154 results about "Gene expression matrix" patented technology

The invention discloses a space transcriptomics cell type deconvolution method based on a generative adversarial network and a comparative learning framework. The method comprises the following steps: firstly, enhancing a gene expression matrix by using a histological image and spatial position information; then, in combination with GraphSAGE and self-supervised comparative learning, optimizing feature representation of space transcriptomics data, and reconstructing high-quality space gene expression data; single-cell RNA sequencing data gene expression features are extracted by using a variational automatic encoder, and meanwhile, the quality of a generated sample is optimized by using a discriminator, so that efficient feature extraction and accurate gene expression reconstruction are realized; and finally, mapping single-cell RNA sequencing data into spatial points through comparative learning and mapping matrix learning based on low-rank representation, thereby predicting the cell type of each point. According to the model, multi-modal information in single-cell RNA sequencing and space transcriptomics data is comprehensively considered, and a mapping matrix is constructed and optimized, so that the cell type of each space point is predicted.

The invention discloses a transcriptomics spatial domain identification method, and belongs to the technical field of transcriptomics. The objective of the invention is to solve the problems of low data noise reduction precision and poor recognition effect of an existing spatial transcriptional spatial domain recognition method. The method comprises the following steps: firstly, obtaining an undirected neighborhood graph according to a gene expression matrix, obtaining embedded representation of the gene expression matrix by utilizing an encoder, obtaining a corresponding reconstruction matrix by utilizing a decoder, and further determining reconstruction loss; meanwhile, a ZINB model is used for fitting a reconstruction matrix, and a ZINB loss function is obtained; then, an augmented graph is constructed based on the undirected neighborhood graph, respective embedded matrixes are obtained through an encoder, the comparison loss of the undirected neighborhood graph and the comparison loss of the augmented graph are obtained through a comparison representation learning mechanism, and then the neighbor comparison loss is obtained; total target loss is obtained based on all losses, a joint optimization strategy is adopted for training, and after training of the whole model is completed, dimensionality reduction and spatial domain recognition are carried out on a generated reconstruction matrix.

The invention discloses a spatial domain identification method based on a multi-view weighted fusion GCN network, and the method comprises the steps: taking the gene expression and spatial position of spatial transcriptome data as input, and combining the spatial information based on different similarity measurement modeling with the gene expression after modeling to construct a multi-view weighted fusion graph convolutional network model; and reconstructing a gene expression matrix by using a decoder, capturing global information of ST data, calculating spatial regularization constraint loss by using similarity information and spatial neighbor information, and completing identification of a spatial transcriptome spatial domain. According to the method provided by the scheme, the model can extract the spatial information from the whole situation and the local situation, so that the spatial information is more fully and comprehensively mined, and accurate spatial domain identification is realized.

The invention provides a clustering method and system for transcriptome sequencing data. The method is applied to the technical field of data processing, and comprises the following steps: collecting cervical adenocarcinoma and para-carcinoma tissue specimens of a plurality of patients, and carrying out data preprocessing to obtain a standardized gene expression matrix; performing clustering operation on the standardized gene expression matrix based on a clustering analysis method combining a potential category model and sub-alliance division to obtain a clustered gene set; performing gene function annotation on the clustered gene set, and performing correlation analysis on a clustering result after function annotation and clinical characteristics of cervical adenocarcinoma to obtain correlation between gene expression and the clinical characteristics; and on the basis of clustering analysis and correlation analysis results, constructing a deep clustering prediction model based on a variational auto-encoder and a Gamma hybrid model so as to predict the prognosis risk or chemotherapy sensitivity of the patient. The problems that high-dimensional transcriptome data is poor in clustering stability and low in prognosis prediction precision are solved.

The invention relates to the technical field of bioinformatics, in particular to a data classification method and system for single cell sequencing. The method comprises the following steps: acquiring an original gene expression matrix; performing gene data preprocessing on the original gene expression matrix to obtain a preprocessed gene expression matrix; constructing a cell topological graph according to the preprocessed gene expression matrix to obtain the cell topological graph; performing cell division rate calculation according to the cell topological graph and the preprocessed gene expression matrix, and performing RNA rate calculation to obtain RNA rate data; adding node rates to the cell topological graph according to the cell division rate data and the RNA rate data to obtain an enhanced cell topological graph. According to the method, the dynamic graph is constructed and the change rule of the dynamic graph is analyzed, so that the dynamic change of the cell state is better captured, the sensitivity to parameters is reduced, a complex cell population structure is processed, and a quantitative cell heterogeneity evaluation index is provided.

A gene regulation inference method based on causal diagram embedding and conditional cellular network relates to the technical field of gene regulation network prediction, and comprises the following steps: 1, obtaining a gene expression matrix from scRNA-seq, and generating a causal diagram; 2, generating a local feature embedding matrix of a gene by using a graph neural network model based on a causal graph and a known gene regulation and control network graph; 3, constructing a CCSN based on scRNA-seq, converting the CCSN into gene connectivity vectors, and integrating the gene connectivity vectors of all cells to form a CNDM as a global feature matrix; 4, integrating the local feature embedding matrix and the global feature matrix to form a final gene feature matrix; 5, screening a core gene from the gene feature matrix, and constructing a regulation edge matrix; and 6, inputting the regulatory edge matrix into a gene link prediction module to realize inference of the gene regulatory network. By applying the method, the causal relationship and the cell specificity can be integrated, the core gene is effectively screened, the feature fusion is optimized, and the accuracy and the biological interpretation of network inference are improved.

The invention provides a multi-scale causal discovery method and system based on collective behavior modeling, and relates to the technical field of causal discovery, and the method comprises the steps: obtaining single-cell RNA sequencing data, the single-cell RNA sequencing data being a gene expression matrix of a plurality of cells; single-cell RNA sequencing data is input into a multi-scale causal model for reasoning, a multi-scale causal diagram is obtained, and the multi-scale causal diagram is used for describing the multi-scale causal relationship between genes and between genes and gene programs; wherein the multi-scale causal model takes a single gene as an individual variable, through collective behavior modeling, the single gene is aggregated into a collective variable representing a gene program, and a multi-scale collaborative learning strategy is adopted to collaboratively learn the multi-scale causal relationship between individuals and between individuals and collective. According to the method, a collective behavior modeling framework is innovatively constructed, key collective behaviors driving state changes can be effectively identified, and the causal discovery accuracy, rationality and robustness are improved.

The invention discloses a genetic state cancer clustering analysis method based on deep learning, which comprises the following steps: S1, constructing a single cell sequencing data set which comprises a gene expression matrix; s2, constructing a deep neural network composed of an input layer, a hidden layer and an output layer to obtain converted low-dimensional high-variation gene expression features; s3, constructing a gridding preprocessing model, and performing networked preprocessing including grid segmentation and isolated point removal on the low-dimensional high-variation gene expression features; constructing a clustering algorithm model, inputting the preprocessed low-dimensional high-variation gene expression features into the clustering algorithm model, and designing an optimization strategy based on a target function by the clustering algorithm model to enhance label entropy on a KL divergence index and amplify the ratio weight of auxiliary probability distribution and clustering distribution of the gene features; and iteratively realizing clustering result optimization as the output of the clustering algorithm model. Compared with the prior art, the clustering precision can be improved.

Provided are a gene expression matrix optimization method, an electronic device, and a storage medium. The method comprises: acquiring cell data of a plurality of cells, wherein the cell data comprises an initial gene expression matrix of each of the plurality of cells; determining a preset number of nearest neighbor cells of a target cell among the plurality of cells on the basis of the cell data; determining a gene expression distance between the target cell and each nearest neighbor cell, determining a distance parameter on the basis of a target non-zero value among the gene expression distances, and determining, on the basis of the distance parameter, a weight parameter corresponding to each nearest neighbor cell; and on the basis of the weight parameter corresponding to each nearest neighbor cell and the initial gene expression matrix of each nearest neighbor cell, obtaining a target gene expression matrix obtained by smoothing of the target cell. By using the described method, the noise in a gene expression matrix can be reduced to improve the accuracy of performing downstream analysis on the basis of the gene expression matrix.

The invention is applicable to the technical field of cell type annotation, and provides an interpretable single cell annotation method, which comprises the following steps: based on a source cancer data set, performing cell type label prediction on a target cancer data set through a first module of an annotation model to obtain a prediction label of each cell in the target cancer data set, the annotation model is constructed by adopting an information bottleneck-based graph interpretation technology, and based on the health data set and the prediction label, interpretability analysis is performed on the to-be-interpreted cancer data set through a second module of the annotation model to obtain an interpretation result, the to-be-explained cancer data set is composed of the cancer cell gene expression matrix and the prediction label of the target cancer data set, so that the explanatory mechanism is integrated into the model architecture, the prediction accuracy is ensured, the explanatory stability and reliability are improved, the inconsistency caused by dependence on a post explanatory method is avoided, and the prediction accuracy is improved. And the prediction logic of the model can be understood.

The invention discloses a spatial transcriptome data spatial domain identification method based on multi-space self-supervised contrast learning, and the method comprises the steps: carrying out the modeling to generate a spatial neighborhood graph, keeping the graph structure unchanged, disorganizing node features, and carrying out the data enhancement, thereby obtaining an enhanced graph; constructing an encoder based on a graph neural network, extracting spatial transcriptome data fused with spatial information and gene information to obtain potential embedding, and sending the potential embedding into a multi-space generator to generate multiple groups of rich graph feature representations; fusing graph feature representation and potential embedding to obtain refined representation, reconstructing a gene expression matrix through a decoder, and adding contrast learning loss and reconstruction loss as a total objective function; and updating network parameters by adopting an Adam optimizer according to the obtained total objective function to complete spatial transcriptome spatial domain identification. Spatial transcriptome data are fully mined from global and local angles, and accurate spatial domain identification is realized.

The invention discloses a spatial transcriptome data analysis method based on artificial intelligence, and belongs to the technical field of spatial transcriptomics data analysis. Firstly, self-adaptive normalization and hypervariant gene screening preprocessing are carried out on original gene expression data; then constructing a hierarchical map integrating spatial proximity and transcription similarity, and ensuring the connectivity and robustness of the map through a dynamic radius pruning and neighborhood inheritance strategy; dividing positive and negative sample sets based on the atlas, and inputting a type modulation contrast graph auto-encoder for training; and finally, spatial domain identification and downstream function analysis are completed based on the low-dimensional potential representation or reconstructed gene expression matrix output by the model. The method effectively improves the accuracy and stability of spatial domain recognition, adapts to multi-technology-source data, enhances the biological interpretability of model output, and can be widely applied to biomedical scenes such as tumor microenvironment analysis and organ development research.

The invention belongs to the technical field of single cell data analysis, and particularly relates to a single cell pseudo-trajectory identification method based on a gene co-expression network. Comprising the following steps: S1, constructing a single cell transcription data matrix of behavior gene names and cell names; s2, preprocessing the single-cell transcription data matrix, constructing a cell expression matrix, and performing characteristic gene screening on the cell expression matrix to obtain a single-cell gene expression matrix; s3, constructing a gene co-expression network; s4, constructing a gene rank differential expression network in combination with the single-cell gene expression matrix and the gene co-expression network; and S5, carrying out integrated clustering on the gene rank differential expression network, fusing by using a similarity network to obtain a final clustering result, determining an initial cluster, distributing pseudo time for cells in each cluster, and deducing a cell pseudo trajectory. According to the method, the differentiation track of the cell can be identified, and compared with an original gene expression matrix, a more stable identification result can be obtained by utilizing a relative expression sequence.

The invention discloses a prediction model generation method and device of a gene regulatory network, a server and a medium, and relates to the technical field of computers. Gene activity changes at different time points in a gene expression time sequence are captured through a gene expression matrix encoder to obtain global gene expression characteristics; a text encoder is used for analyzing the corresponding relation between a gene expression mode and text semantics, multi-time-point gene expression feature data and biological text feature data are aligned through a comparative learning method, the learning rate is gradually and smoothly reduced through a cosine recession learning rate scheduling mechanism, it is ensured that the model is closer to a globally optimal solution, and the optimal solution is obtained. Relevance modeling between multi-time-point gene expression data and biological text description is achieved through the prediction model of the gene regulation and control network, and the precision and generalization ability of a statistical model are improved.

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

The invention belongs to the field of spatial transcriptome spatial domain recognition, and provides an idling data feature extraction method, a spatial domain recognition method and a spatial domain recognition system in order to solve the problem of poor recognition accuracy of a spatial transcriptome spatial domain. The idling data feature extraction method comprises the following steps: acquiring idling data of a pathological section, wherein the idling data comprises an initial gene expression matrix and spatial position information; performing denoising processing on the initial gene expression matrix to obtain a denoised gene expression matrix; calculating a position code for each site according to the spatial position information, and splicing the position code with the denoised gene expression matrix to obtain an enhanced gene expression matrix; a mask auto-encoder is utilized to encode an enhanced gene expression matrix, low-order potential representation is obtained and serves as extracted idle data features, accurate recognition of a spatial domain can be achieved, and a good upstream analysis basis is provided for downstream tasks.

The invention provides a subcellular level space transcriptome function analysis method and system, and relates to the field of bioinformatics, the method comprises the following steps: automatically processing core information of a sample, and optimizing space transcriptome expression matrix data to realize expression matrix data reception and automatic processing; timed polling and the like are added to realize automation of standard analysis from a space gene expression matrix to a space transcriptome, and gene ID conversion, cell unit type selection and cell unit selection are performed according to expression matrix data to obtain data after selection; and according to the converted and selected data, carrying out statistics on spatial data features, and visualizing the data features. According to the method, through a multi-module automatic process, the analysis efficiency, accuracy and biological interpretation of space transcriptomics and single cell data are remarkably improved, and the method is suitable for multi-sample, high-resolution and big data analysis scenes.

The method disclosed by the invention comprises the following steps: S1, collecting a gene expression matrix of scRNA-seq, a gene activity matrix of scATAC-seq and cell type annotation of the scRNA-seq; s2, the scRNA-seq data and the scATAC-seq data are subjected to preprocessing, and a gene shared by the scRNA-seq data and the scATAC-seq data is obtained; s3, constructing an encoder network, and realizing joint representation of different omics data in the shared embedding space; s4, performing supervised learning guidance on the embedded space by adopting a cell type label in the scRNA-seq data; s5, in the embedding space and the label space, performing alignment on the scRNA-seq data and the scATAC-seq data by adopting an unbalanced optimal transmission algorithm; and S6, according to the matching probability matrix, endowing each cell in the scATAC-seq data with a cell type annotation, and realizing integration of the scRNA-seq data and the scATAC-seq data. The problem that the accuracy and reliability of data integration are affected due to distortion of biological related signals caused by application of an existing OT frame to single cell data integration is solved.

The invention discloses a cell interaction identification method based on cell space transcriptome data, which comprises the following steps: constructing cell pairs according to cell space proximity, and calculating an interaction signal matrix and space information of each pair of cells by combining a ligand-receptor database; then generating cell pair embedding representation by using a graph neural network, calculating the similarity between cell pairs, and calculating the similarity between cells based on a gene expression matrix; updating the similarity matrix through iteration until convergence to obtain a final similarity matrix between cells and between cell pairs; and clustering the similarity matrix, and constructing a cell type and cell pair type model, thereby realizing accurate cell interaction identification. According to the method, space and expression information can be fully utilized, inter-cell heterogeneity and context specificity interaction can be captured, the analysis precision and biological authenticity of a cell communication network are improved, and an effective tool is provided for research on tissue development, disease mechanisms, immune response and the like.

The invention discloses a method and a device for identifying a transcription factor of a tissue gene, relates to the technical field of biological information, and mainly aims to solve the problem of poor identification accuracy of the transcription factor of the existing tissue gene. Comprising the following steps: acquiring an initial gene expression matrix of tissue genes, and constructing a full-connection directed graph based on the initial gene expression matrix; after the full-connection directed graph is converted into an undirected role graph, the undirected role graph and a feature matrix are reconstructed based on a graph neural network model after model training is completed, a reconstructed target gene expression matrix is obtained, and a loss value of the graph neural network model is obtained through calculation after edges are dynamically removed; and generating a directed gene regulatory network based on the target gene expression matrix, and identifying a transcription factor of the tissue gene based on the directed gene regulatory network.

The present application relates to a kind of intercellular interaction reconstruction method and system based on graph convolution and long short-term memory attention network.The method comprises the following steps: collecting spatial transcriptome data under single cell or subcellular resolution, obtaining the gene expression matrix corresponding to each cell;Determine the spatial proximity between each cell, determine whether there is interaction between cells, thereby constructing cell graph;Using graph convolution and long short-term memory attention network combination, form feature extraction network to extract features from cell graph, obtain latent feature representation;Using inner product to decode latent feature representation, generate new adjacency matrix, obtain the intercellular interaction after reconstruction.By introducing the combination of graph convolution and long short-term memory attention network, the spatial correlation in cell graph structure can be captured while learning intercellular interaction, and the intercellular interaction is reconstructed, so that the intercellular interaction and the organization homeostasis of organism can be better understood.

This invention belongs to the field of bioinformatics and relates to a single-cell trajectory inference method based on adaptive feature selection. First, an initial gene expression matrix is ​​obtained through data preprocessing and screening for highly variable genes. Second, a two-dimensional evaluation strategy is employed to calculate the scores of highly variable genes with gene expression variability and the trajectory importance score related to differentiation trajectories. Then, a dynamic weight fusion mechanism is introduced, adaptively adjusting the fusion weights of the two scores based on performance feedback, and highlighting key genes through nonlinear enhancement. Next, an intelligent inflection point detection algorithm adaptively determines the optimal number of features. Finally, trajectory inference is performed based on a variational autoencoder model reconstructed from feature subsets, and a performance-driven feature selection closed loop is formed through multiple rounds of iterative optimization. This invention achieves high-precision, adaptive single-cell trajectory inference, solving the technical problems of single feature selection and fixed weights in traditional methods.

The present application discloses a method, device, server, and medium for generating a prediction model of a gene regulatory network, which relates to the field of computer technology. The gene expression matrix encoder is used to capture the gene activity changes at different time points in the gene expression time series to obtain global gene expression features. The text encoder is used to analyze the correspondence between gene expression patterns and text semantics. Through the method of contrastive learning, the gene expression feature data at multiple time points is aligned with the biological text feature data. The cosine decay learning rate scheduling mechanism is used to gradually and smoothly reduce the learning rate to ensure that the model is closer to the global optimal solution. The prediction model of the gene regulatory network is used to establish the correlation modeling between the gene expression data at multiple time points and the biological text description, improving the accuracy and generalization ability of the statistical model.

The invention relates to a structural network-genetic map biological network model for predicting ischemic stroke and a construction method thereof, and the method comprises the steps: extracting and calculating seven multi-scale morphological features and pairwise Pearson correlation coefficients among the features from T1 weighted imaging data and diffusion tensor imaging data; constructing a 308 * 308 morphological similarity network matrix and a brain network module for identifying ischemic stroke neural dysfunction; 1782 sampling points are extracted from the Airy human brain map, and each sampling point comprises expression data of 10185 genes; the method comprises the following steps: mapping space coordinates of AHBA sampling points to a cortex package of a Desikan-Killiany map, carrying out normalization processing to output 308 * 10185 brain region gene-by-gene expression matrixes, and constructing a structural network-gene map biological network model for predicting ischemic stroke by adopting a partial least square regression method and a bootstrap method. Compared with the prior art, the model determines the specific molecular mechanism related to the phenotypic structure change of ischemic stroke injury, and the stroke occurrence probability is predicted according to the specific molecular mechanism.

The invention belongs to the technical field of biomedical engineering, and discloses a gene regulatory network optimization method based on a diffusion model, which comprises the following steps: acquiring gene data of cells under a steady state condition, and constructing a gene expression matrix according to the gene data; injecting Gaussian noise into the gene expression matrix based on a diffusion model method to generate a series of noisy data sequences; performing noise estimation and structure estimation on the noisy data sequence by a noise estimator and a structure estimator based on a gene regulation and control network, and performing reverse denoising processing according to the noise estimation and the structure estimation to obtain gene structure estimation after reverse denoising; performing structure optimization on the gene structure estimation after reverse denoising by adopting an acyclic constraint function and a regularization substitution method; and outputting the optimized gene structure estimation. According to the method, the regulation and control relation between the genes is accurately recognized from high-dimensional gene expression data, and the modeling precision of the regulation and control relation between the genes is improved.

The invention relates to the technical field of biomedical detection and bioinformatics, and discloses a detection method and system for social anxiety disorder risk assessment, and the method comprises the steps: obtaining transcriptome data of a peripheral blood sample of a to-be-detected object, and carrying out preprocessing and normalization to obtain a standardized gene expression matrix; extracting minimum gene set expression data containing 10 genes such as HSF5 and FADS2, and performing Z-score standardization processing by using the solidified model parameters; calling a preset weight coefficient and an intercept item to perform linear weighting and probability conversion calculation on the standardized data to obtain a disease prediction probability of the subject; and carrying out risk layering according to the optimal critical value and generating an auxiliary diagnosis report. According to the method, stable features are screened through a machine learning algorithm, the scoring model is constructed, subjectivity of traditional clinical diagnosis is overcome, and objective, quantitative and automatic evaluation of social anxiety disorder risks is achieved.

This application discloses a single-cell identification method and related devices based on gene waves, relating to the field of single-cell identification technology. The method includes: preprocessing the gene expression matrix of the object to be annotated to obtain a preprocessed matrix; sorting the genes in the preprocessed matrix based on the human reference genome to obtain the gene wave of each single cell to be annotated; using the gene wave of the single cell to be annotated as input, and using a trained single-cell recognition model to determine the identification result of the single cell to be annotated, the identification result being the cell type and / or cell subtype. This application can quickly and accurately complete the automated annotation of cell types and cell subtypes.

The invention discloses a method and device suitable for tumor glucose metabolism reprogramming driving factor analysis, and the method comprises the steps: generating a gene expression matrix for a biological pathway under a sample; performing dimension reduction processing on the gene expression matrix to obtain a first low-dimensional representation and a second low-dimensional representation; inputting the first low-dimensional representation and the second low-dimensional representation into a causal direction inference structure, and outputting a plurality of initial causal direction inference results; selecting from the plurality of initial causal direction inference results to obtain a target causal direction inference result corresponding to the sample; and finally, analyzing and comparing a target causal direction inference result under a normal sample and a target causal direction inference result under a tumor sample to obtain an analysis result, and indicating that Fenton reaction in cells drives tumor glucose metabolism reprogramming. Therefore, key factors of tumor glucose metabolism reprogramming can be quickly and accurately identified, and the fact that the Fenton reaction in cells is a main reason of tumor glucose glycolipid metabolism reprogramming is found.

The invention discloses a gene regulatory network prediction method and system based on explicit correlation modeling, and the method comprises the steps: obtaining a gene expression matrix of single-cell RNA sequencing data and an adjacent matrix of a prior regulatory graph constructed based on prior knowledge, and inputting the matrixes into a graph neural network model; wherein the graph neural network model is configured to perform explicit modeling on a link in the prior regulation and control graph through an intra-layer message passing space and an inter-layer message passing space so as to obtain link representation; predicting whether a regulation relation exists between the gene pairs through a classifier on the basis of link characterization so as to deduce a gene regulation network; wherein the architecture of the graph neural network model is adaptively determined through an automatic architecture search algorithm according to input data. According to the framework provided by the scheme, modeling link representation is displayed in the message passing process, the regulation and control relation of the gene pair is inferred by MLP based on link embedding, utilization and organization of complex connection information of the prior regulation and control graph are enhanced from the source, and the inference accuracy is effectively improved.

The invention provides a single cell multi-omics integration method and device, and relates to the technical field of multi-omics. The method comprises the following steps: preprocessing a gene expression matrix of a single cell to obtain a standardized feature matrix of genomics; the method comprises the following steps: preprocessing a protein expression matrix of a single cell to obtain a proteomics standardized characteristic matrix, then obtaining an adjacent matrix which is used for indicating a neighbor relation between cells, and then, carrying out a genomics standardized characteristic matrix, the proteomics standardized characteristic matrix and the adjacent matrix to obtain a single cell standard characteristic matrix; inputting into a single-cell multi-omics data integration model, and finally outputting to obtain a common characteristic matrix integrating single-cell multi-omics. In this way, the single-cell multi-omics is integrated through the adjacent matrix, and the single-cell multi-omics is integrated through the cell neighbor relation.