Multi-type cell gene expression quantification method and apparatus, and storage medium

Abstract

A multi-type cell gene expression quantification method and apparatus, and a storage medium. The method comprises: acquiring a sample gene expression matrix and a reference gene expression matrix; determining an intersection gene between the sample gene expression matrix and the reference gene expression matrix, and obtaining a sample intersection gene expression matrix and a reference intersection gene expression matrix on the basis of the intersection gene; on the basis of the sample intersection gene expression matrix and the reference intersection gene expression matrix, calculating a cell proportion of various cell types of at least one sample to be tested; and calculating the gene expression level of the intersection gene in various cell types of cells on the basis of the cell proportion of the various cell types of said at least one sample and gene expression level data of the intersection gene of said sample.

Description

Methods, devices, and storage media for quantifying gene expression in multiple cell types Technical Field

[0001] This disclosure relates to, but is not limited to, the field of bioinformatics, and particularly to a method, apparatus, and storage medium for quantifying gene expression in multiple cell types. Background Technology

[0002] In gene expression studies, inferring gene expression levels across different cell types from RNA sequencing data plays a crucial role in understanding cell type function and disease mechanisms. Overall gene expression in a sample is the result of the aggregation of various cell types. Current methods, such as single-cell sequencing, can provide detailed information on various cell types, but they are costly and technically limited, preventing their application in large clinical cohorts. Furthermore, existing deconvolution methods can only infer cell proportions and cannot further analyze gene expression levels within each individual cell type. Summary of the Invention

[0003] The following is an overview of the subject matter described in detail herein. This overview is not intended to limit the scope of the claims.

[0004] This disclosure provides a method for quantifying gene expression in multiple cell types, including:

[0005] Obtain a sample gene expression matrix and a reference gene expression matrix. The sample gene expression matrix includes gene expression data of multiple genes in at least one sample to be tested, and the reference gene expression matrix includes gene expression data of multiple genes in a reference sample in multiple cell types.

[0006] Identify the intersection genes between the sample gene expression matrix and the reference gene expression matrix, and obtain the sample intersection gene expression matrix and the reference intersection gene expression matrix based on the intersection genes;

[0007] Calculate the proportion of multiple cell types in at least one test sample based on the sample intersection gene expression matrix and the reference intersection gene expression matrix;

[0008] The gene expression levels of the intersection genes of the at least one test sample and the proportion of cells of multiple cell types are fitted and calculated to obtain the gene expression levels of the intersection genes in cells of multiple cell types.

[0009] This disclosure also provides a multi-cell gene expression quantification device, including a memory; and a processor connected to the memory, the memory being used to store instructions, the processor being configured to execute the steps of the multi-cell gene expression quantification method according to any embodiment of this disclosure based on the instructions stored in the memory.

[0010] This disclosure also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for quantifying gene expression in multiple cell types as described in any embodiment of this disclosure.

[0011] This disclosure also provides a multi-type cell gene expression quantification device, including: a data acquisition module, an intersection processing module, a cell ratio calculation module, and an intersection gene calculation module, wherein:

[0012] The data acquisition module is configured to acquire a sample gene expression matrix and a reference gene expression matrix. The sample gene expression matrix includes gene expression data of multiple genes of at least one sample to be tested, and the reference gene expression matrix includes gene expression data of multiple genes of a reference sample in cells of multiple cell types.

[0013] The intersection processing module is configured to determine the intersection genes between the sample gene expression matrix and the reference gene expression matrix, and to obtain the sample intersection gene expression matrix and the reference intersection gene expression matrix based on the intersection genes.

[0014] The cell proportion calculation module is configured to calculate the cell proportion of multiple cell types in at least one test sample based on the sample intersection gene expression matrix and the reference intersection gene expression matrix.

[0015] The intersection gene calculation module is configured to fit and calculate the gene expression level of the intersection gene in the at least one test sample and the cell proportion of multiple cell types in the test sample to obtain the gene expression level of the intersection gene in the cells of multiple cell types.

[0016] After reading and understanding the accompanying diagrams and detailed descriptions, other aspects can be understood.

[0017] Overview of the attached figures

[0018] The accompanying drawings are provided to further illustrate the technical solutions of this disclosure and form part of the specification. They are used together with the embodiments of this disclosure to explain the technical solutions of this disclosure and do not constitute a limitation on the technical solutions of this disclosure. The shapes and sizes of the components in the drawings do not reflect actual proportions and are only intended to illustrate the content of this disclosure.

[0019] Figure 1 is a flowchart illustrating a method for quantifying gene expression in multiple cell types according to an exemplary embodiment of this disclosure;

[0020] Figure 2 is a schematic diagram of a sample gene expression matrix containing gene expression data of multiple genes from four test samples provided by this disclosure.

[0021] Figure 3 is a schematic diagram of a reference gene expression matrix provided by an exemplary embodiment of the present disclosure;

[0022] Figure 4 is a schematic diagram of the cell proportion matrix of 22 cell types in the 4 test samples in Figure 2;

[0023] Figure 5 is a schematic diagram of the gene expression levels of 496 genes in the sample gene expression matrix of Figure 2 and the reference gene expression matrix of Figure 3 in cells of 22 cell types.

[0024] Figure 6 is a schematic diagram of the gene expression levels of all genes in a certain sample to be tested in 22 cell types in Figure 2;

[0025] Figure 7 is a schematic diagram of the structure of a multi-cell gene expression quantification device provided in an exemplary embodiment of the present disclosure;

[0026] Figure 8 is a schematic diagram of another multi-cell gene expression quantification device provided by an exemplary embodiment of this disclosure.

[0027] Detailed Explanation

[0028] To make the objectives, technical solutions, and advantages of this disclosure clearer, the embodiments of this disclosure will be described in detail below with reference to the accompanying drawings. It should be noted that, unless otherwise specified, the embodiments and features described in this disclosure can be arbitrarily combined with each other.

[0029] Unless otherwise defined, the technical or scientific terms used in the embodiments of this disclosure shall have the ordinary meaning understood by one of ordinary skill in the art to which this disclosure pertains. The terms "first," "second," and similar terms used in the embodiments of this disclosure do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" indicate that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, but do not exclude other elements or objects.

[0030] As shown in Figure 1, this disclosure provides a method for quantifying gene expression in multiple cell types, including:

[0031] Step 101: Obtain the sample gene expression matrix and the reference gene expression matrix. The sample gene expression matrix includes gene expression data of multiple genes in at least one sample to be tested, and the reference gene expression matrix includes gene expression data of multiple genes in multiple cell types of the reference sample.

[0032] Step 102: Determine the intersection genes between the sample gene expression matrix and the reference gene expression matrix, and obtain the sample intersection gene expression matrix and the reference intersection gene expression matrix based on the determined intersection genes;

[0033] Step 103: Calculate the proportion of multiple cell types in at least one test sample based on the sample intersection gene expression matrix and the reference intersection gene expression matrix;

[0034] Step 104: Calculate the gene expression level of the intersection gene in the cells of the multiple cell types based on the proportion of cells of multiple cell types in at least one test sample and the gene expression level data of the intersection gene of the test sample.

[0035] The multi-cell gene expression quantification method provided in this disclosure obtains a sample gene expression matrix and a reference gene expression matrix, determines the intersection genes between the two matrices, and obtains a sample intersection gene expression matrix and a reference intersection gene expression matrix based on the determined intersection genes. It then calculates the cell proportions of multiple cell types in at least one test sample based on these matrices and the gene expression levels of the intersection genes in the test sample. This method can further obtain the gene expression levels of multiple genes in different cell types based on the cell proportions of the test sample, thereby acquiring more biological data. This disclosure eliminates the need for single-cell sequencing, reducing experimental costs and complexity.

[0036] The multi-cell gene expression quantification method of this disclosure has wide applications and can be applied to different types of gene expression data. For example, the sample gene expression matrix can be obtained from transcriptome sequencing (RNA-seq) data or microarray data, and the sample to be tested can be any type of sample such as blood sample, tissue sample, or body fluid sample; this disclosure does not limit this.

[0037] Taking obtaining a sample gene expression matrix based on transcriptome sequencing data as an example, in some exemplary embodiments, step 101, obtaining the sample gene expression matrix includes:

[0038] Perform quality control and filtering on sequencing data (remove low-quality sequences and redundant adapters, etc.);

[0039] The quality control and filtering processed sequencing data are aligned to the human reference genome to generate an alignment file;

[0040] The transcripts in the alignment file are assembled and quantified to determine the expression level of each gene, and a sample gene expression matrix is ​​generated based on the expression level of each gene.

[0041] For example, cells are isolated from the blood of the test sample for RNA library construction and sequencing. Quality control software (such as Fastqc) and data cleaning software (such as Trim_galore) are used to perform sequencing data quality control and cleaning. Then, alignment software (such as Hisat2) is used to align the cleaned sequencing data to the human reference genome (such as hg38) to obtain sequencing reads aligned to the human reference genome. Transcriptome assembly and quantification software (such as Stringtie) is used to assemble and quantify transcripts to determine the expression levels of all genes in each test sample and obtain the sample gene expression matrix.

[0042] Figure 2 is a schematic diagram of a sample gene expression matrix containing gene expression data of multiple genes from four test samples, provided by an example of this disclosure. As shown in Figure 2, the sample gene expression matrix includes four columns, where each column represents a test sample, and each row represents the gene expression data of a certain gene from the four test samples.

[0043] In this embodiment of the disclosure, the dimension of the sample gene expression matrix can be represented by the total number of genes G. t *Sample size N s In this example, N s =4, G t This represents the row number of the gene expression matrix of the sample in Figure 2.

[0044] In some exemplary embodiments, in step 101, obtaining the reference gene expression matrix includes:

[0045] Download a cell type-specific expression matrix from a public database or create your own cell type-specific expression matrix, and use the cell type-specific expression matrix as a reference gene expression matrix.

[0046] In this embodiment of the disclosure, the reference gene expression matrix can be obtained by downloading a publicly available cell type-specific expression matrix or by creating a custom cell type-specific expression matrix.

[0047] In some exemplary embodiments, a self-constructed cell type-specific expression matrix is ​​included, including:

[0048] Transcriptome sequencing or microarray analysis was performed on the cell populations isolated from the reference sample to obtain the gene expression profiles of each cell type;

[0049] Gene expression profiles for each cell type were standardized, and a list of genes showing significant differences in different cell types was obtained through differential expression analysis.

[0050] We constructed a cell type-specific expression matrix based on a list of genes that showed significant differences across different cell types.

[0051] For example, taking the publicly available cell type-specific expression matrix LM22 as an example, LM22 provides specific gene expression datasets for 22 immune cell subtypes. LM22 includes a total of 547 genes, and the reference gene expression matrix constructed using this dataset is shown in Figure 3. The reference gene expression matrix in Figure 3 consists of 547 rows and 22 columns, where each column represents an immune cell subtype, and each row represents the gene expression level data of one gene in the cells of the 22 immune cell subtypes.

[0052] In this embodiment of the disclosure, the dimension of the reference gene expression matrix can be represented as the total number of genes G. r *Number of cell types N c In this example, G r =547, N c =22.

[0053] In this embodiment, in step 102, the intersection of the gene list contained in the sample gene expression matrix and the gene list contained in the reference gene expression matrix is ​​obtained to obtain the intersection genes. Furthermore, the remaining genes in the sample gene expression matrix besides the intersection genes can also be obtained; assuming the number of intersection genes is G. o The number of remaining genes is G a Then G o +G a =G t .

[0054] For example, taking the sample gene expression matrix in Figure 2 and the reference gene expression matrix in Figure 3 as examples, the intersection of multiple genes in the sample gene expression matrix in Figure 2 and multiple genes in the reference gene expression matrix in Figure 3 is calculated, resulting in 496 intersecting genes, i.e., G. o =496. Obtain the positions of the intersecting genes in the sample gene expression matrix and the reference gene expression matrix, and generate a sample intersecting gene expression matrix and a reference intersecting gene expression matrix that only contain the gene expression data of the above intersecting genes.

[0055] In some exemplary embodiments, in step 103, calculating the cell proportions of multiple cell types in at least one test sample based on the sample intersection gene expression matrix and the reference intersection gene expression matrix includes:

[0056] For at least one sample to be tested, perform the following operations:

[0057] The gene expression data of the intersection genes in the test sample are used as the target vector, and the intersection gene expression matrix is ​​used as the feature matrix. The data are input into a pre-established optimization model for solution. The optimal coefficient vector obtained is used as the initial cell proportion of various cell types in the test sample. The optimization model can be a linear programming model, a quadratic programming model, or a non-negative least squares model.

[0058] The initial cell proportions of various cell types in the test sample are normalized to obtain the cell proportions of various cell types in the test sample.

[0059] In this embodiment of the disclosure, when calculating the proportion of cells of multiple cell types in at least one test sample, the pre-established optimization model can be a linear programming model, a quadratic programming model, or a non-negative least squares model; however, this disclosure does not limit this. In some other exemplary embodiments, the pre-established optimization model can also be a deconvolution model.

[0060] For example, taking a pre-established optimization model as a non-negative least squares model, the regression coefficient vector is obtained by solving the non-negative least squares method. This regression coefficient vector is the initial cell proportion of various cell types in the test sample that needs to be predicted.

[0061] For example, the non-negative least squares model is calculated for each test sample as follows: min x ||Ax-b|| 2 subject to x≥0(Formula 1)

[0062] Where, min represents finding the minimum value, subject to(st) indicates that it is constrained by, and A is the feature matrix, i.e., the reference intersection gene expression matrix (with dimension G). o *N c ), where b is the target vector, i.e., the gene expression levels of the genes at the intersection of the samples to be tested (dimension G). o *1), where x is the coefficient vector to be optimized, i.e., the initial cell proportions of various cell types in the test sample to be calculated (dimension N). c *1). The x value at which the formula is minimized is the optimal coefficient vector, which is the initial cell proportion of multiple cell types in the test sample. The initial cell proportions of multiple cell types in each test sample are merged column by column and normalized column by column (each element is divided by the sum of its column, so that the sum of each column is 1), resulting in the final cell proportion matrix of multiple cell types in the test sample, with dimension N. c *N s .

[0063] In this embodiment of the disclosure, the calculation formula for the non-negative least squares model can also be in other forms, and this disclosure does not limit this. For example, the calculation formula for the non-negative least squares model can also be: min x ‖Ax-b‖2subject to x≥0.

[0064] For example, taking the sample gene expression matrix in Figure 2 and the reference gene expression matrix in Figure 3 as examples, the cell proportions of multiple cell types in the test sample are calculated based on the sample intersection gene expression matrix and the reference intersection gene expression matrix. The optimal coefficient vector is solved using the non-negative least squares method. The feature matrix is ​​the reference intersection gene expression matrix (dimension 496*22), and the target vector is the gene expression data of multiple intersection genes in each test sample (dimension 496*1). The obtained optimal coefficient vector serves as the initial cell proportions of multiple cell types in the test sample. The initial cell proportions of multiple cell types in the test sample are normalized in each column to obtain the cell proportion matrix of multiple cell types in multiple test samples as shown in Figure 4. The cell proportion matrix in Figure 4 consists of 22 rows and 4 columns, where each row represents one immune cell type, and each column represents one test sample. The element in the i-th row and j-th column represents the cell proportion of the i-th immune cell type in the j-th test sample.

[0065] In some exemplary embodiments, in step 104, calculating the gene expression level of the intersection gene in the cells of the multiple cell types based on the cell proportions of at least one test sample and the gene expression level data of the intersection gene of the test sample includes:

[0066] For at least one overlapping gene, perform the following operation:

[0067] A linear regression model was used to fit the cell proportions of various cell types in the test sample to the gene expression data of the intersection genes of the test sample, and the regression coefficient vector was obtained. The obtained regression coefficient vector was used as the gene expression level of the intersection genes in various cell types.

[0068] In this embodiment of the disclosure, the linear regression model can be a linear regression model with regularization parameters; however, this disclosure is not limited to this, and the linear regression model can be set to other linear regression models as needed. By using a linear regression model with regularization parameters, overfitting can be reduced.

[0069] For example, a linear regression model with regularization parameters is established, and for each intersection gene of each test sample, N is used. c Cell proportions of different cell types (dimension 1*N) c *1) The gene expression data of the intersection genes in the test sample (dimension 1*1) are fitted to obtain the regression coefficient vector (dimension N). c *1) represents the gene expression level of the intersection gene in cells of different cell types.

[0070] The formula for calculating the linear regression model is as follows:

[0071] Where y is the target vector, i.e. the gene expression data of a certain intersection gene, X is the feature matrix, i.e. the proportion of cells of different cell types, w is the regression coefficient vector, i.e. the gene expression of the intersection gene in different cell types, and λ is the regularization hyperparameter, used to reduce overfitting.

[0072] For example, taking the aforementioned 496 intersecting genes as an example, the ridge regression algorithm is used, with the regularization strength parameter alpha set to 1 and the principal parameter positive set to True for non-negativity constraint (when the principal parameter positive is True, all parameters are positive). For each intersecting gene of each test sample, the cell proportions of 22 cell types (dimension 1*22) and the gene expression level of the intersecting gene (dimension 1*1) are fitted on the above algorithm. The resulting regression coefficient vector is the gene expression level of each intersecting gene of each test sample in the 22 cell types. This process is repeated, and finally, each sample can obtain the gene expression levels of 496 intersecting genes in the 22 cell types, with a dimension of 496*22, as shown in Figure 5.

[0073] In some exemplary embodiments, the method further includes:

[0074] Determine the remaining genes in the sample, where the remaining genes are the genes in the sample to be tested excluding the intersection genes;

[0075] Determine the correlation weight vector of the remaining genes in each sample. The correlation weight vector is used to represent the correlation between the remaining genes in the sample and all intersection genes.

[0076] The gene expression levels of the remaining genes in each sample across multiple cell types are determined based on the gene expression levels of the intersection genes and the relevant weight vectors in cells of multiple cell types.

[0077] In some exemplary implementations, determining the relevant weight vector of the remaining genes for each sample includes:

[0078] For the remaining genes in each sample, perform the following operations:

[0079] Calculate the correlation coefficient between the gene expression level of the remaining gene in each sample in all test samples and the gene expression level of each intersection gene in all test samples;

[0080] Check if the calculated correlation coefficient is less than 0. If the calculated correlation coefficient is less than 0, replace the correlation coefficient less than 0 with 0.

[0081] The correlation coefficients between the gene expression levels of the remaining genes in the sample and the gene expression levels of all intersecting genes are normalized to obtain the correlation weight vector of the remaining genes in the sample.

[0082] In some exemplary embodiments, the correlation coefficient may be the Pearson correlation coefficient or the Spearman correlation coefficient; however, this disclosure is not limited thereto.

[0083] In this embodiment of the disclosure, when the correlation coefficient between the gene expression level of at least one sample remaining gene in all test samples and the gene expression level of at least one intersection gene in all test samples cannot be calculated, the correlation coefficient between the gene expression level of at least one sample remaining gene in all test samples and the gene expression level of at least one intersection gene in all test samples is set to 0.

[0084] In some exemplary embodiments, determining the gene expression levels of the remaining genes in each sample across multiple cell types based on the gene expression levels of the intersection genes and the relevant weight vectors in cells of multiple cell types includes:

[0085] The gene expression levels of the remaining genes in each sample across multiple cell types are obtained by performing a dot product operation between the relevant weight vector of the remaining genes in each sample and the gene expression levels of all intersecting genes in multiple cell types.

[0086] In this embodiment of the disclosure, the remaining genes in the sample are obtained by calculating the difference between all genes in the sample and the intersection genes. For the remaining genes in the sample, this embodiment of the disclosure uses similarity weights to complete the expression level, ensuring that the gene expression level of all genes in the sample to be tested can be estimated under different cell types.

[0087] For each sample's remaining genes, the correlation between each sample's remaining genes and each intersection gene at the gene expression level is calculated; that is, the gene expression levels of the remaining genes in all test samples are calculated (length N). s Gene expression levels of each intersection gene (of length N) s The correlations between the elements are given by array G, where the length of the correlation array is G. o Since the above calculation process may result in a situation where gene expression levels are constant, making it impossible to define the correlation coefficient, the correlation coefficient can be defined as 0 in such cases. In the calculated correlation array, correlation coefficients greater than or equal to 0 remain unchanged, while correlation coefficients less than 0 are converted to 0. The converted correlation data is then normalized and used as the correlation weight vector. Finally, the correlation weight vector (of length G) is calculated. o Gene expression matrix of all intersecting genes in different cell types (dimension G) o *N cThe dot product of N and N is used as the gene expression level of the remaining genes in different cell types of the sample (length N). c ), and so on, to calculate the gene expression levels of the remaining genes in different cell types for all samples.

[0088] The specific calculation formula is as follows:

[0089] For the gene expression level g of each intersection gene across all samples... o and the gene expression level g of the remaining genes in each sample a The correlation coefficient w after conversion is calculated using the following formula. oa w oa =max(0,corr(g) o ,g a ))(Formula 3)

[0090] For each sample of remaining genes, there exists G. o Each correlation coefficient is related to G. o The correlation coefficients are normalized to form the correlation weights, as shown in the following formula:

[0091] Calculate the relevant weight vector of the remaining genes in each sample. (length is G) o ).

[0092] Finally, the relevant weight vectors of the remaining genes in each sample are calculated. With all intersecting genes in N c Gene expression matrix M (with dimensions G) in cells of various cell types o *N c Calculate the dot product to obtain the remaining genes in N for each sample. c Gene expression levels in cells of different cell types.

[0093] For example, taking the sample gene expression matrix in Figure 2 and the reference gene expression matrix in Figure 3 as examples, for each remaining gene in the sample, the Pearson correlation coefficient between the gene expression level of the gene in the four test samples and the gene expression level of each intersection gene is calculated.

[0094] For example, the gene expression levels of the remaining genes in one of the four samples to be tested are as follows: array([0.674655, 0.604969, 1.361255, 0.959226])

[0095] The gene expression levels of a certain intersection gene in the four test samples are as follows: array([0.34436, 0.025082, 0.059417, 0.127633])

[0096] Calculate the Pearson correlation coefficient between the gene expression levels of the remaining genes in the four test samples and the gene expression levels of the intersection genes.

[0097] Since correlation calculations were performed on the remaining genes of each sample, with the number of correlations equal to the number of genes in all intersections, a correlation coefficient vector of length 496 was obtained after transforming the correlation data. This correlation coefficient vector was then normalized, and the result was used as the correlation weight vector (length 496) for the remaining genes of each sample. The correlation weight vector is as follows: [0.00452246 0.00814345 0.……0.01039216 0.0.00359411]

[0098] Finally, the dot product (496*22) of the relevant weight vector of the remaining genes in each sample and the gene expression matrix of all intersecting genes in each sample across 22 cell types is calculated. The resulting dot product is taken as the gene expression level of the remaining genes in that sample across the 22 cell types. This process is repeated to calculate the gene expression levels of all remaining genes in each sample across different cell types.

[0099] The embodiments disclosed herein reduce the interference of noise and unreasonable biological results on downstream processes through strategies such as nonnegativity constraints, regression analysis, and correlation calculations.

[0100] In some exemplary embodiments, the method further includes:

[0101] The gene expression levels of the intersection genes in cells of multiple cell types and the gene expression levels of the remaining genes in the sample in cells of multiple cell types are combined to form the gene expression levels of all genes in the sample to be tested in cells of multiple cell types.

[0102] In this embodiment, for each test sample, the gene expression levels of the intersection genes in cells of different cell types and the gene expression levels of the remaining genes in the sample from cells of different cell types are combined to form gene expression data of all genes in cells of different cell types in the test sample (dimension G). t *N c ).

[0103] For example, taking the sample gene expression matrix in Figure 2 and the reference gene expression matrix in Figure 3 as examples, the gene expression levels of all intersecting genes in each test sample in the cells of 22 cell types and the gene expression levels of the remaining genes in all samples in the cells of 22 cell types are combined to form the gene expression levels of all genes in the test sample in the cells of 22 cell types. Figure 6 is a schematic diagram of the gene expression level matrix of all genes in a test sample in the cells of 22 cell types.

[0104] The multi-cell gene expression quantification method of this disclosure estimates the gene expression level at the cellular level based on the overall gene expression level of the sample to be tested. It can obtain the gene expression level of all genes under different cell types based on the cell composition ratio, thus obtaining more biological data. Moreover, it does not require single-cell sequencing, reducing experimental costs and complexity.

[0105] As shown in Figure 7, this embodiment of the present disclosure also provides a multi-type cell gene expression quantification device, including: a data acquisition module 701, an intersection processing module 702, a cell ratio calculation module 703, and an intersection gene calculation module 704, wherein:

[0106] The data acquisition module 701 is configured to acquire a sample gene expression matrix and a reference gene expression matrix. The sample gene expression matrix includes gene expression data of multiple genes of at least one sample to be tested, and the reference gene expression matrix includes gene expression data of multiple genes of a reference sample in cells of multiple cell types.

[0107] The intersection processing module 702 is configured to determine the intersection genes between the sample gene expression matrix and the reference gene expression matrix, and to obtain the sample intersection gene expression matrix and the reference intersection gene expression matrix based on the intersection genes.

[0108] The cell proportion calculation module 703 is configured to calculate the cell proportion of multiple cell types in at least one test sample based on the sample intersection gene expression matrix and the reference intersection gene expression matrix.

[0109] The intersection gene calculation module 704 is configured to calculate the gene expression level of the intersection gene in the cells of the multiple cell types based on the cell proportions of the at least one test sample and the gene expression level data of the intersection gene of the test sample.

[0110] In some exemplary embodiments, the cell proportion calculation module 703 calculates the cell proportions of multiple cell types in at least one test sample based on the sample intersection gene expression matrix and the reference intersection gene expression matrix, including:

[0111] For at least one sample to be tested, perform the following operations:

[0112] The gene expression data of the intersection genes in the test sample are used as the target vector, and the reference intersection gene expression matrix is ​​used as the feature matrix. The data are input into a pre-established optimization model for solution. The optimal coefficient vector obtained is used as the initial cell proportion of various cell types in the test sample. The optimization model is a linear programming model, a quadratic programming model, or a non-negative least squares model.

[0113] The initial cell proportions of various cell types in the test sample are normalized to obtain the cell proportions of various cell types in the test sample.

[0114] In some exemplary embodiments, the intersection gene calculation module 704 calculates the gene expression level of the intersection gene in cells of multiple cell types based on the cell proportions of the at least one test sample and the gene expression level data of the intersection gene of the test sample, including:

[0115] For at least one overlapping gene, perform the following operation:

[0116] A linear regression model is used to fit the cell proportions of various cell types in the test sample and the gene expression data of the intersection genes in the test sample to obtain a regression coefficient vector. The obtained regression coefficient vector is used as the gene expression level of the intersection genes in the cells of various cell types.

[0117] In some exemplary embodiments, the apparatus further includes: a residual gene calculation module 705, configured to: determine sample residual genes, wherein the sample residual genes are genes of a sample to be tested excluding the intersection genes; determine a correlation weight vector for each sample residual gene, wherein the correlation weight vector is used to represent the correlation between the sample residual gene and all the intersection genes; and determine the gene expression level of each sample residual gene in the multiple cell types based on the correlation weight vector and the gene expression levels of the intersection genes in the cells of multiple cell types.

[0118] In some exemplary embodiments, the apparatus further includes a merging module 706, configured to merge the gene expression levels of the intersection genes in cells of multiple cell types and the gene expression levels of the remaining genes of the sample in cells of multiple cell types into the gene expression levels of all genes of the sample to be tested in cells of multiple cell types.

[0119] In some exemplary embodiments, the remaining gene calculation module 705 determines the relevant weight vector of the remaining genes for each sample, including:

[0120] For the remaining genes in each of the samples, perform the following operations:

[0121] Calculate the correlation coefficient between the gene expression level of the remaining gene in each sample in all test samples and the gene expression level of each intersection gene in all test samples;

[0122] Check if the calculated correlation coefficient is less than 0. If the calculated correlation coefficient is less than 0, replace the correlation coefficient less than 0 with 0.

[0123] The correlation coefficients between the gene expression levels of the remaining genes in the sample and the gene expression levels of all the intersecting genes are normalized to obtain the correlation weight vector of the remaining genes in the sample.

[0124] In some exemplary embodiments, the correlation coefficient is the Pearson correlation coefficient or the Spearman correlation coefficient.

[0125] In some exemplary embodiments, the remaining gene calculation module 705 determines the gene expression level of the remaining gene of each sample in multiple cell types based on the gene expression levels of the intersection genes of the relevant weight vector and the genes in multiple cell types, including:

[0126] The gene expression levels of the remaining genes in each sample are obtained by performing a dot product operation between the relevant weight vector of each sample and the gene expression levels of all the intersection genes in cells of multiple cell types.

[0127] In some exemplary embodiments, the data acquisition module 701 acquires the sample gene expression matrix, including:

[0128] Perform quality control and filtering on sequencing data;

[0129] The sequencing data, after quality control and filtering, are aligned to the human genome to generate an alignment file;

[0130] The transcripts in the alignment file are assembled and quantified to determine the expression level of each gene, and the sample gene expression matrix is ​​generated based on the expression level of each gene.

[0131] In some exemplary embodiments, the data acquisition module 701 acquires a reference gene expression matrix, including:

[0132] Download a cell type-specific expression matrix from a public database or create a custom cell type-specific expression matrix, and use the cell type-specific expression matrix as the reference gene expression matrix. The custom cell type-specific expression matrix includes:

[0133] Transcriptome sequencing or microarray analysis was performed on the cell populations isolated from the reference sample to obtain the gene expression profiles of each cell type;

[0134] The gene expression profiles of each cell type were standardized and differentially expressed to obtain a list of genes with significant differences in different cell types.

[0135] For the list of genes that show significant differences in the different cell types, construct a cell type-specific expression matrix.

[0136] This disclosure also provides a multi-cell gene expression quantification device, including a memory; and a processor connected to the memory, the memory being used to store instructions, the processor being configured to execute the steps of the multi-cell gene expression quantification method as described in any embodiment of this disclosure based on the instructions stored in the memory.

[0137] As shown in Figure 8, in one example, a multi-cell gene expression quantification device may include: a processor 810, a memory 820, a bus system 830, and a transceiver 840. The processor 810, the memory 820, and the transceiver 840 are connected through the bus system 830. The memory 820 is used to store instructions, and the processor 810 is used to execute the instructions stored in the memory 820 to control the transceiver 840 to send and receive signals. Specifically, transceiver 840, under the control of processor 810, can acquire a sample gene expression matrix and a reference gene expression matrix. The sample gene expression matrix includes gene expression data of multiple genes of at least one test sample, and the reference gene expression matrix includes gene expression data of multiple genes of a reference sample in multiple cell types. Processor 810 determines the intersection genes between the sample gene expression matrix and the reference gene expression matrix, and obtains a sample intersection gene expression matrix and a reference intersection gene expression matrix based on the intersection genes. Based on the sample intersection gene expression matrix and the reference intersection gene expression matrix, it calculates the cell proportions of multiple cell types of at least one test sample. Based on the cell proportions of multiple cell types of at least one test sample and the gene expression data of the intersection genes of the test samples, it calculates the gene expression levels of the intersection genes in multiple cell types.

[0138] It should be understood that processor 810 can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), off-the-shelf programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0139] Memory 820 may include read-only memory and random access memory, and provides instructions and data to processor 810. A portion of memory 820 may also include non-volatile random access memory. For example, memory 820 may also store device type information.

[0140] In addition to the data bus, the bus system 830 may also include a power bus, a control bus, and a status signal bus. However, for clarity, all buses are labeled as bus system 830 in Figure 8.

[0141] In implementation, the processing performed by the processing device can be accomplished through integrated logic circuits in the hardware of the processor 810 or through software instructions. That is, the method steps of this embodiment can be executed by the hardware processor, or by a combination of hardware and software modules within the processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other storage media. This storage medium is located in memory 820, and the processor 810 reads information from memory 820 and, in conjunction with its hardware, completes the steps of the above method. To avoid repetition, further details are omitted here.

[0142] This disclosure also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method for quantifying gene expression in multiple cell types as described in any embodiment of this disclosure. The method for quantifying gene expression in multiple cell types driven by executing executable instructions is essentially the same as the method for quantifying gene expression in multiple cell types provided in the above embodiments of this disclosure, and will not be described in detail here.

[0143] In some possible implementations, various aspects of the multi-cell gene expression quantification method provided in this disclosure can also be implemented as a program product comprising program code that, when run on a computer device, causes the computer device to perform the steps in the multi-cell gene expression quantification method according to various exemplary embodiments of this disclosure as described above. For example, the computer device can perform the multi-cell gene expression quantification method described in the embodiments of this disclosure.

[0144] The program product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0145] It will be understood by those skilled in the art that all or some of the steps, systems, or apparatuses disclosed above, and their functional modules / units, can be implemented as software, firmware, hardware, or suitable combinations thereof. In hardware implementations, the division between functional modules / units mentioned above does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed collaboratively by several physical components. Some or all components may be implemented as software executed by a processor, such as a digital signal processor or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit (ASIC). Such software may be distributed on a computer-readable medium, which may include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and can be accessed by a computer. Furthermore, it is well known to those skilled in the art that communication media typically contain computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0146] It should be noted that the above embodiments or implementation methods are merely exemplary and not restrictive. Therefore, this disclosure is not limited to the content specifically shown and described herein. Various modifications, substitutions, or omissions can be made to the form and details of the implementations without departing from the scope of this disclosure.

Claims

1. A method for quantifying gene expression in multiple cell types, comprising: Obtain a sample gene expression matrix and a reference gene expression matrix. The sample gene expression matrix includes gene expression data of multiple genes in at least one sample to be tested, and the reference gene expression matrix includes gene expression data of multiple genes in a reference sample in multiple cell types. Identify the intersection genes between the sample gene expression matrix and the reference gene expression matrix, and obtain the sample intersection gene expression matrix and the reference intersection gene expression matrix based on the intersection genes; Calculate the proportion of multiple cell types in at least one test sample based on the sample intersection gene expression matrix and the reference intersection gene expression matrix; The gene expression levels of the intersection genes of the at least one test sample and the proportion of cells of multiple cell types are fitted and calculated to obtain the gene expression levels of the intersection genes in cells of multiple cell types.

2. The method according to claim 1, wherein, The step of calculating the proportion of multiple cell types in at least one test sample based on the sample intersection gene expression matrix and the reference intersection gene expression matrix includes: For at least one sample to be tested, perform the following operations: The gene expression data of the intersection genes in the test sample are used as the target vector, and the reference intersection gene expression matrix is ​​used as the feature matrix. The data are input into a pre-established optimization model for solution. The optimal coefficient vector obtained is used as the initial cell proportion of various cell types in the test sample. The optimization model is a linear programming model, a quadratic programming model, or a non-negative least squares model. The initial cell proportions of various cell types in the test sample are normalized to obtain the cell proportions of various cell types in the test sample.

3. The method according to claim 2, wherein, When the optimization model is a non-negative least squares model, the calculation formula for the non-negative least squares model is: min x ‖Ax-b‖ 2 s.t. x ≥ 0; or, min x ‖Ax-b‖₂ s.t. x ≥ 0; Where min represents finding the minimum value, st represents being constrained by, and A is the gene expression matrix of the reference intersection, with dimension G. o *N c , b represents the gene expression levels of the intersection genes in the samples to be tested, and the dimension of b is G. o *1, x is the coefficient vector to be optimized, and the dimension of x is N. c *1, G o N represents the number of genes in the intersection. c The number of cells of that type.

4. The method according to claim 1, wherein, The process of fitting and calculating the gene expression levels of the intersection genes of the at least one test sample with the cell proportions of multiple cell types and the data of the intersection genes of the test samples to obtain the gene expression levels of the intersection genes in the cells of multiple cell types includes: For at least one overlapping gene, perform the following operation: A linear regression model with regularization parameters is used to fit the cell proportions of various cell types in the test sample and the gene expression levels of the intersection genes in the test sample. The regression coefficient vector obtained by fitting is used as the gene expression level of the intersection genes in the cells of various cell types.

5. The method according to claim 1, wherein, The method further includes: Determine the remaining genes in the sample, which are the genes in the sample to be tested excluding the intersection genes; Determine the correlation weight vector for each remaining gene in the sample, the correlation weight vector being used to represent the correlation between the remaining gene in the sample and all the intersection genes; The gene expression levels of the remaining genes in each sample in multiple cell types are determined based on the gene expression levels of the intersection genes and the relevant weight vectors in cells of multiple cell types.

6. The method according to claim 5, wherein, The method further includes: The gene expression levels of the intersection genes in cells of multiple cell types and the gene expression levels of the remaining genes in the sample in cells of multiple cell types are combined to obtain the gene expression levels of all genes in the test sample in cells of multiple cell types.

7. The method according to claim 5, wherein, Determining the relevant weight vector of the remaining genes in each sample includes: For the remaining genes in each of the samples, perform the following operations: Calculate the correlation coefficient between the gene expression levels of the remaining genes in the sample and the gene expression levels of each intersection gene in all the samples to be tested; Check if the calculated correlation coefficient is less than 0. If the calculated correlation coefficient is less than 0, replace the correlation coefficient less than 0 with 0. The correlation coefficients between the gene expression levels of the remaining genes in the sample and the gene expression levels of all the intersecting genes are normalized to obtain the correlation weight vector of the remaining genes in the sample.

8. The method according to claim 7, wherein, When the correlation coefficient between the gene expression level of at least one remaining gene of the sample in all test samples and the gene expression level of at least one intersection gene in all test samples cannot be calculated, the correlation coefficient between the gene expression level of at least one remaining gene of the sample in all test samples and the gene expression level of at least one intersection gene in all test samples is set to 0.

9. The method according to claim 7, wherein, The correlation coefficient is either the Pearson correlation coefficient or the Spearman correlation coefficient.

10. The method according to claim 5, wherein, The step of determining the gene expression levels of the remaining genes in each sample across multiple cell types based on the gene expression levels of the intersection genes with the relevant weight vector in cells of multiple cell types includes: The relevant weight vector of the remaining genes of each sample is multiplied by the gene expression levels of all the intersection genes in cells of multiple cell types, and the result of the dot product is taken as the gene expression level of the remaining genes of each sample in cells of multiple cell types.

11. The method according to claim 1, wherein, The process of obtaining the sample gene expression matrix includes: The sequencing data of the sample to be tested are subjected to quality control and filtering. The sequencing data, after quality control and filtering, are aligned to the human genome to generate an alignment file; The transcripts in the alignment file are assembled and quantified to determine the expression level of each gene, and the sample gene expression matrix is ​​generated based on the expression level of each gene.

12. The method according to claim 1, wherein, The sample to be tested includes at least one of the following: blood sample, tissue sample, and body fluid sample.

13. The method according to claim 1, wherein, The process of obtaining the reference gene expression matrix includes: Transcriptome sequencing or microarray analysis was performed on the cell populations isolated from the reference sample to obtain the gene expression profiles of each cell type; The gene expression profiles of each cell type were standardized and differentially expressed to obtain a list of genes with significant differences in different cell types. A reference gene expression matrix is ​​constructed based on the list of genes that show significant differences in the different cell types.

14. The method according to claim 1, wherein, The step of obtaining the reference gene expression matrix includes: downloading a public cell type-specific expression matrix from a public database, and using the cell type-specific expression matrix as the reference gene expression matrix.

15. A multi-cell gene expression quantification device, comprising a memory; and a processor connected to the memory, the memory for storing instructions, the processor being configured to perform the steps of the multi-cell gene expression quantification method as claimed in any one of claims 1 to 14 based on the instructions stored in the memory.

16. A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for quantifying gene expression in multiple cell types as described in any one of claims 1 to 14.

17. A device for quantifying gene expression in multiple cell types, comprising: The module includes a data acquisition module, an intersection processing module, a cell ratio calculation module, and an intersection gene calculation module, among which: The data acquisition module is configured to acquire a sample gene expression matrix and a reference gene expression matrix. The sample gene expression matrix includes gene expression data of multiple genes of at least one sample to be tested, and the reference gene expression matrix includes gene expression data of multiple genes of a reference sample in cells of multiple cell types. The intersection processing module is configured to determine the intersection genes between the sample gene expression matrix and the reference gene expression matrix, and to obtain the sample intersection gene expression matrix and the reference intersection gene expression matrix based on the intersection genes. The cell proportion calculation module is configured to calculate the cell proportion of multiple cell types in at least one test sample based on the sample intersection gene expression matrix and the reference intersection gene expression matrix. The intersection gene calculation module is configured to fit and calculate the gene expression level of the intersection gene in the at least one test sample and the cell proportion of multiple cell types in the test sample to obtain the gene expression level of the intersection gene in the cells of multiple cell types.

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