Single-cell RNA sequencing data clustering method and system based on multi-scale hypergraph

Abstract

The invention provides a single-cell RNA sequencing data clustering method and system based on a multi-scale hypergraph. The method comprises the following steps: constructing a multi-scale similarity matrix through a gene expression matrix; generating a cell hypergraph through the multi-scale similarity matrix; carrying out cell hypergraph fusion; carrying out SVD (Singular Value Decomposition) on the hypergraph Laplacian matrix fused with the cell hypergraph; and extracting feature vectors corresponding to the first # imgabs0 # minimum feature values, and performing K-means to obtain a clustering result, thereby realizing the function of improving the clustering effect of the single-cell RNA sequencing data. The invention provides a novel hypergraph generation method, the high-order correlation among a plurality of cells is better captured, and the correlation among the cells is more accurately captured by using a plurality of scales. Compared with an existing single-cell RNA sequencing data clustering method, the single-cell RNA sequencing data clustering method has the advantages that a unified clustering framework is provided, the framework comprehensively learns topological information between cells on multiple scales, cell hypergraphs are constructed for clustering, and a good clustering result can be obtained.

Description

Technical Field

[0001] The present invention belongs to the technical field of single-cell RNA sequencing data clustering, and in particular relates to a single-cell RNA sequencing data clustering method and system based on a multi-scale hypergraph. Background Art

[0002] Single-cell RNA sequencing (scRNA-seq) is a method that uses next-generation sequencing technology to analyze single cells and understand cell functions at the genetic and cellular levels. Unlike traditional large-scale RNA sequencing technology, scRNA-seq can provide a more comprehensive insight into cell heterogeneity and diversity within cell populations. scRNA-seq provides information on the transcriptome of a single cell, which can be used to identify cell subtypes, track cell differentiation stages, and monitor the progression of individual cells. This method provides a new perspective for studying gene expression patterns at the cellular level.

[0003] The focus of single-cell research is on accurately identifying cell types, which is a key requirement for understanding the diversity of cell populations. However, scRNA-seq data poses some challenges, such as loss events and the curse of dimensionality, which makes subsequent analysis difficult. Even in the same group of cells, there are significant differences in gene expression levels, which poses a challenge to downstream analysis and it is not always possible to obtain reliable clustering results.

[0004] In recent years, hypergraphs have received increasing attention due to their ability to model high-order relationships between complex data. Yang et al. proposed the SAFE-clustering method, which is an integrated clustering method that integrates the results of four clustering methods and adopts three hypergraph partitioning algorithms. This method provides a flexible, accurate and robust solution for clustering single-cell RNA-seq data. However, its method of constructing a hypergraph using the results of four clustering methods is too complicated and still needs further improvement. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a single-cell RNA sequencing data clustering method and system based on a multi-scale hypergraph, so as to improve the clustering effect of scRNA-seq data.

[0006] The technical solution adopted by the present invention to solve the above technical problems is: a single-cell RNA sequencing data clustering method based on a multi-scale hypergraph, comprising the following steps: S1: Convert the gene expression matrix into a multi-scale similarity matrix through multi-scale similarity learning; S2: Generate cell hypergraph based on multi-scale similarity matrix; S3: A fusion algorithm is used according to the cell hypergraph to obtain a fused cell hypergraph; S4: Calculate the hypergraph Laplacian matrix by fusing the cell hypergraph and perform SVD decomposition to obtain the eigenvector; S5: Extract the eigenvectors corresponding to the first K smallest eigenvalues ​​and perform K-means to obtain the clustering results.

[0007] According to the above scheme, in step S1, the specific steps are: S11: Obtain gene expression data and normalize them, then represent them as a gene expression matrix, where each row of the matrix represents a gene expression vector of a cell, and each column represents the gene expression level of the same gene in different cells; S12: construct a similarity matrix with the same three dimensions for the gene expression data and convert it into a multi-scale similarity matrix, and symmetrize the multi-scale similarity matrix.

[0008] According to the above scheme, in step S2, the specific steps are: A multi-scale cell hypergraph described by a triple {vertex set, hyperedge set, hyperedge weight matrix} and the corresponding association matrix are generated through a multi-scale similarity matrix.

[0009] Furthermore, in step S2, Each vertex in the vertex set is used to represent a cell; The hyperedge set represents the set of hyperedges consisting of the neighbors of the cell vertices in the similarity matrix; The association matrix is ​​used to represent the importance of cell vertices in the hyperedge.

[0010] According to the above scheme, in step S3, the specific steps are: The association matrices corresponding to the cell hypergraphs are averaged to fuse the association matrices, and the multi-scale cell hypergraphs are fused to construct a fused cell hypergraph.

[0011] According to the above scheme, in step S4, the specific steps are: S41: Construct the hypergraph Laplacian matrix by fusing the incidence matrix, hyperedge degree matrix, vertex degree matrix and hyperedge weight matrix of the cell hypergraph; S42: Perform SVD decomposition on the hypergraph Laplacian matrix to obtain eigenvectors.

[0012] Furthermore, in step S41, Each element of the hyperedge degree matrix represents the weight of a hyperedge, which is equal to the sum of the weights of the vertices connected by the hyperedge; Each element of the vertex degree matrix represents the weight of a vertex, which is equal to the weight of the vertex in the hyperedge connected to the vertex multiplied by the hyperedge weight.

[0013] According to the above scheme, in step S5, the specific steps are: S51: Select the eigenvectors corresponding to the first K smallest eigenvalues ​​of the hypergraph Laplacian matrix according to the number of cell categories: S52: performing row normalization on the feature vector selected in step S51 to obtain a normalized feature vector; S53: Use the K-means method to cluster the normalized feature vectors to obtain clustering results.

[0014] A multi-scale hypergraph-based clustering system for single-cell RNA sequencing data. A matrix conversion submodule, which is used to convert the gene expression matrix into a multi-scale similarity matrix through multi-scale similarity learning; A hypergraph generation submodule, used to generate a cell hypergraph based on a multi-scale similarity matrix; A fusion submodule, used for obtaining a fused cell hypergraph by adopting a fusion algorithm according to the cell hypergraph; The decomposition submodule is used to calculate the hypergraph Laplacian matrix by fusing the cell hypergraph and perform SVD decomposition to obtain the eigenvector; The clustering submodule is used to extract the eigenvectors corresponding to the first K smallest eigenvalues ​​and perform K-means to obtain the clustering results.

[0015] A computer memory stores a computer program executable by a computer processor, wherein the computer program executes a single-cell RNA sequencing data clustering method based on a multi-scale hypergraph.

[0016] The beneficial effects of the present invention are: 1. A single-cell RNA sequencing data clustering method and system based on a multi-scale hypergraph of the present invention comprises the following steps: constructing a multi-scale similarity matrix from a gene expression matrix; generating a cell hypergraph through the multi-scale similarity matrix; performing cell hypergraph fusion; performing SVD decomposition on the hypergraph Laplacian matrix of the fused cell hypergraph; extracting the previous The eigenvector corresponding to the smallest eigenvalue is clustered by K-means, which improves the clustering effect of scRNA-seq data.

[0017] 2. The present invention proposes a novel hypergraph generation method, which better captures the high-order correlations between multiple cells and uses multiple scales to more accurately capture the associations between cells.

[0018] 3. Comparison with existing single-cell RNA sequencing data clustering methods shows that the present invention proposes a unified clustering framework that comprehensively learns the topological information between cells at multiple scales and constructs a cell hypergraph for clustering, which can obtain better clustering results.

[0019] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0021] Figure 1 is a flow chart of an embodiment of the present invention.

[0022] Figure 2 It is a comparative bar chart of clustering performance indicators of the embodiment of the present invention and the classical method on the Treutlein data set.

[0023] Figure 3 It is a comparison diagram of clustering effects of the embodiment of the present invention and the classical method on the Treutlein dataset. DETAILED DESCRIPTION

[0024] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention 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 invention and are not used to limit the present invention.

[0025] Example 1 See also Figure 1 ,The specific steps of a single-cell RNA sequencing data clustering method based on multi-scale hypergraph are as follows: S1: Convert the gene expression matrix into a multi-scale similarity matrix through multi-scale similarity learning; S2: Generate cell hypergraph based on multi-scale similarity matrix; S3: A fusion algorithm is used according to the cell hypergraph to obtain a fused cell hypergraph; S4: Calculate the hypergraph Laplacian matrix by fusing the cell hypergraph and perform SVD decomposition to obtain the eigenvector; S5: Extract the eigenvectors corresponding to the first K smallest eigenvalues ​​and perform K-means to obtain the clustering results.

[0026] Furthermore, in step S1, the specific steps are: S11: Obtain gene expression data and normalize them, then represent them as a gene expression matrix, where each row of the matrix represents a gene expression vector of a cell, and each column represents the gene expression level of the same gene in different cells; S12: construct a similarity matrix with the same three dimensions for the gene expression data and convert it into a multi-scale similarity matrix, and symmetrize the multi-scale similarity matrix.

[0027] In step S2, the specific steps are: A multi-scale cell hypergraph described by a triple {vertex set, hyperedge set, hyperedge weight matrix} and the corresponding association matrix are generated through a multi-scale similarity matrix.

[0028] Further, in step S2, Each vertex in the vertex set is used to represent a cell; The hyperedge set represents the set of hyperedges consisting of the neighbors of the cell vertices in the similarity matrix; The association matrix is ​​used to represent the importance of cell vertices in the hyperedge.

[0029] In step S3, the specific steps are: The association matrices corresponding to the cell hypergraphs are averaged to fuse the association matrices, and the multi-scale cell hypergraphs are fused to construct a fused cell hypergraph.

[0030] In step S4, the specific steps are: S41: Construct the hypergraph Laplacian matrix by fusing the incidence matrix, hyperedge degree matrix, vertex degree matrix and hyperedge weight matrix of the cell hypergraph; S42: Perform SVD decomposition on the hypergraph Laplacian matrix to obtain eigenvectors.

[0031] Further, in step S41, Each element of the hyperedge degree matrix represents the weight of a hyperedge, which is equal to the sum of the weights of the vertices connected by the hyperedge; Each element of the vertex degree matrix represents the weight of a vertex, which is equal to the weight of the vertex in the hyperedge connected to the vertex multiplied by the hyperedge weight.

[0032] In step S5, the specific steps are: S51: Select the eigenvectors corresponding to the first K smallest eigenvalues ​​of the hypergraph Laplacian matrix according to the number of cell categories: S52: performing row normalization on the feature vector selected in step S51 to obtain a normalized feature vector; S53: Use the K-means method to cluster the normalized feature vectors to obtain clustering results.

[0033] In this embodiment, a multi-scale similarity matrix is ​​constructed from a gene expression matrix; a cell hypergraph is generated from the multi-scale similarity matrix; cell hypergraph fusion is performed; a hypergraph Laplacian matrix of the fused cell hypergraph is decomposed by SVD; and the front The eigenvector corresponding to the smallest eigenvalue is clustered by K-means, which improves the clustering effect of scRNA-seq data.

[0034] Example 2 The steps of this embodiment are the same as those of embodiment 1, except that each step is applied to a specific example. Specifically, the following steps are included: S1: Convert the gene expression matrix into a multi-scale similarity matrix through multi-scale similarity learning; the specific steps are: S11: Taking the Treutlein (GSE52583) dataset as an example, the normalized gene expression data is represented as , where each row represents the gene expression vector of a cell, i.e. , each column represents the gene expression level of the same gene in different cells, =80 indicates the number of cells, =959 represents the number of genes, that is , .

[0035] S12: Gene expression data Construct 3 sizes of The similarity matrix , and ,set up Represents cells Under the corresponding similarity calculation method = 4 nearest neighbors, select the value of the nearest neighbor The value is determined by the number of cells = 80 and the ratio of the nearest neighbors selected Determining,computing cells in gene expression data by multi-scale similarity,learning and cells Similarities between: (1) (2) In formula (1) to formula (2): represents the similarity matrix calculated by Euclidean distance, Represents cells and cells The Euclidean distance between A scaling metric to control the overall similarity; (3) (4) (5) In formula (3) to formula (5): represents the similarity matrix calculated by Pearson's correlation coefficient, Represents cells and cells The Pearson correlation coefficient between Indicates The cell The expression level of a gene, Indicates all The average value, M=959, A scaling metric to control the overall similarity; (6) (7) (8) In formula (6) to formula (8): represents the similarity matrix calculated by the spearman correlation coefficient, Represents cells and cells The spearman correlation coefficient between Indicates The gene in The expression level ranking of each cell, A scaling indicator to control the overall similarity.

[0036] For the multi-scale similarity matrix , and Symmetrize: (9) (10) (11) S2: Generate a cell hypergraph based on a multi-scale similarity matrix; the specific steps are: Through the multi-scale similarity matrix , and Generating multi-scale cell hypergraphs , and and their corresponding incidence matrices , and ; A vertex set representing each vertex as a cell; (12) (13) (14) (15) (16) (17) In formula (12) to formula (17): Indicates The hyperedge set corresponding to the hypergraph generated by the similarity matrix, Indicates The cell vertices in the similarity matrix of = a hyperedge set consisting of 4 neighbors, , and Represents the hyperedge weight matrix corresponding to the generated cell hypergraph, which is a unit matrix with a dimension of =80×80, , and Represents the association matrix corresponding to the generated cell hypergraph, with dimension =80×80, representing the cell vertices On the super edge The importance of.

[0037] S3: Fusion of cell hypergraphs through fusion algorithm; the specific steps are: Multiscale Cellular Hypergraph , and Fusion structure fusion cell hypergraph , achieved by averaging the fusion correlation matrix: (18) (19) (20) In formula (18)-formula (20): represents the hyperedge set of the fused cell hypergraph, Indicates the first A super edge, is the first cell in the 3-cell hypergraph The sum of the superedges, Represents the association matrix of the fused cell hypergraph, with dimension =80×80, Represents the hyperedge weight matrix of the fused cell hypergraph, which is a unit matrix with dimension =80×80.

[0038] S4: Calculate the hypergraph Laplacian matrix by calculating the fused cell hypergraph obtained in step S3, and perform SVD decomposition to obtain the eigenvector; the specific steps are: S41: By fusing the cell hypergraph association matrix Constructing the Hypergraph Laplacian Matrix : (twenty one) In formula (21): Represents the hyperedge degree matrix of the fused cell hypergraph, which is a diagonal matrix with dimension =80×80, Indicates The weight of a hyperedge is equal to the sum of the weights of the vertices connected by the hyperedge. Represents the vertex degree matrix of the fused cell hypergraph, which is a diagonal matrix with dimension =80×80, Indicates The weight of a vertex is equal to the weight of the vertex in the hyperedge connected to the vertex multiplied by the hyperedge weight. represents the identity matrix, with dimensions =80×80.

[0039] S42: Laplacian Matrix for Hypergraphs Perform SVD decomposition: (twenty two) In formula (22): represents a left singular matrix, represents the singular value matrix, represents a right singular matrix.

[0040] S5: Extract the feature vectors with the first K=5 smallest eigenvalues ​​from the feature vectors obtained in step S4, and perform K-means to obtain the clustering result; the specific steps are: S51: Select the hypergraph Laplacian matrix forward = The eigenvectors corresponding to the 5 smallest eigenvalues ​​are of size : ,Pick (twenty three) In formula (23): Indicates The eigenvector corresponding to the smallest eigenvalue is taken represents the eigenvector corresponding to the fifth smallest eigenvalue, =5 means the number of cell types is 5, Pick The row vector represents the embedding representation of the cell.

[0041] S52: Perform row normalization on the feature vector selected in step S51 to obtain a normalized feature vector : ,Pick (twenty four) In formula (24): Representation Matrix No. OK, Representation Matrix No. OK, Representation Matrix No. OK norm, Representation Matrix No. OK norm, Represents a very small constant used to avoid division by zero.

[0042] The K-means method is used to normalize the feature vector Perform clustering and obtain clustering results .

[0043] It should be understood that the size of the serial numbers of the steps in the above embodiments does not 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.

[0044] Four performance evaluation indicators such as Adjusted Rand Index (ARI), Accuracy (ACC), Normalized Mutual Information (NMI), and Purity were used to compare the clustering results of the clustering method proposed in the present invention with those of three classic methods, MTGDC, MPSSC, and SIMLR, to verify the effectiveness of the method of the present invention in clustering single-cell RNA sequencing data.

[0045] Performance comparison results are as follows Figure 2 As shown, in the comparison of the four evaluation indicators, the method of the present invention shows a significant advantage, all of which are the highest values. This shows that the method of the present invention has a good effect in clustering single-cell RNA sequencing data and has a high accuracy.

[0046] Figure 3 The figure is a comparison of the clustering effects of the method of the present invention and the classical method based on t-SNE. The distance between the cell clusters of the method of the present invention is large and the visualization effect is good, indicating that the method of the present invention can effectively distinguish different types of cells. The above results show that compared with the existing single-cell RNA sequencing data clustering method, the present invention can obtain better clustering results.

[0047] Example 3 This embodiment is used to implement the principle of the above method embodiment to construct a single-cell RNA sequencing data clustering system based on a multi-scale hypergraph, including a matrix conversion submodule, a hypergraph generation submodule, a fusion submodule, a decomposition submodule and a clustering submodule; A matrix conversion submodule, which is used to convert the gene expression matrix into a multi-scale similarity matrix through multi-scale similarity learning; A hypergraph generation submodule, used to generate a cell hypergraph based on a multi-scale similarity matrix; A fusion submodule, used for obtaining a fused cell hypergraph by adopting a fusion algorithm according to the cell hypergraph; The decomposition submodule is used to calculate the hypergraph Laplacian matrix by fusing the cell hypergraph and perform SVD decomposition to obtain the eigenvector; The clustering submodule is used to extract the eigenvectors corresponding to the first K smallest eigenvalues ​​and perform K-means to obtain the clustering results.

[0048] Each sub-module is mainly used to implement each step of the method embodiment, which will not be described in detail here.

[0049] It should be pointed out that, according to the needs of implementation, the various steps / components described in this application can be split into more steps / components, and two or more steps / components or partial operations of steps / components can be combined into new steps / components to achieve the purpose of the present invention.

[0050] This embodiment also includes a processor, a communication interface, a memory and a communication bus; wherein the processor, the communication interface and the memory communicate with each other via the communication bus; a computer program is stored in the memory, and when the program is executed by the processor, the processor executes the steps of a method for rapid verification of the AEB system of an off-line vehicle.

[0051] This embodiment also provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, enables the processor to implement a method for rapid verification of an AEB system for off-line vehicles.

[0052] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware.

[0053] Moreover, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0054] The present application is described with reference to the block diagram of the device (system) according to Embodiment 1 of the present application and the flowchart of the method and computer program product according to Embodiment 2. It should be understood that each process or box in the flowchart or block diagram, as well as the combination of processes or boxes in the flowchart or block diagram, can be implemented by computer program instructions.

[0055] These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the process Figure 1 A process or multiple processes or boxes Figure 1 A system of functions specified in one or more boxes.

[0056] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes or boxes Figure 1A function specified in one or more boxes.

[0057] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes or boxes Figure 1 The steps of a method are specified in one or more boxes.

[0058] The above embodiments are only used to illustrate the design ideas and features of the present invention, and their purpose is to enable those skilled in the art to understand the content of the present invention and implement it accordingly. The protection scope of the present invention is not limited to the above embodiments. Therefore, any equivalent changes or modifications made based on the principles and design ideas disclosed by the present invention are within the protection scope of the present invention.

Claims

1. A single-cell RNA sequencing data clustering method based on a multi-scale hypergraph, characterized by: The following steps are involved: S1: Convert the gene expression matrix into a multi-scale similarity matrix through multi-scale similarity learning; S2: Generate cell hypergraph based on multi-scale similarity matrix; S3: A fusion algorithm is used according to the cell hypergraph to obtain a fused cell hypergraph; S4: Calculate the hypergraph Laplacian matrix by fusing the cell hypergraph and perform SVD decomposition to obtain the eigenvector; S5: Extract the eigenvectors corresponding to the first K smallest eigenvalues ​​and perform K-means to obtain the clustering results.

2. The method for clustering single-cell RNA sequencing data based on a multi-scale hypergraph according to claim 1, characterized in that: In the step S1, the specific steps are: S11: Obtain gene expression data and normalize them, then represent them as a gene expression matrix, where each row of the matrix represents a gene expression vector of a cell, and each column represents the gene expression level of the same gene in different cells; S12: construct a similarity matrix with the same three dimensions for the gene expression data and convert it into a multi-scale similarity matrix, and symmetrize the multi-scale similarity matrix.

3. The method for clustering single-cell RNA sequencing data based on a multi-scale hypergraph according to claim 1, characterized in that: In the step S2, the specific steps are: A multi-scale cell hypergraph described by a triple {vertex set, hyperedge set, hyperedge weight matrix} and the corresponding association matrix are generated through a multi-scale similarity matrix.

4. The method for clustering single-cell RNA sequencing data based on a multi-scale hypergraph according to claim 3, characterized in that: In the step S2, Each vertex in the vertex set is used to represent a cell; The hyperedge set represents the set of hyperedges consisting of the neighbors of the cell vertices in the similarity matrix; The association matrix is ​​used to represent the importance of cell vertices in the hyperedge.

5. The method for clustering single-cell RNA sequencing data based on a multi-scale hypergraph according to claim 1, characterized in that: In the step S3, the specific steps are: The association matrices corresponding to the cell hypergraphs are averaged to fuse the association matrices, and the multi-scale cell hypergraphs are fused to construct a fused cell hypergraph.

6. The method for clustering single-cell RNA sequencing data based on a multi-scale hypergraph according to claim 1, characterized in that: In the step S4, the specific steps are: S41: Construct the hypergraph Laplacian matrix by fusing the incidence matrix, hyperedge degree matrix, vertex degree matrix and hyperedge weight matrix of the cell hypergraph; S42: Perform SVD decomposition on the hypergraph Laplacian matrix to obtain eigenvectors.

7. The method for clustering single-cell RNA sequencing data based on a multi-scale hypergraph according to claim 6, characterized in that: In the step S41, Each element of the hyperedge degree matrix represents the weight of a hyperedge, which is equal to the sum of the weights of the vertices connected by the hyperedge; Each element of the vertex degree matrix represents the weight of a vertex, which is equal to the weight of the vertex in the hyperedge connected to the vertex multiplied by the hyperedge weight.

8. The method for clustering single-cell RNA sequencing data based on a multi-scale hypergraph according to claim 1, characterized in that: In the step S5, the specific steps are: S51: Select the eigenvectors corresponding to the first K smallest eigenvalues ​​of the hypergraph Laplacian matrix according to the number of cell categories: S52: performing row normalization on the feature vector selected in step S51 to obtain a normalized feature vector; S53: Use the K-means method to cluster the normalized feature vectors to obtain clustering results.

9. A single-cell RNA sequencing data clustering system based on a multi-scale hypergraph, characterized by: A matrix conversion submodule, which is used to convert the gene expression matrix into a multi-scale similarity matrix through multi-scale similarity learning; A hypergraph generation submodule, used to generate a cell hypergraph based on a multi-scale similarity matrix; A fusion submodule, used for obtaining a fused cell hypergraph by adopting a fusion algorithm according to the cell hypergraph; The decomposition submodule is used to calculate the hypergraph Laplacian matrix by fusing the cell hypergraph and perform SVD decomposition to obtain the eigenvector; The clustering submodule is used to extract the eigenvectors corresponding to the first K smallest eigenvalues ​​and perform K-means to obtain the clustering results.

10. A computer memory, characterized in that: A computer program executable by a computer processor is stored therein, and the computer program executes a single-cell RNA sequencing data clustering method based on a multi-scale hypergraph as described in any one of claims 1 to 8.

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