Analysis method, device and equipment based on single cell transcriptome sequencing data

Abstract

The application provides an analysis method, device and equipment based on single-cell transcriptome sequencing data, which comprises the following steps: performing quality control, downstream analysis and visual display on single-cell transcriptome sequencing expression quantitative data, performing cell filtering by using Grubbs test method to obtain effective single-cell transcriptome sequencing quantitative data, performing initialization clustering analysis on the data to obtain single-cell subgroup classification results; performing screening on the single-cell subgroup classification results to obtain target single-cell subgroups, and performing re-clustering analysis to obtain single-cell sub-subgroup classification results; performing significant difference gene screening analysis on the single-cell transcriptome sequencing quantitative data between single-cell subgroups; and performing regression analysis based on characteristic genes of cell cycles to predict cell division periods corresponding to different cell types. The application effectively solves the technical complex problems of existing single-cell transcriptome sequencing quantitative data analysis, makes data analysis more simple and reliable, and reduces the difficulty of data analysis.

Description

Technical Field

[0001] This application relates to the fields of biostatistical data analysis and computer information technology, and in particular to an analysis method, apparatus and equipment based on single-cell transcriptome sequencing data. Background Technology

[0002] With the continuous development of the bio-industry, the advancement of statistical methods, and the rapid progress of computational and information science, the need for scientifically effective methods to study life processes has become increasingly urgent. Most researchers in the biological field possess a strong foundation in biology, but their mastery of statistics, computational science, and computer programming is relatively weak. Currently, biological researchers are increasingly at a loss when faced with large sample sizes, multiple observations, and high-throughput molecular data analysis. Therefore, it is crucial to select appropriate analytical strategies that are flexible, easy to operate, and effective in analyzing single-cell transcriptome sequencing quantitative data to assist researchers in solving existing biological problems.

[0003] Current solutions for single-cell transcriptome quantitative data have the following drawbacks: (1) The analysis is mainly performed on the backend, requiring proficiency in Linux system operation and learning of numerous programming languages, resulting in poor interactivity and user experience. (2) A large number of statistical analysis methods need to be mastered. In summary, there are currently no technical means to solve the above difficulties at the frontend, which greatly hinders the progress of life science research. Summary of the Invention

[0004] In view of the shortcomings of the prior art described above, the purpose of this application is to provide an analysis method, device and equipment based on single-cell transcriptome sequencing data, to solve any of the technical problems of high complexity and low efficiency in the analysis of quantitative data from single-cell transcriptome sequencing in the prior art.

[0005] To achieve the above and other related objectives, a first aspect of this application provides an analysis method based on single-cell transcriptome sequencing data. The method includes: acquiring single-cell transcriptome sequencing expression quantification data and performing quality control, downstream analysis, and visualization; using the Grubbs test to filter cells to obtain effective single-cell transcriptome sequencing quantification data; performing initial clustering analysis based on the single-cell transcriptome sequencing quantification data to obtain single-cell subpopulation classification results; screening the single-cell subpopulation classification results to obtain target single-cell subpopulations, and performing further clustering analysis to obtain single-cell sub-subpopulation classification results; performing significant differential gene screening analysis on the single-cell transcriptome sequencing quantification data among single-cell subpopulations; and performing regression analysis based on characteristic genes of the cell cycle to predict the cell division phase corresponding to different cell types.

[0006] In some embodiments of the first aspect of this application, the single-cell transcriptome sequencing data is subjected to quality control and cell filtering is performed using the Grubbs test. Specifically, this includes: calculating the expression ratio of mitochondrial genes in each cell and the number of genes detected in each cell, and performing quality control according to preset indicators; using statistical methods to depict the distribution and detection of mitochondrial genes, and using the Grubbs test to iteratively remove outlier values ​​from the gene detection results for cell filtering.

[0007] In some embodiments of the first aspect of this application, the initial clustering analysis based on the single-cell transcriptome sequencing quantitative data specifically includes: 1) Data standardization: using an optimized Relative Counts algorithm to perform scaling preprocessing on gene expression data; 2) Feature gene screening: using the nearZeroVar function to remove feature genes with near-zero variance to retain feature genes with significant variations; 3) Data centering: using an optimized min-max normalization algorithm to center the preprocessed data; 4) Principal component analysis: using principal component analysis to reduce the dimensionality of the centered data to reduce variables; 5) Cell clustering: using graph algorithms to cluster and group cells; 6) Data visualization: using two nonlinear dimensionality reduction methods, t-SNE and UMAP, to visualize the single-cell subpopulation classification results and display the cell population on a two-dimensional plane.

[0008] In some embodiments of the first aspect of this application, the standardized data obtained by the optimized Relative counts algorithm is expressed as follows: Where, count i This indicates the expression level of gene i in the corresponding cell.

[0009] In some embodiments of the first aspect of this application, the calculation formula for the optimized minimum-maximum normalization algorithm is as follows: Where max(x) represents the maximum value of the sample data; min(x) represents the minimum value of the sample data.

[0010] In some embodiments of the first aspect of this application, after classifying single-cell subpopulations based on the single-cell transcriptome sequencing quantitative data, subpopulation screening analysis can also be performed, specifically including: setting different subpopulation adjustment parameters, and screening out the number of target subpopulations based on the corresponding subpopulation changes under different subpopulation adjustment parameters.

[0011] In some embodiments of the first aspect of this application, the quantitative data of single-cell transcriptome sequencing is analyzed for significant differential gene screening among single-cell subpopulations. Specifically, this includes: performing a significance test on the genes between single-cell subpopulations and combining the fold difference in the average gene expression levels between single-cell subpopulations to screen for significantly differential genes; wherein the screening conditions for significantly differential genes are: 1) significance level threshold P value ≤ 0.05; 2) average fold difference avg log2FC ≤ 0.25.

[0012] To achieve the above and other related objectives, a second aspect of this application provides an analysis device based on single-cell transcriptome sequencing data, comprising: a data quality control and cell screening module, used to acquire single-cell transcriptome sequencing expression quantification data and perform quality control, downstream analysis, and visualization on it, and to use the Grubbs test to filter cells to obtain effective single-cell transcriptome sequencing quantification data; a clustering analysis module, used to perform initial clustering analysis based on the single-cell transcriptome sequencing quantification data to obtain single-cell subpopulation classification results; a sub-subpopulation analysis module, used to screen the single-cell subpopulation classification results to obtain target single-cell subpopulations, and to perform further clustering analysis on them to obtain single-cell sub-subpopulation classification results; a differential gene screening analysis module, used to perform significant differential gene screening analysis on the single-cell transcriptome sequencing quantification data among single-cell subpopulations; and a cell cycle analysis module, used to perform regression analysis based on characteristic genes of the cell cycle to predict the cell division phase corresponding to different cell types.

[0013] To achieve the above and other related objectives, a third aspect of this application provides a computer-readable storage medium storing a computer program that, when run on a computer, causes the computer to perform the methods described above.

[0014] To achieve the above and other related objectives, a fourth aspect of this application provides a computer device, comprising: a memory and a processor; the memory being used to store a computer program; and the processor being used to execute the computer program stored in the memory to cause the device to perform the method described above.

[0015] In summary, the analysis method, apparatus, and device based on single-cell transcriptome sequencing data provided in this application have the following beneficial effects:

[0016] 1. This application establishes an online, code-free single-cell transcriptome sequencing quantification and data mining analysis platform. Users do not need to undergo special bioinformatics training to analyze complex single-cell transcriptome sequencing data and perform targeted data mining according to research objectives, which greatly improves the progress of scientific research.

[0017] 2. This application improves and upgrades some existing algorithms, thereby enhancing the effectiveness of cell clustering;

[0018] 3. The analysis device / platform based on single-cell transcriptome sequencing quantitative data provided in this application effectively solves the technical complexity problem of single-cell transcriptome sequencing quantitative data analysis, making data analysis simpler and more reliable, reducing the difficulty of data analysis, optimizing quality control standards, improving standardization methods, enhancing feature gene screening strategies, improving centralization methods, optimizing the clustering results of single-cell data, and making inter-cell differences more sparse and intra-cell differences smaller.

[0019] 4. This application requires no computer programming skills to complete data analysis and no strong statistical knowledge background. The platform recommends the best statistical methods for data analysis. It is simple to operate, and data analysis can be completed on the front end without the need for manual parameter setting or modification. It automatically filters data based on preset statistical indicators; it uses an improved data standardization method to make the data more convergent; and it uses an improved normalization algorithm to further optimize the improved relative expression data, so that the distance within the observation category is closer and the distance between categories is sparser, thereby optimizing the classification of the groups. Attached Figure Description

[0020] Figure 1 The diagram shown is a flowchart illustrating an analysis method based on single-cell transcriptome sequencing data according to one embodiment of this application.

[0021] Figure 2A The diagram shows the result obtained based on an existing unimproved algorithm in one embodiment of this application.

[0022] Figure 2B The diagram shows the result obtained based on the optimization algorithm of this application in one embodiment.

[0023] Figure 3 The diagram shown is a schematic representation of an analysis device based on single-cell transcriptome sequencing data according to one embodiment of this application.

[0024] Figure 4 The diagram shown is a structural schematic of a computer device according to an embodiment of this application. Detailed Implementation

[0025] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, unless otherwise specified, the following embodiments and features described therein can be combined with each other.

[0026] It should be noted that in the following description, reference is made to the accompanying drawings, which illustrate several embodiments of this application. It should be understood that other embodiments may also be used, and changes in mechanical composition, structure, electrical system, and operation may be made without departing from the spirit and scope of this application. The following detailed description should not be considered limiting, and the scope of the embodiments of this application is defined only by the claims of the published patent. The terminology used herein is for describing particular embodiments only and is not intended to limit the scope of this application.

[0027] Throughout this specification, unless otherwise expressly specified and limited, the terms "installation," "connection," "linking," "fixing," and "holding" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0028] Furthermore, as used herein, the singular forms “a,” “an,” and “the” are intended to include the plural forms as well, unless the context indicates otherwise. The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification, claims, and accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a particular order or sequence. It should be understood that such data used can be interchanged where appropriate so that the embodiments described herein can be implemented in orders other than those illustrated or described herein. It should be further understood that the terms “comprising,” “including,” indicate the presence of the stated features, operations, elements, components, items, kinds, and / or groups, but do not exclude the presence, occurrence, or addition of one or more other features, operations, elements, components, items, kinds, and / or groups. The terms “or” and “and / or” as used herein are to be interpreted as inclusive, or mean any one or any combination thereof. Thus, “A, B, or C” or “A, B, and / or C” means “any one of: A; B; C; A and B; A and C; B and C; A, B, and C.” An exception to this definition will only occur if the combination of elements, functions, or operations is inherently mutually exclusive in some way.

[0029] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions in the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only for explaining the present invention and are not intended to limit the invention.

[0030] To address the existing problems, this application proposes an analysis method, apparatus, and device based on single-cell transcriptome sequencing data, which solves any of the technical problems in the prior art, such as high complexity and low efficiency in the analysis of quantitative data from single-cell transcriptome sequencing.

[0031] like Figure 1 The diagram shown illustrates a flowchart of an analysis method based on single-cell transcriptome sequencing data according to an embodiment of this application. The method includes the following steps:

[0032] Step S1: Obtain single-cell transcriptome sequencing expression quantification data and perform quality control, downstream analysis, and visualization. Use the Grubbs test to filter cells to obtain effective single-cell transcriptome sequencing quantification data.

[0033] In one embodiment of this application, the single-cell transcriptome sequencing data is subjected to quality control, and cell filtering is performed using the Grubbs test, specifically including:

[0034] a. Calculate the expression ratio of mitochondrial genes in each cell and the number of genes detected in each cell, and perform quality control according to preset indicators;

[0035] Specifically, the quality of single-cell transcriptome sequencing data and cellular data is statistically analyzed and controlled, and compared with a reference genome to quantify the expression levels of each gene. Based on this, the gene detection distribution, molecule detection distribution, and mitochondrial gene detection distribution for each cell are obtained, and these are displayed using violin plots to depict the overall quality control status of the single-cell transcriptome sequencing data.

[0036] Among them, the preset indicators include setting the expression ratio range of mitochondrial genes, preferably in the range of 10 to 20, in order to avoid the appearance of mitochondrial gene cell populations due to excessive mitochondrial gene expression, thereby obtaining more reliable single-cell transcriptome sequencing data.

[0037] b. Statistical methods were used to depict the distribution and detection of mitochondrial genes, and the Grubbs test was used to filter the gene detection results by iteratively removing outliers.

[0038] It should be noted that the Grubbs test is a statistical method. Specifically, when there are individual data points in a set of measurement data that deviate significantly from the mean, these data points are called "suspicious values." The Grubbs test can remove these "suspicious values" from the set of measurement data so that they are not included in the calculation of the mean. In this case, the "suspicious values" are also called "outliers / gross errors / disruptive values."

[0039] Based on the violin diagram, the Grubbs test is used to examine the number of genes detected in each cell, and abnormal cells are iteratively removed to achieve the purpose of cell filtering.

[0040] Step S2: Perform initial clustering analysis based on the single-cell transcriptome sequencing quantitative data to obtain single-cell subpopulation classification results.

[0041] In one embodiment of this application, step S2 specifically includes:

[0042] 1) Data standardization: Gene expression data were preprocessed using an optimized Relative counts algorithm to reduce its size;

[0043] It should be noted that single-cell transcriptome sequencing quantitative data generally exhibit a zero-inflated negative binomial distribution, with a relatively large range of data variation, making calculation inconvenient. This application uses an optimized Relative counts algorithm to scale the data to reduce the computational burden.

[0044] Specifically, the standardized data is obtained through the optimized Relative counts algorithm, and its expression is:

[0045]

[0046] Where, count i This indicates the expression level of gene i in the corresponding cell.

[0047] 2) Feature gene screening: Use the nearZeroVar function to remove feature genes with near-zero variance in order to retain feature genes with significant changes;

[0048] It should be noted that in the data analysis process, the focus is generally on differentiated feature information in order to effectively distinguish differences. The nearZeroVar function is used to detect variables with zero or near-zero variance; therefore, this application preferably uses the nearZeroVar machine learning algorithm to remove feature genes with zero or near-zero variance, that is, genes whose relevant data have basically no fluctuation, thereby retaining feature genes with significant changes and achieving the purpose of feature gene screening.

[0049] 3) Data centralization: The preprocessed data is centralized using an optimized min-max normalization algorithm;

[0050] The calculation formula for the optimized min-max normalization algorithm is as follows:

[0051]

[0052] Where max(x) represents the maximum value of the sample data; min(x) represents the minimum value of the sample data.

[0053] Since the z-score method is mostly suitable for data centering of data with a distribution close to a normal distribution, while single-cell data mostly exhibit a zero-inflated negative binomial distribution, this application uses an optimized Min-Max noemalization algorithm to avoid being limited by the normal distribution.

[0054] 4) Principal Component Analysis: Principal component analysis is used to reduce the dimensionality of centered data, thereby reducing the number of variables;

[0055] It should be noted that Principal Components Analysis (PCA) is a method that uses linear algebra to reduce the dimensionality of data. It transforms multiple variables into a few uncorrelated composite variables to more comprehensively reflect the entire dataset. PCA results are presented as a rolling stone plot, primarily used to select suitable principal components for data visualization. The inflection points in the plot represent the appropriate number of principal components. This application uses unsupervised linear dimensionality reduction using PCA to reduce the dimensionality of centered data, reducing computational burden, maximizing the preservation of feature information, and improving computational speed. Based on the standard deviation changes of each component in the rolling stone plot, a reasonable number of principal components is selected for downstream data analysis. Inflection points are identified in the rolling stone plot, and the number of principal components corresponding to these inflection points represents the appropriate number of principal components.

[0056] 5) Cell clustering: Cells are clustered and grouped based on graph algorithms; specifically including:

[0057] a. Constructing cluster relationships between cells: Constructing a KNN cluster relationship diagram based on Euclidean distance based on the preset number of most significant principal components obtained from PCA analysis;

[0058] b. Clustering and grouping: The Louvain algorithm is used to optimize cell population clustering; the community is initialized, and a greedy algorithm is used to iterate until the community converges and does not change, so as to obtain the final cell grouping and clustering results.

[0059] 6) Data visualization: Two non-linear dimensionality reduction methods, t-SNE and UMAP, are used to visualize the classification results of single-cell subpopulations and display the cell population on a two-dimensional plane.

[0060] It should be noted that since single-cell transcriptome sequencing data are generally high-dimensional data, and most high-dimensional data cannot be displayed in two-dimensional space, this application uses two non-linear dimensionality reduction methods, t-SNE (t-distributed Stochastic Neighbor Embedding) and UMAP (Uniform Manifold Approx-imation and Projection), to reduce the high-dimensional spatial data to two-dimensional space for visualization and to display the cell clustering results.

[0061] like Figure 2A The diagram illustrates the results obtained using an existing, unimproved algorithm in one embodiment of this application. The cell subpopulations obtained through classification clustering analysis are numerous because existing methods are not precise enough in data processing, causing data that could have clustered together to become scattered. Figure 2B The diagram shown illustrates the results obtained using the optimization algorithm described in this application in one embodiment. By using the optimization algorithm (including optimized Relativecounts algorithm, optimized min-max normalization algorithm, etc.), the number of cell type subpopulations is reduced, thus accurately reflecting the potential true categories of the data. The numerical identifiers represent different types of cell clusters.

[0062] Step S3: Filter the classification results of the single-cell subpopulation to obtain the target single-cell subpopulation, and perform further clustering analysis on it to obtain the classification results of the single-cell subpopulation.

[0063] In one embodiment of this application, after classifying single-cell subpopulations based on the quantitative data of single-cell transcriptome sequencing, subpopulation screening analysis can also be performed, specifically including: setting different subpopulation adjustment parameters, and screening out the number of target subpopulations based on the corresponding subpopulation changes under different subpopulation adjustment parameters.

[0064] For example, the changes in subgroups under different subgroup adjustment parameters can be displayed using a dendrogram, which can be used to filter out the number of target subgroups.

[0065] Specifically, a) Subgroup screening: Based on the single-cell subgroup classification results, target single-cell subgroups of interest and their corresponding group labels are screened. b) Subgroup adjustment parameters: By adjusting the number of groups, the target single-cell subgroups are re-clustered, and the single-cell subgroup classification results are visualized using two nonlinear dimensionality reduction methods, t-SNE and UMAP, to achieve the purpose of subgroup analysis.

[0066] In addition, for subpopulation screening analysis of quantitative data from single-cell transcriptome sequencing, Clustree software can be preferred to show the changes in cells or observations among different subpopulations.

[0067] Step S4: Perform differential gene screening analysis on the quantitative data of the single-cell transcriptome sequencing among the single-cell subpopulations.

[0068] In one embodiment of this application, step S4 includes: screening for significantly different genes by performing a significance test on the genes among single-cell subpopulations and combining the fold change in the average gene expression levels among the single-cell subpopulations; specifically:

[0069] a. Significance test of differential gene differences: The statistical Wilcox nonparametric method and the t-test parametric method were used to test the significance of gene differences among single cell subsets.

[0070] b. Comparison of gene expression levels: Calculate the fold difference in average expression among single-cell subsets;

[0071] c. Differential gene screening: Genes are screened for differences by combining statistical significance test indicators and fold differences; wherein, the screening criteria for significantly differentially expressed genes are as follows:

[0072] 1) The selected gene is expressed in the target subgroup or control subgroup and in more than 25% of the samples;

[0073] 2) The significance level threshold P-value ≤ 0.05;

[0074] 3) Average difference multiple avg log2FC≤0.25.

[0075] In addition, visualization of the expression distribution of significantly differentially expressed genes includes:

[0076] ① Bubble chart display: The significantly differentially expressed genes selected can be displayed in a bubble chart, showing the average expression and the proportion of increased expression in cell populations.

[0077] ② Heatmap display: The significantly differentially expressed genes selected can be displayed in a heatmap, showing the expression levels in different cell populations.

[0078] Step S5: Perform regression analysis based on characteristic genes of the cell cycle to predict the cell division phase corresponding to different cell types.

[0079] In summary, this application provides an analysis method based on single-cell transcriptome sequencing data that can complete data analysis without computer programming skills or a strong statistical background. The platform recommends the optimal statistical method for data analysis, making it simple to operate. Data analysis can be completed on the front end without manual parameter setting or modification. Automated data filtering is performed based on the statistical indicator P-value ≤ 0.05. The data standardization method is improved by using an optimized Relative Counts algorithm to transform the original quantitative data and using log2(x+1) data transformation processing to make the data more convergent. Moreover, the +1 processing here avoids the problem that log(0) cannot take the logarithm. The data normalization processing is improved. Since z-score normalization is more suitable for data with an approximately normal distribution, this application uses an optimized min-max normalization algorithm to further optimize the expression data, achieving closer distances within observation categories and sparser distances between categories, thereby optimizing the classification of groups.

[0080] like Figure 3 The diagram shown illustrates the structure of an analysis device based on single-cell transcriptome sequencing data according to an embodiment of this application. The analysis device 300 based on single-cell transcriptome sequencing data includes:

[0081] The data quality control and cell screening module 310 is used to acquire single-cell transcriptome sequencing expression quantitative data and perform quality control, downstream analysis and visualization. The Grubbs test method is used to filter cells to obtain effective single-cell transcriptome sequencing quantitative data.

[0082] Clustering analysis module 320 is used to perform initial clustering analysis based on the single-cell transcriptome sequencing quantitative data to obtain single-cell subpopulation classification results;

[0083] The subgroup analysis module 330 is used to screen the single-cell subgroup classification results to obtain the target single-cell subgroup, and to perform further subgrouping and clustering analysis to obtain the single-cell subgroup classification results.

[0084] The differential gene screening and analysis module 340 is used to perform significant differential gene screening and analysis on the single-cell transcriptome sequencing quantitative data among single-cell subpopulations.

[0085] The cell cycle analysis module 350 is used for regression analysis based on characteristic genes of the cell cycle to predict the cell division phase corresponding to different cell types.

[0086] It should be noted that the device 300 can be used for application interaction of single-cell transcriptome sequencing quantitative data. By acquiring local experimental data (.h5 format), it can perform multiple data analyses, output the analysis results and save them in .rds format.

[0087] It should be understood that the division of the various modules in the above system is merely a logical functional division. In actual implementation, they can be fully or partially integrated into a single physical entity, or they can be physically separated. Furthermore, these modules can be implemented entirely in software via processing element calls; they can be fully implemented in hardware; or some modules can be implemented by processing element calls to software, while others are implemented in hardware. For example, the cell cycle analysis module 350 can be a separate processing element, or it can be integrated into a chip within the above system. Alternatively, it can be stored as program code in the system's memory, and its functions can be called and executed by a processing element within the system. The implementation of other modules is similar. Moreover, these modules can be fully or partially integrated together, or they can be implemented independently. The processing element mentioned here can be an integrated circuit with signal processing capabilities. During implementation, each step of the above method or each of the above modules can be completed through integrated logic circuits in the hardware of the processor element or through software instructions.

[0088] For example, these modules can be one or more integrated circuits configured to implement the above methods, such as one or more Application Specific Integrated Circuits (ASICs), one or more digital signal processors (DSPs), or one or more Field Programmable Gate Arrays (FPGAs). As another example, when a module is implemented using processing element scheduler code, the processing element can be a general-purpose processor, such as a Central Processing Unit (CPU) or other processor capable of calling program code. Furthermore, these modules can be integrated together to form a system-on-a-chip (SOC).

[0089] Based on the above embodiments, this application also provides a computer-readable storage medium storing a computer program, which, when executed by a computer, causes the computer to perform the methods described in the embodiments of this application.

[0090] The computer-readable and writable storage medium may include read-only memory (ROM), random access memory (RAM), EEPROM, CD-ROM or other optical disc storage devices, disk storage devices or other magnetic storage devices, flash memory, USB flash drive, portable hard drive, or any other medium capable of storing desired program code in the form of instructions or data structures and accessible by a computer. Additionally, any connection may be appropriately referred to as a computer-readable medium. For example, if instructions are transmitted from a website, server, or other remote source using coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of medium. However, it should be understood that computer-readable and writable storage media and data storage media do not include connections, carrier waves, signals, or other transient media, but are intended for non-transient, tangible storage media. The disks and optical discs used in the application include compact discs (CDs), laser discs, optical discs, digital multifunction discs (DVDs), floppy disks, and Blu-ray discs, where disks typically copy data magnetically, while optical discs use lasers to copy data optically.

[0091] For example, the computer-readable storage medium stores model programs, analysis method programs, and related data.

[0092] like Figure 4 The diagram shown illustrates the structure of a computer device 400 according to an embodiment of this application. The computer device 400 includes a memory 410 and a processor 420; the memory 410 stores computer instructions; the processor 420 executes the computer instructions to implement... Figure 1 The method described.

[0093] In some embodiments, the number of the memory 410 and the processor 420 in the computer device 400 can be one or more, while Figure 4 Each example is taken as an instance.

[0094] In one embodiment of this application, the processor 420 in the computer device 400 will perform as follows: Figure 1 The steps described involve loading one or more instructions corresponding to the process of an application into memory 410, and then having the processor 420 run the application stored in memory 410, thereby achieving the following: Figure 1 The method described.

[0095] The memory 410 may include random access memory (RAM) or non-volatile memory, such as at least one disk storage device. The memory 410 stores an operating system and operating instructions, executable modules or data structures, or subsets thereof, or extended sets thereof. The operating instructions may include various operation instructions used to implement various operations. The operating system may include various system programs used to implement various basic services and handle hardware-based tasks.

[0096] The processor 420 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0097] In some specific applications, the various components of the computer device 400 are coupled together through a bus system, which may include, in addition to a data bus, a power bus, a control bus, and a status signal bus, etc. However, for clarity, in... Figure 4 All kinds of buses are referred to as bus systems.

[0098] Based on the above embodiments, this application also provides a chip for reading computer programs stored in a memory to implement the methods described in the embodiments of this application.

[0099] Based on the above embodiments, this application provides a chip system including a processor for supporting a computer device in implementing the methods described in the embodiments of this application. In one possible design, the chip system further includes a memory for storing necessary programs and data of the computer device. This chip system may be composed of chips or may include chips and other discrete devices.

[0100] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0101] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to this application. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0102] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0103] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0104] In summary, this application discloses an analysis method, apparatus, and device based on single-cell transcriptome sequencing data, comprising: acquiring single-cell transcriptome sequencing expression quantitative data and performing quality control, downstream analysis, and visualization; using the Grubbs test to filter cells to obtain effective single-cell transcriptome sequencing quantitative data; performing initial clustering analysis based on the single-cell transcriptome sequencing quantitative data to obtain single-cell subpopulation classification results; screening the single-cell subpopulation classification results to obtain target single-cell subpopulations, and performing further clustering analysis to obtain single-cell sub-subpopulation classification results; performing significant differential gene screening analysis on the single-cell transcriptome sequencing quantitative data among single-cell subpopulations; and performing regression analysis based on characteristic genes of the cell cycle to predict the cell division phase corresponding to different cell types.

[0105] This application establishes an online, code-free platform for single-cell transcriptome sequencing quantification and data mining analysis. Users can analyze complex single-cell transcriptome sequencing data without specialized bioinformatics training and perform targeted data mining according to research objectives, significantly improving the progress of scientific research. This application improves and upgrades existing algorithms to enhance cell clustering effectiveness; effectively solves the technical complexity problem of single-cell transcriptome sequencing quantitative data analysis, making data analysis simpler and more reliable, reducing the difficulty of data analysis, optimizing quality control standards, improving standardization methods, enhancing feature gene screening strategies, improving centralization methods, optimizing the clustering results of single-cell data, and making inter-cell differences sparser and intra-cell differences smaller.

[0106] This application effectively overcomes the various shortcomings of the prior art and has high industrial application value.

[0107] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. An analysis method based on single-cell transcriptome sequencing data, characterized in that, The method includes: Single-cell transcriptome sequencing expression quantification data were obtained and subjected to quality control, downstream analysis and visualization. The Grubbs test was used to filter cells to obtain effective single-cell transcriptome sequencing quantification data. Initial clustering analysis was performed based on the quantitative data from the single-cell transcriptome sequencing to obtain the classification results of single-cell subpopulations; The single-cell subpopulation classification results are screened to obtain the target single-cell subpopulation, and then subjected to re-clustering analysis to obtain the single-cell subpopulation classification results. The single-cell subpopulation classification results are visualized using two nonlinear dimensionality reduction methods, t-SNE and UMAP. The quantitative data from the single-cell transcriptome sequencing were used to screen for significantly differentially expressed genes among single-cell subpopulations. Regression analysis based on characteristic genes of the cell cycle was used to predict the cell division phase corresponding to different cell types. The single-cell transcriptome sequencing quantitative data were subjected to initial clustering analysis, specifically including: 1) Data Standardization: Gene expression data were preprocessed using an optimized Relative Counts algorithm, which was scaled down. The expression for the optimized Relative Counts algorithm is as follows: ； in, This indicates the expression level of gene i in the corresponding cell; 2) Feature gene screening: Use the nearZeroVar function to remove feature genes with near-zero variance, in order to retain feature genes with significant changes; 3) Data Centralization: The preprocessed data is centralized using an optimized min-max normalization algorithm; the calculation formula for the optimized min-max normalization algorithm is as follows: ； Where max(x) represents the maximum value of the sample data; min(x) represents the minimum value of the sample data; 4) Principal Component Analysis: Principal component analysis is used to reduce the dimensionality of centered data, thereby reducing the number of variables; 5) Cell clustering: Cells are clustered and grouped based on graph algorithms; 6) Data visualization: The classification results of single-cell subpopulations are visualized using two non-linear dimensionality reduction methods, t-SNE and UMAP, and the cell populations are displayed on a two-dimensional plane.

2. The analysis method based on single-cell transcriptome sequencing data according to claim 1, characterized in that, The single-cell transcriptome sequencing data underwent quality control, and cell filtering was performed using the Grubbs test, specifically including: Calculate the expression ratio of mitochondrial genes and the number of genes detected in each cell, and perform quality control according to preset indicators; Statistical methods were used to depict the distribution and detection of mitochondrial genes, and the Grubbs test was used to filter the gene detection results by iteratively removing outliers.

3. The analysis method based on single-cell transcriptome sequencing data according to claim 1, characterized in that, After classifying single-cell subpopulations based on the quantitative data from single-cell transcriptome sequencing, subpopulation screening analysis can be performed, specifically including: Different subpopulation adjustment parameters are set, and the target subpopulation number is selected based on the corresponding subpopulation changes under different subpopulation adjustment parameters.

4. The analysis method based on single-cell transcriptome sequencing data according to claim 1, characterized in that, The quantitative transcriptome sequencing data of the single cells were used to perform differential gene screening analysis among single cell subpopulations, specifically including: Significantly different genes were screened by performing a significance test on the genes among single-cell subsets and combining the fold difference in the average gene expression levels among single-cell subsets. The screening criteria for the significantly differentially expressed genes are as follows: 1) Significance level threshold P-value ≤ 0.05; 2) Average difference multiple avg log2FC≤0.

25.

5. An analysis device based on single-cell transcriptome sequencing data, characterized in that, The method for analyzing data based on single-cell transcriptome sequencing data as described in any one of claims 1 to 4; the analysis apparatus includes: The data quality control and cell screening module is used to acquire single-cell transcriptome sequencing expression quantitative data and perform quality control, downstream analysis and visualization. The Grubbs test method is used to filter cells to obtain effective single-cell transcriptome sequencing quantitative data. The clustering analysis module is used to perform initial clustering analysis based on the single-cell transcriptome sequencing quantitative data to obtain single-cell subpopulation classification results; the sub-subpopulation analysis module is used to screen the single-cell subpopulation classification results to obtain target single-cell subpopulations, and perform further clustering analysis on them to obtain single-cell sub-subpopulation classification results. The differential gene screening and analysis module is used to perform significant differential gene screening and analysis on the quantitative data of the single-cell transcriptome sequencing among single-cell subpopulations. The cell cycle analysis module is used to perform regression analysis based on characteristic genes of the cell cycle to predict the cell division phase corresponding to different cell types.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when run on a computer, causes the computer to perform the method as described in any one of claims 1 to 4.

7. A computer device, characterized in that, The device includes: a memory and a processor; The memory is used to store a computer program; the processor is used to execute the computer program stored in the memory to cause the device to perform the method as described in any one of claims 1 to 4.

Images