspect of the present application, a computer program product is provided, which, when executed, implements the method described in the first aspect of the present application.

[0012] By employing the relevant embodiments of this application, a more comprehensive evaluation of the quality of single-cell sequencing data and the quality of library information constructed based on single-cell sequencing data can be achieved. Attached Figure Description

[0013] Figure 1 This is a flowchart illustrating an analysis method based on single-cell sequencing data according to an embodiment of this application; Figure 2 This is a flowchart illustrating a cloning annotation method according to an embodiment of this application; Figure 3 This is a flowchart illustrating a method for adjusting a single-cell immunoassay reagent according to an embodiment of this application; Figure 4 This is a flowchart illustrating the method for analyzing single-cell sequencing data and the method for clonal annotation according to embodiments of this application; Figure 5 This is a schematic diagram of an analysis device based on single-cell sequencing data according to an embodiment of this application; Figure 6 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. Detailed Implementation

[0014] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0015] It should be understood that "multiple" as mentioned herein refers to two or more. In the description of the embodiments of this application, unless otherwise stated, " / " means "or," for example, A / B can mean A or B; "and / or" in this document is merely a description of the relationship between related objects, indicating that three relationships can exist, for example, A and / or B can represent: A alone, A and B simultaneously, and B alone. In addition, in order to facilitate a clear description of the technical solutions of the embodiments of this application, the terms "first," "second," etc., are used in the embodiments of this application to distinguish the same or similar items with substantially the same function and effect. Those skilled in the art will understand that the terms "first," "second," etc., do not limit the quantity or execution order, and the terms "first," "second," etc., do not necessarily mean different. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion, for example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or units that are not explicitly listed or inherent to these processes, methods, products, or devices.

[0016] First, the terminology used in the embodiments of this application will be introduced.

[0017] Single-cell sequencing is a technique that performs sequencing analysis of the genome, transcriptome, etc., at the level of a single cell, and can reveal cellular heterogeneity.

[0018] TCR (T Cell Receptor): The T cell receptor is responsible for recognizing the antigen peptide-MHC complex on the surface of antigen-presenting cells.

[0019] BCR (B Cell Receptor): B cell receptor, also known as membrane-bound immunoglobulin, is responsible for recognizing specific antigens.

[0020] Cell clustering: The process of grouping cells based on the similarity of their gene expression profiles.

[0021] Cell annotation: Identifying the biological function of cell clusters based on known marker genes.

[0022] Immune repertoire: A complete collection of the diversity of functional T cell receptors (TCRs) and / or B cell receptors (BCRs) contained in all T lymphocytes and / or B lymphocytes.

[0023] VDJ sequence: V(D)J sequence is a general term that refers to the specific nucleotide or amino acid sequence generated through the rearrangement process of V, D, and J gene fragments in lymphocytes. Specifically, it refers to the general term for TCR sequence and BCR sequence.

[0024] GEX: Gene Expression, which in this patent mainly refers to the gene expression matrix.

[0025] Q30: A core quality assessment metric in the field of high-throughput sequencing, specifically referring to a quality standard where the correct identification probability of a single base sequencing result is not less than 99.9% during the sequencing process.

[0026] Barcode: A cell barcode, a unique molecular identifier that marks each cell.

[0027] Umi: Unique molecular identifier, a short random nucleotide sequence introduced along with cell barcodes to label each original messenger RNA molecule, primarily used for gene quantification.

[0028] Clonal type: In the fields of immunology and immunogenomics, it specifically refers to the population of all lymphocytes and their progeny produced by clonal expansion of a common progenitor cell. The same clonal type can be considered as the same cell.

[0029] CDR3 sequence: The most variable and core region in the variable regions of T-cell and B-cell receptors. In immune repertoire analysis, the CDR3 sequence is the most fundamental molecular basis for defining "clonal type".

[0030] α / β chain (γ / δ chain): refers to the combination of two different types of polypeptide chains of T cell receptor, which constitute two T cell subsets with different functions and distributions. Currently, the main detection is mainly αβ T cells.

[0031] The above describes the terminology that may be involved in the embodiments of this application. Next, the various embodiments involved in this application will be described in detail.

[0032] This application provides an analysis method based on single-cell sequencing data, referring to... Figure 1 The flowchart shown illustrates the following processing steps.

[0033] 100: Obtain annotation results based on single-cell sequencing data. These annotation results include single-cell transcriptome annotation results and single-cell VDJ annotation results. Single-cell transcriptome annotation results identify the primary cell type for each cell, such as T cells, B cells, NK cells, myeloid cells, etc.; cell subtypes: further subdivisions within the primary type, for example, within T cells, subdivisions include CD4+ T cells, CD8+ T cells, Tregs, naïve T cells, memory T cells, exhausted T cells, etc. Single-cell VDJ annotation results primarily define all clonoids, including clonoid number (clonotype1, clonoid2), strand information (TRA and TRB strands for T cells, IGH and IGL / IGK strands for B cells), VDJC genes contained in the sequence (e.g., TRBV12-3, TRBJ2-1) and CDR3 sequences, and clonal amplification information, mainly referring to the number of cells contained in each clonoid; and, based on known clonoids, the clonoid information corresponding to each cell.

[0034] Optionally, the gene expression matrix (GEX) and V(D)J sequence data of cells can be acquired simultaneously using a single-cell sequencing platform, and then preprocessed to obtain single-cell transcriptome annotation results and single-cell VDJ annotation results. Alternatively, single-cell transcriptome annotation results and single-cell VDJ annotation results can be obtained directly from a third party.

[0035] 102: Determine the values ​​of the evaluation parameters based on the annotation results. The evaluation parameters include the number of target cells and their comparative parameters. Target cells are cells containing the target functional chain or cells in which the target cell receptor sequence has been detected.

[0036] 104: Determine the evaluation index value of single-cell sequencing data based on the number of target cells and their comparison parameters.

[0037] The method provided in this embodiment can perform a more complete quality assessment of single-cell sequencing data based on the number of target cells, thereby accurately identifying information biases in library information obtained based on single-cell sequencing data.

[0038] Optionally, in one implementation of this embodiment, the evaluation index has at least one component, different evaluation indices have different comparison parameters, and the evaluation index is logically related to the number of target cells and the comparison parameters. For example, the evaluation index includes a first type of index that analyzes the distribution characteristics (e.g., percentage of numbers) of specific cells within the target cell as a whole, and / or a second type of index that analyzes the distribution characteristics (e.g., percentage of numbers) of the target cell as the analysis object within the whole.

[0039] Optionally, in one implementation of this embodiment, the target functional chain is a TCR chain or a BCR chain, and the target cell receptor sequence is a TCR sequence or a BCR sequence. In the various embodiments described below, those examples using T cells as the target cell, T cell receptors as the target cell receptor, TCR chains as the target functional chain, and TCR sequences as the target cell receptor sequence are used for illustration. Those skilled in the art should understand that the same logic can be used to determine the same parameters and indicators for B cells, and this embodiment will not describe these in detail.

[0040] Optionally, in one implementation of this embodiment, the following preprocessing can be used to obtain single-cell transcriptome annotation results and single-cell VDJ annotation results.

[0041] First, gene expression matrix (GEX) and V(D)J sequence data of cells were simultaneously acquired using a single-cell sequencing platform. Then, the GEX data underwent quality control, normalization, dimensionality reduction, and cell clustering. Genes were labeled based on standard cell types, and cell annotation was performed for each cell. Next, the V(D)J data was assembled and annotated to identify the CDR3 nucleotide / amino acid sequences of the α and β chains (or γ / δ chains) of the T cell receptor (TCR) for each cell.

[0042] Optionally, in one implementation of this embodiment, the target cell is a cell that includes a target functional chain, for example, a cell in which at least one functional TCR chain has been successfully detected.

[0043] In this case, processing 102 can be implemented in the following ways: The number of target cells can be determined solely based on single-cell VDJ annotation results, for example, the number of cells in which at least one functional TCR chain was successfully detected; or, the number of target cells jointly identified by single-cell VDJ annotation results and single-cell transcriptome annotation results can be determined, i.e., the number of cells in the intersection of the target cells identified by each of them can be determined.

[0044] In this case, processing 104 can be done as follows: calculate the ratio of the number of target cells to the number of cells with reliable RNA expression, and use this as the value of the first evaluation index for single-cell sequencing data. The first evaluation index is used to evaluate the detection rate of the target cell receptor.

[0045] The first evaluation index obtained using this implementation method can comprehensively assess the experimental technique's ability to capture target cell receptor sequences, serving as the foundation for the validity of subsequent analyses. Taking the target cell receptor sequence as TCR as an example, the first evaluation index can be generalized to calculate the T cell detection rate. The proportion of T cells is a key factor in this value, and it can be combined with sample type for comprehensive analysis to determine whether it matches the T cell content in the sample.

[0046] Optionally, in one implementation of this embodiment, the target cell is a cell that includes a target functional chain. For example, the target cell is a cell in which at least one functional TCR chain has been successfully detected.

[0047] In this context, processing 102 can be achieved as follows: The number of target cells jointly identified by single-cell VDJ annotation and single-cell transcriptome annotation results is determined; the number of target cells annotated as conventional adaptive immune cells is determined based on the single-cell transcriptome annotation results. Taking cells containing the TCR chain as an example, the comparison parameter is the number of target cells annotated as conventional T cells (i.e., αβ T cells, excluding γδ T cells) by single-cell transcriptome annotation.

[0048] In this context, processing 104 can be achieved by calculating the ratio of the number of conventional adaptive immune cells to the number of target cells, using this ratio as a second evaluation metric for the single-cell sequencing data. This second metric is used to evaluate the target cell receptor capture efficiency. For example, the percentage of target cells annotated as conventional T cells by the single-cell transcriptome can be calculated.

[0049] The second evaluation index obtained using this implementation method, taking cells with detected functional TCR chains as an example, can serve as a key quality control indicator for evaluating the quality of single-cell TCR sequencing libraries. This index can objectively reflect the effective capture degree of full-length TCR transcripts and can be used to compare the quality of TCR library construction between different samples or experimental batches. Incorporating TCR capture efficiency as a systematic quality control indicator into the single-cell TCR sequencing data analysis workflow provides a standardized and quantifiable evaluation criterion for assessing TCR library quality and experimental technique stability.

[0050] Optionally, in one implementation of this embodiment, the target cell is a cell that has detected a target cell receptor sequence. For example, the target cell is a cell that has detected a TCR sequence.

[0051] In this context, processing 102 can be achieved as follows: The number of target cells jointly identified by single-cell VDJ annotation and single-cell transcriptome annotation results is determined; the number of target cells annotated as conventional adaptive immune cells is determined based on the single-cell transcriptome annotation results. Taking cells that detect TCR sequences as an example, the comparison parameter is the number of target cells annotated as conventional T cells (i.e., αβ T cells, excluding γδ T cells) by single-cell transcriptome annotation.

[0052] In this case, processing 104 can be achieved by calculating the ratio of the number of conventional adaptive immune cells to the number of target cells, and using this ratio as the value of the third evaluation index for single-cell sequencing data. The third evaluation index is used to evaluate the annotation purity of the target cells.

[0053] Using the third evaluation metric provided in this implementation, taking cells where TCR sequences were detected as the target cells as an example, it can reveal inconsistencies between gene expression annotation and TCR sequence capture, thus indicating potential annotation errors. If the error rate is too high, further investigation of the double-cell ratio and reassessment of cell type annotation accuracy are necessary.

[0054] Optionally, in one implementation of this embodiment, the evaluation index has a corresponding value range. By comparing the value of the evaluation index with its value range, the analysis result corresponding to the evaluation index can be output based on the comparison result.

[0055] For example, in a specific application, the first evaluation metric can be called the target cell receptor detection rate, the second evaluation metric can be called the target cell receptor capture efficiency, and the third evaluation metric can be called the target cell annotation purity. The following explanation uses TCR as the target cell receptor and T cells as the target cells.

[0056] The range of values ​​for the first evaluation indicator is related to the sample type. Based on research using collected public data, the inventors found that the TCR detection rate for normal samples is between 30% and 70%. If it falls outside this range, the specific characteristics of the sample need to be considered.

[0057] The second evaluation metric can be set at a value no lower than a certain threshold, for example, 80%. If the value is not lower than this threshold, it indicates that the TCR library has been successfully constructed. If the value is lower than this threshold, there may be problems such as insufficient TCR primer coverage, low 5' end capture efficiency, and poor cell viability leading to RNA degradation.

[0058] The value of the third evaluation indicator can be no less than a certain threshold, for example, 90%. If it is not lower than this threshold, it indicates that the T cell annotation is consistent with the TCR detection results. If it is lower than this threshold, there may be a problem with low annotation quality.

[0059] Those skilled in the art should understand that the above evaluation indicators can be used as evaluation indicators for TCR / BCR immune repertoires based on single-cell sequencing.

[0060] In one implementation of this embodiment, taking the second evaluation index as an example, by comparing the value of the second evaluation index with the range of values ​​of the second evaluation index, analysis results containing improved target cell receptor primer coverage can be output when the value of the second evaluation index is lower than the minimum range of values ​​of the second evaluation index.

[0061] For example, the analysis results processing at this time may include not only improving the coverage of the target cell receptor primer, but also addressing issues such as low 5' end capture efficiency and poor cell viability. These analysis results can provide researchers with targeted optimization directions.

[0062] Figure 2 This is a flowchart illustrating a cloning annotation method according to an embodiment of this application. (Refer to...) Figure 2 The method includes the following processing steps.

[0063] 200: Obtain annotation results based on single-cell sequencing data, including single-cell transcriptome annotation results and single-cell VDJ annotation results.

[0064] 202: Cells belonging to the same clone type were identified based on the single-cell VDJ annotation results.

[0065] 204: Determine the cellular functional status of individual cells within the same clone based on single-cell transcriptome annotation results.

[0066] 206: Determine the clone information containing cell functional status based on the cell functional status of each cell in the same clone.

[0067] The method provided in this embodiment, compared with the traditional clonality defined solely based on the CDR3 sequence, achieves deep coupling between clonality information and cellular functional status by integrating cellular functional status, thereby improving the accuracy and biological insight of the correlation analysis between clonality information and immune response.

[0068] Those skilled in the art will understand that the embodiments of this application can be used as independent embodiments or incorporated into other embodiments. Figure 1 The illustrated embodiment is not described in detail here.

[0069] Optionally, in one implementation of this embodiment, processing 206 can be implemented in the following way: Based on the distribution of different cell functional states within the same clonal type, determine the clonal information of the cell functional state. For example, for cells within the same clonal type, count the annotation type and frequency of each cell in the single-cell transcriptome annotation results; then, use a dominant type induction algorithm to determine the cell functional state corresponding to the clonal type. For example, if the number of cells in a certain cell subpopulation type (such as "CD8+ Effector Memory T cells") is significantly dominant (e.g., exceeding 50% of the total number of cells or being the only highest frequency type), then the clonal type is marked as this single dominant type; if there are two subpopulation types with similar cell numbers and both being dominant, then the clonal type is marked as a combination of these two types. For example, a clone containing 100 cells is labeled Clone_A_CD8_EffectorMemory if 80 of them are labeled as "CD8+ Effector Memory T cells"; and Clone_B_CD8_EffectorMemory_CD8_CentralMemory if it consists of 45 effector memory cells and 40 central memory T cells.

[0070] Figure 3 This is a schematic flowchart illustrating a method for adjusting a single-cell immunoassay reagent according to an embodiment of this application. (Refer to...) Figure 3 The methods include the following processing.

[0071] 300: Obtain annotation results based on single-cell sequencing data, including single-cell transcriptome annotation results and single-cell VDJ annotation results.

[0072] 302: Determine the values ​​of the evaluation parameters based on the annotation results. The evaluation parameters include the number of target cells and their comparison parameters. The target cells are cells that include the target functional chain.

[0073] Specifically, 302 includes: determining the number of target cells jointly identified by single-cell VDJ annotation results and single-cell transcriptome annotation results; and determining the number of target cells annotated as conventional adaptive immune cells based on single-cell transcriptome annotation results.

[0074] 304: Calculate the ratio of the number of conventional adaptive immune cells to the number of target cells as the value of the second evaluation index for single-cell sequencing data. The second evaluation index is used to evaluate the target cell receptor capture efficiency.

[0075] 306: When the value of the second evaluation index is lower than the minimum range of the second evaluation index, increase the coverage of the target cell receptor primers in the single-cell immunoassay reagent, ensuring that the target cell receptor primers correspond to the target cells. For example, T cell receptor primers correspond to T cells, and B cell receptor primers correspond to B cells.

[0076] Using the method provided in this embodiment, single-cell immunoassay reagents can be optimized based on a second evaluation index.

[0077] Optionally, in one implementation of this embodiment, before increasing the coverage of the target cell receptor primer in the single-cell immunoassay reagent, existing technical means can be used to eliminate possible causes such as low 5' end capture efficiency and poor cell activity, so as to more effectively ensure the effectiveness of increasing the coverage of the target cell receptor primer.

[0078] Optionally, in one implementation of this embodiment, single-cell sequencing can be performed using a single-cell immunoassay reagent that increases the coverage of the target cell receptor primer, and the value of the second evaluation index can be determined again using the method provided in this application embodiment. The effectiveness of increasing the coverage of the target cell receptor primer can be determined by comparing the results before and after.

[0079] This application also provides a single-cell immunoassay reagent, which, through... Figure 3 The embodiment shown is an optimized version.

[0080] The methods related to the embodiments of this application have been described above. The following is an example of a specific application of the embodiments of this application. In this specific application, the three indicators mentioned above are used in combination and a deep coupling between clonal information and cell functional state is achieved.

[0081] The inventors discovered significant limitations in two key areas: data quality control and downstream analysis. First, the technical quality control is limited to a single dimension, remaining at the level of general sequencing. Specifically, the quality control process in existing solutions is essentially limited to assessing the general quality of the raw sequencing data itself. This mainly includes: sequencing base quality (e.g., Q30 score), cell barcode and UMI recognition efficiency, sequence alignment rate, and transcriptome-based general quality control indicators (e.g., number of genes detected per cell, UMI count, and mitochondrial gene ratio). These quality control indicators are "general" and "technical." They can answer whether the sequencing experiment itself was successful, but cannot answer whether the capture of specific functional elements like TCR / BCR was successful and effective. A dataset with good general quality control indicators may still have an extremely low actual TCR / BCR sequence capture rate due to low TCR / BCR-specific enrichment efficiency, poor probe specificity, or cell viability issues, resulting in severely biased and incomplete immune repertoire information. Second, the immune analysis is rudimentary, remaining at the clonal population level. Specifically, after successfully obtaining the TCR / BCR sequence, existing analytical methods focus heavily on the abstract concept of "clonotype" and use it as the basic unit of analysis. A clonotype is typically defined solely by the CDR3 amino acid sequence. This analytical approach leads to a severe disconnect between "clonotype" and "cellular functional state." This lack of information keeps the analysis at a macroscopic level of population size, making it difficult to delve into the microscopic levels of function and mechanism, thus greatly limiting a deep understanding of the fine-tuned regulation of adaptive immune responses.

[0082] To address the above problems, the inventors researched and proposed the following solution. The process is as follows: Figure 4 As shown, the process includes: inputting single-cell multi-omics data, standard preprocessing, data integration, calculating three evaluation metrics (TCR detection rate, TCR capture efficiency, and T cell annotation purity), obtaining high-quality integrated data, constructing "annotated clonoids," and outputting a list of "annotated clonoids." A detailed explanation follows.

[0083] Step 1: Acquisition and preprocessing of single-cell multi-omics data.

[0084] Data source: Gene expression matrix (GEX) and V(D)J sequence data of cells were obtained simultaneously through a single-cell sequencing platform.

[0085] Standard preprocessing: GEX data underwent quality control, normalization, dimensionality reduction, and cell clustering; cell annotation was performed for each cell based on standard cell type marker genes (resulting in single-cell transcriptome annotation results). V(D)J data were assembled and annotated to identify the CDR3 nucleotide / amino acid sequences of the α and β chains (or γ / δ chains) of the T cell receptor (TCR) for each cell (resulting in single-cell VDJ annotation results).

[0086] Step Two: Data Integration and Calculation of Evaluation Indicators This scheme defines an evaluation system consisting of three core indicators. These indicators are designed to assess the biological quality of the immune repertoire itself. At the same time, the identity of the cells (transcriptome annotation) is cross-correlated with their clonality (immune repertoire sequence) to generate quantitative values ​​with clear biological significance.

[0087] Data integration: GEX data and V(D)J data are precisely matched using the unique cell barcode of each cell to ensure that the gene expression profile of each cell corresponds one-to-one with its TCR sequence information.

[0088] 2. Calculate the three evaluation indicators: a) TCR detection rate This protocol counts the number of cells that successfully exhibit at least one functional TCR chain (typically TCRαβ) among all quality-controlled cells (defined as the number of "TCR cells"). Simultaneously, it counts the number of cells with reliable RNA expression that passed GEX data quality control (defined as the "RNA cell count").

[0089] The calculation formula is: TCR detection rate = number of TCR cells / number of RNA cells.

[0090] Purpose: This indicator comprehensively assesses the experimental technique's ability to capture TCR sequences, serving as a basis for the validity of subsequent analyses. It generalizes the calculation of the T-cell detection rate, where the proportion of T cells is the most critical factor. This value can be analyzed in conjunction with sample type to determine whether it matches the T-cell content in the sample.

[0091] b) TCR capture efficiency: In the integrated data, all “TCR cells” were first screened out. Then, among these cells, those cells that were annotated as conventional T cells (i.e., αβ T cells, excluding γδ T cells) in the GEX data were further identified.

[0092] The calculation formula is: TCR capture efficiency = number of TCR cells (excluding γδT cells) / total number of TCR cells.

[0093] Purpose: TCR capture efficiency is a key quality control indicator for evaluating the quality of single-cell TCR sequencing libraries. This ratio objectively reflects the effective capture degree of full-length TCR transcripts and can be used to compare the quality of TCR library construction between different samples or experimental batches. Incorporating TCR capture efficiency as a systematic quality control indicator into the single-cell TCR sequencing data analysis workflow provides a standardized and quantifiable evaluation criterion for assessing TCR library quality and experimental technique stability.

[0094] c) T cell annotation purity Similarly, in the integrated data, the focus was on all "cells with detected TCR sequences". The number of cells that were successfully annotated as conventional T cells (excluding γδ T cells) in the GEX data was counted.

[0095] The calculation formula is: T cell annotation purity = number of cells annotated as T cells and with detected TCR sequences / total number of cells with detected TCR sequences.

[0096] Purpose: This indicator primarily reveals inconsistencies between gene expression annotation and TCR sequence capture, suggesting potential annotation errors. If the error rate is too high, further investigation of the double-cell ratio and reassessment of cell type annotation accuracy are necessary.

[0097] This approach establishes a complete quality control triangle using these three indicators. It comprehensively evaluates the dataset from three dimensions: technical sensitivity (detection rate), target capture specificity (capture efficiency), and annotation consistency (annotation purity), a method unprecedented in existing technologies. The normal range can be determined based on collected public data. As an example, the TCR detection rate of normal samples is between 30% and 70%; exceeding this threshold requires consideration of sample specificity. A high TCR capture rate (>80%) indicates successful TCR library construction; a low TCR capture rate suggests potential issues such as insufficient TCR primer coverage, low 5' end capture efficiency, and poor cell viability leading to RNA degradation. High T cell annotation purity (>90%) indicates high T cell purity. Data that simultaneously meets all three evaluation indicators can be considered "high-quality integrated data," meaning it guarantees the accuracy and effectiveness of subsequent processing.

[0098] Step 3: Construct "annotated clone" analysis units This step upgrades the traditional clonoid, which is defined solely based on the TCR CDR3 sequence, to an "annotated clonoid" that integrates cellular functional states. The construction process is as follows: First, based on the traditional clonoid, all cells within it are traversed, and the annotation type and frequency of each cell in the single-cell transcriptome (GEX) data are counted. Then, a "dominant type induction" algorithm is used for labeling: if a certain cell subpopulation type (such as "CD8+ Effector Memory T cells") has a significantly dominant cell number (e.g., exceeding 50% of the total cell number or being the only highest frequency type), the clonoid is labeled as this single dominant type; if two subpopulation types with similar cell numbers and jointly dominant exist, the clonoid is labeled as a combination of these two types. For example, a clone containing 100 cells, if 80 of them are annotated as "CD8+ Effector Memory T cells," is labeled Clone_A_CD8_EffectorMemory; if it consists of 45 effector memory T cells and 40 central memory T cells, it is labeled Clone_B_CD8_EffectorMemory_CD8_CentralMemory. Ultimately, a structured "annotated clone list" is output. This output achieves a deep coupling between clonal information and cellular functional status, and through data integration, improves the accuracy and biological insights of more complete clonal information and its correlation with immune responses.

[0099] Based on the same technical principles and process architecture, the multi-dimensional evaluation indicators (which can be defined as BCR detection rate, BCR capture efficiency, and B cell annotation purity) and the "annotated clonal" construction method of this method are also applicable to single-cell data analysis of B cell receptor immune repertoire. The implementation logic is similar to the aforementioned TCR analysis process, and will not be repeated here.

[0100] This solution creatively introduces a multidimensional data quality control system and constructs a brand-new core analysis object, thereby systematically solving two major technical problems in existing single-cell immune repertoire analysis: coarse data quality assessment and the disconnect between clonality and cell functional status.

[0101] A system of indicators, comprised of three specific formulas—TCR detection rate, TCR capture efficiency, and T cell annotation purity—is used to systematically evaluate the quality of single-cell multi-omics (GEX+V(D)J) data. This system effectively distinguishes between problems stemming from experimental library construction (e.g., poor V(D)J enrichment specificity) and bioinformatics analysis (e.g., cell annotation errors), providing clear diagnostic criteria for data optimization and result interpretation, and ensuring that subsequent "annotated clonogenic" analysis is based on reliable data. It addresses the pain point of existing technologies lacking a standardized, systematic quality control system for multi-omics integrated data. Furthermore, existing methods often rely on general sequencing quality indicators, failing to effectively assess the consistency and specificity between TCR data and transcriptome annotation.

[0102] The core of constructing "annotated clonoids" lies in integrating traditional clonoids defined based on TCR sequences with cell annotation information obtained from single-cell transcriptome sequencing. Through a "dominant type induction" algorithm (such as based on cell number percentage or highest frequency), each clonoid is assigned one or more dominant functional subgroup labels, thus outputting a structured list with "annotated clonoids" as the basic unit. This solves the problem of the separation between "clonotype" and "cell functional state" in existing technologies. Traditional methods can only answer "which clones exist," but cannot precisely answer "which functional subgroup these clones belong to" or "whether the same clone exhibits different functional states."

[0103] Figure 5 This is an analysis device based on single-cell sequencing data according to an embodiment of this application, such as... Figure 5 As shown, the device includes the following modules.

[0104] The data acquisition module is used to acquire annotation results based on single-cell sequencing data, including single-cell transcriptome annotation results and single-cell VDJ annotation results. The parameter value determination module is used to determine the values ​​of evaluation parameters based on the annotation results. The evaluation parameters include the number of target cells and their comparison parameters. The target cells are cells that include the target functional chain or cells that have detected the target cell receptor sequence. The indicator value determination module is used to determine the value of the evaluation indicator for single-cell sequencing data based on the number of target cells and their comparison parameters.

[0105] Figure 6 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. (Refer to...) Figure 6 The electronic device includes a memory 50 and a processor 10.

[0106] The memory is used to store one or more computer instructions.

[0107] A processor for invoking and executing computer instructions to implement at least one of the methods mentioned in the foregoing embodiments of this application.

[0108] Optionally, in one implementation of this embodiment, such as Figure 6 As shown, the electronic device includes a processor 10, at least one communication bus 20, a user interface 30, at least one external communication interface 40, and a memory 50. The communication bus 20 is configured to enable communication between these components. The user interface 30 may include a display screen, and the external communication interface 40 may include standard wired and wireless interfaces.

[0109] This application also provides a computer-readable storage medium storing one or more computer instructions, which, when executed, implement at least one of the methods provided in this application.

[0110] This application also provides a computer program product, which, when run, implements at least one of the methods provided in this application.

[0111] The descriptions of the above-described apparatus, computer program products, computer-readable storage media, and electronic devices are similar to those of the method embodiments described above, and have similar beneficial effects. For any technical details not disclosed in the apparatus, computer program products, computer-readable storage media, and electronic devices provided in the embodiments of this application, please refer to the descriptions of the method embodiments of this application for understanding.

[0112] In the above embodiments of this application, the descriptions of each embodiment have their own emphasis. Parts not described in detail in a certain embodiment can be referred to in the relevant descriptions of other embodiments. The steps illustrated in the related flowcharts can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowcharts, in some cases, the steps shown or described may be performed in a different order than that shown here. In other words, the order of steps described in the foregoing embodiments is merely an example. Reasonable adjustments to the order of steps based on the content of the embodiments of this application are also within the protection scope of the embodiments of this application.

[0113] The sequence numbers or order of description of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0114] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some interfaces; indirect couplings or communication connections between units or modules may be electrical or other forms.

[0115] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0116] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0117] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions. When the computer instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer, or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., digital versatile disc (DVD)), or a semiconductor medium (e.g., solid state disk (SSD)). It is worth noting that the computer-readable storage medium mentioned in the embodiments of this application can be a non-volatile storage medium; in other words, it can be a non-transient storage medium.

[0118] It should be noted that the information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, stored data, displayed data, etc.), and signals involved in the embodiments of this application are all authorized by the user or fully authorized by all parties, and the collection, use, and processing of related data must comply with the relevant laws, regulations, and standards of the relevant countries and regions. For example, the scene data of the current frame in the 3D virtual scene involved in the embodiments of this application, the client's device information, and the scene interaction information are all obtained with full authorization.

[0119] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. An analysis method based on single-cell sequencing data, characterized by, The method comprises: obtaining annotation results based on single-cell sequencing data, the annotation results comprising single-cell transcriptome annotation results and single-cell VDJ annotation results; determining values of evaluation parameters according to the annotation results, the evaluation parameters comprising a number of target cells and a comparative parameter thereof, the target cells being cells comprising target functional chains or cells detecting target cell receptor sequences; determining values of evaluation indexes of the single-cell sequencing data according to the number of the target cells and the comparative parameter thereof.

2. The method of claim 1, wherein, The evaluation indexes have at least one, different evaluation indexes have different comparative parameters, and the evaluation indexes are related to comparative logic of the number of the target cells and the comparative parameter.

3. The method of claim 2, wherein, The target functional chains are TCR chains or BCR chains, and the target cell receptor sequences are TCR sequences or BCR sequences.

4. The method of claim 1 or 2, wherein the target cells are cells comprising target functional chains; the determining values of evaluation parameters according to the annotation results comprises: determining the number of the target cells according to the single-cell VDJ annotation results, or determining the number of the target cells jointly identified by the single-cell VDJ annotation results and the single-cell transcriptome annotation results; determining the number of cells with reliable RNA expression as the comparative parameter according to the single-cell transcriptome annotation results; the determining values of evaluation indexes of the single-cell sequencing data according to the number of the target cells and the comparative parameter thereof comprises: calculating a ratio of the number of the target cells to the number of the cells with reliable RNA expression as a value of a first evaluation index of the single-cell sequencing data, the first evaluation index being used to evaluate a detection rate of target cell receptors.

5. The method of claim 1 or 2, wherein the target cells are cells comprising target functional chains; the determining values of evaluation parameters according to the annotation results comprises: determining the number of the target cells jointly identified by the single-cell VDJ annotation results and the single-cell transcriptome annotation results, determining the number of the target cells annotated as conventional adaptive immune cells according to the single-cell transcriptome annotation results; the determining values of evaluation indexes of the single-cell sequencing data according to the number of the target cells and the comparative parameter thereof comprises: calculating a ratio of the number of the conventional adaptive immune cells to the number of the target cells as a value of a second evaluation index of the single-cell sequencing data, the second evaluation index being used to evaluate a capture efficiency of target cell receptors.

6. The method of claim 1 or 2, wherein the target cells are cells detecting target cell receptor sequences; the determining values of evaluation parameters according to the annotation results comprises: determining the number of the target cells jointly identified by the single-cell VDJ annotation results and the single-cell transcriptome annotation results, determining the number of the target cells annotated as conventional adaptive immune cells according to the single-cell transcriptome annotation results; The value of the evaluation index of the single-cell sequencing data is determined according to the number of the target cells and the contrast parameters thereof, and the method comprises the following steps: The ratio of the number of the conventional adaptive immune cells to the number of the target cells is calculated as the value of a third evaluation index of the single-cell sequencing data, and the third evaluation index is used to evaluate the annotation purity of the target cells.

7. The method according to claim 1 or 2, characterized in that, The evaluation index has a corresponding value range, and the method further comprises the following steps: The value of the evaluation index is compared with the value range of the evaluation index; An analysis result corresponding to the evaluation index is output according to the comparison result.

8. The method of claim 5, wherein, The second evaluation index has a corresponding value range, and the method further comprises the following steps: The value of the second evaluation index is compared with the value range of the second evaluation index; In the case that the value of the second evaluation index is lower than the minimum value range of the second evaluation index, an analysis result of increasing the coverage of the target cell receptor primer is output.

9. The method of claim 1, wherein, The method further comprises the following steps: Cells belonging to the same clonotype are determined according to the single-cell VDJ annotation result; The cell functional state of each cell in the same clonotype is determined according to the single-cell transcriptome annotation result; Clonotype information containing the cell functional state is determined according to the cell functional state of each cell in the same clonotype.

10. The method of claim 9, wherein, The clonotype information containing the cell functional state is determined according to the distribution of different cell functional states in the same clonotype. The method comprises the following steps:

11. A method of adjusting a single-cell immunoassay reagent, characterized by, The value of the second evaluation index is determined by the method of claim 5; In the case that the value of the second evaluation index is lower than the minimum value range of the second evaluation index, the coverage of the target cell receptor primer in the single-cell immune analysis reagent is increased, and the target cell receptor primer corresponds to the target cells.

12. A single-cell immune analysis reagent obtained by the method of claim 11. The device comprises:

13. An analysis device based on single-cell sequencing data, characterized in that a data acquisition module configured to acquire annotation results based on single-cell sequencing data, wherein the annotation results comprise single-cell transcriptome annotation results and single-cell VDJ annotation results; a parameter value determination module configured to determine values of evaluation parameters according to the annotation results, wherein the evaluation parameters comprise the number of target cells and contrast parameters thereof, and the target cells are cells containing target functional chains or cells in which target cell receptor sequences are detected; an index value determination module configured to determine values of evaluation indexes of the single-cell sequencing data according to the number of the target cells and the contrast parameters thereof. The electronic device comprises:

14. An electronic device, comprising: a memory configured to store one or more computer instructions; a processor configured to invoke and execute the computer instructions to implement the method according to any one of claims 1-10. The memory stores one or more computer instructions, and the computer instructions are executed to implement the method according to any one of claims 1-10.

15. A computer-readable storage medium, characterized in that, The computer program product is executed to implement the method according to any one of claims 1-10.

16. A computer program product, characterised in that, ​

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