29 results about "Cluster cell" patented technology

The invention provides a cell type annotation method and device, equipment and a storage medium, and the method comprises the steps: clustering cell sequencing data into a plurality of cell populations; analyzing a differential high-expression component list of each cell population, wherein the differential high-expression component list comprises differential high-expression components (components refer to genes or proteins) which are ranked from high to low according to specificity scores in the cell populations; for each cell population, determining the probability that the cell population belongs to each cell type according to the sequence of the preset marking component corresponding to each cell type in the difference high expression component list of the cell population, and determining the cell type with the maximum corresponding probability as the cell type to which the cell population belongs; the sorting of the marked components corresponding to the cell types in the differential high-expression component list of the cell population is positively correlated with the probability that the cell population belongs to the cell types. According to the scheme, the influence of the marking component on the cell type to which the cell population belongs is correspondingly enhanced or weakened according to the specificity of the marking component in the cell population, and the accuracy of the annotation result is improved.

The invention discloses an scRNA-seq cell clustering method based on multi-scale feature fusion. The scRNA-seq cell clustering method comprises the following steps: 1, acquiring and preprocessing an scRNA-seq data matrix; 2, using a ZINB-based auto-encoder to extract global expression features, and constructing ZINB loss; 3, constructing a cell-cell adjacency matrix based on a Gaussian kernel function, extracting local structure features by using a graph automatic encoder, and constructing graph loss; and 4, fusing the global expression features from the ZINB auto-encoder and the local structure features from the graph auto-encoder by using a double-end attention mechanism. 5, the cells are clustered based on a self-optimization clustering method, and clustering loss is constructed. And 6, performing clustering result analysis by using t-SNE. According to the method, scRNA-seq data can be well processed, multi-scale features are fused, tag prediction is more accurate, and analysis of complex biological characteristics of single cell data is facilitated.

The invention provides a single-cell multi-omics data clustering method based on a diffusion auto-encoder, and belongs to the technical field of biological single-cell data classification. Comprising the following steps: carrying out dimension reduction processing on paired single-cell omics A and omics B data to obtain two types of omics data with consistent dimensions; integrating the features of the two types of omics data by using a feature fusion module based on a bidirectional cross attention mechanism to obtain common semantic embedding z of the cells; generating random encoder features based on the two types of omics data, and reconstructing original data based on z and the random encoder features by using two conditional diffusion models; updating parameters of the conditional diffusion model by using a loss function to obtain a common semantic embedding z'which minimizes the loss function; and dividing the cells into different cell subgroups by using z'by using a clustering algorithm. According to the method, the semantic features and random noise of the cells are separated by using decoupling learning, and the cells are clustered by using cross-omics invariant features, so that different subtypes can be accurately identified.

The invention provides an application of a carcino-embryonic antigen related cell adhesion molecule 5 (CEACAM5) in treatment of pancreatic cancer. Research finds that a novel stem cell-like cluster cell subtype (hereinafter referred to as' stem cell-like TCs') is found, and the subtype is significantly related to poor prognosis of pancreatic ductal adenocarcinoma patients. Therefore, the invention provides a novel pancreatic ductal adenocarcinoma treatment medicine, namely the carcino-embryonic antigen related cell adhesion molecule 5, which has very positive significance on the treatment of the pancreatic ductal adenocarcinoma.

The invention provides an application of angiopoietin (ANG) in treatment of pancreatic cancer. Research finds that a novel stem cell-like cluster cell subtype (hereinafter referred to as' stem cell-like TCs') is found, and the subtype is significantly related to poor prognosis of pancreatic ductal adenocarcinoma patients. Therefore, the invention provides a novel pancreatic ductal adenocarcinoma treatment medicine, namely angiopoietin, which has very positive significance in treatment of pancreatic ductal adenocarcinoma.

The invention provides a space transcriptome cell type identification and annotation method based on dimension reduction and downsampling, and relates to the technical field of biological information analysis. Comprising the following steps: S101, providing a spatial transcriptome gene expression matrix and a single cell transcriptome gene expression matrix annotated with cell types; the cell type comprises two levels, the first level is a cell large class, and the second level is a cell subtype; s102, splitting the single cell transcriptome gene expression matrix according to cell subtypes, clustering cells of the same cell subtype, and generating a meta-cell gene expression matrix; s103, identifying the cell category of each spatial site in the spatial transcriptome marker gene expression matrix; and S104, identifying the cell subtype of each spatial site in the spatial transcriptome marker gene expression matrix under each cell large class one by one. According to the technical scheme, the calculation amount in the analysis process is greatly reduced, the analysis efficiency is improved, and the accuracy and stability of annotation are improved.

The invention discloses a spatial omics cell type annotation method and device and a medium, and belongs to the technical field of biological information. The method comprises the following steps: splitting unicellular omics data with cell type annotations according to cell types, clustering cells of the same cell type and generating meta-cells, and obtaining a meta-cell characteristic counting matrix; calculating specific mark characteristics of each cell type relative to other cell types; and calculating a basis matrix of each cell type mark feature, calculating a cell type weight value corresponding to each spatial position based on the feature counting matrix of the spatial omics data to be annotated, and taking the cell type with the highest weight value as the cell type annotation of the spatial position. By utilizing the method, the equipment or the medium to carry out space cell type annotation, the calculation amount in the analysis process is greatly reduced, the analysis efficiency is improved, and the accuracy and the stability of annotation are improved.

The invention discloses a space transcriptome cell type annotation method and device based on meta-cells and a medium, and belongs to the technical field of biological information. The method comprises the following steps: splitting unicellular omics data with cell type annotations according to cell types, clustering cells of the same cell type and generating meta-cells, and obtaining a meta-cell characteristic counting matrix; calculating specific mark characteristics of each cell type relative to other cell types; and calculating a basis matrix of each cell type mark feature, calculating a cell type weight value corresponding to each spatial position based on the feature counting matrix of the spatial omics data to be annotated, and taking the cell type with the highest weight value as the cell type annotation of the spatial position. By utilizing the method, the equipment or the medium to carry out space cell type annotation, the calculation amount in the analysis process is greatly reduced, the analysis efficiency is improved, and the accuracy and the stability of annotation are improved.

The invention relates to a modularized fluorescence microscopic imaging device capable of imaging in real time, which can realize lossless long-term imaging by using a mild LED light source and intelligent exposure control, reduce the influence of phototoxicity on a biological sample and keep the activity of the sample. According to the invention, continuous fluorescence signal detection can be carried out on cells in the detection hole, the same group of cells or the same cluster of cells. All events occurring in the whole time period can be completely recorded and reflected, and the method is particularly suitable for observing the growth and development process of all cells, especially the growth and development of neuronal cells.

The invention belongs to the field of power grid planning, and provides a power grid configuration generation and optimization method and system based on a static reference model, and the method comprises the steps: generating the static reference model based on objective functions and constraint conditions considered in different stages; the method comprises the following steps: dividing a planning area into a set containing a plurality of grid units with the same size, estimating the load density and the new energy density of the planning area, further constructing a three-dimensional dynamic weight, dividing clustering units by using a clustering algorithm and taking the grid load data of the planning area as a basis and combining the load density grading characteristics, and constructing a three-dimensional dynamic weight model by taking each stage constraint as a boundary condition. The method comprises the following steps: carrying out iterative convergence to obtain the number of initial transformer substations, carrying out solution by using a particle swarm algorithm, adjusting the site and capacity configuration of the transformer substations, synchronously embedding ring network redundancy constraint verification and topological form adaptation constraint by taking the minimization of the total path length of the ring network as a target, and carrying out iterative optimization of node pair exchange in a centralized manner through a 2-opt algorithm. Obtaining a looped network path optimization result; the optimal configuration result meets the actual demand of the future receiving end power grid.

The present application discloses a battery cluster SOC balancing control method and device for an energy storage system. The method of the present invention sets two balancing control strategies to achieve SOC coarse adjustment and fine adjustment. The first balancing control strategy achieves SOC balancing within the battery cluster: coarsely adjusts the system SOC, and dynamically adjusts the charge and discharge power of each battery cell in high and low segments based on the SOC standard deviation of the single battery cells in the battery cluster, so that the power of all battery cells is roughly consistent, achieving SOC balance within the battery cluster, avoiding overcharging or undercharging of the single cells, reducing the risk of thermal runaway, and extending the life of the battery cluster; the second balancing control strategy achieves SOC balancing between battery clusters: finely adjusts the system SOC, and dynamically adjusts the charge and discharge power of each target battery cluster based on the standard deviation of the SOC of the target battery cluster, so that the SOC of each battery cluster is basically consistent, the battery clusters are used in the best way, and the system is in the best working state, thereby improving the life of the system battery cluster.

The invention provides a single-cell omics feature amplification method and device and a storage medium, and relates to the technical field of biological information. Comprising the following steps: S101, providing a gene expression matrix of a single cell transcriptome, classifying cells in the gene expression matrix, and labeling classification labels; s102, splitting the gene expression matrix according to classification labels, clustering cells labeled with the same classification label, and generating a gene expression matrix of meta-cells; s103, performing normalization processing on the gene expression matrix of the element cell, and calculating a marker gene of each classification tag in the element cell; filtering non-symbolic genes in the gene expression matrix of the meta-cells to obtain a marker gene expression matrix; and S104, calculating a meta-cell dimension reduction matrix based on the marker gene expression matrix, wherein the meta-cell dimension reduction matrix is used for model training of a subsequent machine learning classifier. According to the technical scheme, the calculation amount in the analysis process is greatly reduced, the analysis efficiency is improved, and the accuracy and stability of a subsequent machine learning classifier can be improved.

Cells of a network can be clustered based on signaling behavior, and abnormal signaling conditions against cells can be detected and mitigated. Signal measurement data from a group of cell devices in a cellular network can be applied to a neural network. The neural network can be selected to detect abnormal conditions in the cellular network based on reduced dimensionality encoding and decoding of prior signal measurement data from the cellular network. The neural network can generate encoded reduced dimensionality vectors from the signal measurement data. A cluster of cell devices from the group of cell devices can be generated based on a relative proximity of the encoded reduced dimensionality vectors. The neural network determines potential abnormal conditions in the cluster of cell devices based on an application of new signal measurement data from the cluster of cell devices.

The application discloses a single cell transcriptome low-quality cell filtering method, and relates to a biological data processing method. The method comprises the following steps: clustering cells; taking the expression value average of genes to generate a characteristic expression profile of the cell group; randomly combining the characteristic expression profile of the cell group in pairs to generate artificial multicells; merging the artificial multicell expression profile and the real cell expression profile to calculate the distance between each cell; setting a plurality of equidistant neighborhoods within a specified range, calculating the proportion of artificial multicells of each real cell within the neighborhood under each neighborhood; counting the distribution of the proportion of artificial multicells under each neighborhood, calculating the bimodal coefficient, and taking the neighborhood with the maximum bimodal coefficient as the optimal neighborhood; in the optimal neighborhood, a specified number of real cells with the maximum proportion of artificial multicells are identified as multicells, and the multicells are deleted from the real cell expression profile. The filtering standard and precision of the single cell transcriptome data are improved, and the reliability of the data is enhanced.

Provided is a method for establishing an estimation model of a total number of cells of a three-dimensional cell cluster in a thyroid puncture smear. The method for establishing the estimation model of the total number of the cells of the three-dimensional cell cluster in the thyroid puncture smear includes following steps: S1: selecting a three-dimensional cell cluster; S2: first drawing a big circle, and enclosing the three-dimensional cell cluster into the big circle; S3: drawing small circles with actual diameters of follicular cells, and surrounding the small circles around the big circle; adjusting a diameter of the big circle properly during a drawing process, making the small circles completely surround the big circle with a total number of the small circles are divisible by 4; and S4: marking black circles with a same size as the small circles to supplement cell-free areas.

The application discloses a metacell-based spatial transcriptome cell type annotation method, device and medium, and belongs to the technical field of bioinformatics. The method comprises the following steps: splitting single cell omics data with cell type annotation according to cell types, clustering cells of the same cell type and generating metacells, and obtaining a metacell feature count matrix; calculating marker features specific to each cell type relative to other cell types; calculating a basis matrix of the marker features of each cell type, and based on a feature count matrix of spatial omics data to be annotated, calculating a cell type weight value corresponding to each spatial position, and taking the cell type with the highest weight value as the cell type annotation of the spatial position. The spatial cell type annotation method, device and medium are used to greatly reduce the calculation amount of the analysis process, improve the analysis efficiency, and increase the annotation accuracy and stability.

The utility model relates to the technical field of endoscope ultrasonic biopsy equipment, in particular to an ultrasonic biopsy device which comprises a handle, a hollow needle tube, a three-way Luer seat and a needle core, a plurality of cell brushes are fixedly arranged at the other end of the needle core, a through hole is formed in the end portion of the hollow needle tube, and at least part of the cell brushes penetrate through the through hole to be exposed outside the hollow needle tube. Aiming at the technical problem that an existing ultrasonic puncture needle has use defects, according to the ultrasonic biopsy device, a traditional ultrasonic biopsy needle is improved, the hollow needle tube and the needle core are integrated, the cell brush is arranged on the needle core, and if the amount of diseased tissue or exudate falling off from the hollow needle tube in the puncture process is small, the hollow needle tube can be used as a cell brush. Therefore, the cell brush exposed outside the needle head repeatedly rubs a lesion area to perform brush detection and sampling, so that more lesion tissues are additionally collected to perform cell biopsy.

The present invention provides a cell fate mapping research method based on CytoEnergy technology, which belongs to the field of biorecognition technology, and includes the following steps: step S1, preprocessing single cell transcriptome data; step S2, constructing a local gene network based on the Hopfield neural network; step S3, calculating the local gene network energy and cell energy, and normalizing the cell energy; step S4, introducing a Gaussian mixture model, identifying the potential state of the cell by analyzing the energy distribution difference of the cell, and clustering cells in the same differentiation potential state; step S5, identifying cells in different fate terminal states; step S6, inferring the cell lineage trajectory. Through the above method, without relying on prior knowledge, the technology provides directional information for de novo inference of cell fate transformation from the perspective of cell plasticity, and can accurately restore the dynamic change process of cell fate.

The present invention relates to the technical field of analysis methods for single-cell transcriptome data, and particularly relates to a method for analyzing disease-specific cells in single-cell transcriptome based on iterative EM clustering. The method mainly includes the following steps: First, use the graph clustering method and cell marker genes to cluster cells. Then, obtain the up-regulated genes and down-regulated genes of a certain cell population of interest in the disease environment. Next, use a scoring algorithm to score the cells and reduce the dimension to the score space. Then, use the iterative EM clustering algorithm to divide the analyzed cells into two groups, and subsequently classify the results into disease-specific cells and control group normal cells. The present invention proposes a research method applicable to single-cell transcriptome data sets under all disease conditions, providing a new modular idea for the analysis of this type of data sets.

This invention discloses a comprehensive temperature sensing method for energy storage battery clusters based on a hybrid expert model. The method includes: acquiring full thermal-electrical data of the target battery cluster; using the acquired full thermal-electrical data as input, classifying all cells using a clustering algorithm, selecting the central cell of the cluster, and installing a temperature sensor at the central cell to form a sparse temperature measurement arrangement; processing the acquired data using a sliding window with a preset time window length to construct a model input feature matrix; constructing a multi-source time-series feature matrix and a T-MoE model, using a multi-gated, multi-task structure for classification output; training the T-MoE model based on the main loss function and auxiliary loss function; and outputting the temperature of unmeasured cells or the temperature distribution of the entire cluster cells based on the trained T-MoE model. This invention enables high-precision, stable, and scalable sensing of the temperature distribution of large-scale energy storage battery clusters under low-sensor configuration conditions.

The Image Activated Cell Sorting (IACS) classification workflow involves extracting features from cell images using a neural network-based feature encoder (or extractor), automatically clustering cells based on the extracted cell features, identifying clusters based on the cell images and selecting which cluster(s) to sort, fine-tuning a classification network based on the selected cluster(s), and, once refined, using the classification network to sort cells for real-time live sorting.

Cells of a network can be clustered based on signaling behavior, and abnormal signaling conditions against cells can be detected and mitigated. A security management component (SMC) can determine a neural network (NN) of NNs that can be representative of the cell network based on analysis of first signal measurement data associated with the cells. The NN can cluster respective cells into respective clusters based on analysis of second signal measurement data associated with the cells. The NN can determine whether an abnormal signaling condition associated with a cell is occurring based on analysis of third signal measurement data associated with the cells, information relating to the cluster to which the cell is assigned, and a defined network security criterion. SMC can perform feature reforming on the first, second, and / or third signal measurement data to reduce dimensionality of such data to facilitate processing by the NN.

The present application relates to a kind of breast cancer cell group identification, and metastasis or drug-resistant subtype tumor cell feature identification method and system, belong to biological information analysis technical field.Single-cell sequencing expression matrix data is dimensionally reduced and clustered, and clustering cell group is obtained;Correlation calculation is carried out between the obtained clustering cell group and each cell type in breast cancer tissue cell specificity marker data set, and the cell type corresponding to each clustering cell group is judged.The present application solves the problems of inaccurate classification, high classification cost and non-uniform classification standard in the prior art.

This application relates to the field of cell type identification technology and discloses a cell type identification method based on multi-omics decoupled representation and graph embedding, including the following steps: Step 1, acquiring single-cell multi-omics data and preprocessing it; Step 2, obtaining cell spectrum embedding representation using a graph embedding model, calculating single-cell sample similarity based on the spectrum embedding representation, and then constructing a sample similarity graph; Step 3, using a variational autoencoder model to map different omics data to the same dimensional space for data integration, obtaining a single-cell shared latent representation; Step 4, clustering cells using a Gaussian mixture model based on the shared latent representation to obtain cell cluster assignments; Step 5, constructing an objective function based on maximum likelihood estimation. Through a scalable model architecture and high-quality cell type identification, this method improves the performance efficiency of single-cell multi-omics data integration and analysis, providing new directions and possibilities for single-cell multi-omics data integration.

The application discloses a plant growth gene and development stage and cell type relationship determination method, relates to the field of plant development cell data research, and comprises the following steps: taking expression and non-expression of a key gene as a division condition, dividing a plant development single cell data set into X 表达 and X 未表达 , using a partial least square method to extract difference characteristic components V 表达 and V 未表达 between the two data sets; using a clustering analysis method to cluster cells; using a hypergeometric distribution test to calculate the correlation value of V 表达 and V 未表达 with marked cell types and marked plant development states; and using Xuan Tu to represent significant correlations obtained by using lines. The method is based on statistical modeling analysis, and can preliminarily infer the role of a target key gene in a plant growth process.

The present application discloses a method and device for optimizing cell configuration parameters, which belongs to the field of communication technology. The method includes: clustering cells based on user distribution characteristics, and determining the cells included in each cluster obtained by clustering; learning the relationship between the configuration data and performance index data of the cells included in the target cluster, and obtaining a relationship model between the configuration data and performance index data corresponding to the target cluster, wherein the target cluster is a target cell belonging cluster, and the target cell matches a first load state; according to the relationship model, determining the configuration parameter optimization scheme of the target cell. This method, by combining big data processing methods such as machine learning, classifies the cell scenarios of the cells, and then mines the intrinsic relationship between the cell configuration parameters and performance indicators for each cell scenario through machine learning methods, and searches for the optimized configuration parameters of the high-load cells based on the mined intrinsic relationship, thereby improving the efficiency of cell configuration parameter optimization.

The present application relates to a kind of quality analysis methods of vRBT video ringback tone, and the present application relates to a kind of methods for discovering video ringback tone clustering problem based on different dimension indicators. Including: step one: original data acquisition, step two: data cleaning, step three: clustering threshold statistics, step four: statistics influence user number, step five: statistics comprehensive score, step six: output clustering SDR, step seven: interface presents clustering problem. The present application is based on the actual business appeal of customer, refers to the specification of China Mobile Communication Group, adopts big data technology, automatically calculates and outputs the clustering cell of vRBT video ringback tone, clustering terminal brand, clustering core network and clustering city list, simultaneously utilizes the presentation of interface graphics, helps customer to quickly discover, locate the location of the clustering problem of vRBT video ringback tone, effectively reduces cost and improves work efficiency.

The embodiment of the invention provides an image table restoration method and device, electronic equipment and a storage medium, and belongs to the field of information processing. The method comprises the following steps: acquiring a table image containing a target table area, and performing cell detection on the target table area by using a table detection model to obtain cell data; and obtaining a first table character number of the target table area, and performing clustering processing on the first table character number based on the table cell data to obtain a clustering table cell label and clustering evaluation data. And according to the clustering cell label and the clustering evaluation data, performing data selection on the first table character data, and determining second table character data and edge character data. And performing cell label detection on the edge character data, and determining an edge cell label of the edge character data. And integrating the second table character data, the clustering cell tag, the edge character data and the edge cell tag to generate complete target table data. According to the embodiment of the invention, the image table restoration accuracy can be improved.

The present invention provides a method for evaluating the heterogeneity of a cell population based on an energy index, which belongs to the field of biorecognition technology, and comprises the following steps: step S1, preprocessing single cell transcriptome data; step S2, feature selection of the preprocessed data, and definition of cell state; step S3, constructing a local gene network based on a Hopfield neural network; step S4, calculating the local gene network energy and cell energy, and normalizing the cell energy; step S5, introducing a Gaussian mixture model, identifying the potential state of the cell by analyzing the energy distribution difference of the cell, and clustering cells in the same differentiation potential state; step S6, evaluating the heterogeneity of the cell population. The present invention adopts the above-mentioned method for evaluating the heterogeneity of a cell population based on an energy index, which can more accurately reveal the complexity and differences within the cell population.