Cell differentiation trajectory analysis method based on topological entropy

Abstract

The invention relates to the technical field of data analysis, in particular to a cell differentiation trajectory analysis method based on topological entropy, which comprises the following steps: acquiring gene expression data of cells in the same cell type; standardizing the gene expression data to obtain a cell standardized expression matrix, and constructing a single-cell gene regulation network according to the cell standardized expression matrix; constructing a single-cell gene regulatory network representation matrix according to the single-cell gene regulatory network; obtaining a single cell differentiation trajectory and a cell topological entropy matrix according to the single cell gene regulation and control network characterization matrix; and analyzing according to the cell topological entropy matrix and the single cell differentiation trajectory to obtain the key evolution determining gene of the adjacent state in the differentiation trajectory. According to the method, the key evolution determining gene is identified based on the topological entropy, and the fluctuation key nodes influencing the stability of the adjacent state network structure can be effectively screened.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and particularly to a method for analyzing cell differentiation trajectories based on topological entropy. Background Art

[0002] In recent years, single-cell transcriptomics has become an important technical means in the research of immunology and developmental biology. Among them, how to clarify the differentiation patterns of different cell types and effectively explain the key determinant molecules in the process of the evolution of low-differentiation state cells to high-differentiation state cells is a key problem to be solved in this field. Although differentiation trajectory inference algorithms such as Slingshot and scanpy have been widely used in cell differentiation inference, the above tools mainly use the strength of cell gene expression status as the key to infer cell status, without considering the evolution of the gene regulatory network during the process of cells from immature to mature and the key genes that determine cell differentiation. Therefore, proposing a method for analyzing cell differentiation trajectories based on topological entropy is of great significance in cell differentiation research. Summary of the Invention

[0003] The object of the present invention is to provide a method for analyzing cell differentiation trajectories based on topological entropy, which can effectively screen key nodes that affect the fluctuation of the stability of the adjacent state network structure.

[0004] To achieve the above object, the present invention provides the following solution:

[0005] A method for analyzing cell differentiation trajectories based on topological entropy, comprising:

[0006] Obtaining gene expression data of cells in the same cell type;

[0007] Normalizing the gene expression data to obtain a cell normalized expression matrix, and constructing a single-cell gene regulatory network according to the cell normalized expression matrix, wherein the single-cell gene regulatory network includes a single-cell gene regulatory relationship adjacency matrix and a single-cell gene regulatory relationship weight matrix;

[0008] Constructing a single-cell gene regulatory network characterization matrix according to the single-cell gene regulatory network;

[0009] Obtaining a single-cell differentiation trajectory and a cell topological entropy matrix according to the single-cell gene regulatory network characterization matrix;

[0010] Analyzing according to the cell topological entropy matrix and the single-cell differentiation trajectory to obtain key evolution determinant genes in the adjacent state of the differentiation trajectory.

[0011] Optionally, normalizing the gene expression data includes: normalizing the gene expression data through the NormalizeData function and the ScaleData function to obtain the cell-normalized expression matrix.

[0012] Optionally, constructing a single-cell gene regulatory network based on the cell-normalized expression matrix includes:

[0013] Inputting the cell-normalized expression matrix into the CSNet algorithm to obtain the single-cell gene regulatory relationship weight matrix;

[0014] Defining the edge weight values in the cell gene regulatory relationship weight matrix according to a preset threshold to obtain the single-cell gene regulatory relationship adjacency matrix;

[0015] Obtaining the single-cell gene regulatory network based on the single-cell gene regulatory relationship adjacency matrix and the single-cell gene regulatory relationship weight matrix.

[0016] Optionally, constructing a single-cell gene regulatory network representation matrix based on the single-cell gene regulatory network includes:

[0017] Using the Degree algorithm to calculate the degree value of each gene in a single cell with the single-cell gene regulatory relationship adjacency matrix as the input data;

[0018] Combining the degree values of each gene in the single cell to construct the single-cell gene regulatory network representation matrix.

[0019] Optionally, constructing a single-cell differentiation trajectory based on the single-cell gene regulatory network representation matrix includes:

[0020] Using the reduceDimension algorithm to perform feature dimensionality reduction on the degree values in the single-cell gene regulatory network representation matrix;

[0021] Processing the degree values after feature dimensionality reduction and using the orderCells algorithm to obtain the cell differentiation trajectory.

[0022] Optionally, obtaining the cell topological entropy matrix includes: calculating the topological entropy value of each gene in a single cell using the topological entropy algorithm based on the single-cell gene regulatory relationship adjacency matrix and the single-cell gene regulatory relationship weight matrix to obtain the cell topological entropy matrix.

[0023] Optionally, calculating the topological entropy value of each gene in a single cell using the topological entropy algorithm includes:

[0024] ;

[0025] Among them, represents the deviation of the degree value of and its first-order neighborhood genes, represents the mean value of the first-order neighborhood edge weights, represents the mean value of the second-order neighborhood edge weights, represents the i-th gene in the adjacency matrix of single-cell gene regulatory relationships, is the degree of gene i in the adjacency matrix of single-cell gene regulatory relationships,

[0026] Optionally, obtaining the key evolution-determining genes in the differentiation trajectory includes:

[0027] Using the cell topological entropy matrix as input data, according to the cell differentiation trajectory, and adopting the wilcox test algorithm to obtain the key evolution-determining genes.

[0028] Optionally, after obtaining the key evolution-determining genes, it includes: performing GO enrichment analysis on the key evolution-determining genes.

[0029] The beneficial effects of the present invention are as follows: Currently, the inference of cell differentiation patterns mainly takes the strength of cell gene expression states as the key to inferring cell states, without considering the evolution of gene regulatory networks during cell differentiation and the key gene nodes that determine cell differentiation. To solve the above technical problems, the present invention first obtains a single-cell dataset of the same type of cells. Then, a single-cell gene regulatory network is constructed through the CSNet algorithm. Next, a single-cell gene regulatory network characterization matrix is constructed through the Degree algorithm. Key evolution-determining genes in the adjacent cell states are screened by combining topological entropy with the wilcox test. Finally, biological annotation of the above genes is performed through GO enrichment analysis. Compared with traditional methods, the present invention constructs a gene regulatory network for each cell, and identifies the evolution of cell differentiation through the complex states of different cell gene regulatory networks. In addition, the present invention identifies key evolution-determining genes based on topological entropy, and can effectively screen key nodes that affect the stability of the adjacent state network structure. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0031] Figure 1Flow chart of a method for analyzing cell differentiation trajectories based on topological entropy according to an embodiment of the present invention;

[0032] Figure 2 Schematic diagram of cell differentiation trajectories according to an embodiment of the present invention;

[0033] Figure 3 Schematic diagram of the GO enrichment analysis results of the states from S1 to S7 and from S1 to S2 according to an embodiment of the present invention. Detailed implementation manners

[0034] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0035] To make the above objects, features, and advantages of the present invention more obvious and understandable, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific implementation manners.

[0036] As Figure 1 shown, this embodiment provides a method for analyzing cell differentiation trajectories based on topological entropy, including:

[0037] Obtain gene expression data of the same cell type;

[0038] Normalize the gene expression data to obtain a cell normalized expression matrix, and construct a single-cell gene regulatory network based on the cell normalized expression matrix, where the single-cell gene regulatory network includes a single-cell gene regulatory relationship adjacency matrix and a single-cell gene regulatory relationship weight matrix;

[0039] Construct a single-cell gene regulatory network representation matrix based on the single-cell gene regulatory network;

[0040] Obtain a single-cell differentiation trajectory and a cell topological entropy matrix based on the single-cell gene regulatory network representation matrix;

[0041] Analyze based on the cell topological entropy matrix and the single-cell differentiation trajectory to obtain the key evolution-determining genes in adjacent states of the differentiation trajectory.

[0042] Furthermore, normalizing the gene expression data includes: normalizing the gene expression data through the NormalizeData function and the ScaleData function to obtain a cell normalized expression matrix.

[0043] Specifically, the obtained dataset is the gene expression matrix of all cells in a cell type. The dataset source is the gene expression matrix of 6,500 cells with 35,000 genes measured by 10X Genomics for the same type of cells. The gene expression matrix contains the gene expression information of each cell. In this embodiment, a Seurat instance object is first created for the dataset; then, the gene expression information is normalized through the NormalizeData function and the ScaleData function to obtain the normalized expression matrix of the same type of cells. Among them, the ScaleData function and the NormalizeData function are implemented through ScaleData() and NormalizeData().

[0044] Further, constructing a single-cell gene regulatory network based on the cell normalized expression matrix includes: inputting the cell normalized expression matrix into the CSNet algorithm to construct a single-cell gene regulatory network.

[0045] Specifically, taking the normalized expression matrix of the same type of cells as the input, applying the CSNet algorithm to construct each cell gene regulatory network, including: inputting the normalized expression matrix of the same type of cells into the CSNet algorithm to obtain the single-cell gene regulatory relationship weight matrix, defining the edge weight values in the single-cell gene regulatory relationship weight matrix that meet the preset threshold as 1, and those that do not meet the preset threshold as 0, to obtain the single-cell gene regulatory relationship adjacency matrix, where the preset threshold is: p < 0.05, boxsize = 0.1, p represents the verification value of the pairwise gene relationship in each cell, and boxsize represents the range of the gene neighborhood in the network.

[0046] Further, constructing a single-cell gene regulatory network characterization matrix based on the single-cell gene regulatory network includes:

[0047] Taking the single-cell gene regulatory relationship adjacency matrix as the input data, using the Degree algorithm to calculate the degree value of each gene within a single cell;

[0048] Combining the degree values of each gene within a single cell to construct a single-cell gene regulatory network characterization matrix.

[0049] Specifically, taking the adjacency matrix of each cell gene regulatory relationship as the input data, applying the Degree algorithm to calculate the degree value of each gene within each cell, and combining them to obtain a single-cell gene regulatory network characterization matrix.

[0050] Further, constructing a single-cell differentiation trajectory based on the single-cell gene regulatory network characterization matrix includes:

[0051] Using the reduceDimension algorithm to perform feature dimensionality reduction on the degree values in the single-cell gene regulatory network characterization matrix;

[0052] Process according to the degree value after feature dimensionality reduction, and use the orderCells algorithm to obtain the cell differentiation trajectory.

[0053] Specifically, input the single-cell gene regulatory network characterization matrix. First, apply the reduceDimension algorithm to perform feature dimensionality reduction on the degree value, and then apply the orderCells algorithm to construct the cell differentiation trajectory. Among them, the reduceDimension algorithm is implemented through the reduceDimension() function, and the orderCells algorithm is implemented through the orderCells() function.

[0054] Furthermore, obtaining the cell topological entropy matrix includes: calculating the topological entropy value of each gene in a single cell using the topological entropy algorithm according to the single-cell gene regulatory relationship adjacency matrix and the single-cell gene regulatory relationship weight matrix, and obtaining the cell topological entropy matrix.

[0055] Even further, calculating the topological entropy value of each gene in a single cell using the topological entropy algorithm includes:

[0056] ;

[0057] Among them, represents the deviation of the degree value of and its first-order neighborhood genes, represents the average value of the first-order neighborhood edge weights, represents the average value of the second-order neighborhood edge weights, represents the i-th gene in the single-cell gene regulatory relationship adjacency matrix, is the degree of gene i in the single-cell gene regulatory relationship adjacency matrix,

[0058] Even further, obtaining the key evolution determining genes in the adjacent states of the differentiation trajectory includes:

[0059] Using the cell topological entropy matrix as the input data, according to the cell differentiation trajectory, use the wilcox test algorithm to obtain the key evolution determining genes.

[0060] Specifically, referring to the differentiation state results in the obtained differentiation trajectory, using the cell topological entropy matrix as the input data, applying the wilcox test, calculating the topological entropy difference genes in adjacent states, and defining them as the key evolution determining genes. Among them, the threshold is , represents the entropy value difference of adjacent states.

[0061] Further, after obtaining the key evolution-determining genes, it includes: performing GO enrichment analysis on the key evolution-determining genes.

[0062] Specifically, for the key evolution-determining genes in each differentiation state, the Metascape online analysis platform is used for GO function annotation.

[0063] Example 2:

[0064] Using the method for analyzing cell differentiation trajectories based on topological entropy in Example 1, data analysis is performed on the single-cell gene expression data of natural killer cells (NK). The specific steps are as follows:

[0065] (1) Data acquisition:

[0066] ① Download the single-cell data of NK cells from the GEO database (https: / / www.ncbi.nlm.nih.gov / geo / query / ). Among them, there are 6,500 NK cells, and each sample contains 35,000 genes.

[0067] ② Normalize through the NormalizeData function and the ScaleData function to obtain the processed expression profile. Part of the processed expression profile data is shown in Table 1.

[0068] Table 1

[0069]

[0070] (2) Construction of the single-cell gene regulatory network:

[0071] ① Apply the CSNet algorithm to construct the adjacency matrix of the gene regulatory relationship of each cell and the weight matrix of the gene regulatory relationship of all cells. Part of the weight matrix results are shown in Table 2, and part of the adjacency matrix results of the gene regulatory relationship are shown in Table 3.

[0072] ② Threshold selection , .

[0073] Table 2

[0074]

[0075] Table 3

[0076]

[0077] (3)Construction of the single-cell gene regulatory network characterization matrix:

[0078] ① Calculate the gene regulatory network representation matrix of each cell using the Degree algorithm, and the results are shown in Table 4.

[0079] Table 4

[0080]

[0081] (4)Construction of single-cell differentiation trajectories:

[0082] ① Apply the reduceDimension algorithm to reduce the dimension of the gene regulatory network representation matrix obtained in the previous step, and then use the orderCells function to construct its differentiation trajectory.

[0083] ② Display the results of dimension reduction by the reduceDimension algorithm through the plot_cell_trajectory function. The specific results are shown in Figure 2 .

[0084] (5)Identification of key evolution-determining genes:

[0085] Specifically, it includes the following steps:

[0086] ① Calculate the topological entropy value of each gene in each cell using the topological entropy algorithm. Some of the results are shown in Table 5.

[0087] Table 5

[0088]

[0089] ② Apply wilcox to calculate the key evolution-determining genes in adjacent states, where the threshold is set to .

[0090] Among them, State obtained 3126 key evolution-determining genes, State obtained 3012 key evolution-determining genes, State obtained 22 key evolution-determining genes, State obtained 351 key evolution-determining genes, State obtained 2754 key evolution-determining genes, State obtained 194 key evolution-determining genes. The analysis results of key evolution-determining genes are shown in Table 6:

[0091] Table 6

[0092]

[0093] (6)GO enrichment analysis:

[0094] Specifically, it includes the following steps:

[0095] Perform GO enrichment analysis on the key evolution-determining genes of different cell state evolutions and select the top 10 GO terms with Figure 3 values for subsequent in-depth research. The enriched biological processes of some evolution processes are shown as . Among them,

[0096] the state is mainly involved in chaperone-mediated protein folding, PCP / CE pathway (a kind of intracellular signal transduction pathway), cell response to hypoxia, proteasomal degradation of zinc finger protein GLI; the

[0097] state is mainly involved in B cell receptor signaling, FCERI-mediated NF-kB activation, FCERI signal transduction, granulocyte colony-stimulating factor G-CSF pathway, interleukin-1 signaling pathway, Hedgehog signaling pathway, C-type lectin receptor, non-canonical NF-kB signaling pathway, KEAP1-NFE2L2 signaling pathway, TCR signaling pathway, Dectin-1-mediated non-canonical NF-kB pathway, MAPK6 / MAPK4 signaling pathway, interleukin-12 signaling pathway, regulation of RAS gene by enzyme activator protein GAPs, etc. pathways. Thus, it is confirmed that there are significant differences in the evolution directions of different states. The above-described embodiments are only descriptions of the preferred embodiments of the present invention and do not limit the scope of the present invention. Without departing from the design spirit of the present invention, various deformations and improvements made by those of ordinary skill in the art to the technical solutions of the present invention shall fall within the protection scope determined by the claims of the present invention.

Claims

1. A method for analyzing cell differentiation trajectories based on topological entropy, characterized in that, Including: Obtaining gene expression data of cells in the same cell type; Normalizing the gene expression data to obtain a cell normalized expression matrix, and constructing a single-cell gene regulatory network according to the cell normalized expression matrix, wherein the single-cell gene regulatory network includes a single-cell gene regulatory relationship adjacency matrix and a single-cell gene regulatory relationship weight matrix; Constructing a single-cell gene regulatory network representation matrix according to the single-cell gene regulatory network; Obtaining a single-cell differentiation trajectory and a cell topological entropy matrix according to the single-cell gene regulatory network representation matrix; Analyzing according to the cell topological entropy matrix and the single-cell differentiation trajectory to obtain key evolution determining genes in adjacent states in the differentiation trajectory.

2. The method for analyzing cell differentiation trajectories based on topological entropy according to claim 1, wherein Normalizing the gene expression data includes: normalizing the gene expression data through the NormalizeData function and the ScaleData function to obtain the cell normalized expression matrix.

3. The method for analyzing cell differentiation trajectories based on topological entropy according to claim 1, characterized in that Constructing a single-cell gene regulatory network according to the cell normalized expression matrix includes: Inputting the cell normalized expression matrix into the CSNet algorithm to obtain the single-cell gene regulatory relationship weight matrix; Defining the edge weight values in the cell gene regulatory relationship weight matrix according to a preset threshold to obtain the single-cell gene regulatory relationship adjacency matrix; Obtaining the single-cell gene regulatory network according to the single-cell gene regulatory relationship adjacency matrix and the single-cell gene regulatory relationship weight matrix.

4. The method for analyzing cell differentiation trajectories based on topological entropy according to claim 1, characterized in that, Constructing a single-cell gene regulatory network representation matrix according to the single-cell gene regulatory network includes: Using the Degree algorithm to calculate the degree value of each gene in a single cell with the single-cell gene regulatory relationship adjacency matrix as input data; Merging the degree values of each gene in the single cell to construct the single-cell gene regulatory network representation matrix.

5. The method for analyzing cell differentiation trajectories based on topological entropy according to claim 1, wherein Constructing a single-cell differentiation trajectory according to the single-cell gene regulatory network representation matrix includes: Using the reduceDimension algorithm to perform feature dimensionality reduction on the degree values in the single-cell gene regulatory network representation matrix; Processing according to the degree values after feature dimensionality reduction and using the orderCells algorithm to obtain the cell differentiation trajectory.

6. The method for analyzing cell differentiation trajectories based on topological entropy according to claim 4, wherein Obtaining the cell topological entropy matrix includes: calculating the topological entropy value of each gene in a single cell using the topological entropy algorithm according to the single-cell gene regulatory relationship adjacency matrix and the single-cell gene regulatory relationship weight matrix to obtain the cell topological entropy matrix.

7. The method for analyzing cell differentiation trajectories based on topological entropy according to claim 1, wherein Calculating the topological entropy value of each gene in a single cell using the topological entropy algorithm includes: ； Among them, represents the deviation of the degree value of and its first-order neighborhood genes, represents the average value of the first-order neighborhood edge weights, represents the average value of the second-order neighborhood edge weights, represents the i-th gene in the adjacency matrix of single-cell gene regulatory relationships, is the degree of gene i in the adjacency matrix of single-cell gene regulatory relationships, is the topological entropy value of each gene in a single cell.

8. The method for analyzing cell differentiation trajectories based on topological entropy according to claim 1, wherein Obtaining the key evolution determining genes in adjacent states in the differentiation trajectory includes: Using the cell topological entropy matrix as input data, and according to the cell differentiation trajectory, using the wilcox test algorithm to obtain the key evolution determining genes.

9. The method for analyzing cell differentiation trajectories based on topological entropy according to claim 8, wherein After obtaining the key evolution determining genes includes: performing GO enrichment analysis on the key evolution determining genes.

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