330 results about "Single cell transcriptome" patented technology

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.

The invention relates to the technical field of biological information, in particular to a method and system for online integration of multi-source single cell data. The method comprises the following steps: acquiring multi-source single cell transcriptome data; performing data quality evaluation based on gene expression quantity, mitochondrial gene proportion and cell data depth on the single cell transcriptome data to obtain initial quality control data; setting standardized filtering parameters according to the initial quality control data, and performing quality control parameter filtering on the single cell transcriptome data to obtain filtered cell data; performing gene expression characteristic analysis on the filtered cell data to generate high-variation gene expression characteristic data; and performing gene expression spatio-temporal distribution statistics according to the high-variation gene expression feature data to generate gene expression statistical feature data. According to the method, through refined quality control and batch effect correction, data from different sources can be seamlessly integrated on the premise of ensuring data quality, so that noise caused by different batches and technical platforms is eliminated.

This disclosure provides a novel method and system for characterizing morphodynamic profiles of objects, such as biological entities. This disclosure provides a shape, appearance, and motion (SAM) phenotype Observation Tool (SPOT). SPOT establishes a standardized SAM “phenome,” image descriptors resembling single-cell transcriptomes, to comprehensively quantify a cell's instantaneous state without prior knowledge. SPOT also establishes a standardized workflow for temporal analysis. SPOT is a generalist tool, applicable to any live-cell imaging and advances biomedical discovery through its standardized, unbiased, streamlined workflow to quantify phenotypic heterogeneity and predict phenotype-genotype-function coupling.

The embodiment of the invention discloses a single cell transcriptome sequencing data annotation method. The method comprises the following steps: acquiring single cell transcriptome sequencing data associated with a target disease, and clustering a plurality of cells corresponding to the single cell transcriptome sequencing data to obtain a plurality of cell clusters; aiming at each marker gene corresponding to the target disease, according to single cell transcriptome sequencing data, obtaining the cell cluster gene expression quantity of the marker gene in each cell cluster; for each cell cluster and each cell type corresponding to the target disease, accumulating the cell cluster gene expression quantity of each marker gene corresponding to the cell type in all the marker genes in the cell cluster to obtain the total gene expression quantity; and aiming at each cell cluster, according to the total gene expression quantity corresponding to the cell cluster under each cell type, annotating a target cell type for the cell cluster from all the cell types. According to the technical scheme provided by the embodiment of the invention, the accuracy of cell type annotation can be ensured.

The invention relates to the technical field of bioinformatics, and provides a visual analysis method and system for a rice multi-tissue single cell expression profile. The method comprises the following steps: comparing sequencing data of an original single cell transcriptome of a rice tissue to obtain a standardized transcriptome data set; performing batch effect correction and integration on the standardized transcriptome data set to obtain a whole plant expression matrix; performing cell type annotation on the whole plant expression matrix to obtain a cell type annotation system; carrying out visual dimension reduction processing on the whole plant expression matrix fused with the cell type annotation system, and carrying out co-expression network construction to obtain a modular tissue correlation analysis model; and establishing an interaction end based on the module organization correlation analysis model, and realizing data visualization analysis through the interaction end. The invention provides a one-stop analysis platform for rice cell heterogeneity research, functional gene mining and molecular breeding.

According to the single-cell transcriptome cell annotation method and system fused with the large language model, cell type annotation is carried out by constructing the proprietary cue word and utilizing the large language model, and the accuracy and universality of cell annotation are improved. The method has remarkable advantages on cell annotation of non-mode species, and an automatic and intelligent cell annotation process is realized.

The invention provides a single-cell multi-omics cell type annotation method based on distribution and knowledge alignment, and belongs to the technical field of single-cell type annotation, the method comprises the following steps: obtaining single-cell transcriptome data and single-cell chromatin accessibility sequencing data, and pre-training and training a multi-omics variation auto-encoder model, the multi-omics variational auto-encoder model is combined with a variational auto-encoder and a knowledge distillation technology, and multi-omics single cell data is integrated and annotated through distribution and knowledge alignment. And performing cell type prediction on the single cell transcriptome data and the single cell chromatin accessibility sequencing data which are input at the same time by using the trained multi-omics variational auto-encoder model. According to the method, the problem of limitation of a method only depending on single omics is solved, the synergistic effect between the omics is enhanced, the accuracy of annotation is improved, and the calculation overhead is reduced through knowledge distillation.

The invention relates to the technical field of cell data analysis, and discloses a single-cell transcriptome data and text description conjoint analysis method based on a multi-modal language model, which comprises the following steps: acquiring a single-cell RNA sequencing expression matrix and a corresponding cell text description, preprocessing the single-cell RNA sequencing expression matrix and the corresponding cell text description, and analyzing the single-cell transcriptome data and the corresponding cell text description; according to the method, a multi-modal data set is constructed, deep fusion of gene expression data and text knowledge is realized by constructing a double-model and cross-modal projection module, limitation of a single mode is avoided, a gene expression value and an index sequence are reserved during preprocessing, a rough coding mode is changed, and the cell type identification accuracy is improved; based on a pre-training strategy of comparative learning, matching learning and a cross-modal projection module, fine-grained cross-modal information interaction and sharing are realized, and cross-modal task effects of text generation cells or cell generation texts and the like are optimized.

The invention discloses a multi-omics analysis method and device, electronic equipment and a storage medium, and relates to the technical field of biological information, and the method comprises the following steps: obtaining a spatial single cell function map set based on multi-omics data, the multiple omics data at least comprise single cell transcriptome data, spatial transcriptome data, single cell chromatin transposase accessibility sequencing ATAC data and whole genome association research GWAS data, and the spatial single cell function map set comprises a plurality of maps used for indicating spatial distribution of different omics data; and establishing a mapping relationship from the spatial transcriptome data to the single cell transcriptome data, wherein the mapping relationship indicates spatial position information of each cell in the single cell transcriptome data. By utilizing the method, the flexibility during multi-omics analysis is improved, and the data integration capability is improved.

The invention relates to a single-cell transcriptome data clustering method based on graph contrast representation learning, and belongs to the technical field of single-cell transcriptome data clustering. Comprising the following steps: acquiring single-cell transcription data; preprocessing the acquired single cell transcription data; obtaining a first enhanced view and a second enhanced view based on the preprocessed single cell transcription data; constructing a clustering model, wherein the clustering model comprises a first encoder, a second encoder, a first decoder and a second decoder; sequentially inputting the first enhanced view into a first encoder and a first decoder for training, and sequentially inputting the second enhanced view into a second encoder and a second decoder for training; and carrying out single cell transcriptome data clustering by using the first encoder and the first decoder of the trained clustering model. According to the method, the influence of the characteristics of the single cell transcriptome data on the clustering effect is weakened, so that a better clustering effect is achieved.

The invention provides a cell development process dynamic modeling method and device based on time sequence single cell transcriptome data and a medium, and relates to the crossing field of bioinformatics and computational biology. The method comprises the following steps: constructing a Shenchang differential equation learning framework; adjusting parameters of the single cell development state change model based on the Shenxuan differential equation learning framework so as to construct a population cell development state change model; obtaining a cell specific gene regulation network and a population cell gene regulation network based on the population cell development state change model so as to predict occurrence opportunity of cell lineage differentiation and a molecular decision mechanism of cell differentiation; therefore, the problems of incomplete modeling mechanism, insufficient noise processing and lack of energy principle in the existing cell development process are solved.

The invention discloses a method and system for screening drug targets with space-time specificity and computer equipment, and relates to the technical field of bioinformatics and computational biology. The screening method is based on single cell transcriptome sequencing data, and comprises the following steps: (1) quantitatively reconstructing spatial positioning and functional modes of cells in tissues, namely 1.1) carrying out data preprocessing on the single cell transcriptome sequencing data; 1.2) reconstructing the spatial positioning of the single cell; 1.3) reconstructing a single cell biological function mode; (2) screening a drug target with space-time specificity, wherein the screening comprises the following steps: 2.1) cell-cell communication analysis; the invention discloses a single-cell data analysis method based on a GRN (Gene Regulatory Network), which is characterized by comprising the following steps of (1) establishing a single-cell data analysis method, (2) establishing a GRN (Gene Regulatory Network) taking a specific tissue microenvironment state as a core, and (3) discovering a target spot. The single-cell data analysis method is innovative, provides a new thought and a technical path for research and development of drugs for metabolic diseases and other systemic diseases, and has a popularization and application basis.

The invention discloses a preparation method of fresh taxus chinensis stem and leaf protoplasts. The preparation method comprises a preparation process and a purification process. The preparation method of the taxus chinensis stem and leaf protoplast is simple and easy to operate, raw materials are easy to obtain and low in price, reagent components have good biocompatibility, and the taxus chinensis stem and leaf protoplast is free of harmful components, safe and environmentally friendly. The invention provides a simple and rapid taxus chinensis stem and leaf protoplast enzymolysis preparation method, which adopts vacuum filtration, accelerates the permeation of enzymatic hydrolysate and improves the enzymolysis efficiency, so that the protoplast can be rapidly obtained. Meanwhile, high-purity separation of the protoplast is carried out in combination with density gradient sedimentation of the iodixanol solution, so that the protoplast with uniform size and complete form is obtained, and convenience is provided for scientific researches such as subsequent conversion and single cell transcriptome sequencing.

The present application relates to the field of bioinformatics. In particular, the present application relates to methods and apparatus for speculating variable splicing functionality based on single cell transcriptome data. The method comprises the following steps: determining a variable splicing mode of each gene in a data set in a cell; determining the incidence relation between the variable splicing mode and the gene expression of each gene; a variable splicing mode module is determined according to the incidence relation between the variable splicing modes and the gene expression, and the variable splicing mode module is a variable splicing mode set obtained through clustering according to the correlation between the variable splicing modes and the cell phenotypes; displaying the cell splicing heterogeneity according to the variable splicing mode module; and / or determining a potential regulatory mechanism between the variable splicing mode and the gene expression according to the variable splicing mode module, the potential regulatory mechanism being used for embodying key splicing factors in the gene expression, and a biological approach in which the variable splicing mode affects the cell phenotype.

The invention discloses a single-cell multi-modal data integration method based on an attention mechanism and a graph variation auto-encoder. The method comprises the following steps: step 1, pre-processing multi-modal data and constructing a cell relation graph; step 2, cross-modal adjacency matrix fusion based on multi-head attention; step 3, performing graph variation auto-encoder training and multi-objective optimization; 4, performing multi-target loss calculation and model joint optimization; and 5, carrying out low-dimensional embedding extraction and clustering analysis on the cells. According to the method, single-cell transcriptome and epigenetic group data are fused through a multi-head attention mechanism, and low-dimensional embedding representation of cells is learned by using a graph variation auto-encoder, so that efficient integration and clustering analysis of single-cell multi-modal data are realized.

The application provides a single-cell transcriptome cell annotation method and system of a fusion large language model, cell type annotation is performed through construction of special prompt words and use of a large language model, and the accuracy and universality of cell annotation are improved. The application has a significant advantage for cell annotation of non-model species, and realizes an automatic and intelligent cell annotation process.

The invention discloses a chemical labeling system (HIM-IP) for detecting interaction between cells. According to the system, tyrosinase and phenol are respectively modified on the surfaces of two to-be-detected cells to construct capturing cells and prey cells, and then biotin or single-stranded DNA with nucleophilic groups is used as a molecular tag for labeling. The system not only can perform detection through traditional flow cytometry, but also can be compatible with a commercialized single cell transcriptome sequencing platform to realize association of intercellular interaction information and transcriptome information on a single cell level. The system does not need to carry out complex gene engineering modification on cells, the whole modification and marking process is simple, convenient and efficient, the detection sensitivity of intercellular interaction is high, and the biological mechanism of intercellular interaction regulation and control can be deeply excavated by integrating the single cell sequencing technology.

The invention discloses a BERT-based cell type deconvolution method and a BERT-based cell type deconvolution system, which effectively overcome the problems of data noise and unstable gene expression in single cell transcriptome sequencing data, enhance the adaptability to gene-gene interaction in complex single cell data, effectively capture gene-gene complex relationship, and improve the accuracy of gene-gene deconvolution. The problem of dependence on hypervariant gene selection and data dimension reduction in a traditional method is solved, and the generalization ability and robustness of the model are remarkably improved. By means of the design of gene embedding, unstable gene expression is converted into more stable feature expression, the accuracy and robustness of cell type recognition are remarkably improved, and high performance is shown in complex heterogeneous cell sample annotation.

The invention provides a cell type annotation method and device based on plant single cell transcriptome data and a readable storage medium. Annotation is achieved through multi-level data integration, wherein preliminary annotation is expressed based on cell type marker genes or homologous genes; calculating expression correlation auxiliary annotations of the to-be-analyzed data and the known transcriptome data set; performing function enrichment on the cell cluster differential genes to deduce cell types; carrying out quasi-timing analysis on the heterogeneous cell clusters and annotating subgroups; and finally integrating and generating a comprehensive annotation. The method solves the problem that the prior art depends on artificial experience and is insufficient in basic data set, and is suitable for mode and non-mode plants.

The invention relates to a single cell transcriptome data assisted AD analysis and classification method. The method comprises the following steps: acquiring a brain tissue sample from an AD patient and an age-matched healthy control, and treating the brain tissue by using a cell separation technology; then, sequencing the separated single cells by adopting a high-throughput single cell transcriptome sequencing platform, performing data quality control, and removing low-quality cells and low-expression genes; performing standardization processing on gene expression data, and eliminating technical deviation among different samples by using Log2 conversion and Z-score standardization; dimensionality reduction processing is carried out on the high-dimensional data by using dimensionality reduction algorithms of principal component analysis (PCA), t-SNE and UMAP; differential expression including DESeq2 and EdgeR is utilized to analyze and identify genes with significant difference between the AD patient and the healthy control; gSEA is adopted for gene set enrichment analysis, KEGG and GO are adopted for pathway analysis, and a key gene pathway and a biological process related to AD are determined; and inputting single cell transcriptome data of a new sample into the trained classification model, and performing AD classification prediction.

The invention provides a method for evaluating cell population heterogeneity based on an energy index, and belongs to the technical field of biological recognition, and the method comprises the following steps: S1, preprocessing single cell transcriptome data; s2, carrying out feature selection on the preprocessed data, and defining a cell state; s3, constructing a gene local network based on a Hupfield neural network; s4, calculating gene local network energy and cell energy, and carrying out normalization processing on the cell energy; s5, introducing a Gaussian mixture model, identifying the potential state of the cells by analyzing the energy distribution difference of the cells, and clustering the cells in the same differentiation potential state; and step S6, evaluating the heterogeneity of the cell population. By adopting the method for evaluating the heterogeneity of the cell population based on the energy index, the complexity and the difference in the cell population can be disclosed more accurately.

The invention provides a deconvolution model construction method and device, a terminal and a medium, and the method comprises the steps: generating simulated space transcriptome data with a cell type proportion label based on single cell transcriptome data, and obtaining real space transcriptome data obtained through sampling based on a 3D concentric sphere sampling strategy, and based on a comparative learning strategy and a domain adversarial network, training an encoder and an initial prediction model by using target space transcriptome data containing differential expression genes obtained by screening from simulation data and real data, and obtaining a feature extraction model for extracting embedded information and a prediction model for predicting a cell type proportion. According to the method, deep learning is combined, target space transcriptome data is used as encoder input, encoder output is used as prediction model input, the model is trained based on domain adversarial learning and contrast learning strategies, a model with good generalization ability is obtained, and high-precision deconvolution and cell type proportion prediction of 3D space transcriptome data are realized.

The present invention relates to a method for constructing a gene network through a single-cell transcriptome and a method for discovering key genes in differentiation using same, and a composition for the prevention, alleviation, or treatment of colon cancer using the target discovered through the method. The method for constructing a gene network of key genes according to the present invention employs single-cell transcriptome data and thus can be applied to all single-cell transcriptome data. The combination of MYB / HDAC2 / FOXA2 discovered upon application to colon cells can serve as a cancer treatment target that promotes the differentiation of colon cancer cells to revert same into differentiated normal cells.

The embodiment of the invention provides a gene regulation network inference method and device, a storage medium and electronic equipment. The method comprises the following steps: acquiring a first time sequence corresponding to a target cell type; the first time sequence comprises accessible chromatin sequencing data and single cell transcriptome sequencing data at different first time points; constructing a corresponding first gene regulation network according to the accessible chromatin sequencing data at each first time point; pruning the first gene regulatory network based on single cell transcriptome sequencing data to obtain a second gene regulatory network corresponding to each first time point; and deducing a plurality of second gene regulatory networks corresponding to the first time sequence based on a pre-constructed gene regulatory network prediction model to obtain a target gene regulatory network corresponding to the target cell type at a second time point, the second time point at least comprising a future time point and / or a missing time point in the first time sequence. The method can improve the inference accuracy of the gene regulatory network.

The invention provides a method and system for analyzing lung cancer brain metastasis organoid cell subpopulation and functional characteristics, and the method comprises a series of steps: data quality control, batch effect removal, cell grouping, copy number variation inference, quasi-time sequence analysis, function enrichment analysis, and drug sensitivity analysis before and after radiotherapy. Single cell transcriptome sequencing data obtained after organoid culture is subjected to personalized analysis, heterogeneity of tumor cells and sensitivity of the tumor cells to drugs after radiotherapy are described, and the characteristics of all cell subgroups in the lung cancer brain metastasis organoid and the effect of the cell subgroups in the tumor microenvironment can be accurately analyzed; powerful support is provided for mechanism research and precise medicine of lung cancer brain metastasis.

The invention provides a new bunyavirus neutralizing antibody screening method based on single cell BCR sequencing. The new bunyavirus neutralizing antibody screening method comprises the following steps: S1, collecting peripheral blood B cells of acute stage and recovery stage patients and healthy volunteers of new bunyavirus infected persons; s2, carrying out CD19 < + > CD38 < + > CD138 < + > magnetic bead separation to obtain a single-cell suspension; s3, constructing a single cell transcriptome and a BCR library, separating single cells by using a microfluidic system, and performing high-throughput sequencing; s4, analyzing a B cell subset, a BCR cloning amplification mode and a VJ gene pair enrichment characteristic through bioinformatics analysis; and S5, screening a specific BCR clone type related to the severity of the disease as a neutralizing antibody candidate target. The method provided by the invention provides powerful support for immune monitoring and vaccine design of new bunyavirus infection, and is expected to provide new strategies and methods for clinical treatment and prevention.

The invention provides a multi-element machine learning model-based cross-species lung disease characteristic gene screening method and system, an electronic system and a storage device. The method comprises the following steps of: acquiring single cell / transcriptome data related to mouse lung diseases from a public database and preprocessing the single cell / transcriptome data; training the model by adopting six machine learning algorithms and outputting a gene importance score; calculating the weight according to the model performance and normalizing the score; and integrating the cross-species scores through a weighted fusion formula, and outputting a feature gene list and a visual report. The system comprises a data acquisition and preprocessing module, a multi-element machine learning model training module, a weight calculation and normalization module, a cross-species comprehensive scoring module and a result output module. The screening accuracy, stability and generalization ability are improved through multi-algorithm integration and cross-species fusion, and the method can be widely applied to the fields of mechanism research of lung diseases, diagnosis marker development and drug target verification.

The invention discloses an empty transgene expression filling method based on a conditional variation auto-encoder, which comprises the following steps: designing a unified framework for conjoint analysis of single cell transcriptome data and spatial transcriptome data, obtaining single cell transcriptome sequencing expression profile data, spatial transcriptome expression data and a COVET matrix used for coding local neighborhood covariance in a tissue; projecting single cell transcriptome sequencing expression profile data and spatial transcriptome expression data into a shared potential space through an attention enhancement encoder to obtain potential variables; and decoding gene expression from the potential variables by using a decoder network, filling up missing gene expression information in spatial data, predicting a COVET matrix of single cell transcriptome data, and deducing a spatial context. According to the method, gene expression and spatial information can be coded at the same time, so that spatial context prediction of single cell data and filling of missing genes in spatial data are realized.

The invention relates to the technical field of biological information analysis, in particular to a cell annotation method based on a single cell space transcriptome and a related device. According to the cell annotation method based on the single-cell spatial transcriptome, single-cell transcriptome data, spatial transcriptome data and a plurality of branch annotation models need to be acquired firstly; converting the single cell transcriptome data into a single cell annotation file according to a first format constraint condition; and converting the spatial transcriptome data into a spatial annotation file according to a second format constraint condition. Inputting the single cell annotation file and the space annotation file into corresponding branch annotation models for branch annotation to obtain a branch annotation result output by each branch annotation model, and finally performing comprehensive annotation prediction based on a plurality of branch annotation results to obtain a comprehensive annotation result of the target single cell space transcriptome. According to the method, a more accurate annotation result can be obtained while good efficiency of cell annotation is ensured.

The invention discloses an application of DUOX2 in identification of ileum subtype and colon subtype Crohn's disease. A group of new DUOX2-epi is analyzed and identified based on single cell transcriptome sequencing, the proportion of the cells in the Crohn disease inflammation tissue is obviously increased, and the proportion of the DUOX2-epi in the colon type Crohn disease inflammation tissue is obviously higher than that in the ileum type Crohn disease inflammation tissue; in addition, in ileum type and colon type Crohn diseases, interaction with surrounding cells is carried out through different mechanisms, so that the occurrence and development of the diseases are promoted. Therefore, based on a DUOX2-epi mediated intercellular communication network, a gene prediction module for predicting different subtypes of Crohn diseases and evaluating the treatment effect of a biological agent is developed, and based on a DUOX2 molecular network, an experimental basis and a research direction are provided for subsequent in-depth research, and a new strategy is expected to be provided for diagnosis and treatment of different subtypes of Crohn diseases.