Spatial transcriptome tissue specific expression gene identification method and system based on multi-dimensional statistical index

Abstract

The invention provides a spatial transcriptome tissue specific expression gene identification method and system based on a multi-dimensional statistical index, and belongs to the technical field of gene identification, the method comprises the following steps: obtaining gene expression data corresponding to a plurality of genes in different cells, different cells belonging to different types of tissues; the gene expression data is data processed based on a space transcriptome; for each gene, determining a basic statistical feature and a differential expression feature corresponding to the gene based on the gene expression data corresponding to the gene in different cells; determining specific index characteristics of the gene based on the basic statistical characteristics of the gene; and determining a tissue specific expression gene from the plurality of genes based on the basic statistical characteristics, the differential expression characteristics and the specific index characteristics corresponding to each gene. According to the invention, the accuracy of tissue specific expression gene recognition can be improved.

Description

A method and system for identifying tissue-specific expressed genes in spatial transcriptomics based on multidimensional statistical indices. Technical Field

[0001] This application belongs to the field of gene recognition technology, and more specifically, it relates to a method and system for identifying spatially expressed genes of the transcriptome based on multidimensional statistical indices. Background Technology

[0002] In developmental biology, oncology, and spatial transcriptomics research, accurately identifying biomarkers for specific tissues or cell populations is a crucial step in elucidating cell fate determination mechanisms and mapping cell atlases. Spatial transcriptomics technology can simultaneously capture gene expression information and spatial location information, providing a data foundation for the discovery of tissue-specific biomarkers. However, accurately screening for high-confidence specific genes from massive amounts of data remains a core research need.

[0003] In existing technologies, most screening tools rely on differential expression analysis methods to identify tissue-specific expressed genes. For example, the Wilcoxon rank-sum test uses statistical significance and fold change to screen for highly expressed genes in target tissues as potential tissue biomarkers. However, relying solely on differential expression analysis methods like the Wilcoxon rank-sum test results in a limited selection dimensionality, which can easily lead to false positives for tissue-specific genes, meaning that other non-tissue-specific genes may be present in the screening results. Summary of the Invention

[0004] The purpose of this application is to provide a method and system for identifying tissue-specific expressed genes in spatial transcriptomics based on multidimensional statistical indices, so as to improve the accuracy of tissue-specific gene identification.

[0005] A first aspect of this application provides a method for identifying tissue-specific expressed genes based on spatial transcriptomics using multidimensional statistical indices. The method includes: acquiring gene expression data for multiple genes in different cells, where different cells belong to different types of tissues; the gene expression data is data processed based on spatial transcriptomics; for each gene, determining its basic statistical features and differential expression features based on the gene expression data in different cells; determining its specificity index features based on the basic statistical features; wherein the basic statistical features characterize the gene's expression distribution attributes in various types of tissues; the differential expression features characterize the significant difference in gene expression between the target tissue and other tissues; the specificity index features characterize the gene's expression specificity level among different tissues; and identifying tissue-specific expressed genes from multiple genes based on the basic statistical features, differential expression features, and specificity index features corresponding to each gene.

[0006] A second aspect of this application provides a spatial transcriptome tissue-specific expression gene identification system based on multidimensional statistical indices, comprising: a data acquisition module for acquiring gene expression data of multiple genes in different cells, wherein the different cells belong to different types of tissues; the gene expression data is data processed based on spatial transcriptome; a multidimensional index determination module for determining, for each gene, the basic statistical features and differential expression features corresponding to the gene based on the gene expression data corresponding to the gene in different cells; and determining the specificity index features of the gene based on the basic statistical features; wherein the basic statistical features are used to characterize the expression distribution attributes of the gene in various types of tissues; the differential expression features are used to characterize the significance of the gene's expression difference between the target tissue and other tissues; and the specificity index features are used to characterize the expression specificity level of the gene among various tissues; and a gene identification module for identifying tissue-specific expression genes from multiple genes based on the basic statistical features, differential expression features, and specificity index features corresponding to each gene.

[0007] A third aspect of this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of the above-described method for identifying spatial transcriptome-specifically expressed genes based on multidimensional statistical indices.

[0008] In a fourth aspect of this application, a computer-readable storage medium is provided, which stores a computer program that, when executed by a processor, implements the steps of the above-described method for identifying spatial transcriptome-specifically expressed genes based on multidimensional statistical indices.

[0009] The beneficial effects of the spatial transcriptome tissue-specific gene identification method and system based on multidimensional statistical indices provided in this application are as follows: This application calculates three types of features for each gene: basic statistics, differential expression, and specificity index. These three features comprehensively characterize gene features from three dimensions: expression distribution attributes, significance of inter-tissue expression differences, and level of inter-tissue expression specificity. This overcomes the dimensional limitations of single-dimensional differential analysis in existing technologies and compensates for the deficiency that a single dimension cannot comprehensively measure gene tissue specificity. Finally, this application uses the three types of features to jointly screen specific genes. Through the mutual constraints of multidimensional indicators, it effectively excludes non-specific genes that are only significantly different but lack expression specificity or have distribution characteristics but no statistically significant differences, reducing false positive results. In summary, this method improves the accuracy and confidence of tissue-specific gene identification in spatial transcriptomes by constructing a multidimensional feature analysis and joint screening system, thereby enhancing the accuracy of tissue-specific gene identification. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 is a flowchart illustrating a method for identifying spatial transcriptome tissue-specific expressed genes based on multidimensional statistical indices according to an embodiment of this application; Figure 2 is a structural block diagram illustrating a spatial transcriptome tissue-specific expressed gene identification system based on multidimensional statistical indices according to an embodiment of this application; Figure 3 is a schematic block diagram illustrating an electronic device according to an embodiment of this application. Detailed Implementation

[0012] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0013] Before proceeding, let's clarify the meanings of some terms: spatial transcriptomics and gene expression data. Spatial transcriptomics is a technique that integrates spatial location information with transcriptome sequencing to quantitatively detect gene expression in samples from different tissues and cells. After sequencing quality control, expression quantification, and data standardization, the sequencing analysis data retains gene expression levels, cell spatial location, and tissue origin attributes, resulting in gene expression data. Gene expression data is used to characterize the expression levels of multiple genes in different cells and tissue types, including expression counts and standardized expression values ​​for each gene in single cells or cellular microregions.

[0014] Tissue-specific expressed genes are those whose expression levels are significantly enriched in a specific tissue, while their expression levels are extremely low or almost non-existent in other tissues.

[0015] Leaked expression: refers to the phenomenon that a target gene or protein is still expressed at a low level even when no inducer is added or the expected induction conditions are not met.

[0016] To make the objectives, technical solutions, and advantages of this application clearer, the following description will be provided in conjunction with the accompanying drawings and specific embodiments.

[0017] Please refer to Figure 1. Figure 1 is a flowchart of a method for identifying spatial transcriptome-specific expressed genes based on multidimensional statistical indices according to an embodiment of this application. The method can be executed by an electronic device and may include: S101-S103.

[0018] S101: Obtain gene expression data for multiple genes in different cells.

[0019] In this embodiment, "multiple genes" refers to the entire genome or a set of genes to be analyzed. Different cells belong to different types of tissues. If different cells belong to the same type of tissue, it is impossible to determine tissue-specific expressed genes by analyzing gene expression data corresponding to genes in different tissues.

[0020] In this embodiment, the gene expression data is based on spatial transcriptome processing. The gene expression data not only contains the expression level of each gene in each cell, but also the label information of each cell and the tissue type to which each cell belongs.

[0021] In one embodiment of this application, gene expression data corresponding to multiple genes in different cells correspond to the same developmental stage. In this embodiment, obtaining gene expression data corresponding to multiple genes in different cells includes: obtaining unlabeled gene expression data corresponding to multiple genes; determining metadata corresponding to the developmental stage of multiple genes based on a preset mapping table; the preset mapping table is used to characterize the correspondence between different developmental stages and different metadata; the metadata is used to characterize the tissue type and cell type information of the cells corresponding to each gene expression data in the corresponding developmental stage; and labeling the unlabeled gene expression data based on the metadata to obtain gene expression data corresponding to multiple genes in different cells.

[0022] In this embodiment, since tissue-specific gene expression changes with biological developmental stages—for example, the marker genes in the same tissue may differ at different stages of embryonic development—the gene expression data of multiple genes in different cells are limited to the same developmental stage to eliminate interference from gene expression across developmental stages. In this embodiment, the chicken embryonic development process is used as the research object.

[0023] In this embodiment, unlabeled gene expression data refers to raw expression data, which only contains the numerical correspondence between gene and corresponding cell expression levels, without tissue type and cell type annotation information, and can be stored in the format of Hierarchical Data Format Version 5 (H5AD).

[0024] In this embodiment, metadata is essentially an annotation of the tissue and cell types of biological samples at a specific developmental stage. That is, the metadata contains tissue and cell type information for the corresponding stage. Since the tissue composition, cell type, and spatial distribution of an organism dynamically change with developmental stages, a pre-constructed mapping table is used in this embodiment. This table contains the correspondence between different developmental stages and different metadata. Metadata can be stored in comma-separated values ​​(CSV) format.

[0025] In this embodiment, the annotation and alignment operations for gene expression data without labels based on metadata are essentially to match and associate tissue type labels and cell type labels in the metadata with individual cells in the unlabeled expression data. During this process, missing cell type labels and / or missing tissue type labels can be marked to facilitate verification or further processing by relevant personnel. Finally, complete and annotated standardized expression data is obtained, which is the gene expression data of multiple genes corresponding to different cells.

[0026] S102: For each gene, determine the basic statistical characteristics and differential expression characteristics of the gene based on the gene expression data of the gene in different cells; determine the specificity index characteristics of the gene based on the basic statistical characteristics of the gene.

[0027] In this embodiment, basic statistical features, differential expression features, and specificity index features characterize whether a gene belongs to a tissue-specific expression gene from different dimensions. Basic statistical features characterize the expression distribution attributes of the gene in various tissue types; differential expression features characterize the significance of the gene's expression difference between the target tissue and other tissues; and specificity index features characterize the expression specificity level of the gene among different tissues. In this embodiment, the target tissue refers to the specific tissue type pre-specified by relevant researchers based on their specific research needs and scenarios, and serves as the core control benchmark throughout the analysis process.

[0028] In this embodiment, the basic statistical characteristics may include average expression level and expression percentage. The average expression level refers to the mean expression level of the gene in all cells of each type of tissue, and the expression percentage refers to the percentage of cells in the target tissue where the gene's expression level is greater than a preset expression level threshold. In this embodiment, the preset expression level threshold is set to 0.

[0029] In this embodiment, differential expression characteristics refer to the differential verification indicators obtained through statistical tests after comparing the target tissue with all other tissues in a one-to-many manner. In this embodiment, differential expression characteristics may include fold change and significance index. In one implementation, the gene expression data corresponding to the gene in different cells can be processed and calculated based on the Wilcoxon rank-sum test to finally obtain the fold change and significance index corresponding to the gene. In this embodiment, both the fold change and significance index are indicators for evaluating the difference between the average expression level of the gene in the target tissue and the average expression level of the gene in all other tissues.

[0030] In this embodiment, the significance index is the P-value (probability value) after multiple hypothesis testing correction, which can generally be achieved by using the False Discovery Rate (FDR) correction method.

[0031] In one embodiment, determining the gene's specificity index characteristics based on the gene's basic statistical characteristics includes: determining the gene's specificity index characteristics based on the gene's average expression level.

[0032] In this embodiment, the average expression level of the gene can be used to generate a first matrix with genes as rows and tissues as columns, and then the specificity index characteristics of the gene can be determined based on the first matrix. Each value in the first matrix represents the average expression level of a certain gene in all cells of a certain tissue, which is a quantification of the overall expression level of the gene in a certain tissue.

[0033] In this embodiment, the specificity index features include at least one of the Gini coefficient and the Tau index. The Gini coefficient can be calculated based on the first matrix using a vectorized difference summation formula. The Gini coefficient is used to characterize the degree of heterogeneity in gene expression distribution among different tissue types.

[0034] In one embodiment, the Gini coefficient can be calculated as follows: For each gene, the difference in gene expression levels between every two tissues is calculated, constructing a matrix of expression difference values ​​for the gene across different tissues (for example, the (m, n)th element in the expression difference matrix represents the difference between the gene expression level in tissue m and tissue n), then the absolute values ​​of the difference matrices are summed, and normalized by combining the number of tissues and the total expression level to obtain the Gini index for the gene. This calculation process is based on matrix broadcasting and vectorized operations, eliminating the need for element-by-element loops, thus significantly improving computational efficiency for large-scale gene sets.

[0035] The formula for calculating the Gini index is as follows:

[0036] in, This represents the gene, i.e., the gene for which the Gini coefficient is to be calculated, where N represents the total number of tissue types. This represents the average expression level of the gene in the i-th tissue. This represents the average expression level of the gene in the j-th tissue.

[0037] In one embodiment, the Tau index can be calculated based on the data in the first matrix using the following formula: ,in, This represents the Tau index. This represents the average expression level of the gene in the i-th tissue. This represents the maximum average expression level of the gene across all tissues. It represents the total number of tissue types. The Tau index is used to characterize the degree to which gene expression levels deviate from their maximum expression levels. The Tau index uses a relative value ( The Tau index (expressed as a ratio) focuses on the ratio of the absolute value of gene expression to the maximum value of expression in each tissue, regardless of whether the absolute value of gene expression is high or low. Therefore, fluctuations in the absolute value of low-expressed genes (such as from 0.1 to 0.2) will not significantly affect this ratio, resulting in a more stable calculation. In contrast, the Gini coefficient is calculated based on the absolute value difference of expression. Fluctuations in the absolute value of low-expressed genes can easily interfere with the results. Therefore, compared to the Gini coefficient, the Tau index is more robust when dealing with low-expressed genes.

[0038] S103: Identify tissue-specific expressed genes from multiple genes based on the basic statistical characteristics, differential expression characteristics, and specificity index characteristics corresponding to each gene.

[0039] In this embodiment, tissue-specific expression genes are determined from multiple genes based on the basic statistical characteristics, differential expression characteristics, and specificity index characteristics corresponding to each gene. This includes: obtaining the screening conditions corresponding to the basic statistical characteristics, differential expression characteristics, and specificity index characteristics respectively; and identifying the genes that simultaneously meet the screening conditions corresponding to the basic statistical characteristics, differential expression characteristics, and specificity index characteristics as tissue-specific expression genes.

[0040] In this embodiment, the screening conditions corresponding to the basic statistical characteristics include: the expression ratio of the gene in the target tissue is not less than a first threshold; the maximum expression ratio of the gene in other tissues is not greater than a second threshold.

[0041] In this embodiment, the expression ratio of the gene in the target tissue and the maximum expression ratio of the gene in other tissues are both calculated based on the expression percentage of the gene. Specifically, the expression percentage can be used to generate a second matrix with genes as rows and tissues as columns. This second matrix is ​​a quantification matrix of the expression percentage of each gene in different tissues. Each element value in the matrix uniquely corresponds to the expression percentage of a single gene in a single tissue, that is, the percentage of cells in which the gene expression level in the tissue is greater than a preset threshold (the preset threshold is 0 in this embodiment). This second matrix can be uniformly denoted as pct_df, which is the data frame of gene and tissue expression percentage. For any gene to be analyzed, firstly, based on a pre-specified target tissue, the element value of the gene in the target tissue column of the second matrix is ​​extracted. This element value represents the expression ratio of the gene in the target tissue (pct_in), used to characterize the cellular expression coverage of the gene in the target tissue. Simultaneously, all element values ​​of the gene in all non-target tissue columns of the second matrix (excluding the target tissue column) are extracted, and the expression ratio of the gene in each non-target tissue (i.e., other tissues) is calculated. The maximum value is taken as the maximum expression ratio of the gene in other tissues (pct_other_max), used to characterize the highest level of expression coverage of the gene in non-target tissues, thereby determining whether there is severe leakage expression of the gene in non-target tissues. The above calculation process of pct_in and pct_other_max based on the second matrix can be implemented through vectorized matrix operations, which can significantly improve the computational efficiency of the entire gene set, adapting to the needs of large-scale gene analysis in spatial transcriptomics research. Furthermore, the two calculated indicators will serve as core quantitative values ​​of basic statistical features, participating in the determination of screening conditions corresponding to basic statistical features in subsequent multi-dimensional joint screening.

[0042] In this embodiment, the first threshold can be set to 0.5 to ensure that most cells in the tissue express the expression, and the second threshold can be set to 0.3 to ensure that there is no serious leakage expression.

[0043] In this embodiment, the screening conditions corresponding to the specificity index feature include: the Gini coefficient corresponding to the gene is not less than the third threshold; the Tau index corresponding to the gene is not less than the fourth threshold.

[0044] In this embodiment, the third threshold can be set to 0.6 to ensure that the gene expression distribution among different tissues shows a clear bias, rather than uniform expression. The fourth threshold can be set to 0.8. Compared with the Gini coefficient, the higher Tau index threshold further enhances the criteria for determining gene expression specificity. Utilizing the robustness of the Tau index to low-expression genes, genes whose expression levels deviate significantly from the maximum value and are concentrated in the target tissue are accurately screened. The two thresholds work together to form a dual screening mechanism based on specificity index characteristics, quantifying the tissue exclusivity of genes from the dimension of expression distribution specificity.

[0045] In this embodiment, the screening conditions corresponding to the differential expression features include: the fold change of the gene is not less than the fifth threshold; and the significance index of the gene is not greater than the sixth threshold.

[0046] In this embodiment, the fifth threshold is 1. The fold change in this embodiment is the logarithmic fold change after Log1p logarithmic transformation. If the fold change is not less than the fifth threshold, that is, the fold change is greater than or equal to 1, it means that the expression level of the gene in the target tissue is at least (e-1) times the average expression level of all other tissues (e is the natural constant), ensuring that the gene has a significant upregulation in the target tissue, and excluding genes with weak expression differences from the perspective of the magnitude of the difference. The sixth threshold is 0.05. When the significance index of the difference is not greater than the sixth threshold, that is, less than or equal to 0.05, it indicates that the expression difference of the gene between the target tissue and other tissues is still statistically significant after multiple hypothesis testing correction, excluding false positive differences caused by random fluctuations, and verifying the authenticity of the expression difference from the perspective of statistical reliability.

[0047] In one embodiment of this application, for each type of tissue, a CSV file can be automatically generated, which contains the finally determined tissue-specific expressed genes and the corresponding basic statistical characteristics, differential expression characteristics and specificity index characteristics of the tissue-specific expressed genes, so as to facilitate subsequent biological verification.

[0048] As can be seen from the above, the embodiments of this application calculate three types of features for each gene: basic statistics, differential expression, and specificity index. These three features comprehensively characterize gene features from three dimensions: expression distribution attributes, significance of inter-tissue expression differences, and level of inter-tissue expression specificity. This overcomes the dimensional limitations of single-dimensional differential analysis in existing technologies and compensates for the deficiency that a single dimension cannot comprehensively measure gene tissue specificity. Finally, the embodiments of this application screen specific genes based on the joint screening of these three features. Through the mutual constraints of multi-dimensional indicators, non-specific genes that are only significantly different but have no specific expression or have distribution characteristics but no statistically significant differences are effectively excluded, reducing false positive results. In summary, this method improves the accuracy and confidence of tissue-specific gene identification in spatial transcriptomics by constructing a multi-dimensional feature analysis and joint screening system, thereby improving the accuracy of tissue-specific gene identification.

[0049] Corresponding to the above embodiment of a method for identifying spatial transcriptome tissue-specific expressed genes based on multidimensional statistical indices, Figure 2 is a structural block diagram of a spatial transcriptome tissue-specific expressed gene identification system based on multidimensional statistical indices provided in an embodiment of this application. For ease of explanation, only the parts relevant to the embodiment of this application are shown. Referring to Figure 2, the spatial transcriptome tissue-specific expressed gene identification system 20 based on multidimensional statistical indices includes: a data acquisition module 21, a multidimensional index determination module 22, and a gene identification module 23.

[0050] The data acquisition module 21 is used to acquire gene expression data of multiple genes in different cells, where different cells belong to different types of tissues; the gene expression data is based on spatial transcriptome processing. The multidimensional index determination module 22 is used to determine the basic statistical characteristics and differential expression characteristics of each gene based on the gene expression data of the gene in different cells; and to determine the specificity index characteristics of the gene based on the basic statistical characteristics; wherein, the basic statistical characteristics are used to characterize the expression distribution attributes of the gene in various types of tissues; the differential expression characteristics are used to characterize the significance of the gene's expression difference between the target tissue and other tissues; and the specificity index characteristics are used to characterize the expression specificity level of the gene in various tissues. The gene identification module 23 is used to identify tissue-specific expression genes from multiple genes based on the basic statistical characteristics, differential expression characteristics, and specificity index characteristics of each gene.

[0051] In one embodiment of this application, gene expression data corresponding to multiple genes in different cells correspond to the same developmental stage; the data acquisition module 21 is specifically used to acquire unlabeled gene expression data corresponding to multiple genes; determine metadata corresponding to the developmental stage of multiple genes based on a preset mapping table; the preset mapping table is used to characterize the correspondence between different developmental stages and different metadata; the metadata is used to characterize the tissue type and cell type information of the cells corresponding to each gene expression data in the corresponding developmental stage; and label the unlabeled gene expression data based on the metadata to obtain gene expression data corresponding to multiple genes in different cells.

[0052] In one embodiment of this application, for each gene, the basic statistical characteristics of the gene include: average expression level, which is the average expression level of the gene in all cells of various types of tissues; correspondingly, the multidimensional index determination module 22 is specifically used to determine the specific index characteristics of the gene based on the average expression level of the gene.

[0053] In one embodiment of this application, the differential expression characteristics corresponding to the gene include: the fold change of the gene and the significance index of the gene; the multidimensional index determination module 22 is specifically used to calculate the gene expression data corresponding to the gene in different cells based on the Wilcoxon rank-sum test to obtain the fold change of the gene and the significance index of the gene.

[0054] In one embodiment of this application, the specificity index features include at least one of the following: the Gini coefficient and the Tau index; the Gini coefficient is used to characterize the degree of uneven gene expression distribution among different tissue types; the Tau index is used to characterize the degree to which the gene expression level deviates from its maximum expression level.

[0055] In one embodiment of this application, the basic statistical features further include: expression percentage; the expression percentage is the percentage of cells in the target tissue where the gene is expressed at a level greater than a preset expression level threshold.

[0056] In one embodiment of this application, the gene identification module 23 is specifically used to obtain the screening conditions corresponding to the basic statistical characteristics, differential expression characteristics and specificity index characteristics respectively; and to identify the genes that simultaneously meet the screening conditions corresponding to the basic statistical characteristics, differential expression characteristics and specificity index characteristics as tissue-specific expression genes.

[0057] In one embodiment of this application, the screening conditions corresponding to the basic statistical characteristics include: the expression ratio of the gene in the target tissue is not less than a first threshold; and the maximum expression ratio of the gene in other tissues is not greater than a second threshold.

[0058] In one embodiment of this application, the screening conditions corresponding to the specificity index feature include: the Gini coefficient corresponding to the gene is not less than a third threshold; and the Tau index corresponding to the gene is not less than a fourth threshold.

[0059] Referring to Figure 3, which is a schematic block diagram of an electronic device provided in an embodiment of this application, the electronic device 300 in this embodiment, as shown in Figure 3, may include one or more processors 301, one or more input devices 302, one or more output devices 303, and one or more memories 304. The processors 301, input devices 302, output devices 303, and memories 304 communicate with each other via a communication bus 305. The memories 304 are used to store computer programs, which include program instructions. The processors 301 are used to execute the program instructions stored in the memories 304. Specifically, the processors 301 are configured to invoke the program instructions to execute the functions of each module / unit in the above-described device embodiments, such as the functions of the data acquisition module 21, the multidimensional index determination module 22, and the gene recognition module 23 shown in Figure 2.

[0060] It should be understood that, in the embodiments of this application, the processor 301 may be a central processing unit (CPU), but it may also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0061] Input device 302 may include a touchpad, a fingerprint sensor (for collecting the user's fingerprint information and fingerprint orientation information), a microphone, etc., and output device 303 may include a display (LCD, etc.), a speaker, etc.

[0062] The memory 304 may include read-only memory and random access memory, and provides instructions and data to the processor 301. A portion of the memory 304 may also include non-volatile random access memory. For example, the memory 304 may also store device type information.

[0063] In specific implementations, the processor 301, input device 302, and output device 303 described in the embodiments of this application can execute the implementation method described in the embodiment of this application for a spatial transcriptome tissue-specific gene expression identification method based on multidimensional statistical index, or they can execute the implementation method of the electronic device described in the embodiment of this application, which will not be repeated here.

[0064] In another embodiment of this application, a computer-readable storage medium is provided. This computer-readable storage medium stores a computer program, which includes program instructions. When executed by a processor, the program instructions implement all or part of the processes in the methods described above. Alternatively, the computer program can instruct related hardware to complete the process. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include any entity or device capable of carrying computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0065] The computer-readable storage medium can be an internal storage unit of the electronic device in any of the foregoing embodiments, such as a hard disk or memory of the electronic device. The computer-readable storage medium can also be an external storage device of the electronic device, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on the electronic device. Furthermore, the computer-readable storage medium can include both internal and external storage units of the electronic device. The computer-readable storage medium is used to store computer programs and other programs and data required by the electronic device. The computer-readable storage medium can also be used to temporarily store data that has been output or will be output.

[0066] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this application.

[0067] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the electronic devices and units described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0068] In the several embodiments provided in this application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of units is only 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. In addition, the mutual coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces or units, or it may be an electrical, mechanical, or other form of connection.

[0069] 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 network units. Some or all of the units can be selected to achieve the purpose of the embodiments of this application, depending on actual needs.

[0070] 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.

[0071] It should be noted that the data involved in this application (including but not limited to data used for analysis, data stored, data displayed, etc.) are all authorized information and data, and the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0072] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for identifying spatially transcriptomic tissue-specific expressed genes based on multidimensional statistical indices, characterized in that, include: Obtain gene expression data for multiple genes in different cells; The different cells belong to different types of tissues; the gene expression data is based on spatial transcriptomics processing. For each gene, the basic statistical characteristics and differential expression characteristics of the gene are determined based on the gene expression data of the gene in different cells; The specificity index characteristics of the gene are determined based on its basic statistical characteristics; wherein, the basic statistical characteristics are used to characterize the expression distribution attributes of the gene in various types of tissues; and the differential expression characteristics are used to characterize the significance of the gene's expression differences between the target tissue and other tissues. The specificity index feature is used to characterize the expression specificity level of the gene in various tissues; tissue-specific expression genes are identified from the plurality of genes based on the basic statistical features, differential expression features and specificity index features corresponding to each gene.

2. The method for identifying spatially transcriptomic tissue-specific expressed genes based on multidimensional statistical indices as described in claim 1, characterized in that, The gene expression data of the multiple genes in different cells correspond to the same developmental stage. Obtaining the gene expression data of the multiple genes in different cells includes: obtaining unlabeled gene expression data corresponding to the multiple genes; determining metadata corresponding to the developmental stage of the multiple genes based on a preset mapping table; the preset mapping table is used to characterize the correspondence between different developmental stages and different metadata; the metadata is used to characterize the tissue type and cell type information of the cells corresponding to each gene expression data in the corresponding developmental stage; and labeling the unlabeled gene expression data based on the metadata to obtain the gene expression data of the multiple genes in different cells.

3. The method for identifying spatially transcriptome-specifically expressed genes based on multidimensional statistical indices as described in claim 1, characterized in that, For each gene, the basic statistical characteristics of the gene include: the average expression level, which is the average expression level of the gene in all cells of various types of tissues; correspondingly, the determination of the gene's specificity index characteristics based on the gene's basic statistical characteristics includes: determining the gene's specificity index characteristics based on the gene's average expression level.

4. The method for identifying spatially transcriptomic tissue-specific expressed genes based on multidimensional statistical indices as described in claim 1, characterized in that, The differential expression characteristics of the gene include: the fold change of the gene and the significance index of the difference. The process of determining the differential expression characteristics of the gene includes: calculating the gene expression data of the gene in different cells based on the Wilcoxon rank-sum test to obtain the fold change of the gene and the significance index of the difference.

5. The method for identifying spatially transcriptome-specifically expressed genes based on multidimensional statistical indices as described in claim 1, characterized in that, The specificity index features include at least one of the following: the Gini coefficient and the Tau index; the Gini coefficient is used to characterize the degree of uneven gene expression distribution among different tissue types; the Tau index is used to characterize the degree to which the gene expression level deviates from its maximum expression level.

6. The method for identifying spatially transcriptomic tissue-specific expressed genes based on multidimensional statistical indices as described in claim 3, characterized in that, The basic statistical features also include: expression percentage; the expression percentage is the percentage of cells in the target tissue where the gene is expressed at a level greater than a preset expression threshold.

7. The method for identifying spatially transcriptomic tissue-specific expressed genes based on multidimensional statistical indices as described in claim 1, characterized in that, The step of determining tissue-specific expression genes from the plurality of genes based on the basic statistical features, differential expression features, and specificity index features corresponding to each gene includes: obtaining the screening conditions corresponding to the basic statistical features, differential expression features, and specificity index features respectively; and identifying the genes that simultaneously meet the screening conditions corresponding to the basic statistical features, differential expression features, and specificity index features as the tissue-specific expression genes.

8. The method for identifying spatially transcriptomic tissue-specific expressed genes based on multidimensional statistical indices as described in claim 7, characterized in that, The screening criteria corresponding to the basic statistical characteristics include: the expression ratio of the gene in the target tissue is not less than a first threshold; the maximum expression ratio of the gene in other tissues is not greater than a second threshold.

9. The method for identifying spatially transcriptomic tissue-specific expressed genes based on multidimensional statistical indices as described in claim 7, characterized in that, The screening conditions corresponding to the specificity index features include: the Gini coefficient of the gene is not less than the third threshold; the Tau index of the gene is not less than the fourth threshold.

10. A spatial transcriptome tissue-specific gene expression identification system based on multidimensional statistical indices, characterized in that, include: The data acquisition module is used to acquire gene expression data of multiple genes in different cells, where the different cells belong to different types of tissues; the gene expression data is based on spatial transcriptomics processing. The multidimensional index determination module is used to determine the basic statistical characteristics and differential expression characteristics of each gene based on the gene expression data of the gene in different cells. The specificity index characteristics of the gene are determined based on its basic statistical characteristics; wherein, the basic statistical characteristics are used to characterize the expression distribution attributes of the gene in various types of tissues; and the differential expression characteristics are used to characterize the significance of the gene's expression differences between the target tissue and other tissues. The specificity index feature is used to characterize the expression specificity level of the gene in various tissues; the gene identification module is used to identify tissue-specific expressed genes from the multiple genes based on the basic statistical features, differential expression features and specificity index features corresponding to each gene.

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