Sample analysis method and device, computer equipment and computer readable storage medium

Abstract

The embodiment of the invention relates to a sample analysis method and device, computer equipment and a computer readable storage medium. The method comprises the following steps: acquiring mass spectrum analysis data corresponding to a target sample; clustering the plurality of cells according to the mass spectrometric analysis result corresponding to each cell to obtain a plurality of cell subgroups; calculating the average expression quantity of the labeled protein corresponding to the first cell subset according to the expression quantity of the labeled protein corresponding to each cell contained in the first cell subset; and clustering the plurality of cell subgroups according to the average expression quantities of the labeled proteins corresponding to the plurality of cell subgroups to obtain at least one target cell group and a cell type corresponding to each target cell group. According to the sample analysis method and device, the computer equipment and the computer readable storage medium, the accuracy of sample analysis can be improved.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a sample analysis method, apparatus, computer equipment, and computer-readable storage medium. Background Art

[0002] With the in-depth development of life science research, accurate analysis of cells and their microenvironment has become the key to understanding disease mechanisms, evaluating treatment effects and promoting drug development. As a major breakthrough in the field of single-cell analysis, tissue mass spectrometry imaging (IMC) technology has been widely used in sample and pathology analysis in many clinical-related fields. Based on tissue mass spectrometry imaging technology, the mass spectrometry analysis results of each cell in the sample can be obtained by performing mass spectrometry analysis on the sample.

[0003] However, since the mass spectrometry analysis results of cells contain too much information, using only the mass spectrometry analysis results to classify the cells contained in the sample can easily lead to the obtained cell subpopulations not matching the actual cell population, and the accuracy of the sample analysis is low. Summary of the invention

[0004] The embodiments of the present application disclose a sample analysis method, apparatus, computer device, and computer-readable storage medium, which can improve the accuracy of sample analysis.

[0005] In a first aspect, an embodiment of the present application discloses a sample analysis method, which is applied to a computer device, and the method includes:

[0006] The computer device acquires mass spectrometry data corresponding to the target sample, wherein the mass spectrometry data is obtained by performing mass spectrometry processing on the target sample; the mass spectrometry data includes mass spectrometry results corresponding to a plurality of cells in the target sample, respectively, and the mass spectrometry results include the expression amount of the marker protein;

[0007] The computer device clusters the multiple cells according to the mass spectrometry analysis results corresponding to each of the cells to obtain multiple cell subpopulations;

[0008] The computer device calculates the average expression level of the marker protein corresponding to the first cell subpopulation according to the expression level of the marker protein corresponding to each cell included in the first cell subpopulation; the first cell subpopulation is any of the cell subpopulations;

[0009] The computer device clusters the multiple cell subpopulations according to the average expression levels of the marker proteins respectively corresponding to the multiple cell subpopulations to obtain at least one target cell population and the cell type corresponding to each of the target cell populations.

[0010] As an optional embodiment, in the first aspect of the embodiment of the present application, the mass spectrometry analysis data includes first expression count data, the first expression count data includes a first value corresponding to each of the cells, and the first value is used to characterize the expression level of the marker protein corresponding to the cell; after the computer device obtains the mass spectrometry analysis data corresponding to the target sample, the method also includes: the computer device converts each first value contained in the first expression count data to obtain second expression count data that conforms to the normal distribution, the second expression count data includes a second value corresponding to each of the cells; and, according to the second value corresponding to each of the cells, screens out cells whose second values ​​are within the target standard deviation range from multiple cells in the target sample to obtain multiple first cells; the computer device clusters the multiple cells according to the mass spectrometry analysis results corresponding to each of the cells to obtain multiple cell subpopulations, including: the computer device clusters the multiple first cells according to the mass spectrometry analysis results corresponding to each of the first cells to obtain multiple cell subpopulations.

[0011] As an optional embodiment, in the first aspect of the embodiment of the present application, the mass spectrometry analysis data includes positional morphological data corresponding to multiple cells in the target sample, and the method also includes: the computer device calculates the cell area corresponding to each of the first cells based on the positional morphological data corresponding to each of the first cells; screens out first cells whose cell area is within a preset area range from the multiple first cells to obtain multiple second cells; the computer device clusters the multiple first cells according to the mass spectrometry analysis results corresponding to each of the first cells to obtain multiple cell subpopulations, including: the computer device clusters the multiple second cells according to the mass spectrometry analysis results corresponding to each of the second cells to obtain multiple cell subpopulations.

[0012] As an optional embodiment, in the first aspect of the embodiment of the present application, the mass spectrometry analysis data also includes imaging mass spectrometry flow cytometry IMC image data corresponding to the target sample; after the computer device acquires the mass spectrometry analysis data corresponding to the target sample, the method also includes: the computer device generates a target IMC image based on the IMC image data; the image position of each cell contained in the target IMC image is marked; the computer device clusters the multiple cells according to the mass spectrometry analysis results corresponding to each of the cells to obtain multiple cell subpopulations, including: the computer device clusters the multiple cells according to the image position of each cell in the target IMC image and the mass spectrometry analysis results corresponding to each of the cells to obtain multiple cell subpopulations.

[0013] As an optional embodiment, in the first aspect of the embodiment of the present application, the computer device generates a target IMC image based on the IMC image data, including: the computer device calculates the average pixel intensity corresponding to each mass spectrometry channel and the average overflow pixel intensity between any two mass spectrometry channels based on the pixel intensities corresponding to each pixel point contained in the IMC image data in multiple mass spectrometry channels; the mass spectrometry channel is a channel for detecting marker proteins on the surface or inside of cells; the computer device constructs an overflow matrix corresponding to the IMC image data based on the average pixel intensities corresponding to each mass spectrometry channel and the average overflow pixel intensities between any two mass spectrometry channels; the computer device corrects the pixel intensities corresponding to each pixel point contained in the IMC image data in the multiple mass spectrometry channels based on the overflow matrix to generate a target IMC image.

[0014] As an optional embodiment, in the first aspect of the embodiments of the present application, the mass spectrometry analysis results include the expression level of a marker protein corresponding to at least one marker protein; after obtaining at least one target cell population and the cell type corresponding to each of the target cell populations, the method further includes: the computer device calculates the first expression level of each of the marker proteins in the first target cell population based on the expression level of the marker protein corresponding to each cell contained in the first target cell population and each of the marker proteins, and determines the spatial distribution of each of the marker proteins in the first target cell population; the first target cell population is any of the target cell populations; the computer device displays the cell type, the first expression level and the corresponding spatial distribution corresponding to each of the target cell populations.

[0015] As an optional embodiment, in the first aspect of the embodiment of the present application, the mass spectrometry analysis data also includes slice image data corresponding to the target sample. After obtaining at least one target cell population and the cell type corresponding to each target cell population, the method also includes: the computer device generates a first slice image based on the slice image data; color-labels each cell in the first slice image according to the data type corresponding to each cell contained in the first slice image to obtain a second slice image; the computer device calculates the interaction probability between any two cell types based on the second slice image and the features corresponding to each cell type; the computer device determines two cell types whose interaction probability is higher than a probability threshold.

[0016] In a second aspect, an embodiment of the present application discloses a sample analysis device, which is applied to a computer device, including:

[0017] A data acquisition module, used for the computer device to acquire mass spectrometry data corresponding to the target sample, wherein the mass spectrometry data is obtained by performing mass spectrometry processing on the target sample; the mass spectrometry data includes mass spectrometry results corresponding to a plurality of cells in the target sample, respectively, and the mass spectrometry results include the expression amount of the marker protein;

[0018] A first clustering module, used for the computer device to cluster the multiple cells according to the mass spectrometry analysis results corresponding to each of the cells to obtain multiple cell subpopulations;

[0019] an expression level calculation module, used for the computer device to calculate the average expression level of the marker protein corresponding to the first cell subpopulation according to the expression level of the marker protein corresponding to each cell included in the first cell subpopulation; the first cell subpopulation is any of the cell subpopulations;

[0020] The second clustering module is used for the computer device to cluster the multiple cell subpopulations according to the average expression levels of the marker proteins corresponding to the multiple cell subpopulations, so as to obtain at least one target cell population and the cell type corresponding to each of the target cell populations.

[0021] In a third aspect, an embodiment of the present application discloses a computer device, which is applied to a computer device and includes a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the processor implements the method described in any of the above embodiments.

[0022] In a fourth aspect, an embodiment of the present application discloses a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described in any of the above embodiments is implemented.

[0023] The sample analysis method, apparatus, computer device, and computer-readable storage medium disclosed in the embodiments of the present application, the computer device obtains mass spectrometry analysis data corresponding to the target sample, the mass spectrometry analysis data is obtained by performing mass spectrometry analysis on the target sample; the mass spectrometry analysis data includes mass spectrometry analysis results corresponding to multiple cells in the target sample, the mass spectrometry analysis results include marker protein expression levels; the computer device clusters multiple cells according to the mass spectrometry analysis results corresponding to each cell to obtain multiple cell subpopulations; the computer device calculates the average expression level of the marker protein corresponding to the first cell subpopulation according to the marker protein expression level corresponding to each cell included in the first cell subpopulation; the first cell subpopulation is any cell subpopulation; the computer device clusters multiple cell subpopulations according to the average expression levels of the marker proteins corresponding to the multiple cell subpopulations, so as to obtain at least one target cell population and the cell type corresponding to each target cell population. In an embodiment of the present application, a computer device first performs preliminary clustering of multiple cells according to the mass spectrometry analysis results corresponding to each cell to form multiple cell subpopulations, thereby effectively reducing the amount of data for subsequent analysis and improving processing efficiency; and, based on the average expression level of marker proteins of cells in each cell subpopulation, multiple cell subpopulations are clustered, and the cell subpopulations are further merged into a target cell population that is more in line with the actual classification. Through a hierarchical clustering strategy, both individual differences between cells and common characteristics between groups are taken into account, thereby improving the accuracy and reliability of the overall classification, thereby improving the accuracy of the sample analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0025] Figure 1 An application scenario diagram of a sample analysis method in an embodiment;

[0026] Figure 2 is a flow chart of a sample analysis method in one embodiment;

[0027] Figure 3 is a flow chart of a sample analysis method in another embodiment;

[0028] Figure 4 is a flow chart of a sample analysis method in another embodiment;

[0029] Figure 5 is a flow chart of generating a target IMC image according to IMC image data in one embodiment;

[0030] Figure 6 is a flow chart of a sample analysis method in one embodiment;

[0031] Figure 7a is a flow chart of a sample analysis method in another embodiment;

[0032] Figure 7b A target IMC image generated by a computer device in another embodiment;

[0033] Figure 7c A second slice image generated by a computer device in another embodiment;

[0034] Figure 7d is a distribution diagram of each cell subpopulation in a target sample in another embodiment;

[0035] Figure 7e A cell subpopulation heat map in another embodiment;

[0036] Figure 8 is a block diagram of a sample analysis device in one embodiment;

[0037] Fig. 9 FIG. 4 is a structural block diagram of a computer device in one embodiment. DETAILED DESCRIPTION

[0038] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0039] It should be noted that the terms "including" and "having" and any variations thereof in the embodiments of the present application and the accompanying drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device including a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products or devices.

[0040] It is understood that the terms "first", "second", etc. used in this application may be used herein to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish a first element from another element. For example, without departing from the scope of this application, a first slice image may be referred to as a second slice image, and similarly, a second slice image may be referred to as a first slice image. Both the first slice image and the second slice image are slice images, but they are not the same slice images.

[0041] The embodiments of the present application disclose a sample analysis method, apparatus, computer device, and computer-readable storage medium, so that the target cell population obtained by two clusterings is more consistent with the actual cell population, thereby improving the accuracy of sample analysis.

[0042] Figure 1 FIG. 1 is an application scenario diagram of a sample analysis method in an embodiment. Figure 1 As shown, the sample analysis method can be applied to a computer device 110. The computer device may include but is not limited to various personal computers, smart wearable devices, tablet computers, PCs (Personal Computers), laptop computers, smart phones, and portable wearable devices, etc., which are not limited in the embodiments of the present application.

[0043] When the user undergoes medical examination, he or she may choose to perform mass spectrometry analysis and biological sample analysis according to personal needs or doctor's instructions. The user may go to a sample collection agency, and the collector in the sample collection agency 1 may collect at least one target sample 120 of the user, and transport all collected samples to the laboratory for mass spectrometry analysis. The laboratory staff may use a mass spectrometer 130 to perform mass spectrometry analysis on each target sample 120 delivered by the sample collection agency, and obtain mass spectrometry analysis data corresponding to each target sample 120. The mass spectrometry analysis data may include mass spectrometry analysis results corresponding to multiple cells contained in the target sample 120, and the mass spectrometry analysis results may include the expression level of the marker protein.

[0044] In some embodiments, the computer device 110 can establish a communication connection with the mass spectrometer 130 in the laboratory, so that the mass spectrometer 130 sends the mass spectrometer analysis data corresponding to the target sample 120 to the computer device 110 after completing the mass spectrometer analysis of the target sample 120; the computer device 110 can receive the mass spectrometer analysis data corresponding to the target sample 120 sent by the mass spectrometer 130. Optionally, the available connection methods include but are not limited to data line connection, Bluetooth connection, NFC (NearField Communication) connection and other wired or wireless connection methods.

[0045] The computer device 110 obtains mass spectrometry data obtained by the mass spectrometer 130 performing mass spectrometry analysis on each target sample 120, and performs sample analysis on each target sample 120, including: clustering multiple cells according to the mass spectrometry results corresponding to each cell contained in the target sample 120 to obtain multiple cell subpopulations; the computer device 110 calculates the average expression level of the marker protein corresponding to the first cell subpopulation according to the expression level of the marker protein corresponding to each cell contained in the first cell subpopulation; the first cell subpopulation is any cell subpopulation; the computer device 110 then clusters the multiple cell subpopulations according to the average expression levels of the marker proteins corresponding to the multiple cell subpopulations, so as to obtain at least one target cell population and the cell type corresponding to each target cell population.

[0046] Optionally, the mass spectrometer 130 may simultaneously send mass spectrometry analysis data corresponding to a plurality of target samples 120 to the computer device 110. The plurality of target samples 120 may be biological samples from different parts of the same user, or biological samples from the same part of different users.

[0047] Optionally, the computer device 110 may perform sample analysis on multiple target samples 120 at the same time. In this case, the computer device may use the target samples corresponding to multiple users suffering from a certain symptom as experimental group samples, and use the target samples corresponding to multiple users not suffering from the symptom as control group samples, based on the medical history corresponding to the users to whom the target samples belong. In the process of the computer device 110 performing sample analysis on the multiple target samples, the computer device 110 may compare the expression of marker proteins corresponding to the experimental group samples with the expression of marker proteins corresponding to the control group samples to determine the biological characteristics corresponding to the experimental group samples; the marker protein expression may include the expression amounts of marker proteins corresponding to multiple cells contained in the sample, the spatial distribution of various marker proteins in the sample, the expression amounts of various marker proteins on each cell, and other information related to the marker protein.

[0048] like Figure 2 As shown, in one embodiment, a sample analysis method is provided, which can be applied to the above-mentioned computer device. The method may include the following steps 210 to 240.

[0049] Step 210: The computer device obtains mass spectrometry analysis data corresponding to the target sample.

[0050] After the sample collection personnel of the sample collection agency have collected the target sample, the sample collection agency will send the target sample to the laboratory for mass spectrometry analysis. The laboratory can process the target sample through a mass spectrometer in the laboratory to obtain mass spectrometry analysis data corresponding to the target sample. Therefore, the computer device can obtain the mass spectrometry analysis data corresponding to the target sample sent by the mass spectrometer, and then analyze the mass spectrometry analysis data corresponding to the target sample to obtain the sample analysis result corresponding to the target sample.

[0051] In some embodiments, the mass spectrometry data is obtained by performing mass spectrometry processing on the target sample; the mass spectrometry data includes the mass spectrometry results corresponding to a plurality of cells in the target sample, and the mass spectrometry results include the expression of marker proteins. Specifically, the mass spectrometry results may include the expression of marker proteins corresponding to at least one marker protein. The marker protein expression refers to the number of proteins labeled by a specific labeling technique (such as fluorescent labeling, isotope labeling or chemical labeling, etc.) in the target sample or cell, which is used to characterize the expression level of the marker protein in the target sample or cell. For example, the marker protein can be one or more of CD4, CA_IX, HLA_DR, etc.

[0052] In some embodiments, the computer device may also obtain mass spectrometry analysis data corresponding to the target sample by obtaining input from a staff member performing sample analysis.

[0053] Step 220: The computer device clusters the multiple cells according to the mass spectrometry analysis results corresponding to each cell to obtain multiple cell subpopulations.

[0054] In some embodiments, the clustering methods that can be used by the computer device include but are not limited to hierarchical clustering, PhenoGraph (based on graph theory and Louvain community discovery algorithm) clustering, K-means (K-center) clustering, DBSCAN (Density-Based Spatial Clustering of Applications with Noise, density-based spatial clustering algorithm), etc.

[0055] In some embodiments, the computer device can calculate the similarity between any two cells based on the mass spectrometry analysis results corresponding to each cell; and based on the similarity between the arbitrary cells, hierarchical clustering is performed on multiple cells to obtain multiple cell subpopulations.

[0056] Hierarchical clustering is an unsupervised learning method that can evaluate the affinity between different cells by calculating the similarity or distance between different cells. Computer equipment can cluster cells through hierarchical clustering without pre-specifying the number of clusters. During the clustering process, the computer equipment can first obtain intermediate cell subpopulations based on the similarity between different cells, and then gradually merge or split the intermediate cell subpopulations. It can well capture the relationship between each cell subpopulation, and the multiple cell subpopulations finally obtained are more in line with the association between each cell subpopulation.

[0057] In some embodiments, after obtaining multiple cell subpopulations, the computer device can also perform dimensionality reduction processing on the multiple cell subpopulations; and display the clustering corresponding to each cell subpopulation after dimensionality reduction. Optionally, the clustering situation may include but is not limited to the distribution position and distribution density of multiple cells contained in each cell subpopulation in the target sample, the expression level of various marker proteins in each cell subpopulation, the number of cells expressing various marker proteins in each cell subpopulation, etc., wherein the expression level of various marker proteins in the cell subpopulation refers to the sum of the expression levels of marker proteins corresponding to each cell contained in the cell subpopulation and various marker proteins. Optionally, the display methods that can be used include but are not limited to heat maps, box plots, violin plots, bar graphs, scatter plots, curve graphs, etc.

[0058] In some specific embodiments, the computer device can cluster multiple cells in the target sample through the PhenoGraph clustering method to obtain multiple cell subpopulations; then perform dimensionality reduction processing on the multiple cell subpopulations through the UMAP (Uniform Manifold Approximation and Projection) dimensionality reduction algorithm; the computer device can display the clustering of multiple cell subpopulations corresponding to the target sample based on the multiple cell subpopulations after dimensionality reduction. Specifically, the computer device can draw a cell subpopulation distribution map according to the distribution position of each cell subpopulation in the target sample. Different cell subpopulations can be represented by different colors or shapes, and their distribution positions reflect their relative positional relationship in the target sample. The cell subpopulation distribution map can intuitively reflect the distribution of each cell subpopulation; the computer device can also draw a cell subpopulation heat map according to the marker protein expression level of various marker proteins in each cell subpopulation and the number of cells expressing various marker proteins in each cell subpopulation using a heat map tool (such as the pheatmap package in R language, the seaborn library in Python, etc.). In the cell subpopulation heat map, rows represent cell subpopulations, columns represent marker proteins, and the depth of color represents the marker protein expression level or the number of cells expressing various marker proteins. The cell subpopulation heat map can intuitively reflect the expression differences of various marker proteins in different cell subpopulations in the target sample.

[0059] Step 230: The computer device calculates the average expression level of the marker protein corresponding to the first cell subpopulation according to the expression level of the marker protein corresponding to each cell included in the first cell subpopulation.

[0060] Optionally, the average expression level of the marker protein can be expressed as the mean expression level corresponding to at least one marker protein; the average expression level of the marker protein can also be expressed as the average expression level of all marker proteins corresponding to the cells.

[0061] The computer device can calculate the average expression amount of the marker protein corresponding to each cell subpopulation according to the expression amount of the marker protein corresponding to each cell contained in each cell subpopulation. The average expression amount of the marker protein corresponding to the cell subpopulation can be used to characterize the expression level of the marker protein of the cell subpopulation.

[0062] Taking the first cell subpopulation as an example, the first cell subpopulation can be any cell subpopulation. In some embodiments, the computer device can first determine the expression level of the marker protein corresponding to the first marker protein of each cell included in the first cell subpopulation, and the first marker protein is any marker protein; then divide the sum of the expression levels of the marker protein corresponding to the first marker protein of each cell by the number of cells included in the first cell subpopulation to obtain the average expression level of the first marker protein in the first cell subpopulation.

[0063] In some embodiments, the computer device can add the marker protein expression amounts corresponding to at least one marker protein expressed in the first cell to obtain the total marker protein expression amount corresponding to the first cell, where the first cell is any cell in the first cell subpopulation. The sum of the total marker protein expression amounts corresponding to each cell included in the first cell subpopulation can be divided by the number of cells included in the first cell subpopulation to obtain the average expression amount of all marker proteins in the first cell subpopulation.

[0064] For example, suppose that cell subpopulation A contains cells a, b, and c; the total amount of marker protein expression corresponding to cell a is α; the total amount of marker protein expression corresponding to cell b is β; the total amount of marker protein expression corresponding to cell c is γ; then the average expression of marker protein corresponding to cell subpopulation A is

[0065] Step 240 : The computer device clusters the multiple cell subpopulations according to the average expression levels of the marker proteins corresponding to the multiple cell subpopulations, so as to obtain at least one target cell population and the cell type corresponding to each target cell population.

[0066] The computer device clusters multiple cells according to the mass spectrometry analysis results corresponding to each cell to obtain multiple cell subpopulations; then calculates the average expression of the marker protein corresponding to the first cell subpopulation according to the expression of the marker protein corresponding to each cell included in the first cell subpopulation; clusters the multiple cell subpopulations according to the average expression of the marker protein corresponding to the multiple cell subpopulations, thereby obtaining the sample analysis results corresponding to the target sample. The sample analysis results may include the target cell populations to which the multiple cells in the target sample belong and the cell types corresponding to each target cell population.

[0067] Optionally, the sample analysis results may also include, but are not limited to, one or more of the following: the expression of marker proteins corresponding to the target sample and the corresponding visualization image, the morphology of each cell in the target sample, the spatial distribution of multiple cells contained in each target cell population in the target sample, the cell density corresponding to each target cell population, the interaction between each target cell population, and the IMC image corresponding to the target sample.

[0068] Among them, the expression of marker proteins corresponding to the target sample refers to the expression amount of marker proteins corresponding to each cell contained in the target sample, the expression amount of marker proteins of at least one marker protein in the target sample, the number of cells expressing at least one marker protein in the target sample, and other data related to the expression level of marker proteins in the target sample. The morphology of each cell in the target sample may include an evaluation of the shape, size, nuclear-cytoplasmic ratio, organelle distribution and other characteristics of each cell or cell subpopulation or target cell population in the target sample. The spatial distribution of multiple cells contained in each target cell population in the target sample refers to the relative position and arrangement of cells in each target cell population in the target sample. The cell density corresponding to each target cell population refers to the degree of aggregation of multiple cells contained in each target cell population. The interaction between each target cell population refers to the type and degree of influence between each target cell population, and the interaction may include direct contact (such as cell connection), signal transmission (through cytokines, hormones, etc.) and regulation of the extracellular matrix (ECM), etc. The IMC image corresponding to the target sample can display the precise position and relative abundance information (quantity) of various molecules such as proteins, lipids, metabolites, etc. in the target sample.

[0069] In order to make the final target cell population more consistent with the actual classification, the computer device can cluster multiple cell subpopulations and merge cell subpopulations with similar marker protein expression levels to obtain at least one target cell population.

[0070] In some embodiments, the computer device may first determine the average expression level of various marker proteins in a first cell subpopulation; calculate the difference in average expression level of the same marker protein between any two cell subpopulations; if the computer device finds that the differences in average expression level of various marker proteins between two cell subpopulations are all less than an expression level threshold, the two cell subpopulations are merged into one cell subpopulation, and the difference in average expression level of the same marker protein in each cell subpopulation is repeatedly calculated until the difference in average expression level of at least one marker protein between any two cell subpopulations is greater than the expression level threshold, thereby obtaining at least one target cell population.

[0071] In a specific embodiment, it is assumed that the average expression level of marker protein 1 in cell subpopulation A is 7, the average expression level of marker protein 2 is 9, and the average expression level of marker protein 3 is 6; the average expression level of marker protein 1 in cell subpopulation B is 7, the average expression level of marker protein 2 is 8, and the average expression level of marker protein 3 is 8; the expression level threshold is 3. Therefore, the difference in the average expression levels of various marker proteins in cell B is 0, 1, and 2, which are all less than 3, and the computer device can merge cell subpopulation A with cell subpopulation B.

[0072] In another specific embodiment, it is assumed that the average expression level of marker protein 1 in cell subpopulation A is 7, the average expression level of marker protein 2 is 9, and the average expression level of marker protein 3 is 6; the average expression level of marker protein 1 in cell subpopulation C is 9, the average expression level of marker protein 2 is 14, and the average expression level of marker protein 3 is 7; the expression level threshold is 3. Therefore, if the difference between the average expression levels of marker protein 2 in cell subpopulation C and cell subpopulation A is 5 greater than 3, the computer device does not merge cell subpopulation A with cell subpopulation C.

[0073] Since marker proteins have high specificity and sensitivity, and cells of different cell types express different types of marker proteins. Therefore, in some embodiments, the computer device can determine the cell type corresponding to each target cell population based on the expression level of various marker proteins in each target cell population. For example, under normal circumstances, marker protein CD68 and marker protein F4 / 80 are specific expression cells of macrophages. Therefore, if the expression of CD68 and F4 / 80 in target cell population A is higher than that in other target cell populations, the computer device can consider that the cell type corresponding to the target cell population A is a macrophage.

[0074] Alternatively, cell type refers to the category or type to which cells with similar morphology, structure and functional characteristics belong; wherein the functional characteristics can be reflected in the proteins expressed by the cells, that is, cells with different functional characteristics express different proteins. Specifically, cell types may include but are not limited to B cells, T cells, macrophages, tumor cells, myeloid cells, etc.

[0075] In an embodiment of the present application, a computer device first performs preliminary clustering of multiple cells according to the mass spectrometry analysis results corresponding to each cell to form multiple cell subpopulations, thereby effectively reducing the amount of data for subsequent analysis and improving processing efficiency; and, based on the average expression level of marker proteins of cells in each cell subpopulation, multiple cell subpopulations are clustered, and the cell subpopulations are further merged into a target cell population that is more in line with the actual classification. Through a hierarchical clustering strategy, both individual differences between cells and common characteristics between groups are taken into account, thereby improving the accuracy and reliability of the overall classification, thereby improving the accuracy of the sample analysis.

[0076] like Figure 3 As shown, in another embodiment, a sample analysis method is provided, which can be applied to the above-mentioned computer device. The method may include the following steps 302 to 316.

[0077] Step 302: The computer device obtains mass spectrometry analysis data corresponding to the target sample.

[0078] The description of step 302 may refer to the relevant description of step 210 in the above embodiment, which will not be repeated here.

[0079] Step 304: The computer device converts each first value included in the first expression count data to obtain second expression count data that conforms to a normal distribution.

[0080] During the sample analysis process, it is often easy for many cells in the target sample to show relatively low marker protein expression levels, while a few cells have very high marker protein expression levels. At this time, due to the uneven distribution of marker protein expression levels, it is very likely to cause data value deviations in the sample analysis process, especially for the analysis of highly expressed cells or marker proteins. Therefore, before clustering multiple cells contained in the target sample, the computer device can first screen multiple cells of the target sample and remove cells that abnormally express marker proteins to avoid analysis deviations caused by low marker protein expression levels in most cells in the target sample.

[0081] In some embodiments, the mass spectrometry data includes first expression count data, the first expression count data includes a first value corresponding to each cell, and the first value is used to characterize the expression level of the marker protein corresponding to the cell. Optionally, the mass spectrometry data includes position morphology data corresponding to multiple cells in the target sample. Among them, the position morphology data may include but is not limited to the position coordinates, specific shape, cell area, relative position of the cell nucleus in the cell, etc. of the cell in the target sample. The first expression count data and position morphology data can be used to screen multiple cells in the target sample to avoid abnormal cells causing deviations in sample analysis.

[0082] In some embodiments, the computer device may use an inverse hyperbolic sine function to convert each first value contained in the first expression count data to obtain second expression count data that conforms to a normal distribution. The inverse hyperbolic sine function may map each first value to a more evenly distributed region (conforming to a normal distribution), effectively amplifying data with lower first values, while retaining the relative differences between the expression levels of marker proteins corresponding to each cell, reducing the excessive impact of cells with high first values ​​on the analysis, thereby avoiding serious analysis bias.

[0083] The first value is used to characterize the expression level of the marker protein corresponding to the cell; the second expression count data includes the second value corresponding to each cell. Optionally, the expression level of the marker protein corresponding to the cell can be represented by the total expression of all marker proteins in the cell; it can also be represented by the average expression of at least one marker protein in the cell; it can also be represented by the expression concentration of various marker proteins in the cell, and the representation form of the marker protein expression level corresponding to the cell is not limited here. For example, assuming that the target sample contains cell a, and two marker proteins are expressed in cell a, namely marker protein 1 and marker protein 2, and the expression level corresponding to marker protein 1 is α, and the expression level corresponding to marker protein 2 is β, then the first value corresponding to cell a can be (α+β).

[0084] In some specific embodiments, the computer device substitutes the first values ​​corresponding to the multiple cells in the target sample into formula (1) respectively to obtain the second value corresponding to each cell.

[0085] y = arcsinh(x - μα) (1);

[0086] Among them, x is the first value, y is the second value, μ is the mean (or median) of the first value corresponding to all cells, and α is the scaling factor (also called pseudo count), which is used to adjust the sensitivity of the conversion. The smaller α is, the larger the mapped area is. The specific value can be set according to the actual situation.

[0087] Step 306 : The computer device selects cells whose second values ​​are within a target standard deviation range from a plurality of cells in the target sample according to the second values ​​corresponding to the respective cells, to obtain a plurality of first cells.

[0088] Since the second expression count data obtained by the computer device conforms to the normal distribution, in some embodiments, the computer device can calculate the standard deviation of the second value in the second expression count data, and determine the target standard deviation range based on the standard deviation; then, the cells whose second values ​​are within the target standard deviation range are screened out from the multiple cells in the target sample to obtain multiple first cells. Among them, if the second value corresponding to the cell is within the target standard deviation range, it means that the marker protein expression level of the cell is within the normal range. Therefore, there will be no abnormal cells with too high or too low marker protein expression levels in the multiple first cells screened by the computer device, which can avoid the analysis deviation caused by the low marker protein expression levels of most cells in the target sample.

[0089] Step 308: The computer device calculates the cell area corresponding to each first cell according to the position and morphological data corresponding to each first cell.

[0090] Optionally, the positional morphological data may include, but are not limited to, the positional coordinates of the cell in the target sample, the specific shape, the cell area, the relative position of the cell nucleus in the cell, etc.

[0091] Step 310: The computer device selects first cells whose cell areas are within a preset area range from the plurality of first cells to obtain a plurality of second cells.

[0092] During the sample analysis process, the computer equipment may identify non-cellular structures (such as debris, noise) or abnormal cells (such as overgrown or atrophic cells), which may affect the clustering of normal cells. Therefore, the computer equipment can screen the cell area of ​​the cells to remove non-cellular structures and abnormal cells that do not meet the normal cell area standards, thereby reducing the noise in the target sample and ensuring the consistency and comparability of the analysis.

[0093] Optionally, the preset area range can be set according to the actual sample analysis requirements and is not limited here. Specifically, if the target sample analysis focuses on cells of a specific size (such as large cancer cells, etc.), the preset area range is narrower and the area value is larger. For example, if the preset area range of ordinary size is (α, β), then when the target sample analysis focuses on large cancer cells, the preset area range can be (α+8, β+6).

[0094] Step 312: The computer device clusters the plurality of second cells according to the mass spectrometry analysis results corresponding to the respective second cells to obtain a plurality of cell subpopulations.

[0095] The description of clustering the plurality of second cells in step 312 may refer to the related description of clustering the plurality of cells in step 220 of the above embodiment, which will not be described in detail here.

[0096] In some embodiments, the computer device may not execute steps 308 to 310, that is, the computer device only performs cell screening once, and after executing step 306 to obtain multiple first cells, directly executes the step of clustering the multiple first cells according to the mass spectrometry analysis results corresponding to each first cell to obtain multiple cell subpopulations; and then executes step 314.

[0097] In some embodiments, after executing step 302 to obtain mass spectrometry data corresponding to the target sample, the computer device may calculate the cell area corresponding to each cell according to the positional morphological data corresponding to each cell in the target sample; screen out cells whose cell area is within a preset area range from multiple cells in the target sample to obtain multiple second cells; and then directly execute step 312.

[0098] Step 314: The computer device calculates the average expression level of the marker protein corresponding to the first cell subpopulation according to the expression level of the marker protein corresponding to each cell included in the first cell subpopulation.

[0099] In step 316 , the computer device clusters the multiple cell subpopulations according to the average expression levels of the marker proteins corresponding to the multiple cell subpopulations, so as to obtain at least one target cell population and the cell type corresponding to each target cell population.

[0100] The description of steps 314 to 316 may refer to the relevant description of steps 230 to 240 of the above embodiment, and will not be repeated here.

[0101] In some embodiments, after obtaining at least one target cell population and the cell type corresponding to each target cell population, the computer device can also calculate the first expression level of various marker proteins in the first target cell population based on the marker protein expression levels corresponding to each cell contained in the first target cell population and each marker protein, and determine the spatial distribution of various marker proteins in the first target cell population; the first target cell population is any target cell population; the computer device displays the cell type, first expression level and corresponding spatial distribution corresponding to each target cell population.

[0102] Optionally, the display methods that can be used by the computer device include but are not limited to heat maps, box plots, violin plots, bar graphs, scatter plots, curve graphs, etc.

[0103] In some specific embodiments, when a computer device performs sample analysis on multiple target samples, the computer device may use a bar graph and / or a box plot on the same interface to display the cell number and average cell area of ​​the second cells corresponding to the multiple target samples, so that staff performing sample analysis can intuitively compare the cell number and average cell area of ​​the second cells respectively contained in the multiple samples based on the content displayed on the interface, thereby facilitating the staff to observe and analyze the multiple target samples.

[0104] In an embodiment of the present application, the computer device can effectively reduce the analysis bias caused by the uneven distribution of the marker protein expression level by converting the first expression count data (such as using an inverse hyperbolic sine function) into the second expression count data that conforms to the normal distribution; and / or, through cell area screening, it can ensure that the cells with cell areas within a preset range are subsequently clustered. By screening the marker protein expression level and / or cell area, it can be ensured that the subsequent analysis is performed on cells with relatively normal and stable expression levels and cell areas, which helps to reduce the impact of abnormal cells on the analysis results and improve the overall quality and accuracy of the target sample analysis.

[0105] like Figure 4 As shown, in another embodiment, a sample analysis method is provided, which can be applied to the above-mentioned computer device. The method may include the following steps 402 to 410.

[0106] Step 402: The computer device obtains mass spectrometry analysis data corresponding to the target sample.

[0107] The description of step 402 may refer to the related description of step 210 in the above embodiment, which will not be repeated here.

[0108] Step 404: The computer device generates a target IMC image according to the IMC image data.

[0109] Optionally, the mass spectrometry analysis data also includes imaging mass spectrometry flow cytometry IMC image data corresponding to the target sample. The image position of each cell included in the target IMC image is marked.

[0110] In some embodiments, the IMC image data may include the pixel intensity corresponding to each pixel point in multiple mass spectrometry channels during the mass spectrometry analysis process of the target sample. Among them, the mass spectrometry channel is a channel for detecting marker proteins on the surface or inside of the cell; the pixel intensity can be a parameter value such as grayscale value, peak area or fluorescence intensity. For example, if the target sample co-expresses three marker proteins, and the labeling methods or labeling colors corresponding to these three marker proteins are different, there are three mass spectrometry channels, one marker protein corresponds to one mass spectrometry channel, that is, the pixel intensity corresponding to each pixel point in a certain mass spectrometry channel may refer to the pixel intensity corresponding to each pixel point during the labeling process of the marker protein corresponding to the mass spectrometry channel.

[0111] In some embodiments, Figure 5 As shown, the step of generating a target IMC image according to the IMC image data may include steps 502 to 506.

[0112] Step 502: The computer device calculates the average pixel intensity corresponding to each mass spectrum channel and the average overflow pixel intensity between any two mass spectrum channels according to the pixel intensities corresponding to each pixel point in the IMC image data in multiple mass spectrum channels.

[0113] In the process of collecting IMC image data for mass spectrometry analysis of target samples, it is necessary to collect pixel intensities corresponding to multiple mass spectrometry channels, and there may be pixel overflow between any two mass spectrometry channels, which will affect the measurement of pixel intensities corresponding to each pixel point, and further affect the identification of each cell in the target IMC image. In order to correct this overflow effect, the computer device can calculate the overflow matrix to realize the correction of the pixel intensities corresponding to each pixel point in multiple mass spectrometry channels.

[0114] The average pixel intensity corresponding to each mass spectrometry channel refers to the average pixel intensity that can be detected at multiple pixel points of each mass spectrometry channel during the process of labeling the labeled protein at each pixel point of each mass spectrometry channel.

[0115] The average overflow pixel intensity refers to the influence of the first mass spectrometry channel on the second mass spectrometry channel during the process of labeling the marker protein at each pixel point in the first mass spectrometry channel, so that each pixel point in the second mass spectrometry channel generates a pixel intensity, that is, the average overflow pixel intensity of the first mass spectrometry channel overflowing into the second mass spectrometry channel is the average value of the pixel intensity that can be detected by multiple pixel points in the second mass spectrometry channel at this time. The first mass spectrometry channel is any mass spectrometry channel; the second mass spectrometry channel is any mass spectrometry channel except the first mass spectrometry channel.

[0116] In some embodiments, the average overflow pixel intensity of the first mass spectrum channel overflowing into the second mass spectrum channel may be the same as or different from the average overflow pixel intensity of the second mass spectrum channel overflowing into the first mass spectrum channel. For example, assuming there are two mass spectrum channels (mass spectrum channel A and mass spectrum channel B), the average pixel intensity corresponding to mass spectrum channel A may be α, the average pixel intensity corresponding to mass spectrum channel B may be β, the average overflow pixel intensity of mass spectrum channel A overflowing into mass spectrum channel B is θ, and the average overflow pixel intensity of mass spectrum channel B overflowing into mass spectrum channel A is μ; wherein θ and μ may be the same or different.

[0117] Step 504: The computer device constructs an overflow matrix corresponding to the IMC image data according to the average pixel intensity corresponding to each mass spectrum channel and the average overflow pixel intensity between any two mass spectrum channels.

[0118] In some embodiments, the computer device can calculate the overflow ratio between any two mass spectrometry channels based on the average overflow pixel intensity between any two mass spectrometry channels; and use the average pixel intensity corresponding to each mass spectrometry channel and the overflow ratio between any two mass spectrometry channels as each element in the overflow matrix. The overflow matrix is ​​an N-order matrix, and N is the number of mass spectrometry channels.

[0119] Optionally, when , the diagonal elements of the overflow matrix can be expressed as the average pixel intensity corresponding to each mass spectrometry channel; when , the elements of the overflow matrix can be expressed as the overflow ratio of the i-th mass spectrometry channel to the j-th mass spectrometry channel, where i and j are positive integers not greater than N.

[0120] In some embodiments, the computer device may use the ratio of the average overflow pixel intensity of the first mass spectrum channel overflowing into the second mass spectrum channel to the average pixel intensity corresponding to the first mass spectrum channel as the overflow ratio of the first mass spectrum channel overflowing into the second mass spectrum channel. For example, assuming that the average pixel intensity corresponding to mass spectrum channel A is α, and the average overflow pixel intensity of mass spectrum channel A overflowing into mass spectrum channel B is θ; then the overflow ratio of mass spectrum channel A overflowing into mass spectrum channel B is

[0121] For example, assuming that there are three mass spectrometry channels (mass spectrometry channel A, mass spectrometry channel B, and mass spectrometry channel C), the overflow matrix is ​​a 3×3 matrix. The average pixel intensity corresponding to mass spectrometry channel A can be α, the average pixel intensity corresponding to mass spectrometry channel B can be β, and the average pixel intensity corresponding to mass spectrometry channel C can be γ; the average overflow pixel intensity of mass spectrometry channel A overflowing into mass spectrometry channel B is θ, the average overflow pixel intensity of mass spectrometry channel A overflowing into mass spectrometry channel C is φ, the average overflow pixel intensity of mass spectrometry channel B overflowing into mass spectrometry channel A is μ, the average overflow pixel intensity of mass spectrometry channel B overflowing into mass spectrometry channel C is σ, the average overflow pixel intensity of mass spectrometry channel C overflowing into mass spectrometry channel A is ω, and the average overflow pixel intensity of mass spectrometry channel C overflowing into mass spectrometry channel B is ε. Therefore, the computer device can obtain the overflow matrix as

[0122]

[0123] Step 506: The computer device corrects the pixel intensities corresponding to the multiple mass spectrometry channels of each pixel point included in the IMC image data according to the overflow matrix to generate a target IMC image.

[0124] In some embodiments, the computer device may multiply the pixel intensity of the first pixel point on the i-th mass spectrometry channel by each element of the i-th row of the overflow matrix respectively, and then use the sum of the products as the corrected pixel intensity of the first pixel point on the i-th mass spectrometry channel; the first pixel point is any pixel point.

[0125] Specifically, the computer device can calculate the corrected pixel intensity of the first pixel point on the i-th mass spectrum channel according to formula (2).

[0126]

[0127] Among them, is the pixel intensity of the first pixel point on the i-th mass spectrometry channel before correction, is the element in the i-th row and j-th column of the inverse matrix of the overflow matrix, and N is the order of the overflow matrix, which is also the number of mass spectrometry channels.

[0128] In some embodiments, the computer device can integrate the corrected pixel intensities corresponding to each pixel point on multiple mass spectrometry channels to obtain the target pixel intensity corresponding to each pixel point; and generate a target IMC image according to the target pixel intensity corresponding to each pixel point. By adjusting the signal intensity of the pixel point in each mass spectrometry channel to eliminate the influence of the spillover effect between mass spectrometry channels, the accuracy of marking the image position of each cell contained in the target IMC image is further improved.

[0129] Optionally, the target pixel intensity corresponding to the first pixel may be an average of the corrected pixel intensities corresponding to the first pixel on multiple mass spectrometry channels.

[0130] Optionally, the computer device may first determine the target weights corresponding to the first pixel point in multiple mass spectrometry channels respectively; and then perform weighted summation on the corrected pixel intensities of the first pixel point in each mass spectrometry channel to obtain the target pixel intensity corresponding to the first pixel point.

[0131] Step 406: The computer device clusters the multiple cells according to the image position of each cell in the target IMC image and the mass spectrometry analysis result corresponding to each cell to obtain multiple cell subpopulations.

[0132] The description of step 406 may refer to the related description of step 220 in the above embodiment, which will not be repeated here.

[0133] In some embodiments, after generating the target IMC image, the computer device may mark the cell contours and cell nucleus positions of each cell contained in the target IMC image to generate a marked IMC image; output the marked IMC image, and obtain the input cell clustering indication; the cell clustering indication is used to indicate to the staff whether the cell contours and cell nucleus positions of each cell in the marked IMC image are correctly marked. If the computer device detects that the cell clustering indication is correctly marked, step 406 may be continued; if the cell clustering indication is detected as incorrectly marked, the computer device may regenerate a new marked IMC image until the cell clustering indication received by the computer device is correctly marked. By manually confirming whether the marked cell contours and cell nucleus positions are correct, the erroneous markings caused by algorithm misjudgment or poor image quality can be significantly reduced, so that the computer device can accurately determine each cell, so that when clustering cells, each cell can be accurately classified, thereby improving the accuracy of sample analysis.

[0134] Step 408: The computer device calculates the average expression level of the marker protein corresponding to the first cell subpopulation according to the expression level of the marker protein corresponding to each cell included in the first cell subpopulation.

[0135] In step 410 , the computer device clusters the multiple cell subpopulations according to the average expression levels of the marker proteins corresponding to the multiple cell subpopulations, so as to obtain at least one target cell population and the cell type corresponding to each target cell population.

[0136] The description of steps 408 to 410 may refer to the relevant description of steps 230 to 240 in the above embodiment, and will not be repeated here.

[0137] In an embodiment of the present application, the computer device corrects the spillover effect of each pixel in multiple mass spectrometry channels by calculating the overflow matrix, and can more accurately measure the pixel intensity of each pixel in different mass spectrometry channels, thereby reducing the measurement error caused by pixel overflow, so that the outline and characteristics of each cell can be more clearly displayed in the target IMC image, improving the accuracy of cell identification and labeling, and also enabling the computer device to obtain the target cell population to which each cell belongs when clustering multiple cells, thereby improving the accuracy of target sample analysis.

[0138] like Figure 6 As shown, in another embodiment, a sample analysis method is provided, which can be applied to the above-mentioned computer device. The method may include the following steps 602 to 608.

[0139] Step 602: The computer device generates a first slice image according to the slice image data.

[0140] In some embodiments, the mass spectrometry data corresponding to the target sample acquired by the computer device also includes slice image data corresponding to the target sample. The slice image data can be used to generate a first slice image corresponding to the target sample, which refers to a cell image that can be directly observed through a microscope after the target sample is stained. The slice image data can also be used to generate multiple slice images corresponding to the target sample, so that the staff of the sample analysis can intuitively understand the spatial distribution, morphology and structural characteristics of multiple cells on the target sample.

[0141] Step 604: The computer device performs color marking on each cell in the first slice image according to the data type corresponding to each cell in the first slice image to obtain a second slice image.

[0142] In some embodiments, in order to further improve the accuracy and intuitiveness of the analysis, the computer device can accurately color-label each cell in the first slice image according to the data type to which each cell contained in the first slice image belongs, thereby generating a second slice image. Among them, cells of different data types are labeled with different colors, and cells of the same data type are labeled with the same color; it ensures that cells of different cell types can be clearly distinguished, so that the spatial distribution and abundance of cells of each cell type in the target sample can be clearly observed. The abundance corresponding to the cell type is associated with the density of the cell, that is, if the cell density of a certain cell type in a certain area of ​​the first slice image is high, then the abundance of the cell type in this area is high. Therefore, through the difference in color, the computer device can quickly identify and pay attention to various target cell populations, and then deeply determine their biological characteristics, as well as the spatial relationship and quantitative relationship between different target cell populations.

[0143] Step 606: The computer device calculates the probability of interaction between any two cell types based on the second slice image and the features corresponding to each cell type.

[0144] Characteristics corresponding to cell types may include, but are not limited to, specifically expressed protein types, cell morphology, cell area, cell arrangement, receptor-ligand binding ability on the cell membrane containing the cells, signal transduction pathways, known biological interaction networks, etc.

[0145] In some embodiments, after obtaining the second slice image, the computer device can obtain the cell area corresponding to various cell types by image processing technology, and determine the spatial distribution of each cell area; the computer device calculates the spatial proximity between any two cell types according to the spatial distribution of each cell area, and the spatial proximity is used to characterize the proximity of the cell areas corresponding to any two cell types; according to the spatial neighborhood, the interaction probability between any two cell types is determined. Optionally, the computer device can evaluate the spatial proximity between any two cell types by spatial statistical methods (such as Voronoi Voronoi diagram, Ripley'sK multi-distance spatial clustering analysis function). The higher the spatial proximity of the two cell types, the more likely it is that the interaction will occur, and the greater the probability of interaction between the two cell types.

[0146] Specifically, if two cell types are closely adjacent or intersecting in spatial distribution, and the position distribution of the cells shows high-density aggregation, the computer device may deem that there is a high probability of interaction between the two cell types.

[0147] In some embodiments, after calculating the interaction probability between any two cell types based on the second slice image and the corresponding features of each cell type, the computer device can also adjust or weight the interaction probability based on a known biological interaction network to obtain a more realistic interaction probability, where the biological interaction network contains preset cell type interaction relationships.

[0148] In step 608, the computer device determines two cell types whose probability of interaction is higher than a probability threshold.

[0149] In some embodiments, a probability threshold may be determined based on historical experimental data, historical studies, or expert knowledge and may be used to distinguish between which interactions between cell types are significant and which are likely to be accidental or insignificant.

[0150] Optionally, if the detected probability of interaction between two cell types is higher than a probability threshold, the computer device confirms that the two cell types have a significant interaction relationship.

[0151] In an embodiment of the present application, the computer device finds out the cell types with significant functional relationships, which can help analyze the biological characteristics of multiple target cell populations in the target sample. The biological characteristics include but are not limited to cell morphology, surface markers (marker proteins), gene expression profiles, metabolic activity, etc. By comparing the biological characteristics between different target cell populations, or the biological characteristics of the target cell population with the normal biological characteristics of the corresponding cell type, it can be further determined whether the user to whom the target sample belongs has an abnormality or what kind of abnormality he has.

[0152] In a specific embodiment, Figure 7a FIG. 1 is a flow chart of a sample analysis method in one embodiment. Figure 7a As shown, the specific process of sample analysis may include:

[0153] Laboratory staff use a mass spectrometer or mass spectrometry software to perform mass spectrometry analysis on the target sample, so that the computer device obtains mass spectrometry analysis data corresponding to the target sample; the mass spectrometry analysis data may include: mass spectrometry analysis results corresponding to each cell contained in the target sample, IMC image data after hotspot correction, slice image data of the target sample, first expression count data, position morphology data corresponding to each cell contained in the target sample (morphological features and positions), pixel intensity of each cell on each mass spectrometry channel, etc.

[0154] In order to reduce the impact of abnormal cells on the analysis results, the computer device can first screen the multiple cells contained in the target sample: by screening the marker protein expression level and / or cell area. The computer device can use the inverse hyperbolic sine function to convert each first value contained in the first expression count data to obtain the second expression count data that conforms to the normal distribution; then screen out cells whose second values ​​are within the target standard deviation range from multiple cells in the target sample to obtain multiple first cells. The computer device can also determine the cell area of ​​each cell based on the position morphology data corresponding to each cell; and screen out cells whose cell area is within the preset area range from multiple cells as second cells.

[0155] In order to prevent pixel overflow between multiple mass spectrometry channels from affecting the pixel intensity of each pixel point of the target IMC image, the computer device can realize overflow correction of the pixel intensity of each pixel point by generating an overflow matrix. The computer device can calculate the average pixel intensity corresponding to each mass spectrometry channel and the average overflow pixel intensity between any two mass spectrometry channels based on the pixel intensity corresponding to each pixel point in multiple mass spectrometry channels contained in the IMC image data; then use the average pixel intensity corresponding to each mass spectrometry channel and the overflow ratio between any two mass spectrometry channels as each element in the overflow matrix to construct an N×N overflow matrix (N is the number of mass spectrometry channels) corresponding to the IMC image data; the computer device uses the overflow matrix to correct the pixel intensity corresponding to each pixel point in multiple mass spectrometry channels, and generate a clear and accurate target IMC image (such as Figure 7b As shown, the white outline is the cell outline of each cell in the target IMC image, the blue represents each cell, and the legend on the left is the marker protein expression level of each cell in the target IMC image, the darker the color, the lower the marker protein expression level of the cell), and the image position of each cell contained in the target IMC image is marked.

[0156] After cell screening and pixel intensity correction, the computer device can cluster multiple cells obtained by structural screening in the target sample to obtain multiple cell subpopulations. Afterwards, the computer device can calculate the average expression amount of the marker protein corresponding to the first cell subpopulation based on the expression amount of the marker protein corresponding to each cell contained in the first cell subpopulation; and cluster the multiple cell subpopulations based on the average expression amount of the marker protein corresponding to the multiple cell subpopulations to obtain at least one target cell population. The computer device can also determine the cell type corresponding to each target cell population based on the expression level of each marker protein in each target cell population.

[0157] After determining the corresponding multiple target cell populations in the target sample, the computer device can further perform bioinformatics analysis on the target sample (interaction relationship between each target cell population). The computer device can generate a first slice image of the target sample based on the slice image data; accurately color-label each cell in the first slice image based on the data type corresponding to each target cell population in the first slice image, thereby generating a second slice image (such as Figure 7c As shown, 8 colors represent 8 target cell populations, each colored dot represents a cell, and the horizontal and vertical axes are image position coordinate axes). The computer device calculates the probability of interaction between any two cell types based on the second slice image and the features corresponding to each cell type, and determines that there is interaction between two cell types whose interaction probability is higher than a probability threshold.

[0158] Optionally, the order of steps for cell screening and pixel intensity correction by the computer device is not limited herein.

[0159] Optionally, the computer equipment can also visualize the relevant data in the specific process of the above-mentioned sample analysis to obtain analysis images of different dimensions, so that laboratory staff can intuitively and clearly understand the analysis results of the target sample by the computer equipment, and further analyze the target sample and the analysis results.

[0160] For example, the computer device can draw a cell subpopulation distribution map (such as Figure 7d As shown, Figure 7d The computer equipment can also draw a cell subpopulation heat map (such as the heat map of the cell subpopulation) based on the expression of various marker proteins in each cell subpopulation and the number of cells expressing various marker proteins in each cell subpopulation. Figure 7e As shown, "ncell" indicates the number of cells expressing various marker proteins in each cell subpopulation, and the darker the color, the more cells there are; "pg_chusters_2" indicates the 23 cell subpopulations present in the target sample, and 23 colors are used to represent the 23 cell subpopulations respectively; Figure 7e Each vertical column represents the expression level of various marker proteins in a certain cell subpopulation, and each horizontal column represents the expression level of a certain marker protein in each cell subpopulation. The darker the color, the smaller the expression level. The cell subpopulation heat map can intuitively reflect the expression differences of various marker proteins in each cell subpopulation of the target sample.

[0161] In related technologies, sample analysis of mass spectrometry data of target samples requires not only solid biological and medical knowledge, but also the use of multiple analysis software to obtain different analysis results. To use these analysis software, the staff is required to undergo long-term learning, training and practice, which further increases the difficulty of work for experimental staff who lack programming experience. In addition, when using analysis software to analyze tissue mass spectrometry imaging results, problems such as difficulty in saving, flashbacks and long analysis time are prone to occur, which makes it often take longer for staff to fully analyze an image, especially when encountering a large number of samples, the difficulty of work will increase dramatically.

[0162] If the computer device in the embodiments of the present application can implement the method of any of the above embodiments, then the computer device can solve the problems arising in the above-mentioned related technologies, and can meet various analysis requirements of sample analysis without the need for staff to switch between multiple analysis software, and does not require staff to have software programming capabilities.

[0163] In an embodiment of the present application, the computer device simplifies the complex manual operations of analyzing the mass spectrometry analysis data of the samples by automatically performing cell screening, pixel intensity correction, clustering and other operations on the mass spectrometry analysis data of the samples. The staff no longer have to waste time and effort switching back and forth between multiple software. The computer device can also automatically generate a variety of visual charts of the sample analysis process according to the needs of the staff for analysis by the staff, thereby improving the efficiency of sample analysis.

[0164] like Figure 8 As shown, in one embodiment, a sample analysis device 800 is provided, which can be applied to the above-mentioned computer device. The sample analysis device 800 can include a data acquisition module 810, a first clustering module 820, an expression calculation module 830 and a second clustering module 840.

[0165] The data acquisition module 810 is used for a computer device to acquire mass spectrometry data corresponding to the target sample. The mass spectrometry data is obtained by performing mass spectrometry processing on the target sample. The mass spectrometry data includes mass spectrometry results corresponding to multiple cells in the target sample, and the mass spectrometry results include the expression level of the marker protein.

[0166] The first clustering module 820 is used for the computer device to cluster multiple cells according to the mass spectrometry analysis results corresponding to each cell to obtain multiple cell subpopulations.

[0167] The expression level calculation module 830 is used for the computer device to calculate the average expression level of the marker protein corresponding to the first cell subpopulation according to the expression level of the marker protein corresponding to each cell included in the first cell subpopulation; the first cell subpopulation is any cell subpopulation.

[0168] The second clustering module 840 is used for the computer device to cluster the multiple cell subpopulations according to the average expression levels of the marker proteins corresponding to the multiple cell subpopulations, so as to obtain at least one target cell population and the cell type corresponding to each target cell population.

[0169] Optionally, the mass spectrometry analysis data includes first expression count data, the first expression count data includes a first value corresponding to each cell, and the first value is used to characterize an expression level of a marker protein corresponding to the cell.

[0170] In some embodiments, the sample analysis device 800 may further include a first screening module and a second screening module.

[0171] The first screening module is used for a computer device to convert each first value included in the first expression count data to obtain second expression count data that conforms to a normal distribution, wherein the second expression count data includes a second value corresponding to each cell; and, based on the second value corresponding to each cell, to screen out cells whose second values ​​are within a target standard deviation range from multiple cells in a target sample to obtain multiple first cells.

[0172] In some embodiments, the first clustering module 820 is further used by the computer device to cluster the multiple first cells according to the mass spectrometry analysis results corresponding to the respective first cells to obtain multiple cell subpopulations.

[0173] Optionally, the mass spectrometry analysis data includes position and morphology data corresponding to a plurality of cells in the target sample.

[0174] The second screening module is used for the computer device to calculate the cell area corresponding to each first cell according to the position morphological data corresponding to each first cell; and to screen out the first cells whose cell area is within a preset area range from the multiple first cells to obtain multiple second cells.

[0175] In some embodiments, the first clustering module 820 is further used by the computer device to cluster the plurality of second cells according to the mass spectrometry analysis results corresponding to the respective second cells to obtain a plurality of cell subpopulations.

[0176] Optionally, the mass spectrometry analysis data also includes imaging mass spectrometry flow cytometry IMC image data corresponding to the target sample.

[0177] In some embodiments, the sample analysis device 800 may further include an image generation module.

[0178] The image generation module is used for the computer device to generate a target IMC image according to the IMC image data; the image position of each cell contained in the target IMC image is marked.

[0179] Optionally, the first clustering module 820 is also used by the computer device to cluster multiple cells according to the image position of each cell in the target IMC image and the mass spectrometry analysis result corresponding to each cell to obtain multiple cell subpopulations.

[0180] In some embodiments, the image generation module is also used for a computer device to calculate the average pixel intensity corresponding to each mass spectrometry channel and the average overflow pixel intensity between any two mass spectrometry channels based on the pixel intensity corresponding to each pixel point contained in the IMC image data in multiple mass spectrometry channels; the mass spectrometry channel is a channel for detecting marker proteins on the surface or inside of a cell; and, based on the average pixel intensity corresponding to each mass spectrometry channel and the average overflow pixel intensity between any two mass spectrometry channels, construct an overflow matrix corresponding to the IMC image data; and, based on the overflow matrix, correct the pixel intensity corresponding to each pixel point contained in the IMC image data in multiple mass spectrometry channels to generate a target IMC image.

[0181] Optionally, the mass spectrometry analysis result includes the expression level of a marker protein corresponding to at least one marker protein.

[0182] In some embodiments, the sample analysis device 800 may further include a display module and an interaction module.

[0183] A display module is used for a computer device to calculate the first expression level of various marker proteins in the first target cell population according to the marker protein expression levels corresponding to each cell contained in the first target cell population and each marker protein, and determine the spatial distribution of various marker proteins in the first target cell population; the first target cell population is any target cell population; and, display the cell type, first expression level and corresponding spatial distribution corresponding to each target cell population.

[0184] Optionally, the mass spectrometry analysis data also includes slice image data corresponding to the target sample.

[0185] The interaction module is used for the computer device to generate a first slice image according to the slice image data; and to color-label each cell in the first slice image according to the data type corresponding to each cell contained in the first slice image to obtain a second slice image; and to calculate the interaction probability between any two cell types according to the second slice image and the features corresponding to each cell type; and to determine two cell types whose interaction probability is higher than a probability threshold.

[0186] In an embodiment of the present application, a computer device first performs preliminary clustering of multiple cells according to the mass spectrometry analysis results corresponding to each cell to form multiple cell subpopulations, thereby effectively reducing the amount of data for subsequent analysis and improving processing efficiency; and, based on the average expression level of marker proteins of cells in each cell subpopulation, multiple cell subpopulations are clustered, and the cell subpopulations are further merged into a target cell population that is more in line with the actual classification. Through a hierarchical clustering strategy, both individual differences between cells and common characteristics between groups are taken into account, thereby improving the accuracy and reliability of the overall classification, thereby improving the accuracy of the sample analysis.

[0187] Fig. 9 FIG. 1 is a block diagram of a computer device in one embodiment. Fig. 9 As shown, the computer device 900 may include one or more of the following components: a processor 910, and a memory 920 coupled to the processor 910, wherein the memory 920 may store one or more computer programs, and the one or more computer programs may be configured to implement the methods described in the above embodiments when executed by one or more processors 910.

[0188] The processor 910 may include one or more processing cores. The processor 910 uses various interfaces and lines to connect various parts of the entire computer device 900, and executes various functions and processes data of the computer device 900 by running or executing instructions, programs, code sets or instruction sets stored in the memory 920, and calling data stored in the memory 920. Optionally, the processor 910 can be implemented in at least one hardware form of digital signal processing (DSP), field-programmable gate array (FPGA), and programmable logic array (PLA). The processor 910 can integrate one or a combination of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. Among them, the CPU mainly processes the operating system, user interface, and application programs; the GPU is responsible for rendering and drawing display content; and the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 910, but may be implemented separately through a communication chip.

[0189] The memory 920 may include a random access memory (RAM) or a read-only memory (ROM). The memory 920 may be used to store instructions, programs, codes, code sets or instruction sets. The memory 920 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for implementing at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc. The data storage area may also store data created by the computer device 900 during use, etc.

[0190] It is understandable that the computer device 900 may include more or fewer structural elements than those in the above structural block diagram, for example, including a power supply, input buttons, a camera, a speaker, a screen, an RF (Radio Frequency) circuit, a Wi-Fi (Wireless Fidelity) module, a Bluetooth module, a sensor, etc., and is not limited here.

[0191] An embodiment of the present application discloses a computer-readable storage medium storing a computer program, wherein the computer program implements the methods described in the above embodiments when executed by a processor.

[0192] The embodiments of the present application disclose a computer program product, including a computer program, and the computer program can be executed by a processor to implement the methods described in the above embodiments.

[0193] A person skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. The storage medium can be a disk, an optical disk, a read-only memory (ROM), etc.

[0194] As used herein, any reference to memory, storage, database, or other medium may include nonvolatile and / or volatile memory. Suitable nonvolatile memory may include ROM, Programmable ROM (PROM), Erasable PROM (EPROM), Electrically Erasable PROM (EEPROM), or Flash memory. Volatile memory may include random access memory (RAM), which is used as an external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), memory bus direct RAM (RDRAM), and direct memory bus dynamic RAM (DRDRAM).

[0195] It should be understood that the "one embodiment" or "an embodiment" mentioned throughout the specification means that the specific features, structures or characteristics related to the embodiment are included in at least one embodiment of the present application. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. In addition, these specific features, structures or characteristics can be combined in one or more embodiments in any suitable manner. Those skilled in the art should also be aware that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required for the present application.

[0196] In the various embodiments of the present application, it should be understood that the size of the serial numbers of the above-mentioned processes does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0197] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0198] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0199] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0200] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-accessible memory. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product, which is stored in a memory and includes several requests for a computer device (which can be a personal computer, a server or a network device, etc., specifically a processor in a computer device) to perform some or all of the steps of the above-mentioned methods of various embodiments of the present application.

[0201] The above is a detailed introduction to a sample analysis method, device, computer equipment, and computer-readable storage medium disclosed in the embodiments of the present application. Specific examples are used herein to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method and core idea of ​​the present application. At the same time, for those of ordinary skill in the art, according to the idea of ​​the present application, there will be changes in the specific implementation methods and application scopes. In summary, the content of this specification should not be understood as limiting the present application.

Claims

1. A sample analysis method, characterized in that: Applied to a computer device, the method comprises: The computer device acquires mass spectrometry data corresponding to the target sample, wherein the mass spectrometry data is obtained by performing mass spectrometry processing on the target sample; the mass spectrometry data includes mass spectrometry results corresponding to a plurality of cells in the target sample, respectively, and the mass spectrometry results include the expression amount of the marker protein; The computer device clusters the multiple cells according to the mass spectrometry analysis results corresponding to each of the cells to obtain multiple cell subpopulations; The computer device calculates the average expression level of the marker protein corresponding to the first cell subpopulation according to the expression level of the marker protein corresponding to each cell included in the first cell subpopulation; the first cell subpopulation is any of the cell subpopulations; The computer device clusters the multiple cell subpopulations according to the average expression levels of the marker proteins respectively corresponding to the multiple cell subpopulations, so as to obtain at least one target cell population and the cell type corresponding to each of the target cell populations.

2. The method according to claim 1, characterized in that The mass spectrometry analysis data includes first expression count data, the first expression count data includes a first value corresponding to each of the cells, the first value is used to characterize the expression level of the marker protein corresponding to the cell; after the computer device obtains the mass spectrometry analysis data corresponding to the target sample, the method further includes: The computer device converts each first value included in the first expression count data to obtain second expression count data that conforms to a normal distribution, wherein the second expression count data includes a second value corresponding to each of the cells; The computer device selects cells whose second values ​​are within a target standard deviation range from a plurality of cells in the target sample according to the second values ​​corresponding to the respective cells, to obtain a plurality of first cells; The computer device clusters the multiple cells according to the mass spectrometry analysis results corresponding to each of the cells to obtain multiple cell subpopulations, including: The computer device clusters the multiple first cells according to the mass spectrometry analysis results corresponding to each of the first cells to obtain multiple cell subpopulations.

3. The method according to claim 2, characterized in that The mass spectrometry analysis data includes position morphology data corresponding to a plurality of cells in the target sample, and the method further includes: The computer device calculates the cell area corresponding to each of the first cells according to the position morphology data corresponding to each of the first cells; The computer device selects first cells whose cell areas are within a preset area range from the plurality of first cells to obtain a plurality of second cells; The computer device clusters the plurality of first cells according to the mass spectrometry analysis results corresponding to each of the first cells to obtain a plurality of cell subpopulations, including: The computer device clusters the plurality of second cells according to the mass spectrometry analysis results corresponding to each of the second cells to obtain a plurality of cell subpopulations.

4. The method according to claim 1, characterized in that: The mass spectrometry analysis data also includes imaging mass spectrometry flow cytometry IMC image data corresponding to the target sample; after the computer device acquires the mass spectrometry analysis data corresponding to the target sample, the method further includes: The computer device generates a target IMC image according to the IMC image data; the image position of each cell included in the target IMC image is marked; The computer device clusters the multiple cells according to the mass spectrometry analysis results corresponding to each of the cells to obtain multiple cell subpopulations, including: The computer device clusters the multiple cells according to the image position of each cell in the target IMC image and the mass spectrometry analysis result corresponding to each cell to obtain multiple cell subpopulations.

5. The method according to claim 4, characterized in that The computer device generates a target IMC image according to the IMC image data, including: The computer device calculates the average pixel intensity corresponding to each mass spectrometry channel and the average overflow pixel intensity between any two mass spectrometry channels according to the pixel intensities corresponding to each pixel point in the IMC image data in multiple mass spectrometry channels; the mass spectrometry channels are channels for detecting marker proteins on the surface or inside of cells; The computer device constructs an overflow matrix corresponding to the IMC image data according to the average pixel intensity corresponding to each of the mass spectrometry channels and the average overflow pixel intensity between any two mass spectrometry channels; The computer device corrects the pixel intensities corresponding to the multiple mass spectrometry channels of each pixel point included in the IMC image data according to the overflow matrix to generate a target IMC image.

6. The method according to claim 1, characterized in that The mass spectrometry analysis result includes the expression amount of the marker protein corresponding to at least one marker protein; after obtaining at least one target cell population and the cell type corresponding to each of the target cell populations, the method further includes: The computer device calculates the first expression amount of each marker protein in the first target cell population according to the marker protein expression amount corresponding to each cell contained in the first target cell population and each marker protein, and determines the spatial distribution of each marker protein in the first target cell population; the first target cell population is any of the target cell populations; The computer device displays the cell type, the first expression level and the corresponding spatial distribution corresponding to each of the target cell populations.

7. The method according to claim 1, characterized in that The mass spectrometry analysis data further includes slice image data corresponding to the target sample. After obtaining at least one target cell population and a cell type corresponding to each of the target cell populations, the method further includes: The computer device generates a first slice image according to the slice image data; The computer device performs color marking on each cell in the first slice image according to the data type corresponding to each cell in the first slice image to obtain a second slice image; The computer device calculates the probability of interaction between any two cell types according to the second slice image and the features corresponding to each of the cell types; The computer device determines two cell types that have a probability of interacting above a probability threshold.

8. A sample analysis device, characterized in that: Applied to computer equipment, the device comprises: A data acquisition module, used for the computer device to acquire mass spectrometry data corresponding to the target sample, wherein the mass spectrometry data is obtained by performing mass spectrometry processing on the target sample; the mass spectrometry data includes mass spectrometry results corresponding to a plurality of cells in the target sample, respectively, and the mass spectrometry results include the expression amount of the marker protein; A first clustering module, used for the computer device to cluster the multiple cells according to the mass spectrometry analysis results corresponding to each of the cells to obtain multiple cell subpopulations; an expression level calculation module, used for the computer device to calculate the average expression level of the marker protein corresponding to the first cell subpopulation according to the expression level of the marker protein corresponding to each cell included in the first cell subpopulation; the first cell subpopulation is any of the cell subpopulations; The second clustering module is used for the computer device to cluster the multiple cell subpopulations according to the average expression levels of the marker proteins corresponding to the multiple cell subpopulations, so as to obtain at least one target cell population and the cell type corresponding to each of the target cell populations.

9. A computer device, characterized in that: The method comprises a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the processor implements the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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