Single cell data interaction analysis method and device, equipment and storage medium

Abstract

The embodiment of the invention provides a single cell data interaction analysis method and device, equipment and a storage medium, and belongs to the technical field of bioinformatics. The method comprises the following steps: acquiring a target cell sequence and cell annotation information of the target cell sequence; pre-processing the target cell sequence according to the cell annotation information to obtain a selected cell sequence set; obtaining an analysis category and a default analysis parameter; performing gene expression analysis on the selected cell sequence set according to the analysis category and default analysis parameters to obtain a preliminary cell analysis result; performing visualization processing on the preliminary cell analysis result to obtain a preliminary cell analysis view; receiving analysis adjustment parameters for the preliminary cell analysis view; adjusting analysis parameters of the selected cell sequence set according to the analysis adjustment parameters to obtain an updated cell analysis result; and adjusting the preliminary cell analysis view according to the updated cell analysis result to obtain a target cell analysis view. According to the embodiment of the invention, personalized customized cell analysis can be realized.

Description

Technical Field

[0001] The present application relates to the technical field of bioinformatics, and in particular to a method and apparatus, device and storage medium for interactive analysis of single-cell data. Background Art

[0002] With the development of computers, computer technology has also been used in cell research to analyze gene expression in cells. Gene analysis in cells in related technologies is analyzed through models inside computers. Since model analysis is equivalent to a black box, it is impossible to intuitively grasp the analysis process of cell data, and the models are all single-type analyses, and it is impossible to customize the analysis according to the needs of researchers, reducing the applicability of cell analysis. Therefore, how to customize cell analysis according to needs has become a technical problem that needs to be solved urgently. Summary of the invention

[0003] The main purpose of the embodiments of the present application is to propose an interactive analysis method and device, equipment and storage medium for single-cell data, aiming to achieve personalized cell analysis and improve the convenience of cell analysis.

[0004] To achieve the above objectives, a first aspect of an embodiment of the present application proposes an interactive analysis method for single-cell data, the method comprising:

[0005] Acquire target cell data; wherein the target cell data includes: a target cell sequence and cell annotation information of the target cell sequence;

[0006] Preprocessing the target cell sequence according to the cell annotation information to obtain a selected cell sequence set;

[0007] Obtaining custom analysis parameters; wherein the custom analysis parameters include: analysis category and default analysis parameters;

[0008] Performing gene expression analysis on the selected cell sequence set according to the analysis category and the default analysis parameters to obtain preliminary cell analysis results;

[0009] Visualizing the preliminary cell analysis results to obtain a preliminary cell analysis view;

[0010] receiving analysis adjustment parameters for the preliminary cell analysis view;

[0011] adjusting the analysis parameters of the selected cell sequence set according to the analysis adjustment parameters to obtain updated cell analysis results;

[0012] The preliminary cell analysis view is adjusted according to the updated cell analysis result to obtain a target cell analysis view.

[0013] In some embodiments, obtaining target cell data comprises:

[0014] Acquiring raw cell data and the raw data format of the raw cell data;

[0015] Screening the preset candidate toolkits according to the original data format to obtain a target toolkit;

[0016] The target toolkit is used to convert the data format of the original cell data into a preset data format to obtain the target cell data; wherein the format of the target cell data is unified.

[0017] In some embodiments, the analysis category includes at least one of the following: principal component analysis category, cluster analysis category, differential expression analysis category, enrichment analysis category, pseudo-time series analysis category, and genetic rate analysis category; performing gene expression analysis on the selected cell sequence set according to the analysis category and the default analysis parameters to obtain preliminary cell analysis results includes at least one of the following:

[0018] Performing principal component analysis on the selected cell sequence set according to the principal component analysis category and the default analysis parameters to obtain a cell principal component analysis result;

[0019] Performing cluster analysis on the selected cell sequence set according to the cluster analysis category and the default analysis parameters to obtain a cell cluster analysis result;

[0020] Performing differential expression analysis on the selected cell sequence set according to the differential expression analysis category and the default analysis parameters to obtain a cell differential expression analysis result;

[0021] Performing gene enrichment analysis on the selected cell sequence set according to the enrichment analysis category and the default analysis parameters to obtain a cell gene enrichment analysis result;

[0022] Performing a pseudo-time series analysis on the selected cell sequence set according to the pseudo-time series analysis category and the default analysis parameters to obtain a cell pseudo-time series analysis result;

[0023] The genetic rate analysis is performed on the selected cell sequence set according to the genetic rate analysis and the default analysis parameters to obtain a cell genetic rate analysis result.

[0024] In some embodiments, if the preliminary cell analysis result is a cell differential expression analysis result, the cell differential expression analysis result characterizes the gene expression of each cell cluster; the preliminary cell analysis view is a target cell differential expression view; the preliminary cell analysis result is visualized to obtain a preliminary cell analysis view, including:

[0025] Extracting highly expressed gene information from the cell differential expression analysis results;

[0026] Visualizing the cell differential expression analysis results according to a preset view template to obtain a preliminary cell differential expression view; wherein the view template includes a heat map, a bubble map and a Umap map;

[0027] The preliminary cell differential expression view is annotated according to the highly expressed gene information to obtain the target cell differential expression view.

[0028] In some embodiments, adjusting the analysis parameters of the selected cell sequence set according to the analysis adjustment parameters to obtain updated cell analysis results includes:

[0029] The default analysis parameters are adjusted according to the analysis adjustment parameters to obtain target analysis parameters; wherein the target analysis parameters include any one of the following: a cell type of interest, a threshold range, and gene information of interest;

[0030] Screening out a first cell sequence set from the selected cell sequence set according to the cell category of interest, and performing gene difference analysis on the first cell sequence set to obtain the updated cell analysis result;

[0031] Screening out a second cell sequence set from the selected cell sequence set according to the threshold range, and performing gene difference analysis on the second cell sequence set to obtain the updated cell analysis result;

[0032] A third cell sequence set is screened out from the selected cell sequence set according to the information of the gene of interest, and a gene difference analysis is performed on the third cell sequence set to obtain the updated cell analysis result.

[0033] In some embodiments, after adjusting the preliminary cell analysis view according to the updated cell analysis result to obtain the target cell analysis view, the method further includes:

[0034] Receive a display effect adjustment request for the target cell analysis view; wherein the display effect adjustment request includes at least one of the following: display subject information, display gene information, and display template information;

[0035] Switching the display theme of the target cell analysis view according to the display theme information to obtain a first updated cell analysis view;

[0036] Filtering the genes in the target cell analysis view according to the displayed gene information to obtain a second updated cell analysis view;

[0037] The display template of the target cell analysis view is switched according to the display template information to obtain a third updated cell analysis view.

[0038] In some embodiments, the preprocessing of the target cell sequence according to the cell annotation information to obtain a selected cell sequence set includes:

[0039] Filtering the target cell sequence according to the cell annotation information to obtain a selected cell sequence;

[0040] A plurality of the selected cell sequences are integrated to obtain the selected cell sequence set.

[0041] To achieve the above-mentioned purpose, a second aspect of an embodiment of the present application proposes an interactive analysis device for single-cell data, the device comprising:

[0042] A data acquisition module, used to acquire target cell data; wherein the target cell data includes: a target cell sequence and cell annotation information of the target cell sequence;

[0043] A preprocessing module, used for preprocessing the target cell sequence according to the cell annotation information to obtain a selected cell sequence set;

[0044] A parameter acquisition module, used to acquire custom analysis parameters; wherein the custom analysis parameters include: analysis category and default analysis parameters;

[0045] A gene expression analysis module, used to perform gene expression analysis on the selected cell sequence set according to the analysis category and the default analysis parameters to obtain preliminary cell analysis results;

[0046] A visualization module, used for visualizing the preliminary cell analysis results to obtain a preliminary cell analysis view;

[0047] A parameter receiving module, used for receiving analysis adjustment parameters of the preliminary cell analysis view;

[0048] A parameter adjustment module, used for adjusting the analysis parameters of the selected cell sequence set according to the analysis adjustment parameters to obtain updated cell analysis results;

[0049] The view adjustment module is used to adjust the preliminary cell analysis view according to the updated cell analysis result to obtain a target cell analysis view.

[0050] To achieve the above objectives, a third aspect of an embodiment of the present application proposes an electronic device, which includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the method described in the first aspect is implemented.

[0051] To achieve the above-mentioned purpose, the fourth aspect of an embodiment of the present application proposes a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described in the first aspect is implemented.

[0052] The interactive analysis method, device, equipment and storage medium of single-cell data proposed in this application automatically analyze the target cell data according to the default parameters and analysis categories to obtain preliminary cell analysis results, and visualize the preliminary cell analysis results to obtain a preliminary cell analysis view. The user can adjust the preliminary cell analysis view to obtain analysis adjustment parameters, and adjust the analysis parameters of the target cell data in the analysis process according to the analysis adjustment parameters, and visualize the cell analysis results of the adjusted analysis process until a target cell analysis view that meets the needs is constructed. Therefore, users can realize personalized adjustment of the cell analysis view to assist users in conducting cell genetic research and improve the effect of cell genetic research. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 is a flow chart of the interactive analysis method of single cell data provided in an embodiment of the present application;

[0054] Figure 2 yes Figure 1 Flow chart of step S101 in FIG.

[0055] Figure 3 It is a cell collection view of the interactive analysis method of single cell data provided in the embodiment of the present application;

[0056] Figure 4 yes Figure 1 Flow chart of step S103 in FIG.

[0057] Figure 5 It is a cell filtering state view of the interactive analysis method of single cell data provided in the embodiment of the present application;

[0058] Figure 6 It is a view of gene changes in the interactive analysis method of single-cell data provided in the embodiment of the present application;

[0059] Figure 7 yes Figure 1 Flow chart of step S104 in FIG.

[0060] Figure 8 It is a cell principal component analysis view of the interactive analysis method of single cell data provided in an embodiment of the present application;

[0061] Fig. 9 It is a cell clustering analysis view of the interactive analysis method of single cell data provided in the embodiment of the present application;

[0062] Fig.10 It is a cell gene enrichment analysis view of the interactive analysis method of single cell data provided in the embodiment of the present application;

[0063] Fig.11 It is a cytogenetic rate analysis view of the interactive analysis method of single-cell data provided in an embodiment of the present application;

[0064] Fig.12 yes Figure 1 Flow chart of step S105 in FIG.

[0065] FIG. 13( a ) is a schematic diagram showing a heat map representing the results of cell differential expression analysis in the interactive analysis method for single-cell data provided in an embodiment of the present application;

[0066] FIG. 13( b ) is a schematic diagram showing the results of cell differential expression analysis using a bubble chart in the interactive analysis method for single-cell data provided in an embodiment of the present application;

[0067] FIG. 13( c ) is a schematic diagram showing the results of cell differential expression analysis using a scatter plot in the interactive analysis method for single-cell data provided in an embodiment of the present application;

[0068] FIG13( d ) is a schematic diagram of a scatter plot representing the results of cell differential expression analysis in the interactive analysis method for single-cell data provided in an embodiment of the present application;

[0069] Fig.14 yes Figure 1 Flow chart of step S107 in FIG.

[0070] Fig.15 is a flow chart of an interactive analysis method for single-cell data provided by another embodiment of the present application;

[0071] FIG. 16( a ) and FIG. 16( b ) are target cell analysis views of the interactive analysis method for single-cell data provided in an embodiment of the present application;

[0072] Fig.17 is a schematic diagram of the structure of the interactive analysis device for single-cell data provided in an embodiment of the present application;

[0073] Fig.18 It is a schematic diagram of the hardware structure of the electronic device provided in the embodiment of the present application. DETAILED DESCRIPTION

[0074] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0075] It should be noted that, although the functional modules are divided in the device schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first", "second", etc. in the specification, claims and the above drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0076] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0077] First, some nouns involved in this application are analyzed:

[0078] Gene expression: refers to the process of synthesizing the genetic information from genes into functional gene products. The products of gene expression are usually proteins. All known life forms use gene expression to synthesize macromolecules of life.

[0079] Hypermutable genes: Hypermutable genes are a very important concept in single-cell sequencing. Hypermutable genes refer to genes that have undergone a large number of mutations during cell division, resulting in a significant change in the expression level of the gene. Hypermutable genes are usually expressed at a higher level than other genes because they undergo more mutations during cell division.

[0080] Principal Component Analysis (PCA): is a commonly used data analysis method. PCA transforms the original data into a set of linearly independent representations of each dimension through linear transformation. It can be used to extract the main characteristic components of the data and is often used for dimensionality reduction of high-dimensional data.

[0081] Cluster analysis: It is a typical unsupervised learning method used to classify samples of unknown categories. It divides samples of unknown categories into several clusters according to certain rules, groups similar samples (similar in height) into the same cluster, and divides dissimilar samples into different clusters, thereby revealing the intrinsic properties of samples and the laws of their connections.

[0082] Differential gene analysis: aims to compare the genomes of different species to track the evolutionary changes of genes and the behavioral differences of organisms. It is used to study the differences in gene expression between two different phenotypic groups and two different species. The principle of differential gene analysis is to compare different samples of a specific genome to detect differences.

[0083] Gene Set Enrichment Analysis (GSEA): It is to analyze whether a group of genes are over-represented at a certain functional node compared to the random level. The principle of enrichment analysis can be extended from simple annotation of a single gene to group analysis of multiple gene sets.

[0084] Pseudotime analysis: constructs the cell lineage development according to the changes in gene expression levels of different cell subpopulations over time. However, the time here is not real time, but a virtual time, which refers to the order and trajectory of transformation and succession between cells.

[0085] With the development of computer technology, single-cell data analysis has been combined with computer technology to produce a single-cell data visualization analysis platform, which allows researchers to intuitively grasp the single-cell analysis process. In related technologies, single-cell visualization analysis platforms analyze single-cell data according to a fixed analysis model and display a fixed type of visualization view, which cannot achieve personalized analysis according to the needs of researchers, increasing the difficulty for researchers to analyze gene expression in single cells.

[0086] Based on this, the embodiments of the present application provide an interactive analysis method and apparatus, equipment and storage medium for single-cell data, which obtains preliminary cell analysis results by automatically preprocessing and analyzing the cell data, and generates a preliminary cell analysis view corresponding to the preliminary cell analysis results. When researchers need personalized customized analysis, they can adjust the analysis parameters of the cell data according to the analysis adjustment parameters fed back by the researchers to update the preliminary cell analysis results to obtain updated cell analysis results, and generate a target cell analysis view with the updated cell analysis results. Therefore, after the cell data analysis is completed, the analysis parameters of the analysis process can be adjusted according to demand to adjust the cell analysis view to achieve personalized analysis operations, and generate personalized cell analysis views, so that researchers can analyze according to demand, improve the convenience of the cell data analysis process, and help improve the efficiency of cell analysis.

[0087] The interactive analysis method, device, equipment and storage medium for single-cell data provided in the embodiments of the present application are specifically illustrated by the following embodiments. First, the interactive analysis method for single-cell data in the embodiments of the present application is described.

[0088] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0089] AI basic technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, mechatronics, etc. AI software technologies mainly include computer vision technology, robotics technology, biometrics technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0090] The interactive analysis method of single-cell data provided in the embodiment of the present application relates to the field of bioinformatics technology. The interactive analysis method of single-cell data provided in the embodiment of the present application can be applied to a terminal, can also be applied to a server side, and can also be software running in a terminal or a server side. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc.; the server side can be configured as an independent physical server, or a server cluster or a distributed system composed of multiple physical servers, and can also be configured to provide cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms and other basic cloud computing services. Cloud servers; software can be applications that implement interactive analysis methods for single-cell data, etc., but are not limited to the above forms.

[0091] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0092] It should be noted that in each specific implementation of the present application, when it comes to the need to perform relevant processing based on data related to user identity or characteristics such as user information, user behavior data, user historical data, and user location information, the user's permission or consent will be obtained first, and the collection, use, and processing of these data will comply with relevant laws, regulations, and standards. In addition, when the embodiment of the present application needs to obtain the user's sensitive personal information, the user's separate permission or consent will be obtained through a pop-up window or by jumping to a confirmation page. After clearly obtaining the user's separate permission or consent, the necessary user-related data for the normal operation of the embodiment of the present application will be obtained.

[0093] Figure 1 is an optional flow chart of the interactive analysis method of single cell data provided in the embodiment of the present application, Figure 1 The method may include but is not limited to steps S101 to S108.

[0094] Step S101, acquiring target cell data; wherein the target cell data includes: a target cell sequence and cell annotation information of the target cell sequence;

[0095] Step S102, preprocessing the target cell sequence according to the cell annotation information to obtain a selected cell sequence set;

[0096] Step S103, obtaining custom analysis parameters; wherein the custom analysis parameters include: analysis category and default analysis parameters;

[0097] Step S104, performing gene expression analysis on the selected cell sequence set according to the analysis category and default analysis parameters to obtain preliminary cell analysis results;

[0098] Step S105, visualizing the preliminary cell analysis results to obtain a preliminary cell analysis view;

[0099] Step S106, receiving analysis adjustment parameters for the preliminary cell analysis view;

[0100] Step S107, adjusting the analysis parameters of the selected cell sequence set according to the analysis adjustment parameters to obtain updated cell analysis results;

[0101] Step S108, adjusting the preliminary cell analysis view according to the updated cell analysis result to obtain a target cell analysis view.

[0102] Steps S101 to S108 shown in the embodiment of the present application, by obtaining the cell annotation information of the target cell sequence and the target cell sequence, and pre-processing the target cell sequence according to the cell annotation information, to filter out the low-quality target cell sequence, leaving the high-quality target cell sequence as the selected cell sequence set. Gene expression analysis is performed on the selected cell sequence set according to the pre-set custom analysis parameters to obtain preliminary cell analysis results. In order to intuitively understand the cell gene expression situation, the preliminary cell analysis results are visualized into a preliminary cell analysis view, and the situation of gene expression in the cell can be intuitively understood. Since the preliminary cell analysis results are used as an initial cell gene expression analysis result, they can be adjusted based on the preliminary analysis view, so the preliminary cell analysis results are first adjusted based on the analysis adjustment parameters, and then the preliminary cell analysis view is adjusted to build a cell analysis view that meets the needs. Therefore, after initially generating a cell analysis view, researchers can adjust the analysis parameters in the cell sequence according to the preliminary cell analysis view, and intuitively view the cell analysis view after adjusting the parameters, and see the cell analysis views under different analysis parameters to select the cell analysis view that meets the requirements as the target cell analysis view, which can meet the research needs of users, realize the personalized customization of cell analysis views according to user needs, and assist users in understanding the gene expression in cells.

[0103] It should be noted that the interactive analysis method of single-cell data is applied to the interactive analysis platform of single-cell data, and the interactive analysis platform of single-cell data completes the analysis visualization and customized analysis of single-cell data.

[0104] See also Figure 2 In some embodiments, step S101 may include but is not limited to steps S201 to S203:

[0105] Step S201, obtaining original cell data and the original data format of the original cell data;

[0106] Step S202, screening the preset candidate toolkits according to the original data format to obtain the target toolkit;

[0107] Step S203, converting the data format of the original cell data into a preset data format through a target toolkit to obtain target cell data; wherein the format of the target cell data is unified.

[0108] In step S201 of some embodiments, the raw cell data is uploaded as a file, and the user creates a task on the interactive analysis platform for single-cell data, and each user can create multiple tasks. It should be noted that the types of tasks can be divided into uploading files, merging tasks, and built-in data sets, and the raw cell data is uploaded to the interactive analysis platform for single-cell data through the task of uploading files. It should be further explained that in order to expand the compatibility of raw cell data, the uploaded raw data format can be three file formats: matrix, h5ad, and rds. There is no need for users to convert the format of raw cell data before uploading it. Raw cell data in their own data format can be uploaded, reducing the format restrictions on users uploading raw cell data.

[0109] In step S202 of some embodiments, the candidate toolkit is a toolkit for converting files / data in different data formats into a preset data format, and different candidate toolkits are selected for different data formats. It should be noted that although users can upload raw cell data in different raw data formats, subsequent cell analysis requires a unified data format, so the target toolkit is screened out from the candidate toolkits based on the raw data format, and the target toolkit can convert the raw data format into the preset data format.

[0110] For example, if the original data format can be matrix, h5ad, rds, the candidate toolkits are scanpy, pandas, and scdior. If the original data format is h5ad, the target toolkit is scanpy; if the original data format is matrix, the target toolkits are scanpy and pandas; if the original data format is rds, the target toolkit is scdior.

[0111] In step S203 of some embodiments, the original cell data is formatted by a target toolkit to convert the original data format of the original cell data into a preset data format to construct target cell data with a unified data format. It should be noted that after completing the data format conversion to obtain the target cell data, the target data is stored in a database so that subsequent data analysis can be directly extracted from the database. It should be further noted that when the target cell data is stored in the database, the target cell data will be classified and stored in the database according to the sequence number of the task, so as to facilitate targeted extraction during extraction and improve the efficiency of target cell data extraction.

[0112] For example, if the preset data format is h5ad, if the original data format is h5ad, there is no need to convert, and the scanpy toolkit is used to directly store the original cell data in the database. If the original data format is matrix, use pandas and scanpy toolkits to read the original cell data and convert it into h5ad format before storing it in the database. If the original data format is rds, use the scdior toolkit to convert it into h5ad and then store it in the database. Therefore, although cell data in different data formats can be uploaded, they can be converted into a unified h5ad, and then the target cell data in h5ad format can be stored to facilitate subsequent cell data analysis.

[0113] Specifically, after the target cell data is acquired, the number of target cell data is visualized to obtain a cell acquisition view. Figure 3 As shown, through Figure 3 It can be seen that the cell collection view can be used to know what information the uploaded target cell data contains, and Figure 3 The 2655 in the figure refers to 2655 cells, and the 2000 refers to 2000 genes. Therefore, by generating a cell collection view after uploading the cell data, you can intuitively know the number of cells and the number of genes in the cell data.

[0114] In steps S201 to S203 shown in this embodiment, when receiving raw cell data in different raw data formats, a target toolkit matching the raw data format is first selected from the candidate toolkits, and the target toolkit performs format conversion on the raw cell data to convert it into target cell data in a preset data format. Therefore, in order to be compatible with cell data in different formats but not affect the cell data analysis, the toolkit converts the format of the cell data, which can facilitate users to directly upload the cell data for analysis.

[0115] See also Figure 4 In some embodiments, step S103 may include but is not limited to steps S401 to S402:

[0116] Step S401, filtering the target cell sequence according to the cell annotation information to obtain a selected cell sequence;

[0117] Step S402: Integrate multiple selected cell sequences to obtain a selected cell sequence set.

[0118] In step S401 of some embodiments, the cell annotation information is unstructured annotation information, and the cell annotation information is used to mark or record cell data, and is initially empty. The cell annotation information can be used as judgment information for the quality of the target cell sequence. It should be noted that the cell annotation information is uns, and the cell annotation information includes cell name and cell label information. The target cell sequence is single-cell sequencing, and the single-cell sequencing is divided into X, obs, var, and X is the expression matrix. Obs contains cell metadata, that is, the results of describing the cell from different directions, such as the tissue to which the cell belongs. Var contains gene metadata, that is, the results of describing the gene from different directions. It should be noted that X, obs, and var are all stored in dataframe format. Therefore, the target cell sequence is filtered by the cell annotation information to remove low-quality and double-cell target cell sequences to obtain the selected cell sequence.

[0119] It should be noted that if Figure 5 and Figure 6 As shown, during the filtering process of the target cell sequence, the filtering process of the target cell sequence will be visualized to generate a cell filtering status view, and the user can know the filtering status of the target cell data through the cell filtering status view. Figure 6 Characterize the gene changes of the target cell sequence, and extract the cell sequence of the highly variable gene as the selected cell sequence according to the gene changes.

[0120] In step S402 of some embodiments, a selected cell sequence set is formed by integrating multiple selected cell sequences. Therefore, the selected cell sequences of multiple samples are integrated into one selected cell sequence set to facilitate centralized analysis of cell data.

[0121] In step S401 to step S402 shown in this embodiment, the cell sequences of low quality and double cells in the target cell sequence are removed to leave high quality and single cell sequences as selected cell sequences, and the selected cell sequences of different samples are integrated to form a selected cell sequence. Therefore, a high quality selected cell sequence is constructed for gene expression analysis, thereby improving the analysis effect of cell gene expression.

[0122] In step S103 of some embodiments, the custom analysis parameters are analysis parameters predefined by the user, and the user can adjust the custom analysis parameters according to needs. The custom analysis parameters include analysis categories and default analysis parameters, the analysis category is what kind of analysis needs to be performed on the selected cell sequence, and the default analysis parameters are initialization analysis parameters pre-set by the system automatically selected by the user to perform gene expression analysis on the selected cell sequence set.

[0123] In some embodiments, the analysis category includes at least one of the following: principal component analysis category, cluster analysis category, differential expression analysis category, enrichment analysis category, pseudo-time series analysis category, and genetic rate analysis category. It should be noted that the selected cell sequence set is analyzed according to different analysis categories to increase the scope of cell gene expression analysis.

[0124] See also Figure 7 In some embodiments, step S104 may include at least one of steps S701 to S706:

[0125] Step S701, performing principal component analysis on the selected cell sequence set according to the principal component analysis category and default analysis parameters to obtain the cell principal component analysis result;

[0126] Step S702, performing cluster analysis on the selected cell sequence set according to the cluster analysis category and default analysis parameters to obtain cell cluster analysis results;

[0127] Step S703, performing differential expression analysis on the selected cell sequence set according to the differential expression analysis category and default analysis parameters to obtain cell differential expression analysis results;

[0128] Step S704, performing gene enrichment analysis on the selected cell sequence set according to the enrichment analysis category and default analysis parameters to obtain cell gene enrichment analysis results;

[0129] Step S705, performing a pseudo-time series analysis on the selected cell sequence set according to the pseudo-time series analysis category and default analysis parameters to obtain a cell pseudo-time series analysis result;

[0130] Step S706, performing genetic rate analysis on the selected cell sequence set according to the genetic rate analysis and default analysis parameters to obtain the cell genetic rate analysis results.

[0131] In step S701 of some embodiments, if the analysis category is a principal component analysis category, the principal component analysis model is called to perform principal component analysis on the selected cell sequence set using the principal component analysis model and default analysis parameters to obtain the cell principal component analysis results. It should be noted that the principal component analysis of the selected cell sequence set is mainly performed to prepare for cell clustering, so as to map the high-dimensional selected cell sequence into a low-dimensional space for representation and retain more characteristics of the original data points. For example, Figure 8 As shown, principal component analysis is performed on the selected cell sequence set to obtain the cell principal component analysis results, and the cell principal component analysis results are visualized into a cell principal component analysis view. It should be noted that in order to clearly understand the inflection point in the cell principal component analysis view, the inflection point in the cell principal component analysis view is identified and highlighted. In addition, the user can adjust the inflection point in the cell principal component analysis view. It should be further noted that Figure 8 The horizontal axis in represents the scoring value. The higher the score, the greater the correlation between different selected cell sequences, so that the selected cell sequences with high correlation can be combined to form a cluster.

[0132] In step S702 of some embodiments, if the analysis category is a cluster analysis category, it is determined that the cluster analysis uses a cluster analysis model to perform cluster analysis on the selected cell sequence set using the cluster analysis model and default analysis parameters to obtain a cell cluster analysis result. It should be noted that the cluster analysis model will use the cell principal component analysis result of the previous step as a reference to combine the selected cell sequences with high correlation into a cluster. It should be further noted that after obtaining the cell cluster analysis result, a cell cluster analysis view is obtained by visualizing the cell cluster analysis result. For example, please refer to Fig. 9 , Fig. 9 is the cell clustering analysis view, through Fig. 9 The points represent the location of each cell, and the grayscale of the points indicates which sample the cell comes from. The points clustered together represent that the cells have a certain correlation, and the closer the distance, the higher the correlation. Fig. 9 It can be seen that the cells in the same sample are highly correlated, but there are also correlations between cells in different samples.

[0133] In step S703 of some embodiments, if the analysis category is a differential expression analysis category, it is determined that the differential expression analysis of gene expression uses a differential expression analysis model. The selected cell sequence set is subjected to differential expression analysis using the differential expression analysis model and default analysis parameters to analyze the gene differences within the cells and obtain the cell differential expression analysis results. It should be noted that after obtaining the cell differential expression analysis results, the cell differential expression analysis results are visualized to obtain a cell differential expression analysis view, so as to characterize the gene differences of different cells through the cell differential expression analysis view.

[0134] In step S704 of some embodiments, if the analysis category is an enrichment analysis category, and the enrichment analysis category is also a gene enrichment analysis, that is, gene enrichment analysis is performed on the differential genes to analyze the functional differences of the differential genes. It should be noted that the gene enrichment analysis uses a gene enrichment analysis model, and the gene enrichment analysis model and default parameters are used to perform gene enrichment analysis on the selected cell sequence set to obtain a cell gene enrichment analysis result, that is, a differential gene set is selected from the selected cell sequence set according to the cell differential expression analysis results of the previous step, and the differential gene set is sorted, scored, and verified to see whether there are more up- and down-regulated genes in the differential gene set, or no regulatory genes. It should be further noted that when performing gene enrichment analysis, pathways, species, and gene lists can be selected to adjust the default analysis parameters to perform gene enrichment analysis of different pathways, species, and gene lists to construct cell gene enrichment analysis results that meet user needs. At the same time, the cell gene enrichment analysis results are visualized to obtain a cell gene enrichment analysis view. For example, a cell gene enrichment analysis view such as Fig.10 As shown, on the right side of the cell gene enrichment analysis view, the pathway, species, and gene list can be adjusted to change the default analysis parameters, and the user can intuitively view the gene enrichment analysis results under different pathways, species, and genes.

[0135] In step S705 of some embodiments, if the analysis category is a pseudo-time series analysis category, the selected cell sequence set is analyzed using a pseudo-time series analysis model and default analysis parameters to obtain a cell pseudo-time series analysis result.

[0136] In step S706 of some embodiments, if the analysis category is a genetic rate analysis category, and the genetic rate analysis category is an RNA rate analysis category, the genetic rate analysis model and the default analysis parameters are used to analyze the selected cell sequence set to obtain a cytogenetic rate analysis result. It should be noted that after the cytogenetic rate analysis result is generated, the cytogenetic rate analysis result is visualized to obtain a cytogenetic rate analysis view. For example, the generated cytogenetic rate analysis view is as follows: Fig.11 As shown, the cytogenetic rate analysis view can provide an intuitive understanding of the RNA rate in the cell.

[0137] In steps S701 to S706 shown in this embodiment, by setting the principal component analysis category, cluster analysis category, differential expression analysis category, enrichment analysis category, pseudo-time series analysis category and genetic rate analysis category, corresponding analysis is performed on the selected cell sequence set according to different analysis categories, so as to realize multiple analysis operations on one platform, which is suitable for different analysis needs of users.

[0138] See also Fig.12In some embodiments, if the preliminary cell analysis result is a cell differential expression analysis result, the cell differential expression analysis result characterizes the gene expression of each cell cluster; the preliminary cell analysis view is a target cell differential expression view. Step S105 may include but is not limited to steps S1201 to S1203:

[0139] Step S1201, extracting highly expressed gene information from the cell differential expression analysis results;

[0140] Step S1202, visualizing the cell differential expression analysis results according to a preset view template to obtain a preliminary cell differential expression view; wherein the view template includes a heat map, a bubble map, and a Umap map;

[0141] Step S1203 , annotating the preliminary cell differential expression view according to the highly expressed gene information to obtain the target cell differential expression view.

[0142] In step S1201 of some embodiments, the gene difference of each cell can be known through the cell differential expression analysis results, and the expression amount of different genes in different clusters is different. Therefore, the expression of genes in different clusters is obtained, and the cluster information and gene information with high expression are constructed into high expression gene information. High expression gene information indicates in which cluster the gene is most expressed.

[0143] In step S1202 of some embodiments, the cell difference analysis result is displayed with a pre-set view template to obtain a preliminary cell difference expression view, and the expression of the same gene in different clusters can be known through the preliminary cell difference expression view. It should be noted that the view template includes a heat map, a bubble map and a Umap map, and the heat map is used to characterize the cell difference expression analysis result as shown in Figure 13 (a). The expression level of each gene in different clusters can be seen through the heat map, and the darker the color in the heat map, the higher the expression level of the gene in the cluster. It should be noted that when the user moves the mouse to any position of the heat map, the gene name, cell ID information and the expression level value of the gene in the cell at the position will be displayed. The bubble map characterizes the cell difference expression analysis result as shown in Figure 13 (b), and the expression level of each gene in different clusters can also be seen through the bubble map. The larger the circle in the bubble map, the higher the expression level of the gene in the cluster. Therefore, the expression level of different genes in different clusters can also be intuitively grasped through the bubble map. The Umap graph is also called a scatter plot, and the scatter plot represents the results of the cell differential expression analysis as shown in FIG13( c ), and different genes are represented by different colors. FIG13( c ) can be used to intuitively understand the correlation between genes and different clusters.

[0144] In step S1203 of some embodiments, in order to let the user know more intuitively in which cluster the gene is most expressed, the cluster with the highest gene expression is marked in the preliminary cell differential expression view according to the highly expressed gene information to obtain the target cell differential expression view. Therefore, the target cell differential expression view can be used to intuitively see in which cluster the gene is most expressed.

[0145] In steps S1201 to S1203 shown in this embodiment, different view templates are set to represent the results of cell differential expression analysis, which can adapt to the viewing habits of different users. At the same time, the cluster with the highest gene expression will be marked, so that the user can intuitively understand in which cluster the gene has the highest expression level from the target cell differential expression view, so as to provide a reliable reference for the user in the gene research of the cell.

[0146] In step S106 of some embodiments, the preliminary cell analysis view is only a cell analysis view directly generated according to the default analysis parameters. If the preliminary cell analysis view is not satisfied with the user's analysis needs, the user will feedback the analysis adjustment parameters, and the preliminary cell analysis view will be adjusted after the analysis adjustment parameters are received to meet the user's analysis needs. It should be noted that the preliminary cell analysis views generated by different classification categories are different, but a display adjustment button will be set on each preliminary cell analysis view, and the user can click the display adjustment button to form the display adjustment parameters. Specifically, the display adjustment buttons set in different preliminary cell analysis views are different, so the generated display adjustment parameters are also different, but they are all display adjustments made for different analysis categories.

[0147] See also Fig.14 In some embodiments, step S107 includes but is not limited to steps S1401 to S1404:

[0148] Step S1401, adjusting the default analysis parameters according to the analysis adjustment parameters to obtain target analysis parameters; wherein the target analysis parameters include any one of the following: focus cell category, threshold range, focus gene information;

[0149] Step S1402, selecting a first cell sequence set from the selected cell sequence set according to the cell type of interest, and performing gene difference analysis on the first cell sequence set to obtain an updated cell analysis result;

[0150] Step S1403, selecting a second cell sequence set from the selected cell sequence set according to the threshold range, and performing gene difference analysis on the second cell sequence set to obtain an updated cell analysis result;

[0151] Step S1404 , a third cell sequence set is screened out from the selected cell sequence set according to the information of the gene of interest, and a gene difference analysis is performed on the third cell sequence set to obtain an updated cell analysis result.

[0152] In step S1401 of some embodiments, when the preliminary cell analysis view needs to be adjusted, the preliminary cell analysis results need to be adjusted synchronously. It should be noted that the default analysis parameters are first adjusted according to the analysis adjustment parameters to adjust the analysis parameters of the selected cell sequence set in the analysis process to update the preliminary cell analysis results to obtain the target cell analysis results. It should be further noted that the target analysis parameters include at least one of the cell category of interest, the threshold range, and the gene information of interest. The cell category of interest is the cell category that the user wants to pay attention to, and the threshold range is the range of the gene expression program. Genes with low gene expression levels can be filtered out, and then highly expressed genes can be screened out, and the threshold range can be adjusted by the user at will. The gene information of interest represents the genes of interest to the user, and the expression of the genes of interest in different clusters can be displayed.

[0153] In step S1402 of some embodiments, a first cell sequence matching the cell category is screened out from the selected cell sequence set according to the cell category of interest, and the first cell sequence is combined to form a first cell sequence set. Taking a heat map as an example, please refer to FIG. 13 (a). Different grayscale images are used to represent different cell categories above the heat map. The user can directly click on the cell category to obtain the cell category of interest. The background will re-screen the first cell sequence set that meets the requirements according to the cell category of interest, and update the gene differential expression analysis results of the first cell sequence set to obtain an updated heat map, and the heat map only displays the gene expression differences in the cell category of interest. Therefore, the user can directly click on the cell category that he wants to pay attention to to quickly display the gene differential expression analysis results of the cell category of interest, so that the user can perform cell gene analysis in a targeted manner.

[0154] In step S1403 of some embodiments, if the user needs to adjust the threshold range, and the threshold range is mainly the adjustment of the gene difference program, it is necessary to screen out the second cell sequence whose gene expression level meets the threshold range from the selected cell sequence set according to the threshold range. The second cell sequence set is formed by combining the second cell sequence, and then the second cell sequence set difference analysis is performed to update the preliminary cell analysis result to obtain the updated cell analysis result. Taking the bubble chart expression of cell difference expression analysis results as an example, as shown in Figure 13 (b), the adjustment button of the threshold range will be set on the right side of the bubble chart, and the threshold range will be formed by sliding the adjustment button. When the threshold range changes, some selected cell sequences whose expression levels are not within the threshold range will be filtered out, so the number of circles in Figure 13 (b) will decrease or increase.

[0155] In step S1404 of some embodiments, if the user needs to pay attention to the expression of each gene, select the gene of interest on the user interface to obtain the gene of interest information. According to the gene of interest information, the corresponding cell sequence is screened out from the selected cell sequence set as the third cell sequence set. By performing gene expression analysis on the third cell sequence set, the cell analysis result is updated. It should be noted that the updated cell analysis result is the expression degree of the gene of interest in different clusters (cells). If the Umap diagram characterizes the cell differential expression analysis result as an example, as shown in Figure 13 (c), an adjustment option box will be set on the right, and the user can click the corresponding button in the adjustment option box to form the analysis adjustment parameter. As Figure 13 (c) switches to Figure 13 (d), when the user selects the gene of interest, the Umap diagram switches from Figure 13 (c) to Figure 13 (d), and only the expression of a gene in different clusters is displayed.

[0156] In steps S1401 to S1404 shown in this embodiment, when the preliminary cell analysis view needs to be adjusted, and the cell analysis associated with the selected cell sequence set needs to be performed, the analysis parameters of the selected cell sequence set during the analysis process are adjusted according to different analysis adjustment parameters to obtain updated cell analysis results. The preliminary cell analysis view is switched to the updated cell analysis view according to the updated cell analysis results to quickly and intuitively adjust the cell analysis view until the cell analysis view meets the user's needs.

[0157] In some embodiments, after each analysis adjustment parameter is generated, the target cell analysis view corresponding to the analysis adjustment parameter may be collected, and the target cell analysis view may be downloaded by directly clicking a download button.

[0158] See also Fig.15 In some embodiments, after step S108, the interactive analysis method of single-cell data may also include but is not limited to steps S1501 to S1504:

[0159] Step S1501, receiving a display effect adjustment request for a target cell analysis view; wherein the display effect adjustment request includes at least one of the following: display subject information, display gene information, and display template information;

[0160] Step S1502, switching the display theme of the target cell analysis view according to the display theme information to obtain a first updated cell analysis view;

[0161] Step S1503, filtering the genes in the target cell analysis view according to the displayed gene information to obtain a second updated cell analysis view;

[0162] Step S1504: Switch the display template of the target cell analysis view according to the display template information to obtain a third updated cell analysis view.

[0163] In step S1501 of some embodiments, the display effect adjustment request is used to adjust the display effect of the target cell analysis view to achieve a personalized customized cell analysis view. It should be noted that the display effect involves the appearance of the target cell analysis view and does not involve the adjustment of the target cell analysis results. Therefore, the display effect adjustment request includes at least one of display theme information, display gene information and display template information. Among them, the display theme information includes font size, font type, view size, cluster color, etc., and no specific restrictions are made on the content of the display theme information. The display template information involves a display view template, and the display view template can be a scatter plot, a heat map, a bubble chart and a bar chart, and there is no specific restriction on the display view template.

[0164] In step S1502 of some embodiments, if it is necessary to switch the theme of the target cell analysis view, the target theme can be clicked on the left side of the target cell analysis view to obtain the display theme information, and the color, view size, font size, etc. of the cluster in the target cell analysis view can be switched according to the display theme information. For example, if the display theme information is only the switching of cluster colors, the target cell analysis view is shown in FIG16(a). When the user selects the theme, the colors of different clusters in the target cell analysis view are switched according to the display theme information to obtain the first updated cell analysis view, and the first updated cell analysis view is shown in FIG16(b). It should be noted that the theme of the cell analysis view can be switched by the user directly clicking the theme button to achieve personalized customization of the cell analysis view to meet the user's viewing habits.

[0165] In step S1503 of some embodiments, if the target cell analysis view includes the expression of multiple genes in different clusters, the right side of the target cell analysis view will limit the gene selection button that can be selected for individual display. The user clicks the gene selection button to generate display gene information, and then the view displaying the expression of multiple genes is switched to displaying only the expression of one gene in different clusters.

[0166] In step S1504 of some embodiments, if the user is not used to the display template of the target cell analysis view, the display template that the user is used to reading can be switched to generate display template information. The view template of the target cell analysis view is switched according to the display template information, that is, the target cell analysis result is switched from the original view template to another view template to obtain a third updated cell analysis view.

[0167] In steps S1501 to S1504 shown in this embodiment, after the target cell analysis view is constructed, in order to further adjust the display effect of the cell analysis view, the display effect of the target cell analysis view can be adjusted according to user needs to obtain an updated cell analysis view that meets the user's viewing habits.

[0168] In some embodiments, after constructing the updated cell analysis view, the modified updated cell analysis view can be collected and downloaded to a local device.

[0169] See also Fig.17 The present application also provides an interactive analysis device for single-cell data, which can implement the above-mentioned interactive analysis method for single-cell data. The device includes:

[0170] The data acquisition module 1701 is used to acquire target cell data; wherein the target cell data includes: a target cell sequence and cell annotation information of the target cell sequence;

[0171] A preprocessing module 1702 is used to preprocess the target cell sequence according to the cell annotation information to obtain a selected cell sequence set;

[0172] The parameter acquisition module 1703 is used to acquire the custom analysis parameters; wherein the custom analysis parameters include: analysis category and default analysis parameters;

[0173] Gene expression analysis module 1704, used to perform gene expression analysis on the selected cell sequence set according to the analysis category and default analysis parameters to obtain preliminary cell analysis results;

[0174] A visualization module 1705 is used to visualize the preliminary cell analysis results to obtain a preliminary cell analysis view;

[0175] The parameter receiving module 1706 is used to receive the analysis adjustment parameters of the preliminary cell analysis view;

[0176] A parameter adjustment module 1707, used to adjust the analysis parameters of the selected cell sequence set according to the analysis adjustment parameters to obtain updated cell analysis results;

[0177] The view adjustment module 1708 is used to adjust the preliminary cell analysis view according to the updated cell analysis result to obtain the target cell analysis view.

[0178] The specific implementation of the interactive analysis device for single-cell data is basically the same as the specific implementation of the interactive analysis method for single-cell data described above, and will not be repeated here.

[0179] The embodiment of the present application also provides an electronic device, the electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the above-mentioned interactive analysis method of single cell data when executing the computer program. The electronic device can be any intelligent terminal including a tablet computer, a car computer, etc.

[0180] See also Fig.18 , Fig.18 The hardware structure of an electronic device of another embodiment is illustrated, and the electronic device includes:

[0181] The processor 1801 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (Application Specific Integrated Circuit, ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;

[0182] The memory 1802 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 1802 can store an operating system and other application programs. When the technical solution provided in the embodiments of this specification is implemented by software or firmware, the relevant program code is stored in the memory 1802, and the processor 1801 calls and executes the interactive analysis method of single-cell data in the embodiments of this application;

[0183] Input / output interface 1803, used to implement information input and output;

[0184] The communication interface 1804 is used to realize the communication interaction between the device and other devices. The communication can be realized through a wired manner (such as USB, network cable, etc.) or a wireless manner (such as mobile network, WIFI, Bluetooth, etc.);

[0185] A bus 1805 that transmits information between the various components of the device (e.g., the processor 1801, the storage 1802, the input / output interface 1803, and the communication interface 1804);

[0186] The processor 1801 , the memory 1802 , the input / output interface 1803 and the communication interface 1804 are connected to each other in communication within the device via the bus 1805 .

[0187] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, it implements the above-mentioned interactive analysis method of single-cell data.

[0188] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0189] The interactive analysis method, apparatus, device, and storage medium for single-cell data provided in the embodiments of the present application automatically analyze the target cell data according to default parameters and analysis categories to obtain preliminary cell analysis results, and visualize the preliminary cell analysis results to obtain a preliminary cell analysis view. The user can adjust the preliminary cell analysis view to obtain analysis adjustment parameters, and adjust the analysis parameters of the target cell data in the analysis process according to the analysis adjustment parameters, and visualize the cell analysis results of the adjusted analysis process until a target cell analysis view that meets the needs is constructed. Therefore, the user can realize personalized adjustment of the cell analysis view to assist the user in conducting genetic research on the cell and improve the effect of cell genetic research.

[0190] The embodiments described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0191] Those skilled in the art will appreciate that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0192] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0193] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0194] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0195] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0196] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0197] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0198] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0199] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including multiple instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store programs.

[0200] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the rights of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present invention should be within the scope of the rights of the present invention.

Claims

1. A method for interactive analysis of single-cell data, characterized in that: The method comprises: Acquire target cell data; wherein the target cell data includes: a target cell sequence and cell annotation information of the target cell sequence; Preprocessing the target cell sequence according to the cell annotation information to obtain a selected cell sequence set; Obtaining custom analysis parameters; wherein the custom analysis parameters include: analysis category and default analysis parameters; Performing gene expression analysis on the selected cell sequence set according to the analysis category and the default analysis parameters to obtain preliminary cell analysis results; Visualizing the preliminary cell analysis results to obtain a preliminary cell analysis view; receiving analysis adjustment parameters for the preliminary cell analysis view; adjusting the analysis parameters of the selected cell sequence set according to the analysis adjustment parameters to obtain updated cell analysis results; The preliminary cell analysis view is adjusted according to the updated cell analysis result to obtain a target cell analysis view.

2. The method according to claim 1, characterized in that: The step of obtaining target cell data comprises: Acquiring raw cell data and the raw data format of the raw cell data; Screening the preset candidate toolkits according to the original data format to obtain a target toolkit; The target toolkit is used to convert the data format of the original cell data into a preset data format to obtain the target cell data; wherein the format of the target cell data is unified.

3. The method according to claim 1, characterized in that The analysis category includes at least one of the following: principal component analysis category, cluster analysis category, differential expression analysis category, enrichment analysis category, pseudo-time series analysis category and genetic rate analysis category; the gene expression analysis is performed on the selected cell sequence set according to the analysis category and the default analysis parameters to obtain preliminary cell analysis results, including at least one of the following: Performing principal component analysis on the selected cell sequence set according to the principal component analysis category and the default analysis parameters to obtain a cell principal component analysis result; Performing cluster analysis on the selected cell sequence set according to the cluster analysis category and the default analysis parameters to obtain a cell cluster analysis result; Performing differential expression analysis on the selected cell sequence set according to the differential expression analysis category and the default analysis parameters to obtain a cell differential expression analysis result; Performing gene enrichment analysis on the selected cell sequence set according to the enrichment analysis category and the default analysis parameters to obtain a cell gene enrichment analysis result; Performing a pseudo-time series analysis on the selected cell sequence set according to the pseudo-time series analysis category and the default analysis parameters to obtain a cell pseudo-time series analysis result; The genetic rate analysis is performed on the selected cell sequence set according to the genetic rate analysis and the default analysis parameters to obtain a cell genetic rate analysis result.

4. The method according to claim 3, characterized in that If the preliminary cell analysis result is a cell differential expression analysis result, the cell differential expression analysis result characterizes the gene expression of each cell cluster; The preliminary cell analysis view is a target cell differential expression view; The visualizing the preliminary cell analysis results to obtain a preliminary cell analysis view includes: Extracting highly expressed gene information from the cell differential expression analysis results; Visualizing the cell differential expression analysis results according to a preset view template to obtain a preliminary cell differential expression view; wherein the view template includes a heat map, a bubble map and a Umap map; The preliminary cell differential expression view is annotated according to the highly expressed gene information to obtain the target cell differential expression view.

5. The method according to any one of claims 1 to 4, characterized in that: The step of adjusting the analysis parameters of the selected cell sequence set according to the analysis adjustment parameters to obtain updated cell analysis results includes: The default analysis parameters are adjusted according to the analysis adjustment parameters to obtain target analysis parameters; wherein the target analysis parameters include any one of the following: a cell type of interest, a threshold range, and gene information of interest; Screening out a first cell sequence set from the selected cell sequence set according to the cell category of interest, and performing gene difference analysis on the first cell sequence set to obtain the updated cell analysis result; Screening out a second cell sequence set from the selected cell sequence set according to the threshold range, and performing gene difference analysis on the second cell sequence set to obtain the updated cell analysis result; A third cell sequence set is screened out from the selected cell sequence set according to the information of the gene of interest, and a gene difference analysis is performed on the third cell sequence set to obtain the updated cell analysis result.

6. The method according to any one of claims 1 to 4, characterized in that: After adjusting the preliminary cell analysis view according to the updated cell analysis result to obtain the target cell analysis view, the method further includes: Receive a display effect adjustment request for the target cell analysis view; wherein the display effect adjustment request includes at least one of the following: display subject information, display gene information, and display template information; Switching the display theme of the target cell analysis view according to the display theme information to obtain a first updated cell analysis view; Filtering the genes in the target cell analysis view according to the displayed gene information to obtain a second updated cell analysis view; The display template of the target cell analysis view is switched according to the display template information to obtain a third updated cell analysis view.

7. The method according to any one of claims 1 to 4, characterized in that: The preprocessing of the target cell sequence according to the cell annotation information to obtain a selected cell sequence set includes: Filtering the target cell sequence according to the cell annotation information to obtain a selected cell sequence; A plurality of the selected cell sequences are integrated to obtain the selected cell sequence set.

8. An interactive analysis device for single-cell data, characterized in that: The device comprises: A data acquisition module, used to acquire target cell data; wherein the target cell data includes: a target cell sequence and cell annotation information of the target cell sequence; A preprocessing module, used for preprocessing the target cell sequence according to the cell annotation information to obtain a selected cell sequence set; A parameter acquisition module, used to acquire custom analysis parameters; wherein the custom analysis parameters include: analysis category and default analysis parameters; A gene expression analysis module, used to perform gene expression analysis on the selected cell sequence set according to the analysis category and the default analysis parameters to obtain preliminary cell analysis results; A visualization module, used for visualizing the preliminary cell analysis results to obtain a preliminary cell analysis view; A parameter receiving module, used for receiving analysis adjustment parameters of the preliminary cell analysis view; A parameter adjustment module, used to adjust the analysis parameters of the selected cell sequence set according to the analysis adjustment parameters to obtain updated cell analysis results; The view adjustment module is used to adjust the preliminary cell analysis view according to the updated cell analysis result to obtain a target cell analysis view.

9. An electronic device, characterized in that: The electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the interactive analysis method of single-cell data according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the interactive analysis method of single-cell data according to any one of claims 1 to 7 is implemented.

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