Method and system for constructing dynamic gene regulation network and computer equipment

Abstract

The invention relates to a method and a system for constructing a dynamic gene regulation network, computer equipment, a computer readable storage medium, a computer program product and a computer program. The method comprises the following steps: constructing a plurality of single cell quasi-time sequence differentiation trajectories based on single cell transcriptome data of a plurality of single cells; determining a dynamic change gene in a target quasi-timing sequence differentiation trajectory based on the plurality of single cell quasi-timing sequence differentiation trajectories; constructing an initial static gene interaction network based on the dynamic change gene; and performing inspection based on the initial static gene interaction network to obtain the dynamic gene regulation network. According to the method, key gene regulation mechanisms corresponding to different differentiation stages of the cells can be rapidly determined, and the real dynamic change condition of the gene regulation relationship in the biological process is reflected.

Description

Method and system for constructing dynamic gene regulatory network and computer device Technical Field

[0001] The present invention relates to the field of bioinformatics analysis, and in particular to a method and system for constructing a dynamic gene regulatory network using single-cell transcriptome data, as well as a computer device, a computer-readable storage medium, a computer program product, and a computer program. Background Art

[0002] Numerous studies in biomedical fields such as development and cancer involve the transitions between cell states during biological processes. Cell differentiation or cell state transitions are dynamic processes, with different stages corresponding to distinct gene regulatory relationships. Gene Regulatory Networks (GRNs) are powerful tools for describing and explaining the molecular mechanisms underlying different cell states in these biological processes. With the development and maturity of single-cell RNA-sequencing technology (scRNA-Seq), a method for constructing dynamic gene regulatory networks is needed to study dynamic gene regulatory network relationships across the genome and fully reflect the regulatory characteristics between genes.

[0003] Summary of the Invention

[0004] The present invention aims to solve one of the technical problems in the related art at least to a certain extent.

[0005] To this end, embodiments of the present invention provide methods and systems for constructing dynamic gene regulatory networks, as well as computer devices, computer-readable storage media, computer program products, and computer programs. The methods provided by embodiments of the present invention can improve the accuracy of gene regulatory networks, increase spatiotemporal resolution, and facilitate rapid identification of key dynamic gene regulatory mechanisms corresponding to different stages of cell differentiation.

[0006] In a first aspect, an embodiment of the present invention provides a method for constructing a dynamic gene regulatory network, comprising:

[0007] Construct multiple single-cell pseudo-temporal differentiation trajectories based on the single-cell transcriptome data of multiple single cells;

[0008] Determine the dynamically changing genes within the target pseudo-temporal differentiation trajectory based on multiple single-cell pseudo-temporal differentiation trajectories;

[0009] Construct an initial static correlation gene interaction network based on dynamically changing genes;

[0010] Based on the initial static gene interaction network, a dynamic gene regulatory network was obtained.

[0011] In some embodiments, the step of constructing multiple single-cell pseudo-temporal differentiation trajectories based on single-cell transcriptome data of multiple single cells includes:

[0012] Preprocessing the single-cell transcriptome sequencing data to obtain a preprocessed single-cell gene expression matrix;

[0013] Calculate pseudo-time series values ​​of multiple single cells based on the single-cell gene expression matrix;

[0014] Multiple single-cell pseudo-temporal differentiation trajectories were obtained by arranging the pseudo-temporal values ​​of multiple single cells.

[0015] In some embodiments, preprocessing includes at least one of data quality control, data normalization, screening of highly variable genes, data dimensionality reduction, cell clustering, and cell type annotation.

[0016] In some embodiments, determining dynamically changing genes within a target pseudo-temporal differentiation trajectory based on multiple single-cell pseudo-temporal differentiation trajectories comprises:

[0017] Determine the target pseudo-temporal differentiation trajectory based on multiple single-cell pseudo-temporal differentiation trajectories;

[0018] Extracting pseudo-temporal values ​​of multiple single cells and the single-cell transcriptome data based on the target pseudo-temporal differentiation trajectory;

[0019] Identify dynamically changing genes based on pseudo-time series values ​​and single-cell transcriptome data of multiple single cells.

[0020] In some embodiments, dynamically changing genes are identified using tradeSeq, waddR, uSORT, scDE, or BASICs.

[0021] In some embodiments, the step of constructing an initial static gene interaction network based on the expression of dynamically changing genes includes:

[0022] Calculate the mutual information value between dynamically changing genes based on dynamically changing genes;

[0023] The initial static gene interaction network was constructed based on the mutual information value.

[0024] In some embodiments, the step of obtaining a dynamic gene regulatory network by testing based on an initial static gene interaction network includes:

[0025] The first test is performed based on the initial static gene interaction network to obtain the adjacency matrix of the initial static gene interaction network of the target pseudo-temporal differentiation trajectory;

[0026] A second test was performed based on the adjacency matrix of the initial static gene interaction network to obtain a dynamic gene regulatory network.

[0027] In some embodiments, the first test is a t-test, and performing the first test based on the initial static gene interaction network to obtain an adjacency matrix of the initial static gene interaction network of the target pseudo-temporal differentiation trajectory includes:

[0028] Based on the initial static gene interaction network obeying the normal distribution, the original hypothesis is proposed as follows:

[0029] Constructing statistics

[0030] Based on dynamic genes, we screen the transcription factor gene subset and extract the mutual information value x between genes i and j in the subset. i,j ;

[0031] Calculate the sample size N=n based on the initial static gene interaction network 动态基因数 -1, the mean mutual information value between dynamic gene j and all dynamic genes and its standard deviation σ, then substitute it into the constructed statistic t and calculate the p-value;

[0032] Reject the null hypothesis based on a p-value < 0.05 and fill the adjacency matrix with the value 1;

[0033] The null hypothesis was accepted based on a p-value > 0.05, and the adjacency matrix was filled with the value 0.

[0034] In some embodiments, the second test is a Granger causality test, and performing the second test based on the adjacency matrix of the initial static gene interaction network to obtain a dynamic gene regulatory network includes:

[0035] Arrange the pseudo-time series values ​​of multiple single cells based on the target differentiation trajectory and set a sliding window;

[0036] Traverse the adjacency matrix based on a sliding window;

[0037] Based on the value of 0 in the adjacency matrix, fill 0 into the dynamic gene regulatory network;

[0038] Extract single-cell transcriptome data of dynamic genes based on the value of 1 in the adjacency matrix;

[0039] Based on the single-cell transcriptome data of dynamic genes, the expression data that meets the Granger test is processed;

[0040] Determine Granger causality based on expression data that satisfies the Granger test within the sliding window;

[0041] Obtain dynamic gene regulatory networks based on Granger causality.

[0042] In some embodiments, the sliding window length is greater than or equal to M k / 5, less than Mk -1, where M k is the number of single cells in the target differentiation trajectory k. The left endpoint of the sliding window starts at the first cell of the target differentiation trajectory k, and the right endpoint ends at the last cell of the target differentiation trajectory k. The step length of each sliding is 1 cell.

[0043] In some embodiments, processing single-cell transcriptome data based on dynamic genes includes:

[0044] Obtain detrended and demeaned data based on single-cell transcriptome data of dynamic genes;

[0045] Based on the detrended and demeaned data, the KPSS test for stationarity was performed;

[0046] Based on the non-stationary detrended and demeaned data, first-order difference processing is performed to obtain stationary detrended and demeaned data;

[0047] Based on the stationary detrended and demeaned data, expression data that meets the Granger test were obtained.

[0048] In some embodiments, the step of obtaining a dynamic gene regulatory network based on Granger causality comprises:

[0049] Determine whether Granger causality holds true;

[0050] Based on the fact that Granger causality does not hold, fill 0 into the dynamic gene regulatory network;

[0051] Based on the Granger causality, single-cell transcriptome data of dynamic genes are extracted;

[0052] Based on the single-cell transcriptome data of dynamic genes that are up-regulated or down-regulated, 1 or -1 is filled into the dynamic gene regulatory network.

[0053] In some embodiments, the dynamic gene regulatory network is a three-dimensional dynamic gene regulatory interaction matrix of order G×G×S, where G represents dynamically changing genes and S is the number of steps the sliding window slides.

[0054] In some embodiments, the method for constructing a dynamic gene regulatory network further comprises visualizing the dynamic gene regulatory network using ndtv, GENIE3, or cytoscape software.

[0055] In some embodiments, the single-cell transcriptome data is derived from organs, tissues, saliva, urine, blood, cells, wherein the cells are in different stages of differentiation or different physiological states, optionally the single-cell transcriptome data is derived from a developmental sample or a tumor sample, preferably, from mouse pancreas.

[0056] In a second aspect, an embodiment of the present invention provides a dynamic gene regulatory network construction system, comprising:

[0057] A pseudo-temporal differentiation trajectory construction device is configured to construct multiple single-cell pseudo-temporal differentiation trajectories based on single-cell transcriptome data of multiple single cells,

[0058] a dynamically changing gene determining device configured to determine dynamically changing genes within a target pseudo-temporal differentiation trajectory based on a plurality of single-cell pseudo-temporal differentiation trajectories;

[0059] An initial static gene interaction network construction device configured to construct an initial static gene interaction network based on the expression of dynamically changing genes;

[0060] The dynamic gene regulatory network obtaining device is configured to perform a test based on an initial static gene interaction network to obtain a dynamic gene regulatory network.

[0061] In some embodiments, the quasi-temporal differentiation trajectory acquisition device includes:

[0062] a preprocessing module, configured to preprocess the single-cell transcriptome sequencing data to obtain a preprocessed single-cell gene expression matrix;

[0063] a pseudo-time series value calculation module, configured to calculate the pseudo-time series values ​​of multiple single cells based on the single-cell gene expression matrix;

[0064] The pseudo-temporal differentiation trajectory obtaining module is configured to arrange the pseudo-temporal values ​​of the multiple single cells to obtain the multiple single-cell pseudo-temporal differentiation trajectories.

[0065] In some embodiments, the preprocessing includes at least one of data quality control, data normalization, screening of highly variable genes, data dimensionality reduction, cell clustering, and cell type annotation.

[0066] In some embodiments, the dynamically changing gene determination device comprises:

[0067] a target pseudo-temporal differentiation trajectory determination module, configured to determine a target pseudo-temporal differentiation trajectory based on the multiple single-cell pseudo-temporal differentiation trajectories;

[0068] an extraction module configured to extract the pseudo-temporal values ​​of the plurality of single cells and the single-cell transcriptome data based on the target pseudo-temporal differentiation trajectory;

[0069] The dynamically changing gene determination module is configured to determine the dynamically changing gene based on the pseudo-time series values ​​of the multiple single cells and the single cell transcriptome data.

[0070] In some embodiments, the dynamically changing gene determination module uses tradeSeq, waddR, uSORT, scDE or BASICs to determine the dynamically changing genes.

[0071] In some embodiments, the initial static gene interaction network construction device includes:

[0072] a mutual information value calculation module, configured to calculate the mutual information value between the dynamically changing genes based on the dynamically changing genes;

[0073] A static gene interaction network construction module is configured to construct the initial static gene interaction network based on the mutual information value.

[0074] In some embodiments, the dynamic gene regulatory network obtaining device comprises:

[0075] A first testing module is configured to perform a first test based on the initial static gene interaction network to obtain an adjacency matrix of the initial static gene interaction network of the target pseudo-temporal differentiation trajectory;

[0076] The second testing module is configured to perform a second test based on the adjacency matrix of the initial static gene interaction network to obtain a dynamic gene regulatory network.

[0077] In some embodiments, the first test is a t-test, and the first test module includes:

[0078] The hypothesis testing unit is configured to propose the null hypothesis based on the initial static gene interaction network obeying the normal distribution:

[0079] t statistic construction unit, configured to construct the statistic

[0080] The mutual information value extraction unit is configured to screen the transcription factor gene subset based on the dynamically changing gene and extract the mutual information value x of the dynamically changing gene i and j in the subset. i,j ;

[0081] The t statistic calculation unit is configured to calculate the sample size N=n number of dynamic genes-1, the mutual information value mean of the dynamic change gene j and all dynamic genes based on the initial static gene interaction network and its standard deviation σ, then substitute it into the constructed statistic t and calculate the p-value;

[0082] The adjacency matrix 1 filling unit is configured to reject the null hypothesis based on the p-value being less than 0.05 and fill the adjacency matrix with a value of 1;

[0083] The adjacency matrix zero-filling unit is configured to accept the null hypothesis based on the p-value>0.05 and fill the adjacency matrix with a value of 0.

[0084] In some embodiments, the second test is a Granger causality test, and the second test module includes:

[0085] a Granger test unit configured to arrange the pseudo-time series values ​​of the plurality of single cells based on the target differentiation trajectory and set a sliding window;

[0086] a traversal unit, configured to traverse the adjacency matrix based on the sliding window;

[0087] Dynamic gene regulatory network 0 filling unit, configured as the I based on the adjacency matrix (i,j) The value is 0, filling 0 into the dynamic gene regulatory network;

[0088] The single cell transcriptome data extraction unit is configured to extract the data based on the adjacency matrix I (i,j) The value is 1, and the single-cell transcriptome data of the dynamic gene is extracted;

[0089] a processing unit configured to process the single-cell transcriptome data based on the dynamic gene to obtain expression data that satisfies the Granger test;

[0090] a Granger relationship judgment unit, configured to judge the Granger causality based on the expression data satisfying the Granger test in the sliding window;

[0091] The dynamic gene regulatory network obtaining unit is configured to obtain a dynamic gene regulatory network based on the Granger causality.

[0092] In some embodiments, the sliding window length is greater than or equal to M k / 2-M k / 10, preferred M k / 3-M k / 7, more preferably M k / 5, less than M k -1, where M k is the number of multiple single cells in the target differentiation trajectory k. The left endpoint of the sliding window starts at the first cell of the target differentiation trajectory k, and the right endpoint ends at the last cell of the target differentiation trajectory k. The step size of each sliding is 1 to 5, preferably 1-3, and more preferably 1 cell.

[0093] In some embodiments, the processing unit includes:

[0094] a detrending and demeaning sub-data obtaining sub-unit, configured to obtain detrending and demeaning data based on the single-cell transcriptome data of the dynamic gene;

[0095] a stationarity test subunit, configured to perform a KPSS test for stationarity based on the detrended and demeaned data;

[0096] a first-order difference processing subunit, configured to perform first-order difference processing on the non-stationary detrended and de-meaned data to obtain the stationary detrended and de-meaned data;

[0097] The Granger causality judgment subunit is configured to judge the Granger causality based on the stationary detrended and demeaned data.

[0098] In some embodiments, the dynamic gene regulatory network obtaining unit comprises:

[0099] A dynamic gene regulatory network 0-filling subunit is configured to fill 0 into the dynamic gene regulatory network based on the failure of Granger causality;

[0100] a single-cell transcriptome data extraction subunit, configured to extract the single-cell transcriptome data of the dynamically changing genes based on the Granger causality;

[0101] The dynamic gene regulatory network 1 or -1 filling subunit is configured to fill 1 or -1 into the dynamic gene regulatory network based on the upregulation or downregulation of the single-cell transcriptome data of the dynamically changing gene.

[0102] In some embodiments, the dynamic gene regulatory network is a three-dimensional dynamic gene regulatory interaction matrix of order G×G×S, where G is the dynamically changing gene and S is the number of steps that the sliding window slides.

[0103] In some embodiments, the system further comprises a visualization device, which enables visualization of the dynamic gene regulatory network through ndtv, GENIE3 or cytoscape software.

[0104] In some embodiments, the single-cell transcriptome data is derived from organs, tissues, saliva, urine, blood, cells, wherein the cells are in different stages of differentiation or different physiological states, optionally the single-cell transcriptome data is derived from a developmental sample or a tumor sample, preferably, from mouse pancreas.

[0105] In a third aspect, an embodiment of the present invention provides a device for constructing a dynamic gene regulatory network, comprising:

[0106] processor;

[0107] a memory for storing executable instructions;

[0108] The processor is used to read executable instructions from the memory and execute the executable instructions to implement the method for constructing a dynamic gene regulatory network provided by the first aspect of the implementation scheme.

[0109] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the processor implements the method for constructing a dynamic gene regulatory network provided by the embodiment of the first aspect above.

[0110] In a fifth aspect, an embodiment of the present invention provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the method for constructing a dynamic gene regulatory network provided in the embodiment of the first aspect above.

[0111] In a sixth aspect, an embodiment of the present invention provides a computer program, which includes computer program code. When the computer program code is run on a computer, the computer executes the method for constructing a dynamic gene regulatory network provided in the embodiment of the first aspect above.

[0112] The embodiments of the present invention have the following beneficial effects:

[0113] 1. Detect regulatory relationships across the entire genome in a single experiment, reflecting the true dynamic changes in gene regulatory network structures during biological processes.

[0114] 2. Reduce false positives in interactions caused by indirect regulatory effects and improve the accuracy of determining network relationships.

[0115] 3. Increase the spatiotemporal resolution of gene regulatory networks to facilitate rapid identification of key gene regulatory mechanisms corresponding to cells at different differentiation stages. BRIEF DESCRIPTION OF THE DRAWINGS

[0116] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art. Obviously, the drawings described below are only some embodiments of the present disclosure, and a person skilled in the art can also obtain other drawings based on these drawings.

[0117] FIG1 is a flow chart of a method for constructing a dynamic gene regulatory network according to an embodiment of the present invention.

[0118] FIG2 is a schematic diagram of a process for constructing multiple single-cell pseudo-temporal differentiation trajectories according to an embodiment of the present invention.

[0119] FIG3 is a schematic diagram of a process for determining dynamically changing genes according to an embodiment of the present invention.

[0120] FIG4 is a schematic diagram of a process for constructing an initial static gene interaction network according to an embodiment of the present invention.

[0121] FIG5 is a schematic diagram of a process for obtaining a dynamic gene regulatory network based on an initial static gene interaction network according to an embodiment of the present invention.

[0122] 6 is a flowchart of performing a first test based on the initial static gene interaction network to obtain an adjacency matrix of the initial static gene interaction network of the target pseudo-temporal differentiation trajectory according to an embodiment of the present invention.

[0123] FIG7 is a schematic diagram of a process for obtaining a dynamic gene regulatory network based on an adjacency matrix according to an embodiment of the present invention.

[0124] FIG8 is a schematic diagram of a process for processing expression data satisfying the Granger test according to an embodiment of the present invention.

[0125] FIG9 is a schematic diagram of a process for obtaining a dynamic gene regulatory network based on Granger causality according to an embodiment of the present invention.

[0126] FIG10 is a flow chart of a method for constructing a dynamic gene regulatory network according to an embodiment of the present invention.

[0127] FIG11 is a schematic diagram of a preprocessed single-cell gene expression matrix according to an embodiment of the present invention.

[0128] FIG12 is a diagram showing the single cell differentiation trajectory and the pseudo-time series value of each cell according to an embodiment of the present invention.

[0129] FIG13 is a diagram showing the extracted target differentiation trajectory results according to an embodiment of the present invention.

[0130] FIG14 is a schematic diagram of the screening results of genes that dynamically change along the cell differentiation trajectory according to an embodiment of the present invention.

[0131] FIG15 is a schematic diagram of an adjacency matrix of an initial static gene interaction network according to an embodiment of the present invention.

[0132] FIG16 is a diagram showing the results of data detrending and demeaning according to an embodiment of the present invention.

[0133] FIG17 is a schematic diagram of a sliding window and a dynamic gene regulatory network structure matrix according to an embodiment of the present invention.

[0134] FIG18 is a schematic diagram of the final visualization result of the dynamic gene regulatory network according to an embodiment of the present invention.

[0135] FIG19 is a schematic diagram of the system structure for constructing a dynamic gene regulatory network according to an embodiment of the present invention.

[0136] FIG20 is a schematic structural diagram of a device for acquiring quasi-temporal differentiation trajectories according to an embodiment of the present invention.

[0137] FIG21 is a schematic structural diagram of a device for determining a dynamically changing gene according to an embodiment of the present invention.

[0138] FIG22 is a schematic structural diagram of an apparatus for constructing an initial static gene interaction network according to an embodiment of the present invention.

[0139] FIG23 is a schematic diagram of the structure of a device for obtaining a dynamic gene regulatory network according to an embodiment of the present invention.

[0140] FIG24 is a schematic structural diagram of a first inspection module according to an embodiment of the present invention.

[0141] FIG25 is a schematic structural diagram of a second inspection module according to an embodiment of the present invention.

[0142] FIG26 is a schematic structural diagram of a processing unit according to an embodiment of the present invention.

[0143] FIG27 is a schematic structural diagram of a dynamic gene regulatory network obtaining unit according to an embodiment of the present invention.

[0144] FIG28 is a schematic diagram of the system structure for constructing a dynamic gene regulatory network according to an embodiment of the present invention. DETAILED DESCRIPTION

[0145] The embodiments of the present invention are described in detail below, and examples of the embodiments are shown in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and intended to be used to explain the present invention, but should not be understood as limiting the present invention.

[0146] The present invention is based on the inventor's discovery and understanding of the following facts and problems:

[0147] Technology platforms related to gene regulatory networks based on chromatin accessibility, such as Chip-seq and ATAC-seq, can experimentally determine limited gene regulatory relationships. However, these methods make it difficult to construct a complete gene regulatory network in a single experiment, nor can they study gene regulation at different stages of a cell's biological process. With the development and maturity of single-cell transcriptomics technology, it has become possible to capture genome-wide regulatory network relationships in a single experiment in the early stages of exploration. Single-cell transcriptomics technology has three characteristics: 1) a single experiment can capture cells at multiple consecutive differentiation levels; 2) a single experiment can simultaneously detect regulatory relationships across the entire genome; and 3) transcriptome expression levels, as a medium between genes and phenotypes, can fully reflect the regulatory characteristics between genes.

[0148] Methods for constructing dynamic gene regulatory networks

[0149] According to one aspect of the present invention, a method for constructing a dynamic gene regulatory network based on single-cell transcriptome data is provided. Using this method, key gene regulatory mechanisms corresponding to cells at different differentiation stages can be rapidly determined, reflecting the true dynamic changes in gene regulatory relationships during biological processes. This method can be implemented using a computer program and run on a system for constructing dynamic gene regulatory networks. This computer program can be integrated into an application to implement the application's functionality, or it can be run independently as a tool application.

[0150] The system for constructing a dynamic gene regulatory network may be a terminal of the system for constructing a dynamic gene regulatory network, and the terminal includes, but is not limited to, a personal computer, a handheld device, a tablet computer, a smart phone, an in-vehicle device, a computing device, or other processing device. The system may have different names in different networks.

[0151] According to an embodiment of the present invention, referring to FIG. 1 , the method includes steps S1000 to S4000 .

[0152] S1000: Constructing multiple single-cell pseudo-temporal differentiation trajectories based on single-cell transcriptome data

[0153] Single-cell transcriptome sequencing is performed on biological samples to obtain single-cell transcriptome data of multiple single cells and construct multiple single-cell pseudo-temporal differentiation trajectories.

[0154] It should be noted that the single-cell transcriptome data described in this article can capture cells at multiple levels of differentiation at once, recording the information of multiple single cells in the sample, including short-lived cell types.

[0155] According to an embodiment of the present invention, single-cell transcriptome sequencing includes single-cell full-length transcriptome sequencing, paired-end sequencing, and single-end transcriptome sequencing. The technology platforms include 10x Genomics-Chromium platform, MGI-DNBelab C4 platform, BD Rhapsody TM system, C1 TM Platform, ICELL8 platform, Platform, MobiNova-100 platform, Wancheng Gene-10K genomics platform, Singleron Platform, Seeking Biology Platform, Baichuang-DG1000 platform, Dapu Bio-Galaxy platform.

[0156] According to an embodiment of the present invention, the single-cell transcriptome data herein is derived from organs, tissues, saliva, urine, blood, or cells, wherein the cells are at different stages of differentiation or different physiological states. Optionally, the single-cell transcriptome data is derived from a developmental sample or tumor sample. For example, when the single-cell transcriptome data is derived from mouse pancreas, the dynamic gene regulatory network is the regulatory relationship between genes whose transcriptome expression levels dynamically change among all genes in different pancreatic cells.

[0157] It can be understood that the single-cell transcriptome sequencing described in the embodiments of the present invention refers to the separation of single cells from the samples from the above sources and sequencing at the single-cell level. Each single cell has its own single-cell transcriptome data, and multiple pseudo-temporal differentiation trajectories can be constructed based on multiple different single cells at different differentiation stages.

[0158] Among them, based on the single-cell transcriptome data, the most likely differentiation paths between multiple cell subpopulations that may have differentiation / evolutionary inheritance relationships can be deduced, and thus multiple single-cell pseudo-temporal differentiation trajectories can be constructed.

[0159] According to an embodiment of the present invention, referring to FIG. 2 , S1000 further includes steps S1100 to S1300 .

[0160] S1100: Preprocess the single-cell transcriptome sequencing data to obtain a preprocessed single-cell gene expression matrix.

[0161] In some embodiments, the single-cell transcriptome sequencing data may be the original off-machine data obtained directly after sequencing. In some embodiments, the original off-machine data is preprocessed to obtain a single-cell gene expression matrix, wherein the preprocessing includes at least one of data quality control, data normalization, highly variable gene screening, data dimensionality reduction, cell clustering, and cell type annotation. In other embodiments, the single-cell transcriptome sequencing data may be a single-cell gene expression matrix that has been preprocessed, which can then be directly used to construct multiple single-cell pseudo-temporal differentiation trajectories.

[0162] As shown in FIG11 , the preprocessed single-cell gene expression matrix includes information on single-cell names, gene names, and gene expression levels within the cells.

[0163] S1200: Calculate pseudo-time series values ​​of multiple single cells based on single-cell gene expression matrix

[0164] Calculate pseudo-time series values ​​based on the single-cell gene expression matrix.

[0165] It should be noted that pseudo-time series analysis constructs cell differentiation trajectories based on the time-series changes in gene expression levels of different cell subpopulations. The time series does not refer to the order of chronological order, but rather to the sequence of cell-to-cell transformation and succession. The pseudo-time series value for a single cell can be obtained by calculating the change in transcriptome expression using an algorithm.

[0166] S1300: Arrange multiple single-cell pseudo-time series values ​​to obtain multiple single-cell pseudo-time series differentiation trajectories

[0167] The multiple single cells are arranged according to the time series values ​​to construct multiple single cell pseudo-time series differentiation trajectories.

[0168] According to an embodiment of the present invention, referring to Figure 12, each point in the figure represents a cell, and cells with similar states are clustered together. By performing cell type identification on each cluster, a variety of cells such as Ngn3 high EP cells are identified. From right to left, cells are arranged according to pseudo-temporal values, and each branch point represents a possible decision point in the cell biological process. For example, after the first fulcrum in the figure, cells may differentiate into various types of cells such as Alpha, Beta, Delta, Epsilon, etc. Figure 12 shows the pseudo-temporal distribution of each cluster of pancreatic cells, and different shapes represent different clusters. The target single-cell pseudo-temporal differentiation trajectory can be selected according to the research objectives.

[0169] Usually, there are many forms of pseudo-temporal differentiation trajectories, including tree diagrams, dynamic model diagrams, and latent time diagrams.

[0170] A "cluster" refers to a group of cells with similar gene expression characteristics, usually the same cell type. Due to the similar gene expression characteristics, these cells often show significant clustering characteristics in the dimensionality reduction space.

[0171] S2000: Identifying dynamically changing genes within a target pseudo-temporal differentiation trajectory based on multiple single-cell pseudo-temporal differentiation trajectories

[0172] Based on multiple single-cell pseudo-temporal differentiation trajectories, dynamically changing genes within the target pseudo-temporal differentiation trajectory are identified.

[0173] According to an embodiment of the present invention, referring to FIG. 3 , S2000 further includes S2100 to S2300 .

[0174] S2100: Determine target pseudo-temporal differentiation trajectory based on multiple single-cell pseudo-temporal differentiation trajectories

[0175] Based on multiple single-cell pseudo-temporal differentiation trajectories, the target pseudo-temporal differentiation trajectory is determined according to the biological background of the sample itself and the purpose of the research.

[0176] According to an embodiment of the present invention, referring to Figure 13 , the target pseudo-temporal differentiation trajectory extracted in this embodiment is a differentiation trajectory that ultimately differentiates into a Beta cell type. The left half of Figure 13 shows the proportions of various cells in the cell cluster. The right half of Figure 13 illustrates the internal temporal states of different cells during the evolution of the target differentiation trajectory through latent time analysis.

[0177] S2200: Extract pseudo-temporal values ​​and single-cell transcriptome data of multiple single cells based on target pseudo-temporal differentiation trajectory

[0178] A plurality of single cells within a target pseudo-temporal differentiation trajectory and their pseudo-temporal values ​​are determined, and corresponding transcriptome data of the single cells are extracted from the pre-processed single-cell gene expression matrix.

[0179] S2300: Identifying dynamically changing genes based on pseudo-time series values ​​and single-cell transcriptome data from multiple single cells

[0180] Multiple single cells within the target pseudo-temporal differentiation trajectory are arranged according to their pseudo-temporal values. As the differentiation time gradually increases or decreases, the gene expression of the cells changes, and the dynamically changing genes are determined.

[0181] According to an embodiment of the present invention, referring to FIG14 , the screening results of genes that change dynamically along the differentiation trajectory of target cells include gene name, LogFC mean, degrees of freedom, statistical test value, and p-value, wherein genes with a p-value < 0.05 are determined to be dynamically changing genes.

[0182] It should be noted that there are various strategies for determining dynamically changing genes, including selecting differentially expressed genes (for example, differential genes between subpopulations, or differences between treatment groups); selecting genes with high coefficient of variation, such as genes with an average expression level higher than a set threshold or a coefficient of variation higher than a set value; and selecting preset marker genes. The selection of strategies and thresholds can be flexibly adjusted.

[0183] S3000: Constructing an initial static gene interaction network based on dynamically changing genes

[0184] Based on the dynamically changing genes within the target pseudo-temporal differentiation trajectory, the mutual information values ​​between the dynamically changing genes are calculated, and an initial static gene interaction network is constructed.

[0185] According to an embodiment of the present invention, referring to FIG. 4 , S3000 further includes S3100 to S3200 .

[0186] S3100: Calculate the mutual information value between dynamically changing genes based on dynamically changing genes

[0187] According to the dynamic change genes, the single cell transcriptome data is extracted to calculate the mutual information value between the dynamic change genes. The mutual information value calculation formula is: M (i,j) =MI(E (i,) ,E (j,) ); i, j are dynamic changing genes, E (i,) is the transcriptome data of the dynamically changing gene i, that is, the gene expression level.

[0188] As mentioned above, the pre-processed single-cell gene expression matrix contains the information of single-cell name, gene name, and gene expression level in the cell. After determining the dynamically changing genes, the gene expression level corresponding to each dynamically changing gene can be extracted (i.e., E (i,) 、E (j,) ), and then calculate the mutual information value between gene i and gene j.

[0189] S3200: Constructing an initial static gene interaction network based on mutual information value

[0190] Construct a G×G order mutual information matrix M, where G is the number of dynamically changing genes determined by S3300; fill the mutual information values ​​between dynamically changing genes into the mutual information matrix M to obtain the initial static gene interaction network.

[0191] It should be noted that mutual information is used to measure the correlation between two random variables. In practice, it can detect any relationship, not just linear correlation. As mentioned above, it can be understood that the mutual information value can be calculated based on any two dynamically changing genes and used to measure the correlation between them.

[0192] The initial static gene interaction network described here includes all dynamically changing genes within the target pseudo-temporal differentiation trajectory. The correlations described here refer to the correlations between these dynamically changing genes. It should be noted that correlation is a measure of the degree of association between genes and does not necessarily imply a correlation relationship.

[0193] S4000: Test based on the initial static gene interaction network to obtain a dynamic gene regulatory network

[0194] The initial static gene interaction network still cannot directly reveal the gene regulation corresponding to different cell stages in the biological process. Therefore, it is necessary to construct a dynamic gene regulatory network. The dynamic gene regulatory network described in this article, with reference to Figure 18, can directly obtain the dynamic gene correlation in cells at different cell stages, that is, within different time series value ranges. This is convenient and convenient, and is conducive to the research and functional exploration of important genes in the cell differentiation process.

[0195] It should be noted that the test described herein is a statistical test, including a first test and a second test, which will be described in detail below in conjunction with embodiments.

[0196] According to an embodiment of the present invention, referring to FIG. 5 , S4000 further includes S4100 to S4200 .

[0197] S4100: Perform a first test based on the initial static gene interaction network to obtain an adjacency matrix of the initial static gene interaction network of the target pseudo-temporal differentiation trajectory.

[0198] Performing the first test based on the mutual information value in the initial static gene interaction network can reduce false positive interactions caused by indirect regulatory effects and improve the accuracy of network relationship determination.

[0199] Based on the first test results of the initial static gene interaction network, the adjacency matrix of the initial static gene interaction network is obtained, which can show the regulatory role of dynamically changing genes and further determine the network relationship.

[0200] Among them, the first test in this article is the t test, and its test statistic construction formula is Calculations can be performed based on specific data to test whether there is a significant difference between the sample and the population.

[0201] According to an embodiment of the present invention, referring to FIG. 6 , S4100 further includes S4110 to S4160 .

[0202] S4110: Based on the initial static gene interaction network obeying the normal distribution, propose the null hypothesis Among them, genes i and j are dynamic genes in the initial static gene interaction network. is the mean mutual information value between dynamic gene j and all dynamic genes, x i,j is the mutual information value between genes i and j in the transcription factor gene subset.

[0203] According to an embodiment of the present application, preferably, the original hypothesis is H0: μ j >M (f,j) ; Preferably, the transcription factor corresponds to data x i,j M (f,j) (f∈FTs; f≠j), FTs is a subset of transcription factor genes, μ j .

[0204] S4120: Construct statistics Specify the significance level of the test: α = 0.05, one-sided test.

[0205] According to the embodiment of the present application, preferably, t=(mean(M(,j) )-M (f,j) ) / (var(M (,j) ) / √(G-1)).

[0206] S4130: Based on dynamic genes, screen the transcription factor gene subset and extract the mutual information value x between genes i and j in the subset i,j ;

[0207] A subset of transcription factor genes is selected from the dynamic genes in the initial static gene interaction network, and the mutual information value x between genes i and j in the subset is extracted. i,j , which can represent the regulatory effect of gene i as a transcription factor on gene j.

[0208] S4140: Calculate the sample size N = n number of dynamic genes - 1 based on the initial static gene interaction network, and the mean mutual information value between dynamic gene j and all dynamic genes and its standard deviation σ. Then substitute the constructed statistic t and calculate the p-value.

[0209] S4150: rejecting the null hypothesis based on the p-value < 0.05, and filling the value 1 into the adjacency matrix;

[0210] S4160: Accept the null hypothesis based on the p-value>0.05, and fill the value 0 into the adjacency matrix.

[0211] Get the adjacency matrix, the elements of which are I (i,j) , when I (i,j) =1, it means that gene i acts as a transcription factor and regulates gene j. (i,j) =0 indicates that gene i has no regulatory effect on gene j.

[0212] It should be noted that those skilled in the art can understand that updating of elements in the matrix is ​​achieved by filling data into positions corresponding to the elements in the matrix.

[0213] S4200: Perform a second test based on the adjacency matrix of the initial static gene interaction network to obtain a dynamic gene regulatory network.

[0214] The second test is a Granger causality test. Through the second test, the present invention introduces the individual display of different cell differentiation states into the regulatory network of dynamically changing genes.

[0215] It should be noted that the Granger causality test is used to determine Granger causality. Granger causality generally refers to the observation that, in a stationary time series, the prediction of variable Y, when including past information about variables X and Y, is superior to the prediction of Y based solely on past information. In other words, if variable X helps explain future changes in variable Y, then variable X is considered to Granger cause Y. Therefore, Granger causality testing places strict requirements on the data; the data must be a stationary time series.

[0216] According to an embodiment of the present invention, referring to FIG. 7 , S4200 further includes S4210 to S4270 .

[0217] S4210: Arrange the pseudo-time series values ​​of multiple single cells based on the target differentiation trajectory and set a sliding window

[0218] Extract the pseudo-time series values ​​of multiple single cells of the target differentiation trajectory, arrange the single cells, and set a sliding window to slide in time series. The sliding window length is greater than or equal to M k / 2-M k / 10, preferred M k / 3-M k / 7, more preferably M k / 5, less than M k -1, where M k is the number of multiple single cells in the target differentiation trajectory k. The left endpoint of the sliding window starts at the first cell of the target differentiation trajectory k and the right endpoint ends at the last cell of the target differentiation trajectory k. The step length of each sliding is 1 to 5, preferably 1-3, and more preferably 1 cell. The sliding window is set based on the above parameters, and the dynamic gene regulatory network in the cells within each sliding window is displayed separately. The Granger test can test whether a dynamically changing gene E in multiple single cells corresponding to the sliding window K(i,w) Is it another dynamically changing gene E K(j,w) Granger causes, thereby determining gene regulatory relationships.

[0219] S4220: Traverse the adjacency matrix based on the sliding window to determine the adjacency matrix I (i,j) value.

[0220] When the sliding window moves, the cell data corresponding to the window w is E K(i,w) 、E K(j,w) , that is, the expression of dynamically changing genes i and j in the cells corresponding to the sliding window w sliding along the target pseudo-temporal differentiation trajectory K. Based on the dynamically changing genes i and j, traverse the adjacency matrix and extract I in the adjacency matrix (i,j) , and determine the adjacency matrix I (i,j) Whether it is 0 (i, j are dynamic genes).

[0221] S4230: Based on the adjacency matrix I (i,j) The value is 0, filling 0 into the dynamic gene regulatory network.

[0222] I (i,j) If it is 0, fill 0 into the dynamic gene regulatory network.

[0223] S4240: Based on the adjacency matrix I (i,j) The value is 1, and the single-cell transcriptome data of the corresponding dynamic gene are extracted.

[0224] I (i,j) If it is not 0, the single-cell transcriptome data of the corresponding dynamic genes of multiple single cells in the sliding window and the time series values ​​of multiple single cells in the sliding window are extracted.

[0225] S4250: Process the single-cell transcriptome data of dynamic genes to obtain expression data that meets the Granger test.

[0226] Based on the expression data that meets the Granger test, a Wide / 10-order Granger causality test is performed, where Wide is the sliding window length.

[0227] It can be understood that since the sliding window length is greater than or equal to M k / 2-M k / 10, preferred M k / 3-M k / 7, more preferably M k / 5, less than M k -1, where M k is the number of cells in the target differentiation trajectory k. Therefore, the sliding window length Wide depends on the number of cells. Furthermore, the order of the Granger causality test is calculated using the sliding window length Wide, which is one tenth of the sliding window length (Wide / 10).

[0228] It should be noted that the expression data that meets the Granger test needs to be based on a stationary time series. Therefore, it needs to be processed to meet the conditions.

[0229] According to an embodiment of the present invention, referring to FIG. 8 , S4250 further includes S4251 to S4254 .

[0230] S4251: Obtain detrended and demeaned data from single-cell transcriptome data of dynamic genes.

[0231] The single-cell transcriptome data of the corresponding dynamic genes of multiple single cells in the sliding window were extracted and detrended and demeaned. The results are shown in 15. After processing, the data tended to be stable.

[0232] S4252: Perform KPSS test for stationarity based on detrended and demeaned data.

[0233] S4253: Based on the non-stationary detrended and de-meaned data, first-order difference processing is performed to obtain stationary detrended and de-meaned data.

[0234] S4254: Based on the stationary detrended and demeaned data, expression data that satisfies the Granger test are obtained.

[0235] Based on the stability of the data, it is considered to be expression data that meets the Granger test.

[0236] Based on the non-stationary data, the processed data is subjected to first-order difference processing to obtain stable detrended and de-meaned data, making it the expression data that meets the Granger test.

[0237] S4260: Determine Granger causality based on expression data that satisfies the Granger test within the sliding window.

[0238] S4270: Obtaining dynamic gene regulatory networks based on Granger causality.

[0239] Based on the Granger causality, a dynamic gene regulatory network is obtained. The Granger test can test the dynamic change of a gene E in multiple single cells corresponding to the sliding window. K(i,w) Is it another dynamically changing gene E K(j,w) Granger causes, thereby determining gene regulatory relationships.

[0240] According to an embodiment of the present invention, referring to FIG. 9 , S4270 further includes S4271 to S4274 .

[0241] S4271: Determine whether Granger causality holds.

[0242] S4272: Based on the Granger causality not being established, fill 0 into the dynamic gene regulatory network.

[0243] S4273: Extract single-cell transcriptome data of dynamically changing genes based on Granger causality.

[0244] Based on the Granger causality, it is believed that the dynamic change gene E K(i,w) It is another dynamic change gene E K(j,w) Granger cause.

[0245] S4274: Based on the single-cell transcriptome data of dynamically changing genes that are upregulated or downregulated, fill in 1 or -1 into the dynamic gene regulatory network.

[0246] Based on the Granger causality relationship described in S4273, the dynamic change gene E was extracted. K(j,w) The original data of the dynamic gene j is checked to see if it is upregulated. If the single-cell transcriptome data of the dynamic gene j is upregulated, the dynamic gene regulatory network is filled with 1. If the single-cell transcriptome data of the dynamic gene j is downregulated, the dynamic gene regulatory network is filled with -1.

[0247] After filling in the above steps, a dynamic gene regulatory network is obtained. The gene regulatory network described in this article is a three-dimensional dynamic gene regulatory interaction matrix of order G×G×S, where G is the number of dynamically changing genes and S is the number of steps of the sliding window. The matrix contains the dynamic gene regulatory network in the cells within each sliding window step.

[0248] S5000: Visualize the dynamic gene regulatory network using ndtv, GENIE3 or cytoscape software

[0249] The dynamic gene regulatory network was converted into an input format suitable for visualization software and visualized using ndtv, GENIE3, or cytoscape.

[0250] It should be noted that all the above steps can be completed in a variety of corresponding environments using multiple languages ​​such as R and Python, and the final output includes but is not limited to the visualization results of dynamic gene regulatory networks in multiple formats such as pdf, png, and html.

[0251] In the technical solution of this application, the collection, storage, use, processing, transmission, provision and disclosure of personal information involved are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0252] According to yet another aspect of the present invention, embodiments of the present invention further provide a system for constructing dynamic gene regulatory networks based on single-cell transcriptome data. Using this system, key gene regulatory mechanisms corresponding to cells at different differentiation stages can be rapidly identified, reflecting the true dynamic changes in gene regulatory relationships during biological processes.

[0253] The system for constructing a dynamic gene regulatory network may be a terminal of a device system for constructing a dynamic gene regulatory network, including but not limited to a personal computer, handheld device, tablet computer, smartphone, vehicle-mounted device, computing device, or other processing device. The system may have different names in different networks.

[0254] According to an embodiment of the present invention, referring to FIG19 , the system 10000 includes a pseudo-temporal differentiation trajectory construction device 1000 , a dynamically changing gene determination device 2000 , an initial static gene interaction network construction device 3000 , and a dynamic gene regulatory network acquisition device 4000 .

[0255] The pseudo-temporal differentiation trajectory construction device 1000 is configured to construct multiple single-cell pseudo-temporal differentiation trajectories based on single-cell transcriptome data of multiple single cells.

[0256] The dynamically changing gene determining device 2000 is used to determine the dynamically changing genes in a target pseudo-temporal differentiation trajectory based on multiple single-cell pseudo-temporal differentiation trajectories.

[0257] The initial static gene interaction network construction device 3000 is configured to construct an initial static gene interaction network based on the expression of dynamically changing genes.

[0258] The dynamic gene regulatory network obtaining device 4000 is configured to perform a test based on the initial static gene interaction network to obtain a dynamic gene regulatory network.

[0259] According to an embodiment of the present invention, referring to FIG. 20 , a device 1000 for acquiring a quasi-temporal differentiation trajectory includes:

[0260] A preprocessing module 1100 is configured to preprocess the single-cell transcriptome sequencing data to obtain a preprocessed single-cell gene expression matrix;

[0261] A pseudo-time series value calculation module 1200 is configured to calculate the pseudo-time series values ​​of multiple single cells based on the single-cell gene expression matrix;

[0262] The pseudo-temporal differentiation trajectory obtaining module 1300 is configured to obtain the plurality of single-cell pseudo-temporal differentiation trajectories by arranging the pseudo-temporal values ​​of the plurality of single cells.

[0263] According to an embodiment of the present invention, the preprocessing includes at least one of data quality control, data normalization, highly variable gene screening, data dimensionality reduction, cell clustering, and cell type annotation.

[0264] According to an embodiment of the present invention, referring to FIG. 21 , the dynamically changing gene determining device 2000 includes:

[0265] A target pseudo-temporal differentiation trajectory determination module 2100 is configured to determine a target pseudo-temporal differentiation trajectory based on the multiple single-cell pseudo-temporal differentiation trajectories;

[0266] An extraction module 2200 is configured to extract the pseudo-temporal values ​​of the plurality of single cells and the single-cell transcriptome data based on the target pseudo-temporal differentiation trajectory;

[0267] The dynamically changing gene determination module 2300 is configured to determine the dynamically changing gene based on the pseudo-time series values ​​of the multiple single cells and the single cell transcriptome data.

[0268] According to an embodiment of the present invention, the dynamically changing gene determining module 2300 uses tradeSeq, waddR, uSORT, scDE or BASICs to determine the dynamically changing gene.

[0269] According to an embodiment of the present invention, referring to FIG. 22 , the initial static gene interaction network construction device 3000 includes:

[0270] A mutual information value calculation module 3100 is configured to calculate the mutual information value between the dynamically changing genes based on the dynamically changing genes;

[0271] The static gene interaction network construction module 3200 is configured to construct the initial static gene interaction network based on the mutual information value.

[0272] In some embodiments, referring to FIG. 23 , the dynamic gene regulatory network obtaining device 4000 includes:

[0273] A first testing module 4100 is configured to perform a first test based on the initial static gene interaction network to obtain an adjacency matrix of the initial static gene interaction network of the target pseudo-temporal differentiation trajectory;

[0274] The second testing module 4200 is configured to perform a second test based on the adjacency matrix of the initial static gene interaction network to obtain a dynamic gene regulatory network.

[0275] In some embodiments, referring to FIG. 24 , the first test is a t-test, and the first test module 4100 includes:

[0276] The hypothesis testing unit 4110 is configured to propose the null hypothesis based on the initial static gene interaction network obeying the normal distribution:

[0277] t statistic construction unit 4120, configured to construct the statistic

[0278] The mutual information value extraction unit 4130 is configured to screen the transcription factor gene subset based on the dynamically changing genes and extract the mutual information value x between the dynamically changing genes i and j in the subset. i,j ;

[0279] The t statistic calculation unit 4140 is configured to calculate the sample size N=n number of dynamic genes-1, the mutual information value mean of the dynamic change gene j and all dynamic genes based on the initial static gene interaction network. and its standard deviation σ, then substitute it into the constructed statistic t and calculate the p-value;

[0280] An adjacency matrix 1 filling unit 4150 is configured to reject the null hypothesis based on the p-value being less than 0.05 and fill the adjacency matrix with a value of 1;

[0281] The adjacency matrix zero-filling unit 4160 is configured to accept the null hypothesis based on the p-value>0.05 and fill the value 0 into the adjacency matrix.

[0282] According to an embodiment of the present invention, referring to FIG. 25 , the second test is a Granger causality test, and the second test module 4200 includes:

[0283] A Granger test unit 4210 is configured to arrange the pseudo-time series values ​​of the plurality of single cells based on the target differentiation trajectory and set a sliding window;

[0284] The traversal unit 4220 is configured to traverse the adjacency matrix based on the sliding window to determine the adjacency matrix I (i,j) value;

[0285] Dynamic gene regulatory network 0 filling unit 4230, configured as the I based on the adjacency matrix (i,j) The value is 0, filling 0 into the dynamic gene regulatory network;

[0286] The single cell transcriptome data extraction unit 4240 is configured to extract the data based on the adjacency matrix I (i,j) The value is 1, and the single-cell transcriptome data of the dynamic gene is extracted;

[0287] a processing unit 4250 configured to process the single-cell transcriptome data based on the dynamic gene to obtain expression data that satisfies a Granger test;

[0288] A Granger relationship determining unit 4260 is configured to determine a Granger causality relationship based on the expression data satisfying the Granger test within the sliding window;

[0289] The dynamic gene regulatory network obtaining unit 4270 is configured to obtain a dynamic gene regulatory network based on the Granger causality.

[0290] According to an embodiment of the present invention, the sliding window length is greater than or equal to M k / 2-M k / 10, preferred M k / 3-Mk / 7, more preferably M k / 5, less than M k -1, where M k is the number of multiple single cells in the target differentiation trajectory k. The left endpoint of the sliding window starts at the first cell of the target differentiation trajectory k, and the right endpoint ends at the last cell of the target differentiation trajectory k. The step size of each sliding is 1 to 5, preferably 1-3, and more preferably 1 cell.

[0291] According to an embodiment of the present invention, referring to FIG. 26 , the processing unit 4250 includes:

[0292] a detrending and demeaning sub-data obtaining sub-unit 4251, configured to obtain detrending and demeaning data from the single-cell transcriptome data based on the dynamic genes;

[0293] The stationarity test subunit 4252 is configured to perform a KPSS stationarity test based on the detrended and demeaned data;

[0294] a first-order difference processing subunit 4253 configured to perform first-order difference processing on the non-stationary detrended and de-meaned data to obtain stationary detrended and de-meaned data;

[0295] The expression data obtaining subunit 4254 that satisfies the Granger test is configured to obtain expression data that satisfies the Granger test based on the stationary detrended and de-meaned data.

[0296] According to an embodiment of the present invention, referring to FIG. 27 , the dynamic gene regulatory network obtaining unit 4270 includes:

[0297] Granger causality judgment subunit 4271: configured to judge whether Granger causality is established;

[0298] The dynamic gene regulatory network 0 filling subunit 4272 is configured to fill 0 into the dynamic gene regulatory network based on the failure of Granger causality;

[0299] The single-cell transcriptome data extraction subunit 4273 is configured to extract the single-cell transcriptome data of the dynamically changing genes based on the Granger causality relationship;

[0300] The dynamic gene regulatory network 1 or -1 filling subunit 4274 is configured to fill 1 or -1 into the dynamic gene regulatory network based on the upregulation or downregulation of the single-cell transcriptome data of the dynamically changing gene.

[0301] According to an embodiment of the present invention, the dynamic gene regulatory network is a three-dimensional dynamic gene regulatory interaction matrix of order G×G×S, where G is the dynamically changing gene and S is the number of steps of the sliding window.

[0302] According to an embodiment of the present invention, referring to FIG. 28 , the system further includes a visualization device 5000 for visualizing the dynamic gene regulatory network through ndtv, GENIE3 or cytoscape software.

[0303] According to an embodiment of the present invention, the single-cell transcriptome data is derived from organs, tissues, saliva, urine, blood, cells, wherein the cells are in different stages of differentiation or different physiological states, optionally the single-cell transcriptome data is derived from samples or tumor samples in a developmental period, preferably, from mouse pancreas.

[0304] An embodiment of the present invention further provides a computer device comprising one or more processors and a storage system. The storage system is configured to store one or more programs, which, when executed by the one or more processors, enable the one or more processors to implement the method for constructing a dynamic gene regulatory network as described in any of the above embodiments.

[0305] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the computer program is used to implement the method for constructing a dynamic gene regulatory network described in any embodiment.

[0306] Among them, any reference to memory, database or other media used in the various embodiments provided by the present invention may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical storage. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static random access memory (SRAM) and dynamic random access memory (DRAM).

[0307] An embodiment of the present invention further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the method for constructing a dynamic gene regulatory network as described in any embodiment of the first aspect.

[0308] An embodiment of the present invention also provides a computer program, which includes computer program code. When the computer program code is run on a computer, it enables the computer to execute the method for constructing a dynamic gene regulatory network as described in any embodiment of the first aspect.

[0309] The embodiments of the present invention are described in detail below, and examples of the embodiments are shown in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and intended to be used to explain the present invention, but should not be understood as limiting the present invention.

[0310] Example

[0311] This example demonstrates a method for constructing a dynamic gene regulatory network using scRNA-Seq data.

[0312] 1. Preprocess scRNA-Seq sequencing data and construct single-cell pseudo-temporal differentiation trajectories. The construction of dynamic gene regulatory networks utilizes single-cell whole-transcriptome sequencing data. The sample source should be specific tissue types from specific species, and the captured cells should be at different differentiation stages or different cellular states, such as developmental samples or tumor samples.

[0313] 1.1 Obtain publicly available single-cell whole-transcriptome sequencing data of mouse pancreatic development;

[0314] 1.2 Complete the preprocessing of single-cell whole transcriptome sequencing data in the scanpy framework. The specific process includes data quality control, data normalization, screening of highly variable genes, data dimensionality reduction, cell clustering, and cell type annotation;

[0315] 1.3 Obtain the N×M order single cell gene expression matrix E, where N is the number of identified genes, M is the number of captured cells, and E (n,m) is the expression level of gene n (n = 1, 2, …, N) in cell m (n = 1, 2, …, M) ( Figure 11 );

[0316] 1.4 Use PAGA, scVelo and CellRank to construct single cell differentiation trajectory, and sort cells along the differentiation trajectory in pseudo-time. Each cell corresponds to a pseudo-time series value. Let T (k,m) is the pseudo-temporal value of cell m on differentiation trajectory k (k = 1, 2, …, K) (Figure 12);

[0317] 1.5 Select the target pseudo-temporal differentiation trajectory based on the research objectives. Among the four identified differentiation trajectories, the Beta cell development pathway was selected as the target differentiation trajectory (Figure 13).

[0318] 2. Based on the single-cell gene expression matrix in the target differentiation trajectory, use tradeSeq, waddR, uSORT, scDE or BASICs, preferably the tradeSeq software package, to determine the genes that dynamically change in each pseudo-temporal differentiation trajectory.

[0319] 2.1 Extract single-cell gene expression data of target differentiation trajectory 1-K;

[0320] 2.2 Extract the pseudo-temporal arrangement results of the target differentiation trajectory 1-K;

[0321] 2.3 The R language tradeSeq software package was used to screen out genes that dynamically changed along the target differentiation trajectory 1-K. The screening criterion was pvalue < 0.05. The results were shown in Figure 14 for genes that dynamically changed along the target differentiation trajectory.

[0322] 3. Conduct hypothesis testing based on mutual information values ​​and construct an initial static gene interaction network. The mutual information values ​​between regulatory and regulated genes must follow a certain distribution, assuming this distribution is normal. Genes with mutual information values ​​significantly greater than the mean of this distribution are selected as genes with actual regulatory effects, which helps reduce false positives in identifying gene regulatory relationships.

[0323] 3.1 Construct a G×G order mutual information matrix M, where G is the number of genes that change dynamically along the target differentiation trajectory as described in step 2.3, and the initial M (i,i) =0, update M (i,j) is the mutual information value calculation result of gene i (i=1,2,…,G; i≠j) and gene j (j=1,2,…,G; j≠i), and the calculation formula is: M (i,j) =MI(E (i,) ,E (j,) );

[0324] 3.2 Construct the first interaction matrix I of order G×G, initial I (i,j) =0, the element I in the matrix (i,j) Used to record whether there is a regulatory relationship between gene i and gene j;

[0325] 3.3 Based on the transcription factor database of the species from which the single-cell data originated, a subset of transcription factor genes is screened from the G genes in the matrix M, and this subset is recorded as FTs;

[0326] 3.4 Perform hypothesis testing and update the element I in the first interaction matrix I (i,j) , the steps are as follows:

[0327] (1) Assume that the j-th column data M in the M matrix (,j) Obey normal distribution N j (μ j,σ j2 ), for each transcription factor in the jth column of the M matrix, the corresponding data M (f,j) (f∈FTs; f≠j) propose the null hypothesis and alternative hypothesis:

[0328] H0:μ j >M (f,j) ,

[0329] Hα:μ j ≤M (f,j) ;

[0330] (2) Specify the significance level of the test: α = 0.05, one-sided test;

[0331] (3) The sampling distribution is a t distribution with degrees of freedom df = (G-1)-1;

[0332] (4) Calculate the test statistic: t = (mean (M (,j) )-M (f,j) ) / (var(M (,j) ) / √(G-1));

[0333] (5) Calculate the p-value based on the value of the test statistic;

[0334] (6) If p-value < α, then reject the null hypothesis and assume that M (f,j) Significantly greater than μj, and I (f,j) The value of is updated to 1; in response to the p value being greater than 0.05, I (f,j) The value of is 0.

[0335] 3.5 After updating, the first matrix I is obtained as shown in Figure 15, which is the adjacency matrix of the initial static gene interaction network of differentiation trajectory k. (i,j) If it is equal to 1, it means that gene i has a regulatory effect on gene j as a transcription factor. (i,j) Equal to 0 means that gene i has no regulatory effect on gene j, I (i,i) A value of 0 means ignoring the regulatory effect of the gene on itself.

[0336] 4. Based on the Granger causality test, the dynamic gene interaction matrix is ​​calculated to extract the gene regulatory network at different cell differentiation stages.

[0337] 4.1 Extract the expression matrix E of the genes that dynamically change along the target differentiation trajectory k as described in step 2.3 k , the order of the matrix is ​​G×M k , E k(i,j) is the gene i (i=1,2,…,G) in cell j (j=1,2,…,M k ) and 0≤T (k,j) ≤T(k,j+1) ≤1;

[0338] 4.2 Row-wise matrix E k Detrending and demeaning are performed to make the data meet the requirements of Granger causality test. The steps are as follows:

[0339] (1) Let the expression of gene i in trajectory k be E k(i,) T (k,) As a function of the independent variable, all cells whose expression level of gene i is greater than 0 are selected as a set of observations: obs = [(T (k,1) ,E k(i,1) ),(T (k,2) ,E k(i,2) ),…,(T (k,b) ,E k(i,b) )], and satisfy E k(i,b) ≥0;

[0340] (2) According to obs, the functional relationship between the expression of gene i in trajectory k and the pseudo-time value F is fitted by natural cubic spline. k,i , replace the original expression with the residual, that is, E k(i,b) Replace with E k(i,b) -F k,i (T (k,b) ), FIG16 shows the expression of the example gene Cops5 along with the pseudo-time series change, the natural cubic spline fitting result, and the result after detrending and demeaning.

[0341] 4.3 Design a sliding window W of length wide, where M k / 5≤wide<(M k -1), the left endpoint of window W starts at the first cell of differentiation trajectory k, and the right endpoint of window W ends at the last cell of differentiation trajectory k. The step length of each sliding is 1 cell, so the range of the total step length S is 0≤S≤(M k -wide);

[0342] 4.4 For all genes in the adjacency matrix I of the initial static gene interaction network of a differentiation trajectory k described in step 3.5, construct a three-dimensional dynamic gene regulatory interaction matrix D of order G×G×S, where G is the number of genes that dynamically change along a target differentiation trajectory k described in step 2.3, S is the total step size of the sliding window W, and D (i,j,s) For gene i (i=1,2,…,G;i≠j) to gene j (j=1,2,…,G;j≠i) in window W, slide to the sth (s=0,1,…,(M k -wide)) step effect, D (i,j,s) The initial value is set to 0;

[0343] 4.5 Calculation of D (i,j,s) The specific steps are as follows: when the window W slides to the sth step, the cell data corresponding to the window W is E k(,w) Where w=s+1,s+2,…,s+wide+1, traverse the I matrix, when I (i,j) Extract E when it is equal to 1 k(i,w) As the action vector, extract E k(j,w) As the accepting action vector, the KPSS test is used to determine E k(i,w) With E k(j,w) Are both stable? If not, perform first-order difference processing on the two sequences until E k(i,w) With E k(j,w) All stable;

[0344] 4.6 Determine E through wide / 10th-order Granger causality test k(i,w) Is it E k(j,w) Granger cause, if E k(i,w) It's E k(j,w) Granger cause, then further judge E k(j,w) Is the change trend of the original data upward or downward? If E k(j,w) If the original data shows an upward trend, D (i,j,s) The value of is replaced by 1, if E k(j,w) If the original data shows a downward trend, D (i,j,s) The value of is replaced by -1;

[0345] 4.7 Each step of the sliding window corresponds one-to-one to the third dimension of the matrix D. The corresponding relationship between the sliding window and the matrix D is shown in Figure 17.

[0346] 5. Based on ndtv, GENIE3 or cytoscape, preferably, the ndtv software package is used to construct a dynamic gene regulatory network and visualize the results.

[0347] 5.1 Convert the matrix D into the dynamic network construction input object specified by the ndtv package;

[0348] 5.2 Save the final dynamic network results as an HTML file using the ndtv software package;

[0349] 5.3 The final display result of the dynamic gene regulatory network is shown in Figure 18. The slider at the bottom of the page can be used to query the key gene regulatory mechanisms at different differentiation stages and study the changes of the gene regulatory network during the differentiation process.

[0350] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0351] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art could make various modifications and improvements without departing from the spirit of the present application, all of which fall within the scope of protection of the present application. Therefore, the scope of protection of the present patent application shall be determined by the appended claims.

[0352] All embodiments of the present disclosure may be implemented individually or in combination with other embodiments, and are all considered to be within the scope of protection claimed by the present disclosure.

[0353] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential" and the like to indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the referred system or element must have a specific orientation, be constructed and operate in a specific orientation, and therefore should not be understood as limiting the present invention.

[0354] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "plurality" means at least two, such as two, three, etc., unless otherwise specifically defined.

[0355] In the present invention, unless otherwise specified or limited, the terms "installed," "connected," "connect," "fixed," etc. should be understood in a broad sense. For example, they can refer to fixed connection, detachable connection, or integration; mechanical connection, electrical connection, or communication; direct connection or indirect connection through an intermediate medium; internal communication between two elements or interaction between two elements, unless otherwise specified. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0356] In the present invention, unless otherwise expressly specified or limited, when a first feature is "above" or "below" a second feature, it may mean that the first and second features are in direct contact, or that the first and second features are in indirect contact through an intermediary. Furthermore, when a first feature is "above," "above," or "above" a second feature, it may mean that the first feature is directly above or diagonally above the second feature, or simply means that the first feature is at a higher level than the second feature. When a first feature is "below," "below," or "below" a second feature, it may mean that the first feature is directly below or diagonally below the second feature, or simply means that the first feature is at a lower level than the second feature.

[0357] In the present invention, the terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" mean that the specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0358] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A method for constructing a dynamic gene regulatory network, characterized in that: include: Construct multiple single-cell pseudo-temporal differentiation trajectories based on the single-cell transcriptome data of multiple single cells; Determining dynamically changing genes within a target pseudo-temporal differentiation trajectory based on the multiple single-cell pseudo-temporal differentiation trajectories; Constructing an initial static gene interaction network based on the dynamically changing genes; The initial static gene interaction network is tested to obtain a dynamic gene regulatory network.

2. The method for constructing a dynamic gene regulatory network according to claim 1, wherein: The step of constructing multiple single-cell pseudo-temporal differentiation trajectories based on the single-cell transcriptome data of multiple single cells includes: Preprocessing the single-cell transcriptome sequencing data to obtain a preprocessed single-cell gene expression matrix; Calculating pseudo-time series values ​​of multiple single cells based on the single-cell gene expression matrix; Arrangement is performed based on the pseudo-temporal values ​​of the multiple single cells to obtain the pseudo-temporal differentiation trajectories of the multiple single cells.

3. The method for constructing a dynamic gene regulatory network according to claim 2, wherein: The preprocessing includes at least one of data quality control, data normalization, highly variable gene screening, data dimensionality reduction, cell clustering, and cell type annotation.

4. The method for constructing a dynamic gene regulatory network according to any one of claims 1 to 3, characterized in that: Determining the dynamically changing genes in the target pseudo-temporal differentiation trajectory based on the multiple single-cell pseudo-temporal differentiation trajectories includes: Determining a target pseudo-temporal differentiation trajectory based on the multiple single-cell pseudo-temporal differentiation trajectories; Extracting the pseudo-temporal values ​​of the plurality of single cells and the single-cell transcriptome data based on the target pseudo-temporal differentiation trajectory; The dynamically changing genes are determined based on the pseudo-time series values ​​of the multiple single cells and the single-cell transcriptome data.

5. The method for constructing a dynamic gene regulatory network according to any one of claims 1 to 4, characterized in that: The dynamically changing genes were determined using tradeSeq, waddR, uSORT, scDE, or BASICs.

6. The method for constructing a dynamic gene regulatory network according to any one of claims 1 to 5, characterized in that: The step of constructing an initial static gene interaction network based on the expression of the dynamically changing genes comprises: Calculating mutual information values ​​between the dynamically changing genes based on the dynamically changing genes; The initial static gene interaction network is constructed based on the mutual information value.

7. The method for constructing a dynamic gene regulatory network according to any one of claims 1 to 6, characterized in that: The step of testing based on the initial static gene interaction network to obtain a dynamic gene regulatory network includes: Performing a first test based on the initial static gene interaction network to obtain an adjacency matrix of the initial static gene interaction network of the target pseudo-temporal differentiation trajectory; A second test is performed based on the adjacency matrix of the initial static gene interaction network to obtain a dynamic gene regulatory network.

8. The method for constructing a dynamic gene regulatory network according to claim 7, wherein: The first test is a t-test, and the adjacency matrix of the initial static gene interaction network of the target pseudo-temporal differentiation trajectory obtained by performing the first test based on the initial static gene interaction network includes: Based on the fact that the initial static gene interaction network obeys the normal distribution, the original hypothesis is proposed as follows: Constructing statistics Based on the dynamically changing genes, a transcription factor gene subset is screened and the mutual information value x of the dynamically changing genes i and j in the subset is extracted. i,j ; Based on the initial static gene interaction network, the sample size N=n number of dynamic genes-1, the mean mutual information value of the dynamic change gene j and all dynamic genes is calculated and its standard deviation σ, then substitute it into the constructed statistic t and calculate the p-value; Reject the null hypothesis based on the p-value < 0.05 and fill the adjacency matrix with the value 1; The null hypothesis was accepted based on the p-value > 0.05, and the adjacency matrix was filled with the value 0.

9. The method for constructing a dynamic gene regulatory network according to claim 7 or 8, characterized in that: The second test is a Granger causality test, and the second test based on the adjacency matrix of the initial static gene interaction network to obtain a dynamic gene regulatory network includes: Arrange the pseudo-time series values ​​of the multiple single cells based on the target differentiation trajectory and set a sliding window; The adjacency matrix is ​​traversed based on the sliding window to determine the adjacency matrix I (i,j) value; Based on the adjacency matrix I (i,j) The value is 0, filling 0 into the dynamic gene regulatory network; Based on the adjacency matrix I (i,j) The value is 1, extracting the single-cell transcriptome data of the dynamic gene; Processing the single-cell transcriptome data of the dynamic genes to obtain expression data that satisfies the Granger test; Determining Granger causality based on expression data that satisfies the Granger test within the sliding window; A dynamic gene regulatory network is obtained based on the Granger causality.

10. The method for constructing a dynamic gene regulatory network according to claim 9, wherein: The sliding window length is greater than or equal to M k / 2-M k / 10, preferred M k / 3-M k / 7, more preferably M k / 5, less than M k -1, where M k is the number of multiple single cells in the target differentiation trajectory k. The left endpoint of the sliding window starts at the first cell of the target differentiation trajectory k, and the right endpoint ends at the last cell of the target differentiation trajectory k. The step size of each sliding is 1 to 5, preferably 1-3, and more preferably 1 cell.

11. The method for constructing a dynamic gene regulatory network according to claim 9, wherein: The processing includes: Obtaining detrended and demeaned data based on the single-cell transcriptome data of the dynamic genes; Based on the detrended and demeaned data, a KPSS test for stationarity was performed; Based on the non-stationary detrended and de-meaned data, first-order difference processing is performed to obtain the stationary detrended and de-meaned data; Based on the stable detrended and demeaned data, expression data that meets the Granger test are obtained.

12. The method for constructing a dynamic gene regulatory network according to claim 9, wherein: The step of obtaining a dynamic gene regulatory network based on the Granger causality comprises: Determine whether Granger causality holds true; Based on the failure of Granger causality, the dynamic gene regulatory network is filled with 0; Based on the establishment of Granger causality, single-cell transcriptome data of the dynamically changing genes are extracted; Based on the up-regulation or down-regulation of the single-cell transcriptome data of the dynamically changing genes, 1 or -1 is filled into the dynamic gene regulatory network.

13. The method for constructing a dynamic gene regulatory network according to claims 1 to 12, characterized in that: The dynamic gene regulatory network is a three-dimensional dynamic gene regulatory interaction matrix of order G×G×S, where G is the dynamically changing gene and S is the number of steps of the sliding window. 14 . The method for constructing a dynamic gene regulatory network according to claim 1 , further comprising visualizing the dynamic gene regulatory network using ndtv, GENIE3 or cytoscape software.

15. The method for constructing a dynamic gene regulatory network according to any one of claims 1 to 14, characterized in that: The single-cell transcriptome data is derived from organs, tissues, saliva, urine, blood, cells, wherein the cells are in different stages of differentiation or different physiological states. Optionally, the single-cell transcriptome data is derived from samples or tumor samples in a developmental period, preferably, from mouse pancreas.

16. A system for constructing a dynamic gene regulatory network, characterized in that: include: a pseudo-temporal differentiation trajectory construction device configured to construct a plurality of single-cell pseudo-temporal differentiation trajectories based on the single-cell transcriptome data of a plurality of single cells; a dynamically changing gene determining device configured to determine the dynamically changing genes within a target pseudo-temporal differentiation trajectory based on the plurality of single-cell pseudo-temporal differentiation trajectories; an initial static gene interaction network construction device configured to construct an initial static gene interaction network based on the expression of the dynamically changing genes; The dynamic gene regulatory network obtaining device is configured to perform a test based on the initial static gene interaction network to obtain a dynamic gene regulatory network.

17. The system for constructing a dynamic gene regulatory network according to claim 16, characterized in that: The quasi-temporal differentiation trajectory acquisition device comprises: a preprocessing module, configured to preprocess the single-cell transcriptome sequencing data to obtain a preprocessed single-cell gene expression matrix; a pseudo-time series value calculation module, configured to calculate the pseudo-time series values ​​of multiple single cells based on the single-cell gene expression matrix; The pseudo-temporal differentiation trajectory obtaining module is configured to arrange the pseudo-temporal values ​​of the multiple single cells to obtain the multiple single-cell pseudo-temporal differentiation trajectories.

18. The system for constructing a dynamic gene regulatory network according to claim 17, characterized in that: The preprocessing includes at least one of data quality control, data normalization, highly variable gene screening, data dimensionality reduction, cell clustering, and cell type annotation.

19. The system for constructing a dynamic gene regulatory network according to any one of claims 16 to 18, characterized in that: The dynamically changing gene determining device comprises: a target pseudo-temporal differentiation trajectory determination module, configured to determine a target pseudo-temporal differentiation trajectory based on the multiple single-cell pseudo-temporal differentiation trajectories; an extraction module configured to extract the pseudo-temporal values ​​of the plurality of single cells and the single-cell transcriptome data based on the target pseudo-temporal differentiation trajectory; The dynamically changing gene determination module is configured to determine the dynamically changing gene based on the pseudo-time series values ​​of the multiple single cells and the single cell transcriptome data.

20. The system for constructing a dynamic gene regulatory network according to any one of claims 16 to 19, characterized in that: The dynamically changing genes were determined using tradeSeq, waddR, uSORT, scDE, or BASICs.

21. The system for constructing a dynamic gene regulatory network according to any one of claims 16 to 20, characterized in that: The initial static gene interaction network construction device includes: a mutual information value calculation module, configured to calculate the mutual information value between the dynamically changing genes based on the dynamically changing genes; A static gene interaction network construction module is configured to construct the initial static gene interaction network based on the mutual information value.

22. The system for constructing a dynamic gene regulatory network according to any one of claims 16 to 21, characterized in that: The dynamic gene regulatory network obtaining device comprises: A first testing module is configured to perform a first test based on the initial static gene interaction network to obtain an adjacency matrix of the initial static gene interaction network of the target pseudo-temporal differentiation trajectory; The second testing module is configured to perform a second test based on the adjacency matrix of the initial static gene interaction network to obtain a dynamic gene regulatory network.

23. The system for constructing a dynamic gene regulatory network according to claim 22, wherein: The first test is a t-test, and the first test module includes: The hypothesis testing unit is configured to propose the null hypothesis based on the initial static gene interaction network obeying the normal distribution: t statistic construction unit, configured to construct the statistic The mutual information value extraction unit is configured to screen the transcription factor gene subset based on the dynamically changing gene and extract the mutual information value x of the dynamically changing gene i and j in the subset. i,j ; The t statistic calculation unit is configured to calculate the sample size N=n number of dynamic genes-1, the mutual information value mean of the dynamic change gene j and all dynamic genes based on the initial static gene interaction network and its standard deviation σ, then substitute it into the constructed statistic t and calculate the p-value; The adjacency matrix 1 filling unit is configured to reject the null hypothesis based on the p-value being less than 0.05 and fill the adjacency matrix with a value of 1; The adjacency matrix zero-filling unit is configured to accept the null hypothesis based on the p-value>0.05 and fill the adjacency matrix with a value of 0.

24. The system for constructing a dynamic gene regulatory network according to claim 22 or 23, wherein: The second test is a Granger causality test, and the second test module includes: a Granger test unit configured to arrange the pseudo-time series values ​​of the plurality of single cells based on the target differentiation trajectory and set a sliding window; Traverse the cells, configured as, to determine the adjacency matrix I (i,j) value; Dynamic gene regulatory network 0 filling unit, configured as the I based on the adjacency matrix (i,j) The value is 0, filling 0 into the dynamic gene regulatory network; The single cell transcriptome data extraction unit is configured to extract the data based on the adjacency matrix I (i,j) The value is 1, extracting the single-cell transcriptome data of the dynamic gene; a processing unit configured to process the single-cell transcriptome data based on the dynamic gene to obtain expression data that satisfies the Granger test; a Granger relationship determination unit, configured to determine a Granger causality relationship based on the expression data satisfying the Granger test within the sliding window; The dynamic gene regulatory network obtaining unit is configured to obtain a dynamic gene regulatory network based on the Granger causality.

25. The system for constructing a dynamic gene regulatory network according to claim 24, characterized in that: The sliding window length is greater than or equal to M k / 2-M k / 10, preferred M k / 3-M k / 7, more preferably M k / 5, less than M k -1, where M k is the number of multiple single cells in the target differentiation trajectory k. The left endpoint of the sliding window starts at the first cell of the target differentiation trajectory k, and the right endpoint ends at the last cell of the target differentiation trajectory k. The step size of each sliding is 1 to 5, preferably 1-3, and more preferably 1 cell.

26. The system for constructing a dynamic gene regulatory network according to claim 24, wherein: The processing unit includes: a detrending and demeaning sub-data obtaining sub-unit, configured to obtain detrending and demeaning data based on the single-cell transcriptome data of the dynamic gene; a stationarity test subunit, configured to perform a KPSS test for stationarity based on the detrended and demeaned data; a first-order difference processing subunit, configured to perform first-order difference processing on the non-stationary detrended and de-meaned data to obtain the stationary detrended and de-meaned data; The expression data obtaining unit satisfying the Granger test is configured to obtain the expression data satisfying the Granger test based on the detrended and de-meaned data.

27. The system for constructing a dynamic gene regulatory network according to claim 24, wherein: The dynamic gene regulatory network obtaining unit includes: Granger causality judgment subunit, configured to judge whether Granger causality is established A dynamic gene regulatory network 0-filling subunit is configured to fill 0 into the dynamic gene regulatory network based on the failure of Granger causality; a single-cell transcriptome data extraction subunit, configured to extract the single-cell transcriptome data of the dynamically changing genes based on the Granger causality; The dynamic gene regulatory network 1 or -1 filling subunit is configured to fill 1 or -1 into the dynamic gene regulatory network based on the upregulation or downregulation of the single-cell transcriptome data of the dynamically changing gene.

28. The system for constructing a dynamic gene regulatory network according to claims 16 to 27, characterized in that: The dynamic gene regulatory network is a three-dimensional dynamic gene regulatory interaction matrix of order G×G×S, where G is the dynamically changing gene and S is the number of steps of the sliding window.

29. The system for constructing a dynamic gene regulatory network according to any one of claims 16 to 28, further comprising a visualization device for visualizing the dynamic gene regulatory network using ndtv, GENIE3 or cytoscape software.

30. The system for constructing a dynamic gene regulatory network according to any one of claims 16 to 29, characterized in that: The single-cell transcriptome data is derived from organs, tissues, saliva, urine, blood, cells, wherein the cells are in different stages of differentiation or different physiological states. Optionally, the single-cell transcriptome data is derived from samples or tumor samples in a developmental period, preferably, from mouse pancreas.

31. A device for constructing a dynamic gene regulatory network, characterized in that: include: processor; a memory for storing executable instructions; The processor is configured to read the executable instructions from the memory and execute the executable instructions to implement the method for constructing a dynamic gene regulatory network according to any one of claims 1 to 15.

32. A computer-readable storage medium, characterized in that The storage medium stores a computer program, and when the computer program is executed by a processor, the processor implements the method for constructing a dynamic gene regulatory network according to any one of claims 1 to 15.

33. A computer program product, characterized in that The computer program product includes a computer program, and when the computer program is executed by a processor, the computer program implements the method for constructing a dynamic gene regulatory network according to any one of claims 1 to 15.

34. A computer program, characterized in that The computer program comprises computer program codes, and when the computer program codes are run on a computer, the computer is caused to execute the method for constructing a dynamic gene regulatory network according to any one of claims 1 to 15.

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