Cell interaction analysis method and device, electronic device and storage medium

Abstract

The embodiments of the present application provide a cell interaction analysis method and device, an electronic device and a storage medium, which belong to the field of cell communication technology. The cell interaction analysis method includes: obtaining the spatial transcriptome data of the target slice, and clustering the cells in the target slice according to the spatial transcriptome data to obtain multiple cell groups; calculating the proximity values ​​between different cell groups, and determining the target cell group pairs in the multiple cell groups based on the proximity values; determining the target ligand receptor pairs in the target cell group according to the gene expression levels of different ligand receptor pairs in the target cell group pairs; performing cell interaction analysis based on the target ligand receptor pairs and the target cell group pairs to obtain interaction analysis results. The embodiments of the present application can perform cell interaction analysis at the single-cell level based on spatial transcriptome data.

Description

Technical Field

[0001] The present disclosure relates to the field of cell communication technology, and in particular to a cell interaction analysis method and device, an electronic device and a storage medium. Background Art

[0002] At present, cell communication refers to the process in which cells coordinate with other cells in the surrounding environment or system. In the related technology, cell interaction analysis methods include the following two categories: the first category is the method of migrating single-cell analysis to spatial transcriptome data, such as CellphoneDB (CellphoneDB is a cell communication analysis tool) and cellchat (cellchat is an R package that can analyze intercellular communication networks from single-cell RNA sequencing data). The second category is methods designed for spatial transcriptome data, such as stLearn (stLearn is a method that can reconstruct the evolution of cell types in tissues in space and identify areas with cell-to-cell related effects). Among them, the first type of method does not make good use of the position information in the spatial transcriptome data, and the second type of method uses the position information in the spatial transcriptome data, but lacks single-cell level cell interaction analysis on the spatial transcriptome. Therefore, there is currently a lack of a method that can use spatial transcriptome data for single-cell interaction analysis. Summary of the invention

[0003] The main purpose of the embodiments of the present application is to propose a cell interaction analysis method and device, electronic device and storage medium, aiming to be able to perform cell interaction analysis at the single-cell level based on spatial transcriptome data.

[0004] To achieve the above object, a first aspect of an embodiment of the present application provides a cell interaction analysis method, the method comprising:

[0005] Acquiring spatial transcriptome data of a target slice, and clustering cells in the target slice according to the spatial transcriptome data to obtain a plurality of cell populations;

[0006] Calculating proximity values ​​between different cell populations, and determining a target cell population pair among the plurality of cell populations based on the proximity values;

[0007] Determining a target ligand receptor pair in the target cell population according to the gene expression levels of different ligand receptor pairs in the target cell population;

[0008] Cell interaction analysis is performed based on the target ligand-receptor pair and the target cell population pair to obtain interaction analysis results.

[0009] In some embodiments, calculating the proximity values ​​between different cell populations, and determining a target cell population pair among the plurality of cell populations based on the proximity values, comprises:

[0010] Determine the position information of each cell in each of the cell groups according to the spatial transcriptome data, and calculate the neighboring cells of each cell in each cell group according to the position information;

[0011] The proximity values ​​between different cell groups are calculated based on the number of each cell in each cell group and the corresponding neighboring cells;

[0012] Determine the cell population pair whose proximity value is greater than a preset proximity threshold as the target cell population pair.

[0013] In some embodiments, the step of calculating proximity values ​​between different cell populations based on the number of each cell in each cell population and the corresponding neighboring cells includes:

[0014] Determine the number of neighboring cells of each cell in each cell group that correspond to neighboring cells of different cell groups;

[0015] The numbers of adjacent cells of multiple cells belonging to the same cell group are aggregated to obtain the proximity values ​​between different cell groups.

[0016] In some embodiments, calculating the neighboring cells of each cell in each cell population according to the position information comprises:

[0017] Obtain the morphological characteristics of each cell in each cell population;

[0018] The position information and the morphological features are input into a preset prediction model to obtain neighboring cells of each cell in each cell group.

[0019] In some embodiments, determining the target ligand receptor pair in the target cell population according to the gene expression levels of different ligand receptor pairs in the target cell population comprises:

[0020] Screening a plurality of candidate ligand receptor pairs from a preset ligand receptor database according to the target cell population pairs;

[0021] Determining a first interaction parameter according to the gene expression level of each candidate ligand-receptor pair in the target cell population;

[0022] The target ligand receptor pair is determined from the candidate ligand receptor pairs corresponding to a plurality of the first interaction parameters whose values ​​are less than a preset interaction threshold value according to a preset ratio.

[0023] In some embodiments, the target ligand-receptor pair includes a target ligand gene and a target receptor gene, and the target cell population includes a signal-sending population and a signal-receiving population;

[0024] The cell interaction analysis based on the target ligand receptor pair and the target cell population pair is performed to obtain the interaction analysis result, including:

[0025] The true expression mean is calculated according to the gene expression amount of the target ligand gene in the signal sending group and the gene expression amount of the target receptor gene in the signal receiving group;

[0026] Randomly dividing the cells in the signal sending group and the cells in the signal receiving group to obtain a first cell group and a second cell group;

[0027] Calculating a hypothetical expression mean according to the gene expression level of the target ligand gene in the first cell population and the gene expression level of the target receptor gene in the second cell population;

[0028] The interaction analysis result is calculated based on the true expression mean and the hypothesized expression mean.

[0029] In some embodiments, the calculating the true expression mean according to the gene expression amount of the target ligand gene in the signal sending group and the gene expression amount of the target receptor gene in the signal receiving group comprises:

[0030] Calculating the expression similarity value of the target ligand gene and the target receptor gene;

[0031] The expression weight is calculated based on the expression similarity value, and the true expression mean is calculated based on the expression weight, the gene expression level of the target ligand gene in the signal sending group, and the gene expression level of the target receptor gene in the signal receiving group.

[0032] In some embodiments, the method further comprises:

[0033] Dividing the target cell group pair into a neighbor group and a distant group according to the position information of each cell in the target cell group pair;

[0034] A pleiotropic analysis is performed based on the gene expression level of the target ligand receptor in the distant group to obtain a pleiotropic result.

[0035] In some embodiments, the step of performing pleiotropic analysis on the gene expression level of the target ligand receptor in the distant group to obtain pleiotropic results includes:

[0036] Randomly pairing the cells in the distant group to obtain cell pairs;

[0037] The pleiotropic results are calculated based on the gene expression levels of the target ligand-receptor pair in the cell pair.

[0038] In some embodiments, the pleiotropic results are calculated based on the gene expression level of the target ligand receptor pair in the cell pair, including:

[0039] Calculating a second interaction parameter according to the gene expression level of the target ligand-receptor pair in the cell pair;

[0040] A plurality of the second interaction parameters are obtained according to multiple random pairings, and the second interaction parameter with the largest value is logarithmically processed to obtain the pleiotropic effect result.

[0041] In some embodiments, the method further comprises:

[0042] Respectively obtaining group annotations of the signal receiving group and the signal sending group;

[0043] Visualization processing is performed based on the group annotation corresponding to the signal receiving group, the group annotation corresponding to the signal sending group, the interaction analysis results, and the pleiotropic results to obtain a visualization information graph.

[0044] To achieve the above-mentioned purpose, the second aspect of the embodiment of the present application proposes a cell interaction analysis device method, the device comprising:

[0045] A cell clustering unit, used to obtain spatial transcriptome data of a target slice, and cluster cells in the target slice according to the spatial transcriptome data to obtain a plurality of cell groups;

[0046] A cell group determination unit, used to calculate proximity values ​​between different cell groups, and determine a target cell group pair among the plurality of cell groups based on the proximity values;

[0047] a ligand receptor determination unit, configured to determine a target ligand receptor pair in the target cell population according to the gene expression levels of different ligand receptor pairs in the target cell population;

[0048] The cell interaction analysis unit is used to perform cell interaction analysis based on the target ligand-receptor pair and the target cell population pair to obtain an interaction analysis result.

[0049] To achieve the above-mentioned purpose, the third aspect of an embodiment of the present application proposes an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the cell interaction analysis method described in the first aspect is implemented.

[0050] To achieve the above-mentioned purpose, the fourth aspect of the embodiments of the present application proposes a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the cell interaction analysis method described in the first aspect.

[0051] To achieve the above-mentioned purpose, the fifth aspect of an embodiment of the present application proposes a computer program product, which includes a computer program, and the computer program is read and executed by a processor of a computer device, so that the computer device executes the cell interaction analysis method described in the first aspect.

[0052] The cell interaction analysis method and device, electronic device and storage medium proposed in the embodiments of the present application can cluster the cells in the target slice into multiple cell groups through the spatial transcriptome data of the target slice, that is, the cell group is obtained by clustering the single cells determined by the circle cell results of the target slice based on the spatial transcriptome data. The target cell group pairs are determined by the proximity values ​​between different cell groups, and the target ligand receptor pairs are determined according to the gene expression level, so that cell interaction analysis can be performed based on the target ligand receptor pairs and the target cell group pairs. It can be seen that the embodiments of the present application realize the interaction analysis at the single cell level using spatial transcriptome data. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The accompanying drawings are used to provide further understanding of the technical solution of the present disclosure and constitute a part of the specification. Together with the embodiments of the present disclosure, they are used to explain the technical solution of the present disclosure and do not constitute a limitation on the technical solution of the present disclosure.

[0054] Figure 1 is a flow chart of the cell interaction analysis method provided in the embodiment of the present application;

[0055] Figure 2 yes Figure 1 Flow chart of step S102 in FIG.

[0056] Figure 3 yes Figure 2 Flow chart of step S201 in FIG.

[0057] Figure 4 yes Figure 2 Flow chart of step S202 in FIG.

[0058] Figure 5 is a schematic diagram of a neighbor matrix provided in an embodiment of the present application;

[0059] Figure 6 is a schematic diagram of another embodiment of the neighbor matrix provided in an embodiment of the present application;

[0060] Figure 7 is a schematic diagram of a spatial neighbor matrix provided in an embodiment of the present application;

[0061] Figure 8 yes Figure 1 Flow chart of step S103 in FIG.

[0062] Fig. 9 yes Figure 1 Flow chart of step S104 in FIG.

[0063] Fig.10 yes Fig. 9 Flow chart of step S901 in FIG.

[0064] Fig.11 is a flow chart of another embodiment of the cell interaction analysis method provided in an embodiment of the present application;

[0065] Fig.12 It is a schematic diagram of a near-neighbor group and a far-away group provided in an embodiment of the present application;

[0066] Fig.13 yes Fig.11 Flowchart of step S1102 in FIG.

[0067] Fig.14 yes Fig.13 Flowchart of step S1302 in FIG.

[0068] Fig.15 is a flow chart of another embodiment of the cell interaction analysis method provided in an embodiment of the present application;

[0069] Fig.16 is a schematic diagram of a visual information graph provided in an embodiment of the present application;

[0070] Fig.17 is a schematic diagram of a cell interaction analysis device provided in an embodiment of the present application;

[0071] Fig.18 It is a schematic diagram of the hardware structure of the electronic device provided in the embodiment of the present application. DETAILED DESCRIPTION

[0072] In order to make the purpose, technical solution and advantages of the present disclosure more clear, the present disclosure is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present disclosure and are not used to limit the present disclosure.

[0073] Before further describing the embodiments of the present disclosure in detail, the nouns and terms involved in the embodiments of the present disclosure are described. The nouns and terms involved in the embodiments of the present disclosure are subject to the following interpretations:

[0074] Spatial Transcriptomics (ST) sequencing technology: a technology that analyzes RNA from a spatial level, used to analyze all RNA in a single tissue section. There are four capture areas on each slide used for library construction by 10x Genomics spatial transcriptomics, and the size of each capture area is 6.5x6.5mm. Each capture area contains 5,000 barcoded spots, each with a diameter of 55μm and a center-to-center distance of 100μm. Each spot includes multiple capture probes that can bind to RNA, and each probe carries unique spatial barcodes to mark the spatial position of the captured RNA. Through on-machine sequencing, the sequence of each RNA transcript can be mapped back to the original position in the tissue section, thereby providing data support for downstream tasks. It can be seen that ST sequencing technology is a technology that can simultaneously obtain RNA expression and RNA spatial location information in a single experiment.

[0075] Spatial Transcriptomics with Error-Corrected Barcoding and Sequencing (Stereo-seq): is a method that combines spatial transcriptomics and single-cell RNA sequencing technology. It can analyze gene expression in tissues with spatial resolution, while improving the accuracy and sensitivity of gene expression through error-corrected barcoding and sequencing methods. Stereo-seq includes the design of DNA nanoball (DNB) patterned array chips, in situ sequencing to determine the spatial coordinates of unique barcode oligonucleotides, point connection of UMI-polyT oligonucleotides, in situ capture of RNA from tissues, cDNA amplification, library construction sequencing and data analysis. Among them, DNB sequencing is a patterned array based on in situ sequencing, which can serve as the basis for spatial resolution transcription technology with high resolution and large field of view. Compared with HDST (HDST is a high-resolution spatial transcriptome technology that carves a large number of 2.05μm holes on the surface of the slide and distributes silica magnetic beads in the holes. The magnetic beads are used to capture the mRNA of the cells in the corresponding position for reverse transcription, library construction and transcriptome sequencing), Slide-seqV2 (Slide-seqV2 is a high-sensitivity near-cellular resolution spatial transcriptome analysis that can achieve better RNA capture efficiency through steps such as magnetic bead synthesis and array indexing), Visium (Visium is a spatial transcriptome sequencing technology that integrates gene expression with immunohistochemical images of tissue sections to locate the gene expression levels of different cells in the tissue to the original spatial position of the tissue), DBiT-seq (DBiT-seq is a spatial multi-omics sequencing technology based on microfluidic barcode labeling), etc., Stereo-seq has a high resolution of 100μm. 2 With more spots, smaller spot sizes and smaller center-to-center distances, Stereo-seq has higher resolution.

[0076] Cell communication: refers to the process by which cells coordinate with their surrounding environment and other cells in the system. Cell communication methods include cell gap junctions, membrane surface molecular contact communication, and chemical communication. Among them, in the cell gap junction method, there is a special structure composed of proteins between two adjacent cells-a linker. The two ends of the linker are respectively embedded in two adjacent cells to form a hydrophilic channel. This channel allows the two cells to freely exchange water-soluble molecules so that adjacent cells can share small molecules, thereby promoting the coordinated response of adjacent cells to external signals. In the membrane surface molecular contact communication, each cell has multiple molecules distributed on the outer surface of the membrane. These surface molecules, as cell antennae, can specifically recognize and interact with the molecules on the surface of the adjacent cell membrane to achieve functional coordination. In the chemical communication method, cells can secrete some chemicals outside the cell. These chemicals can act on other cells as chemical signals and regulate the functions of other cells. According to the distance range that chemical signal molecules can act on, chemical communication can be divided into three forms: endocrine, paracrine, and autocrine.

[0077] At present, cell interactions (also known as cell communication, hereinafter referred to as cell interaction) are used to regulate the normal generation of organisms. The development, aging, immunity, anti-tumor and other functions of organisms all involve cell communication.

[0078] In the related art, cell interaction analysis methods include the following three categories:

[0079] The first category is the method of migrating from single-cell analysis to spatial transcriptome sequencing, such as the methods used in single-cell omics research using CellphoneDB, CellChat, etc. This type of method is simple and efficient, but does not utilize the position information obtained by ST processing.

[0080] The second type uses a single spot as a unit, using location information and spatial transcriptome data processed by ST to study cell interactions, such as stLearn. However, this type of method only uses the spatial location information of the spot, and still lacks single-cell level cell interaction analysis on the spatial transcriptome.

[0081] The third type is to infer the cell interaction effect of the spatial transcriptome without considering the cell type annotation of spots or cells, and completely based on the strength of RNA signals in space, such as COMMOT (COMMOT is a method for spatial transcriptome cell-to-cell communication analysis using optimal transport). However, this type of method does not consider cell annotation during the calculation process, resulting in a lack of interpretability of the obtained cell interaction results in biological interpretation.

[0082] Based on this, the embodiments of the present application provide a cell interaction analysis method and device, an electronic device and a storage medium, so as to be able to perform interaction analysis at the single-cell level using spatial transcriptome data.

[0083] The cell interaction analysis method provided in the examples of the present application is described below.

[0084] Reference Figure 1 The cell interaction analysis method provided in the embodiment of the present application includes but is not limited to steps S101 to S104.

[0085] Step S101, obtaining spatial transcriptome data of a target slice, and clustering cells in the target slice according to the spatial transcriptome data to obtain multiple cell groups;

[0086] Step S102, calculating proximity values ​​between different cell populations, and determining a target cell population pair among multiple cell populations based on the proximity values;

[0087] Step S103, determining a target ligand-receptor pair in the target cell population according to the gene expression levels of different ligand-receptor pairs in the target cell population;

[0088] Step S104, performing cell interaction analysis based on the target ligand-receptor pair and the target cell population pair to obtain the interaction analysis result.

[0089] Steps S101 to S104 shown in the embodiment of the present application, the cells in the target slice can be clustered into multiple cell groups through the spatial transcriptome data of the target slice, that is, the cell group is obtained by clustering the single cell determined by the circle cell result of the target slice based on the spatial transcriptome data. The target cell group pairs are determined by the proximity values ​​between different cell groups, and the target ligand receptor pairs are determined according to the gene expression amount, so that cell interaction analysis can be performed based on the target ligand receptor pairs and the target cell group pairs. It can be seen from this that the embodiment of the present application realizes the interaction analysis at the single cell level using spatial transcriptome data.

[0090] In step S101 of some embodiments, the target slice refers to a tissue slice to be subjected to cell interaction analysis. The target slice is a biological slice obtained in compliance with relevant legal provisions, and the biological slice includes a plant slice and an animal slice. The target slice is subjected to ST processing to obtain spatial transcriptome data, or spatial transcriptome data is obtained according to Stereo-seq. A spatial expression matrix can be obtained based on the spatial transcriptome data, and the spatial expression matrix includes the gene expression amount captured by the spot point corresponding to each cell in the target slice, and the spatial position information of each cell. According to the gene expression amount, a plurality of cells are clustered to treat a plurality of cells with similar gene expression amounts as a cell group, so that a plurality of cell groups can be obtained. It is understandable that the spatial position information of each cell in the target slice can be determined completely based on the spatial transcriptome data, or the position information can be determined based on the spatial transcriptome data and the staining image of the target slice, and this embodiment of the present application is not specifically limited to this.

[0091] In step S102 of some embodiments, the proximity value is used to represent the neighbor relationship between different cell groups, and the proximity value can be calculated based on the position information. Two cell groups with close neighbor relationship are determined based on the proximity value, and these two cell groups are used as the target cell group pair.

[0092] Reference Figure 2 In some embodiments, step S102 includes but is not limited to steps S201 to S203.

[0093] Step S201, determining the position information of each cell in each cell group according to the spatial transcriptome data, and calculating the neighboring cells of each cell in each cell group according to the position information;

[0094] Step S202, calculating proximity values ​​between different cell groups according to the number of each cell in each cell group and the number of corresponding neighboring cells;

[0095] Step S203, determining that the cell population pair whose proximity value is greater than a preset proximity threshold is the target cell population pair.

[0096] In step S201 of some embodiments, the cell region of each cell can be determined according to the spatial transcriptome data, and the location information of the cell is obtained according to the position of the spot corresponding to the cell region in the capture area. The neighbor cells of each cell in each cell group are calculated according to the location information. For example, for cell A1 in cell group A, the neighbor cells of cell A1 refer to the N cells closest to cell A1. Wherein, N is the preset number of neighbors, and the distance can be calculated based on the location information by methods such as Euclidean distance, Manhattan distance, and Chebyshev distance. Taking N=10 as an example, the distance between each cell and cell A1 is determined in space according to the location information, and the 10 cells closest to cell A1 are taken as neighbor cells. It is understandable that the neighbor cells may include other cells in cell group A except cell A1, as well as cells in other cell groups. It is understandable that the preset number of neighbors can be adaptively set according to actual needs, and this embodiment of the present application is not specifically limited to this.

[0097] Reference Figure 3 In some embodiments, the step S201 of "calculating neighboring cells of each cell in each cell population based on location information" includes but is not limited to steps S301 to S302.

[0098] Step S301, obtaining the morphological characteristics of each cell in each cell group;

[0099] Step S302, inputting the position information and morphological features into a preset prediction model to obtain neighboring cells of each cell in each cell group.

[0100] In step S301 of some embodiments, the morphological features of the cells may be used to indicate whether the morphology of the cells is complete, etc., and the morphological features may be acquired based on an image of a target slice.

[0101] In step S302 of some embodiments, the prediction model is a pre-trained deep learning model that has the ability to determine whether a cell is a neighboring cell. For example, when it is necessary to determine whether cell B1 is a neighboring cell of cell A1, the distance between cell B1 and cell A1 can be calculated based on the location information of cell B1 and the location information of cell A1, and the distance and the morphological characteristics of cell B1 are used as input data of the prediction model. According to the prediction model, it is determined whether cell B1 is a neighboring cell of cell A1, or the probability of cell B1 being a neighboring cell of cell A1 is determined. The above operation is performed on each cell in different cell groups to obtain multiple probability values. When the preset number of neighbors N=10, the 10 cells with the highest probability values ​​are selected as neighboring cells of cell A1. It can be understood that in addition to location information and morphological characteristics, other features related to the calculation of proximity values ​​can also be selected as input data of the prediction model, and this embodiment of the present application is not specifically limited to this.

[0102] The advantage of step S301 to step S302 is that, in addition to determining neighbor cells based on distance, neighbor cells can also be determined based on distance and morphological characteristics, thereby improving the accuracy of determining neighbor cells, and further improving the accuracy of subsequent determination of proximity values ​​based on neighbor cells.

[0103] In step S202 of some embodiments, the proximity value between the cell group and other cell groups can be calculated based on the number of different neighboring cells corresponding to each cell in the cell group. For example, the target slice includes cell group A, cell group B, cell group C, and cell group D. The proximity value between cell group A and cell group B, the proximity value between cell group A and cell group C, and the proximity value between cell group A and cell group D can be calculated based on the number of neighboring cells corresponding to each cell in cell group A. Repeat the above calculation operation for cell group B, cell group C, and cell group D to obtain the proximity value between any two cell groups.

[0104] Reference Figure 4 In some embodiments, step S202 includes but is not limited to steps S401 to S402.

[0105] Step S401, determining the number of neighboring cells of each cell in each cell group corresponding to neighboring cells of different cell groups;

[0106] Step S402, aggregating the numbers of adjacent cells of multiple cells belonging to the same cell group to obtain proximity values ​​between different cell groups.

[0107] In step S401 of some embodiments, the cell group corresponding to the neighboring cells of each cell in each cell group is determined, and the number of neighboring cells corresponding to the same cell group is determined, thereby obtaining the number of neighboring cells. For example, assuming that the neighboring cells corresponding to cell A1 in cell group A include cells B1, B2, C1, D1, and D3, wherein cells B1 and B2 belong to cell group B, cell C1 belongs to cell group C, and cells D1 and D3 belong to cell group D, then the number of neighboring cells corresponding to cell group B in cell A1 is 2, the number of neighboring cells corresponding to cell group C in cell A1 is 1, and the number of neighboring cells corresponding to cell group D in cell A1 is 2. It can be understood that according to the above operations, a neighbor matrix for representing the neighbor relationship between all cells in a cell group and all cell groups can be constructed. Taking cell group A as an example, assuming that cell group A includes cells A1, A2, and A3, refer to Figure 5 , in the neighbor matrix a i,jrepresents the number of adjacent cells of the ith cell in cell group A and the jth cell group. According to the above description, the a corresponding to cell A1 and cell group B can be determined. i,j The value of is 2, corresponding to the a of cell A1 and cell group C i,j The value of is 1, corresponding to the a of cell A1 and cell group D i,j The value of is 2. If cell A1 has no neighboring cells corresponding to cell group A, then the a corresponding to cell A1 and cell group A i,j The value of is 0. It is understandable that in order to facilitate subsequent data analysis and improve the calculation speed, the number of adjacent cells can also be normalized and other data processing, such as a corresponding to cell A1 and cell group B after data processing. i,j The value of is 0.2, corresponding to the a of cell A1 and cell group C i,j The value of is 0.1, corresponding to the a of cell A1 and cell group D i,j The value of is 0.2. It can be understood that the same operation as cell A1 is performed on other cells in cell group A, so that a neighbor matrix representing the neighbor relationship between all cells in cell group A and all cell groups can be constructed.

[0108] In step S402 of some embodiments, aggregation refers to accumulating the number of neighboring cells corresponding to multiple cells belonging to the same cell group, so as to obtain the neighbor relationship between a cell group and all cell groups, and then construct a neighbor relationship array. i,j After the assignment, we can get Figure 6 The neighbor matrix shown. Aggregate the three neighboring cell data of cells A1, A2, and A3 corresponding to cell cluster A, aggregate the three neighboring cell data of cells A1, A2, and A3 corresponding to cell cluster B, aggregate the three neighboring cell data of cells A1, A2, and A3 corresponding to cell cluster C, and aggregate the three neighboring cell data of cells A1, A2, and A3 corresponding to cell cluster D to obtain the neighbor relationship array {1,5,6,3} of cell cluster A. Perform the above operations on all cell clusters to obtain multiple neighbor relationship arrays. A spatial neighbor matrix can be constructed based on multiple neighbor relationship arrays. For example, the following can be constructed: Figure 7 The spatial neighbor matrix shown in the figure includes multiple C i,j , C i,j represents the proximity value between the ith cell group and the jth cell group. It is understandable that in order to facilitate subsequent data analysis and improve the calculation speed, the proximity value can also be normalized to make 0 < C i,j<1. For example, after data processing, the adjacent value C corresponding to cell cluster A and cell cluster A i,j is 0.1, and after data processing, the proximity value C corresponding to cell cluster A and cell cluster B i,j is 0.5, and after data processing, the proximity value C corresponding to cell cluster A and cell cluster C i,j is 0.6. After data processing, the proximity value C corresponding to cell cluster A and cell cluster D i,j is 0.3.

[0109] In step S203 of some embodiments, when the value of the proximity value is larger, it indicates that the number of cells in the corresponding two cell populations is larger, and at this time, it can be considered that the neighbor relationship between the two cell populations is closer. Therefore, a preset proximity threshold can be set, and the target proximity value can be screened out from multiple proximity values ​​whose values ​​are greater than the preset proximity threshold, and the two cell populations corresponding to the target proximity value are used as the target cell population pair. It is understandable that the specific value of the preset proximity threshold can be adaptively set according to actual needs, and this embodiment of the present application does not specifically limit this.

[0110] The benefit of step S201 to step S203 is that since it can be considered that the cell cluster pairs with larger proximity values ​​have greater value in cell interaction analysis on the spatial transcriptome, determining the target cell cluster pairs from the cell cluster pairs with proximity values ​​greater than the preset proximity threshold can improve the effectiveness of subsequent cell interaction analysis.

[0111] In step S103 of some embodiments, a ligand receptor pair refers to a gene pair that plays a role in cell interaction in a cell group pair. The ligand receptor pair includes a ligand gene and a receptor gene, and the ligand gene and the receptor gene perform cell communication in the cell group pair, thereby affecting the interaction reaction of the cell group pair. The target cell group pair may include multiple ligand receptor pairs, and the cell interaction effects of different ligand receptor pairs are different. The target ligand receptor pair is determined from multiple ligand receptor pairs based on the gene expression levels of different ligand receptor pairs in the target cell group pair. It is understandable that the gene expression level of the ligand receptor pair can be obtained through spatial transcriptome data.

[0112] Reference Figure 8 In some embodiments, step S103 includes but is not limited to steps S801 to S803.

[0113] Step S801, screening a plurality of candidate ligand receptor pairs from a preset ligand receptor database according to the target cell population pairs;

[0114] Step S802, determining a first interaction parameter according to the gene expression level of each candidate ligand-receptor pair in the target cell population;

[0115] Step S803: determining a target ligand receptor pair from the candidate ligand receptor pairs corresponding to a plurality of first interaction parameters whose values ​​are smaller than a preset interaction threshold value according to a preset ratio.

[0116] In step S801 of some embodiments, the preset ligand receptor database refers to a database that can be used to analyze and predict cell interactions, such as CellphoneDB, etc. Candidate ligand receptor pairs corresponding to the cell type of the target cell group pair are screened out from the preset ligand database, wherein the cell type of the target cell group can be obtained based on cell annotation.

[0117] In step S802 of some embodiments, the first interaction parameter represents the significance of the candidate ligand receptor pair in the target cell population pair. The first interaction parameter can be calculated according to the T test, that is, the first interaction parameter is the result of performing a differential analysis between the expression distribution of the candidate ligand receptor pair in the target cell population pair and the expression distribution obtained by the permutation test.

[0118] In step S803 of some embodiments, the first interaction parameter whose value is less than the preset interaction threshold is screened out, and the screened out first interaction parameters are sorted from small to large. The target interaction parameter is screened out according to the preset ratio and the sorting result, and the candidate ligand receptor pair corresponding to the target interaction parameter is used as the target ligand receptor pair. For example, assuming that the preset interaction threshold is 0.05 and the preset ratio is 10%, the first interaction parameter corresponding to the target ligand receptor pair is less than 0.05, and the first interaction parameter is in the top 10% of the sorting result. It can be understood that the specific values ​​of the preset interaction threshold and the preset ratio can be adaptively set according to actual needs, and this embodiment of the present application is not specifically limited to this.

[0119] The benefit of step S801 to step S802 is that usually, when the first interaction parameter is less than 0.05, the corresponding ligand receptor pair can be considered to be significant, while the embodiment of the present application uses the candidate ligand receptor pairs whose first interaction parameter is less than 0.05 and belongs to the 10% range with the smallest value as the target ligand receptor pair. The method of the embodiment of the present application can screen out target ligand receptor pairs that are statistically significant and highly expressed in the target cell population pair. Specifically, the target cell population pair includes a signal sending population and a signal receiving population, and the target ligand receptor pair includes a target ligand gene and a target receptor gene. The target ligand gene is highly expressed in the signal sending population, and the target receptor gene is highly expressed in the signal receiving population.

[0120] In step S104 of some embodiments, cell interaction analysis refers to analyzing whether the target ligand receptor pair has significant interaction in the target cell population pair, and the corresponding interaction analysis results include the target ligand receptor pair having significant interaction in the target cell population pair and the target ligand receptor pair having non-significant interaction in the target cell population pair. According to the interaction analysis results, the influence of different target ligand receptor pairs on the interaction of the same target cell population pair can be understood, and downstream tasks such as cell analysis can be performed.

[0121] Reference Fig. 9 In some embodiments, step S104 includes but is not limited to steps S901 to S904.

[0122] Step S901, calculating the true expression mean according to the gene expression amount of the target ligand gene in the signal sending group and the gene expression amount of the target receptor gene in the signal receiving group;

[0123] Step S902, randomly dividing the cells in the signal sending group and the cells in the signal receiving group to obtain a first cell group and a second cell group;

[0124] Step S903, calculating a hypothetical expression mean value according to the gene expression level of the target ligand gene in the first cell group and the gene expression level of the target receptor gene in the second cell group;

[0125] Step S904, calculating the interaction analysis result according to the true expression mean and the hypothesized expression mean.

[0126] In step S901 of some embodiments, the gene expression of the target ligand gene in the signal sending group is determined according to the spatial transcriptome data, and the gene expression of the target receptor gene in the signal receiving group is determined, and the geometric mean calculation is performed based on the above two gene expression amounts to obtain the true expression mean. Specifically, the true expression mean μ is calculated according to the following formula (1).

[0127]

[0128] Among them, exp l represents the gene expression of the target ligand gene in the signal sending group, exp r Represents the gene expression level of the target receptor gene in the receiving group.

[0129] Reference Fig.10 In some embodiments, step S901 includes but is not limited to steps S1001 to S1002.

[0130] Step S1001, calculating the expression similarity value of the target ligand gene and the target receptor gene;

[0131] Step S1002, the expression weight is calculated based on the expression similarity value, and the real expression mean is calculated based on the expression weight, the gene expression level of the target ligand gene in the signal sending group, and the gene expression level of the target receptor gene in the signal receiving group.

[0132] In step S1001 of some embodiments, the expression similarity value is calculated based on the gene expression level of the target ligand gene in the signal sending group and the gene expression level of the target receptor gene in the signal receiving group to determine the expression difference between the target ligand gene and the target receptor gene.

[0133] In step S1002 of some embodiments, the expression similarity values ​​are subjected to data processing such as normalization to obtain expression weights. A weighted average calculation is performed based on the expression weights, the gene expression of the target ligand gene in the signal sending group, and the gene expression of the target receptor gene in the signal receiving group to obtain the true expression mean.

[0134] The benefit of step S1001 to step S1002 is that, in addition to obtaining the true expression mean value according to the average value calculation method, the true expression mean value can also be obtained according to the weighted average value calculation method. The weighted average value calculation method takes into account the expression difference between the target ligand gene and the target receptor gene, thereby improving the accuracy of calculating the true expression mean value.

[0135] In steps S902 to S903 of some embodiments, all cells in the signal sending group and all cells in the signal receiving group are randomly divided to obtain two new cell groups, namely the first cell group and the second cell group. Specifically, each cell in the signal sending group has a "signal sending label", and each cell in the signal receiving group has a "signal receiving label". The above two labels are randomly disrupted so that some cells that originally belonged to the signal receiving group have a "signal sending label" and some cells that originally belonged to the signal sending group have a "signal receiving label". In this case, all cells with a "signal sending label" are aggregated into a first cell group, and all cells with a "signal receiving label" are aggregated into a second cell group. The hypothetical expression mean μ' under the null hypothesis is calculated based on the gene expression amount of the target ligand gene in the first cell group and the gene expression amount of the target receptor gene in the second cell group. It can be understood that the calculation method of the hypothetical expression mean μ' is similar to that of the true expression mean μ, and this embodiment of the present application will not be repeated.

[0136] It is understandable that the hypothesized expression mean can also be calculated according to the weighted average calculation method,

[0137] In step S904 of some embodiments, the p1 value is calculated according to the actual expression mean, the assumed expression mean and the following formula (2), and the p1 value is used as the interaction analysis result. It can be understood that the numerical value of the p1 value can be used to indicate whether the target ligand-receptor pair has a significant interaction in the target cell population.

[0138]

[0139] The advantage of step S901 to step S904 is that step S901 to step S904 is actually a permutation test, and the permutation test method can perform significance test without knowing the data distribution, making the method more flexible. Secondly, the permutation test takes randomness into account, thereby reducing the problem of overfitting.

[0140] Reference Fig.11 In some embodiments, the cell interaction analysis method provided in the embodiments of the present application also includes but is not limited to steps S1101 to S1102.

[0141] Step S1101, dividing the target cell group pair into a neighbor group and a distant group according to the position information of each cell in the target cell group pair;

[0142] Step S1102, performing pleiotropic analysis based on the gene expression levels of the target ligand receptor pair in the distant group to obtain pleiotropic results.

[0143] In step S1101 of some embodiments, the spatial distance between cells can be determined based on the position information of each cell in the target cell group pair, and all cells in the target cell group pair can be divided into neighbor subgroups (i.e., neighbor groups) and distant subgroups (i.e., distant groups) based on the spatial distance based on the k nearest neighbor algorithm (default setting k=10). Specifically, a cell distribution matrix can be constructed based on the result of circling cells based on spatial transcriptome data, and the cell distribution matrix is ​​used to represent the position information of all cells in space. In the cell distribution matrix, different cell groups are marked with different labels, for example, cells belonging to the signal receiving group and cells belonging to the signal sending group have different labels. Based on the k nearest neighbor algorithm and the cell spatial distance, the cells belonging to the target cell group pair in the cell distribution matrix are searched for adjacent nodes to obtain the neighbor group. All cells except the neighbor group in the target group pair are regarded as cells of the distant group.

[0144] For example, refer to Fig.12, a neighbor group 1201 can be obtained, and cells 1202 (cell 1202 may be a cell belonging to a signal sending group) and cells 1203 (cell 1203 may be a cell belonging to a signal receiving group) outside the neighbor group 1201 are cells in the distant group. It can be understood that the cells in the neighbor group have a close interaction relationship.

[0145] In step S1102 of some embodiments, since various traits and various physiological and biochemical processes of an organism are interrelated and mutually constrained, the effects of genes are also related and mutually constrained on this basis. Pleiotropy means that a gene affects a certain trait and indirectly affects other traits. Pleiotropy analysis is performed on the target ligand receptor according to the gene expression amount of the target ligand receptor in the distant group to obtain the corresponding pleiotropy result.

[0146] Reference Fig.13 In some embodiments, step S1102 includes but is not limited to steps S1301 to S1302.

[0147] Step S1301, randomly pairing cells far away from the group to obtain cell pairs;

[0148] Step S1302, calculating the pleiotropic effect results based on the gene expression levels of the target ligand-receptor pair in the cell pair.

[0149] In step S1301 of some embodiments, cells in the far-away group are randomly paired to obtain a spatial pairing relationship of cells under the null hypothesis, that is, to obtain cell pairs. Fig.12 The cell pair may be in the form of two cells 1202 , two cells 1203 , or one cell 1202 and one cell 1203 .

[0150] In step S1302 of some embodiments, the gene expression amount of the target ligand receptor pair in the cell pair is determined, the geometric expression mean of the target ligand receptor pair in the cell pair is calculated according to the gene expression amount, and the pleiotropic analysis is performed according to the geometric expression mean to obtain the pleiotropic result. It is understandable that the method for calculating the geometric expression mean of the target ligand receptor pair in the cell pair is similar to the description in step S901, and the embodiments of the present application will not be repeated here.

[0151] Reference Fig.14 In some embodiments, step S1302 includes but is not limited to steps S1401 to S1402.

[0152] Step S1401, calculating a second interaction parameter according to the gene expression level of the target ligand-receptor pair in the cell pair;

[0153] Step S1402, obtaining multiple second interaction parameters according to multiple random pairings, performing logarithmic processing on the second interaction parameter with the largest value to obtain a pleiotropic effect result.

[0154] In step S1401 of some embodiments, the second interaction parameter is calculated based on the geometric expression average of the target ligand receptor pair in the cell pair and the geometric expression average of the target ligand receptor pair in the actual situation. It can be understood that the calculation method of the second interaction parameter is similar to the method of calculating the p1 value in step S904, and this embodiment of the present application will not be repeated.

[0155] In step S1402 of some embodiments, assuming that n represents the number of permutations of spatial pairing, and m represents the number of target ligand receptor pairs screened according to steps S801 to S803, then in the embodiment of the present application, n*m random pairing operations will be performed, and correspondingly n*m second interaction parameters can be obtained. The second interaction parameter with the largest value is logarithmically processed according to the following formula (3), and the pleiotropic result S is obtained. p .

[0156]

[0157] Among them, p max represents the second interaction parameter with the largest value. Secondly, the pleiotropic effect S can be calculated from formula (3) p In percentage form.

[0158] The benefit of step S1101 to step S1102 is that the target cell population pairs are divided into neighboring and distant cell subpopulations (i.e., neighboring groups and distant groups), and whether the target ligand-receptor pairs in the target cell population pairs have pleiotropy is calculated based on the permutation test. This pleiotropy can clarify the regulatory mechanism of specific signals in vivo.

[0159] It is understandable that dividing the target cell group into a neighbor group and a distant group also includes the following benefits: when the target ligand receptor pair is highly expressed in cells in the neighbor group and lowly expressed in cells in the distant group, it can be shown that the spatial position relationship has an important influence on the effect of the target ligand receptor. This is because the embodiment of the present application clusters the cells in the target slice according to the gene expression amount to obtain the cell group, rather than clustering according to the position information, so when the target ligand gene and the target receptor gene are highly expressed only in the neighbor group, it can be shown that the spatial position is important.

[0160] Reference Fig.15 In some embodiments, the cell interaction analysis method provided in the embodiments of the present application also includes but is not limited to steps S1501 to S1502.

[0161] Step S1501, respectively obtaining group annotations of the signal receiving group and the signal sending group;

[0162] Step S1502, performing visualization processing based on the group annotations corresponding to the signal receiving group, the group annotations corresponding to the signal sending group, the interaction analysis results, and the pleiotropic results to obtain a visualization information graph.

[0163] In step S1501 of some embodiments, the group annotation indicates which cell type the group belongs to. The spatial transcriptome data can be compared with the known single-cell transcriptome data, and the group annotation can be determined based on the similarity of gene expression. Alternatively, the structural features of the cell can be determined based on the image of the target slice, and the group annotation can be obtained based on the structural features. The group annotation of the signal receiving group is obtained, and the group annotation of the signal receiving group is obtained.

[0164] In step S1502 of some embodiments, visualization processing is performed according to the group annotations corresponding to the signal receiving group, the group annotations corresponding to the signal sending group, the interaction analysis results, and the pleiotropic results to obtain a visualization information graph. It can be understood that the significance score of the target ligand-receptor pair in the target cell group pair (i.e., the interaction analysis result) can be intuitively understood from the visualization information graph, and it can be seen that the ligand gene can simultaneously act on the pleiotropic related action relationship of different receptor genes, thereby improving the practicality of cell interaction analysis.

[0165] In a specific embodiment, the spatial transcriptome data generated on the second day after the salamander brain injury were analyzed according to the cell interaction analysis method provided in the embodiment of the present application. Fig.16 As shown in the figure, it shows the communication results of each cell in the tissue slice after injury (i.e., visual information diagram). Fig.16 It can be seen that there are many cell interactions in stress-sensitive neural stem cell-like ependymal glial cells (reaEGCs) and injured sensory neurons (WSNs), and the pathways related to these cell interactions are mostly related to cell proliferation and cell adhesion. For example, pathways related to the cell periplasm are only activated in spatially adjacent cell groups. In addition, a series of target ligand receptor pairs related to certain genes are simultaneously upregulated and activated in neighboring cell groups, which indicates the pleiotropic effects of these gene molecules on these pathways. It is understandable that Fig.16 The size of the bubble is related to the results of the interaction analysis. The larger the bubble, the stronger the interaction between the corresponding target ligand-receptor pair between the two cell populations.

[0166] The cell interaction analysis method provided in the embodiment of the present application performs cell interaction analysis based on spatial transcriptome data at the single-cell precision of the spatial transcriptome, and can test the spatial connection between different cell groups and ligand receptor pairs. Secondly, the embodiment of the present application simultaneously utilizes the annotation results of the cell group and the position information in the spatial transcriptome data, so that the obtained results are interpretable and the calculation speed is improved. The method provided in the embodiment of the present application is also suitable for analyzing high-throughput spatial transcriptome data represented by Stereo-seq.

[0167] The cell interaction analysis device provided in the embodiments of the present application is described below.

[0168] Reference Fig.17 The present application also provides a cell interaction analysis device, which includes:

[0169] A cell clustering unit 1701 is used to obtain spatial transcriptome data of a target slice, and cluster cells in the target slice according to the spatial transcriptome data to obtain multiple cell groups;

[0170] A cell group determination unit 1702 is used to calculate proximity values ​​between different cell groups, and determine a target cell group pair among multiple cell groups based on the proximity values;

[0171] A ligand receptor determination unit 1703 is used to determine a target ligand receptor pair in the target cell population according to the gene expression levels of different ligand receptor pairs in the target cell population;

[0172] The cell interaction analysis unit 1704 is used to perform cell interaction analysis based on the target ligand-receptor pair and the target cell population pair to obtain the interaction analysis result.

[0173] It can be seen that the contents in the above-mentioned cell interaction analysis method embodiment are all applicable to the embodiments of the present cell interaction analysis device, the functions specifically implemented by the present cell interaction analysis device embodiment are the same as those in the above-mentioned cell interaction analysis method embodiment, and the beneficial effects achieved are also the same as those achieved by the above-mentioned cell interaction analysis method embodiment.

[0174] Reference Fig.18 , Fig.18 The hardware structure of an electronic device of another embodiment is illustrated, and the electronic device includes:

[0175] The processor 1801 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (Application Specific Integrated Circuit, ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;

[0176] The memory 1802 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 1802 can store an operating system and other application programs. When the technical solution provided in the embodiments of this specification is implemented by software or firmware, the relevant program code is stored in the memory 1802, and the processor 1801 calls and executes the cell interaction analysis method of the embodiment of this application;

[0177] Input / output interface 1803, used to implement information input and output;

[0178] The communication interface 1804 is used to realize the communication interaction between the device and other devices. The communication can be realized through a wired manner (such as USB, network cable, etc.) or a wireless manner (such as mobile network, WIFI, Bluetooth, etc.);

[0179] A bus 1805 that transmits information between the various components of the device (e.g., the processor 1801, the memory 1802, the input / output interface 1803, and the communication interface 1804);

[0180] The processor 1801 , the memory 1802 , the input / output interface 1803 and the communication interface 1804 are connected to each other in communication within the device via the bus 1805 .

[0181] The embodiment of the present application further provides a computer program product, which includes a computer program. A processor of a computer device reads and executes the computer program, so that the computer device executes and implements the above-mentioned cell interaction analysis method.

[0182] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present disclosure described herein can, for example, be implemented in an order other than those illustrated or described herein. In addition, the terms "comprises" and "comprising" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0183] It should be understood that in the present disclosure, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0184] It should be understood that in the description of the embodiments of the present application, the meaning of multiple (or multiple items) is more than two, greater than, less than, exceed, etc. are understood to not include the number, and above, below, within, etc. are understood to include the number.

[0185] In the several embodiments provided in the present disclosure, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0186] The units described 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.

[0187] In addition, each functional unit in each embodiment of the present disclosure 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.

[0188] If the 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-readable storage medium. Based on this understanding, the technical solution of the present disclosure is essentially 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, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the various embodiments of the present disclosure. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store program codes.

[0189] It should also be understood that the various implementations provided in the embodiments of the present application can be combined arbitrarily to achieve different technical effects.

[0190] The above is a specific description of the implementation methods of the present disclosure, but the present disclosure is not limited to the above implementation methods. Technical personnel familiar with the art can also make various equivalent modifications or substitutions without violating the spirit of the present disclosure. These equivalent modifications or substitutions are all included in the scope defined by the claims of the present disclosure.

Claims

1. A cell interaction analysis method, characterized in that: The method comprises: Acquiring spatial transcriptome data of a target slice, and clustering cells in the target slice according to the spatial transcriptome data to obtain a plurality of cell populations; Calculating proximity values ​​between different cell populations, and determining a target cell population pair among the plurality of cell populations based on the proximity values; Determining a target ligand receptor pair in the target cell population according to the gene expression levels of different ligand receptor pairs in the target cell population; Cell interaction analysis is performed based on the target ligand-receptor pair and the target cell population pair to obtain interaction analysis results.

2. The method according to claim 1, characterized in that: The calculating of proximity values ​​between different cell populations, and determining a target cell population pair among the plurality of cell populations based on the proximity values, comprises: Determine the position information of each cell in each of the cell groups according to the spatial transcriptome data, and calculate the neighboring cells of each cell in each cell group according to the position information; The proximity values ​​between different cell groups are calculated based on the number of each cell in each cell group and the corresponding neighboring cells; Determine the cell population pair whose proximity value is greater than a preset proximity threshold as the target cell population pair.

3. The method according to claim 2, characterized in that The method of calculating the proximity values ​​between different cell groups according to the number of each cell in each cell group and the corresponding neighboring cells includes: Determine the number of neighboring cells of each cell in each cell group that correspond to neighboring cells of different cell groups; The numbers of adjacent cells of multiple cells belonging to the same cell group are aggregated to obtain the proximity values ​​between different cell groups.

4. The method according to claim 2, characterized in that: The step of calculating neighboring cells of each cell in each cell group according to the position information comprises: Obtain the morphological characteristics of each cell in each cell population; The position information and the morphological features are input into a preset prediction model to obtain neighboring cells of each cell in each cell group.

5. The method according to claim 1, characterized in that The step of determining the target ligand receptor pair in the target cell population according to the gene expression levels of different ligand receptor pairs in the target cell population comprises: Screening a plurality of candidate ligand receptor pairs from a preset ligand receptor database according to the target cell population pairs; Determining a first interaction parameter according to the gene expression level of each candidate ligand-receptor pair in the target cell population; The target ligand receptor pair is determined from the candidate ligand receptor pairs corresponding to a plurality of the first interaction parameters whose values ​​are less than a preset interaction threshold value according to a preset ratio.

6. The method according to claim 1, characterized in that The target ligand-receptor pair includes a target ligand gene and a target receptor gene, and the target cell population includes a signal sending population and a signal receiving population; The cell interaction analysis based on the target ligand receptor pair and the target cell population pair is performed to obtain the interaction analysis result, including: The true expression mean is calculated according to the gene expression amount of the target ligand gene in the signal sending group and the gene expression amount of the target receptor gene in the signal receiving group; Randomly dividing the cells in the signal sending group and the cells in the signal receiving group to obtain a first cell group and a second cell group; Calculating a hypothetical expression mean according to the gene expression level of the target ligand gene in the first cell population and the gene expression level of the target receptor gene in the second cell population; The interaction analysis result is calculated based on the true expression mean and the hypothesized expression mean.

7. The method according to claim 6, characterized in that The calculating the true expression mean according to the gene expression amount of the target ligand gene in the signal sending group and the gene expression amount of the target receptor gene in the signal receiving group comprises: Calculating the expression similarity value of the target ligand gene and the target receptor gene; The expression weight is calculated based on the expression similarity value, and the true expression mean is calculated based on the expression weight, the gene expression level of the target ligand gene in the signal sending group, and the gene expression level of the target receptor gene in the signal receiving group.

8. The method according to claim 1, characterized in that: The method further comprises: Dividing the target cell group pair into a neighbor group and a distant group according to the position information of each cell in the target cell group pair; A pleiotropic analysis is performed based on the gene expression level of the target ligand receptor in the distant group to obtain a pleiotropic result.

9. The method according to claim 8, characterized in that The step of performing pleiotropic analysis on the gene expression level of the target ligand receptor in the distant group to obtain pleiotropic results includes: Randomly pairing the cells in the distant group to obtain cell pairs; The pleiotropic results are calculated based on the gene expression levels of the target ligand-receptor pair in the cell pair.

10. The method according to claim 9, characterized in that The method of calculating the pleiotropic results based on the gene expression of the target ligand receptor pair in the cell pair includes: Calculating a second interaction parameter according to the gene expression level of the target ligand-receptor pair in the cell pair; A plurality of the second interaction parameters are obtained according to multiple random pairings, and the second interaction parameter with the largest value is logarithmically processed to obtain the pleiotropic effect result.

11. The method according to any one of claims 8 to 10, characterized in that: The method further comprises: Respectively obtaining group annotations of the signal receiving group and the signal sending group; Visualization processing is performed based on the group annotation corresponding to the signal receiving group, the group annotation corresponding to the signal sending group, the interaction analysis results, and the pleiotropic results to obtain a visualization information graph.

12. A cell interaction analysis device, characterized in that: The device comprises: A cell clustering unit, used to obtain spatial transcriptome data of a target slice, and cluster cells in the target slice according to the spatial transcriptome data to obtain a plurality of cell groups; A cell group determination unit, used to calculate proximity values ​​between different cell groups, and determine a target cell group pair among the plurality of cell groups based on the proximity values; a ligand receptor determination unit, configured to determine a target ligand receptor pair in the target cell population according to the gene expression levels of different ligand receptor pairs in the target cell population; The cell interaction analysis unit is used to perform cell interaction analysis based on the target ligand-receptor pair and the target cell population pair to obtain an interaction analysis result.

13. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the cell interaction analysis method according to any one of claims 1 to 11 is implemented.

14. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the cell interaction analysis method according to any one of claims 1 to 11 is implemented.

15. A computer program product, comprising a computer program, wherein the computer program is read and executed by a processor of a computer device, so that the computer device executes the cell interaction analysis method according to any one of claims 1 to 11.

Images