A cell differentiation trajectory analysis method based on topological entropy

Abstract

The present invention relates to the field of data analysis technology, and in particular to a method for analyzing cell differentiation trajectories based on topological entropy, comprising the following steps: obtaining gene expression data of cells of the same cell type; standardizing the gene expression data to obtain a cell standardized expression matrix, and constructing a single-cell gene regulatory network based on the cell standardized expression matrix; constructing a single-cell gene regulatory network representation matrix based on the single-cell gene regulatory network; obtaining a single-cell differentiation trajectory and a cell topological entropy matrix based on the single-cell gene regulatory network representation matrix; and analyzing the cell topological entropy matrix and the single-cell differentiation trajectory to obtain key evolution-determining genes of adjacent states in the differentiation trajectory. The present invention identifies key evolution-determining genes based on topological entropy, and can effectively screen key nodes that affect fluctuations in the stability of the adjacent state network structure.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and in particular to a cell differentiation trajectory analysis method based on topological entropy. Background Art

[0002] In recent years, single-cell transcriptomics has become an important technical tool in immunology and developmental biology research. Among these, key challenges remain in elucidating the differentiation patterns of different cell types and effectively identifying the key molecules that determine the progression from a poorly differentiated state to a more highly differentiated state. Although differentiation trajectory inference algorithms such as Slingshot and Scanpy have been widely used to infer cell differentiation, these tools primarily use the strength of gene expression as the key to inferring cell state, without considering the evolution of gene regulatory networks and the key genes that determine cell differentiation as cells transition from immaturity to maturity. Therefore, a topological entropy-based method for analyzing cell differentiation trajectories is proposed, which is of great significance in cell differentiation research. Summary of the Invention

[0003] The purpose of the present invention is to provide a cell differentiation trajectory analysis method 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 solutions:

[0005] A cell differentiation trajectory analysis method based on topological entropy, comprising:

[0006] Obtain gene expression data for cells of 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 based on 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 representation matrix according to the single-cell gene regulatory network;

[0009] Obtaining single-cell differentiation trajectories and cell topology entropy matrices based on the single-cell gene regulatory network representation matrix;

[0010] The cell topology entropy matrix and the single cell differentiation trajectory are analyzed to obtain key evolution-determining genes of adjacent states in the differentiation trajectory.

[0011] Optionally, normalizing the gene expression data includes: normalizing the gene expression data by using a NormalizeData function and a 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 regulation relationship weight matrix;

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

[0015] The single-cell gene regulatory network is obtained according to 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 single-cell gene regulatory relationship adjacency matrix as input data, the degree algorithm is used to calculate the degree value of each gene in the single cell;

[0018] The degree values ​​of each gene in the single cell are merged to construct the single cell gene regulatory network representation matrix.

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

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

[0021] The cell differentiation trajectory is obtained by processing the degree value after feature dimensionality reduction using the orderCells algorithm.

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

[0023] Optionally, the topological entropy algorithm is used to calculate the topological entropy value of each gene in a single cell, including:

[0024] ;

[0025] in, represent and the deviation of the degree value of its first-order neighboring genes, represent The mean of the first-order neighborhood edge weights, represent The mean of the second-order neighborhood edge weights, Represents the i-th gene in the single-cell gene regulatory relationship adjacency matrix, represents the degree of gene i in the adjacency matrix of single-cell gene regulatory relationships, is the topological entropy of each gene in a single cell.

[0026] Optionally, key evolution-determining genes for adjacent states in the differentiation trajectory include:

[0027] The cell topology entropy matrix is ​​used as input data, and the Wilcox test algorithm is used according to the cell differentiation trajectory to obtain the key evolution-determining genes.

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

[0029] The beneficial effects of the present invention are as follows: the current inference of cell differentiation patterns mainly uses the strength of the cell gene expression state as the key to inferring the cell state, without considering the evolution of the gene regulatory network during cell differentiation and the key gene nodes that determine cell differentiation. In order to solve the above technical problems, the present invention first obtains a single-cell data set of the same cell, and then constructs a single-cell gene regulatory network through the CSNet algorithm. Next, a single-cell gene regulatory network characterization matrix is ​​constructed through the Degree algorithm, and the key evolution-determining genes of the cell adjacent state are screened by topological entropy combined with the Wilcox test. Finally, the above genes are biologically annotated by 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, which can effectively screen key nodes that affect the fluctuation of the stability of the adjacent state network structure. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] 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. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0031] Figure 1This is a flowchart of a cell differentiation trajectory analysis method based on topological entropy according to an embodiment of the present invention;

[0032] Figure 2 A schematic diagram of cell differentiation trajectory according to an embodiment of the present invention;

[0033] Figure 3 This is a schematic diagram of the GO enrichment analysis results of the S1 to S7 state and the S1 to S2 state in an embodiment of the present invention. DETAILED DESCRIPTION

[0034] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0035] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0036] like Figure 1 As shown, this embodiment provides a cell differentiation trajectory analysis method based on topological entropy, including:

[0037] Obtain gene expression data for 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. 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] According to the single-cell gene regulatory network, a single-cell gene regulatory network representation matrix is ​​constructed;

[0040] According to the single-cell gene regulatory network representation matrix, the single-cell differentiation trajectory and cell topology entropy matrix are obtained;

[0041] Based on the cell topology entropy matrix and single-cell differentiation trajectory analysis, the key evolution-determining genes of adjacent states in the differentiation trajectory are obtained.

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

[0043] Specifically, the dataset obtained is the gene expression matrix of all cells in a cell type. The dataset is sourced from a gene expression matrix of 6,500 cells of the same cell type, measuring 35,000 genes. The gene expression matrix contains gene expression information for each cell. This example first creates a Seurat instance object for the dataset; then, the gene expression information is normalized using the NormalizeData function and the ScaleData function to obtain a standardized expression matrix for the same cell type. The ScaleData function and the NormalizeData function are implemented using ScaleData() and NormalizeData().

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

[0045] Specifically, the standardized expression matrix of the same type of cells was used as input, and the CSNet algorithm was applied to construct the gene regulatory network of each cell, including: inputting the standardized 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 value in the single-cell gene regulatory relationship weight matrix as 1 if it meets the preset threshold, and defining it as 0 if it does not meet the preset threshold, and obtaining 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] Furthermore, based on the single-cell gene regulatory network, a single-cell gene regulatory network representation matrix is ​​constructed, including:

[0047] Using the single-cell gene regulatory relationship adjacency matrix as input data, the Degree algorithm is used to calculate the degree value of each gene in a single cell;

[0048] The degree values ​​of each gene in a single cell are merged to construct a single-cell gene regulatory network representation matrix.

[0049] Specifically, the adjacency matrix of gene regulation relationships in each cell is used as input data, and the degree algorithm is applied to calculate the degree value of each gene in each cell, and the single-cell gene regulatory network representation matrix is ​​obtained by merging them.

[0050] Furthermore, based on the single-cell gene regulatory network representation matrix, the single-cell differentiation trajectory is constructed, including:

[0051] The reduceDimension algorithm is used to reduce the dimension of the degree value in the representation matrix of the single-cell gene regulatory network;

[0052] The orderCells algorithm is used to obtain the cell differentiation trajectory based on the degree value after feature dimensionality reduction.

[0053] Specifically, the single-cell gene regulatory network representation matrix is ​​input, and the reduceDimension algorithm is first applied to reduce the dimension of the degree value, and then the orderCells algorithm is applied to construct the cell differentiation trajectory. Among them, the reduceDimension algorithm is implemented by the reduceDimension() function, and the orderCells algorithm is implemented by 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 a 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.

[0055] Furthermore, the topological entropy algorithm is used to calculate the topological entropy value of each gene in a single cell, including:

[0056] ;

[0057] in, represent and the deviation of the degree value of its first-order neighboring genes, represent The mean of the first-order neighborhood edge weights, represent The mean of the second-order neighborhood edge weights, Represents the i-th gene in the single-cell gene regulatory relationship adjacency matrix, represents the degree of gene i in the adjacency matrix of single-cell gene regulatory relationships, is the topological entropy of each gene in a single cell.

[0058] Furthermore, the key evolution-determining genes for obtaining adjacent states in the differentiation trajectory include:

[0059] The cell topology entropy matrix was used as input data and the Wilcox test algorithm was used to obtain key evolution-determining genes according to the cell differentiation trajectory.

[0060] Specifically, referring to the differentiation state results obtained in the differentiation trajectory, the cell topological entropy matrix is ​​used as input data, and the Wilcox test is applied to calculate the topological entropy difference genes of adjacent states and define them as key evolution-determining genes, where the threshold is , Represents the entropy difference between adjacent states.

[0061] Furthermore, after obtaining the key evolution-determining genes, the following steps are included: performing GO enrichment analysis on the key evolution-determining genes.

[0062] Specifically, the Metascape online analysis platform was used to perform GO functional annotation on the key evolution-determining genes of each differentiation state.

[0063] Example 2:

[0064] The cell differentiation trajectory analysis method based on topological entropy in Example 1 was used to analyze the single-cell gene expression data of natural killer cells (NK cells), specifically including the following steps:

[0065] (1) Data acquisition:

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

[0067] ② The processed expression profiles were normalized using the NormalizeData function and the ScaleData function. Some of the processed expression profile data are shown in Table 1.

[0068] Table 1

[0069]

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

[0071] ① The CSNet algorithm was used to construct the gene regulation relationship adjacency matrix and cell gene regulation relationship weight matrix for each cell. The results of some weight matrices are shown in Table 2, and the results of some gene regulation relationship adjacency matrices are shown in Table 3.

[0072] ②Threshold selection , .

[0073] Table 2

[0074]

[0075] Table 3

[0076]

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

[0078] ① The Degree algorithm was used to calculate the gene regulatory network representation matrix of each cell. The results are shown in Table 4.

[0079] Table 4

[0080]

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

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

[0083] ② The reduceDimension result is displayed through the plot_cell_trajectory function. For specific results, see Figure 2 .

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

[0085] The specific steps include:

[0086] ① The topological entropy algorithm was applied to calculate the topological entropy value of each gene in each cell. Some of the results are shown in Table 5.

[0087] Table 5

[0088]

[0089] ② Apply Wilcox to calculate the key evolution-determining genes of neighboring states, where the threshold is set to .

[0090] in, The state obtained 3126 key evolution-determining genes, The state obtained 3012 key evolution-determining genes, The state obtained 22 key evolution-determining genes, The state obtained 351 key evolution-determining genes, The state obtained 2754 key evolution-determining genes, The results of the analysis of key evolution-determining genes are shown in Table 6:

[0091] Table 6

[0092]

[0093] (6) GO enrichment analysis:

[0094] The specific steps include:

[0095] The key evolution-determining genes of different cell states were analyzed by GO enrichment analysis, and the The GO terms with the top 10 values ​​were further studied, and some of the evolutionary processes were enriched in biological processes as shown below. Figure 3 ,in, The values ​​represent the enrichment significance of the GO terms in the dataset.

[0096] The enrichment results showed The state is mainly involved in molecular chaperone-mediated protein folding, PCP / CE pathway (an intracellular signal transduction pathway), cellular response to hypoxia, and proteasome degradation of zinc finger protein GLI; The states are 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-classical NF-kB signaling pathway, KEAP1-NFE2L2 signaling pathway, TCR signaling pathway, Dectin-1-mediated non-classical NF-kB pathway, MAPK6 / MAPK4 signaling pathway, interleukin-12 signaling pathway, enzyme-activated protein GAPs regulation of RAS gene and other pathways, thus confirming that there are significant differences in the evolution direction of different states.

[0097] The embodiments described above are merely descriptions of preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by persons skilled in the art should fall within the scope of protection defined by the claims of the present invention.

Claims

1. A cell differentiation trajectory analysis method based on topological entropy, characterized in that: include: Obtain gene expression data for cells of the same cell type; Normalizing the gene expression data to obtain a cell-normalized expression matrix, and constructing a single-cell gene regulatory network based on 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 single-cell differentiation trajectories and cell topology entropy matrices based on the single-cell gene regulatory network representation matrix; The cell topology entropy matrix and the single cell differentiation trajectory are analyzed to obtain key evolution-determining genes of adjacent states in the differentiation trajectory.

2. The cell differentiation trajectory analysis method based on topological entropy according to claim 1, characterized in that: Normalizing the gene expression data includes: normalizing the gene expression data by using a NormalizeData function and a ScaleData function to obtain the cell standardized expression matrix.

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

4. The cell differentiation trajectory analysis method based on topological entropy according to claim 1, characterized in that: According to the single-cell gene regulatory network, constructing a single-cell gene regulatory network representation matrix includes: Using the single-cell gene regulatory relationship adjacency matrix as input data, the degree algorithm is used to calculate the degree value of each gene in the single cell; The degree values ​​of each gene in the single cell are merged to construct the single cell gene regulatory network representation matrix.

5. The cell differentiation trajectory analysis method based on topological entropy according to claim 1, characterized in that: According to the single-cell gene regulatory network representation matrix, constructing a single-cell differentiation trajectory includes: Using the reduceDimension algorithm to perform feature dimensionality reduction on the degree value in the representation matrix of the single-cell gene regulatory network; The cell differentiation trajectory is obtained by processing the degree value after feature dimensionality reduction using the orderCells algorithm.

6. The cell differentiation trajectory analysis method based on topological entropy according to claim 4, characterized in that: Obtaining the cell topology entropy matrix includes: calculating the topology entropy value of each gene in a single cell using a topology entropy algorithm according to the single-cell gene regulation relationship adjacency matrix and the single-cell gene regulation relationship weight matrix to obtain the cell topology entropy matrix.

7. The cell differentiation trajectory analysis method based on topological entropy according to claim 1, characterized in that: The topological entropy algorithm is used to calculate the topological entropy value of each gene in a single cell, including: ； in, represent and the deviation of the degree value of its first-order neighboring genes, represent The mean of the first-order neighborhood edge weights, represent The mean of the second-order neighborhood edge weights, Represents the i-th gene in the single-cell gene regulatory relationship adjacency matrix, represents the degree of gene i in the adjacency matrix of single-cell gene regulatory relationships, is the topological entropy of each gene in a single cell.

8. The cell differentiation trajectory analysis method based on topological entropy according to claim 1, characterized in that: Key evolutionary determinants of the acquisition of adjacent states in the differentiation trajectory include: The cell topology entropy matrix is ​​used as input data, and the Wilcox test algorithm is used according to the cell differentiation trajectory to obtain the key evolution-determining genes.

9. The cell differentiation trajectory analysis method based on topological entropy according to claim 8, characterized in that: After obtaining the key evolution-determining genes, the method includes: performing GO enrichment analysis on the key evolution-determining genes.

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