127 results about "Gene expression matrix" patented technology

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.

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 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.

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 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.

The present application relates to the technical field of bioinformatics and artificial intelligence, in particular to a virtual cell construction method and system, the method comprising: taking single cell gene expression matrix and perturbation condition data as input data; constructing an encoding network based on a structural causal model to obtain latent representation, and constructing a perturbation variable according to the perturbation condition data, modeling the latent representation and the perturbation variable to obtain decoupled latent representation; constructing a continuous time evolution path from an initial distribution to a target distribution based on a flow matching model, and determining state changes according to the decoupled latent representation; numerically solving the continuous time evolution path to output virtual cell expression data. The present application is used to solve the problems of causal aliasing, insufficient distribution out-of-distribution generalization ability, unstable generation process and difficulty in counterfactual reasoning in the existing single cell perturbation prediction method.

The application discloses a kind of screening methods and application of pivot gene related to sheep muscle fatty acid content, to solve the technical problems that local sheep breed sample quantity is limited, traditional single gene analysis method is difficult to analyze fatty acid metabolism regulation from network level.This application carries out transcriptome sequencing to multiple months of muscle tissue of Gangba sheep, constructs gene expression matrix, using weighted gene co-expression network analysis (WGCNA) Combined with module characteristic gene and fatty acid phenotype correlation screening strategy, the pivot gene significantly positively correlated with muscle fatty acid content is obtained.The screening method can construct a robust co-expression network under limited sample size, systematically identify the functional module and core gene related to the content of fatty acids such as linoleic acid, and reveal the dynamics of fatty acid metabolism at different ages, and the method can be extended to other plateau livestock;The screened pivot gene can be used as a molecular breeding marker for early selection of Gangba sheep, and the breeding cycle is shortened.

The invention discloses a cell communication analysis method and system based on single cell transcriptome data. The cell communication analysis method based on the single cell transcriptome data comprises the following steps: data input, standardized screening, stratified analysis and visual output. By means of CellPhoneDB and CellChat software, a cell-cell communication network is established by integrating gene expression with a signal ligand and a receptor according to a single-cell gene expression quantity matrix, and a visual analysis result is provided.

The invention discloses a gene regulation and control network prediction method based on line graph attention and Transform. The method comprises the following steps: firstly, acquiring a gene expression matrix and a gene regulation prior network, and preprocessing the gene expression matrix; then, selecting gene nodes from the gene regulation prior network, and constructing a closed subgraph by taking a target gene node pair as a center; converting the closed sub-graph into a line graph, and constructing a line graph node feature matrix; finally, a gene regulation and control network prediction model is constructed, and the model comprises a graph attention neural network, a Tannformer encoder and a multi-layer perceptron; the graph attention neural network extracts gene local spatial features by using the line graph and the line graph node feature matrix; the Tannform encoder extracts global spatial features of the gene by using the gene expression matrix; and splicing the gene local spatial features and the gene global spatial features, and predicting regulation edges of the spliced features through a multi-layer perceptron. According to the method, the key regulation relation can be distinguished more effectively in the gene regulation network with high noise and sparse structure.

The present invention relates to the field of bioinformatics technology, and in particular to a data classification method and system for single-cell sequencing. The method comprises the following steps: obtaining 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 topology map based on the preprocessed gene expression matrix to obtain a cell topology map; calculating cell division rates based on the cell topology map and the preprocessed gene expression matrix, and performing RNA rate calculation to obtain RNA rate data; adding node rates to the cell topology map based on the cell division rate data and the RNA rate data to obtain an enhanced cell topology map. The present invention better captures the dynamic changes of cell states, reduces sensitivity to parameters, processes complex cell population structures, and provides quantitative cell heterogeneity assessment indicators by constructing a dynamic map and analyzing its changing patterns.

This invention relates to a method for assessing the quality of single-cell sequencing data, which addresses the current difficulty in evaluating the differences in data quality after applying different single-cell sequencing data imputation algorithms without the participation of real samples. The method includes the following steps: First, two single-cell sequencing data imputation algorithms are prepared. Then, a synthesis matrix based on the statistical characteristics of real data is created and normalized preprocessed. The normalized gene expression matrix is ​​input into the two imputation algorithms to be evaluated, and the output feature vectors are extracted to set an optimization function. The gene expression matrix is ​​optimized using the optimization function, and then the optimized matrix is ​​denormalized to obtain the imputed gene expression profile. Finally, the obtained gene expression profile is input into the two algorithms to be evaluated to obtain two sets of predicted feature vectors. The data quality difference value can be calculated using these feature vectors. This invention can accurately assess the data quality difference between two data imputation algorithms without the participation of real samples.

The invention discloses a spatial transcriptome data feature extraction method and system based on a hierarchical variational auto-encoder, and the method comprises the steps: carrying out the preprocessing of a to-be-processed gene expression matrix, obtaining the preprocessed data, and constructing an adjacent matrix of a sparse graph structure based on the preprocessed data; inputting the preprocessed data into a hierarchical variational auto-encoder model for processing, and outputting comprehensive potential representation; and carrying out downstream analysis by utilizing the comprehensive potential representation, wherein the downstream analysis comprises spatial domain identification, batch effect correction, trajectory analysis and differential gene expression analysis. According to the method, the feature extraction efficiency of the spatial transcriptomics data is remarkably improved.

The present invention discloses a spatial domain recognition method based on a multi-view weighted fusion GCN network. First, the gene expression and spatial location of spatial transcriptome data are used as inputs. A multi-view weighted fusion graph convolutional network model is constructed by combining spatial information modeled based on different similarity metrics and the modeled gene expression. A decoder is used to reconstruct the gene expression matrix to capture the global information of ST data. The spatial regularization constraint loss is calculated using similarity information and spatial neighbor information to complete the recognition of the spatial domain of spatial transcriptome. The method proposed in this solution enables the model to extract spatial information globally and locally, thereby more fully and comprehensively mining spatial information and achieving accurate spatial domain recognition.

The invention relates to the technical field of cell recognition, and discloses a rare cell population recognition method, device and equipment and a storage medium, and the method comprises the steps: obtaining an original gene expression matrix, preprocessing the original gene expression matrix to obtain a target gene expression matrix, and constructing a cell leaf node graph and a K neighbor graph; integrating the cell leaf node graph and the K neighbor graph to obtain an enriched cell leaf node graph; performing node embedding on the enriched cell leaf node graph to map all target cells in the target gene expression matrix to a target dimension embedding space to obtain a cell embedding vector set; and performing clustering analysis and neighbor error analysis on all target cells in the cell embedding vector set, and identifying a rare cell population. According to the scheme, the limitation of single-dimension information is overcome, the complex relationship between cells can be more comprehensively and deeply described from two complementary perspectives of a functional mechanism and a phenotype state, and the recognition accuracy of a rare cell population is improved.

The invention provides a bladder cancer subtype classification system and molecular typing method based on machine learning, and the molecular typing method comprises the steps: firstly obtaining transcriptome data and survival information of bladder cancer tissue of a patient, extracting data from a preset amino acid metabolism related gene set, and constructing a gene expression matrix; then, clustering the patients by adopting an unsupervised clustering algorithm, and determining at least two types of amino acid metabolism molecule subtypes in combination with a stability index; thirdly, carrying out survival difference analysis on different subtypes, screening out differential expression genes related to survival outcomes, constructing a survival prediction model based on the differential expression genes, and calculating amino acid metabolism scores of the patients; finally, the patients are grouped according to the scores, and molecular typing based on the amino acid metabolism characteristics is completed. According to the invention, stable and accurate typing of the bladder cancer patient is realized.