Stem cell experimental data analysis method and system

Abstract

The invention relates to the technical field of cell data processing and analysis, in particular to a stem cell experimental data analysis method and system. The method comprises the following steps: acquiring an original experimental data set of stem cells; performing standardization processing on the stem cell original experiment data set to obtain stem cell experiment data; performing multi-dimensional feature recognition on the stem cell experimental data to obtain stem cell multi-dimensional feature data; performing stem cell multi-dimensional differentiation information extraction on the stem cell multi-dimensional feature data to obtain stem cell multi-dimensional differentiation information; and performing differentiation marker identification on the multi-dimensional differentiation information of the stem cells to obtain differentiation marker characteristics. Through a data processing technology, a pattern recognition technology and a machine learning technology, the differentiation process gene expression state comprehensive analysis of the stem cell multi-dimensional feature information is realized, so that the gene expression condition of the stem cell in a specific period can be completely presented.

Description

Technical Field

[0001] The present invention relates to the technical field of cell data processing and analysis, and in particular to a stem cell experimental data analysis method and system. Background Art

[0002] Stem cell sequencing technology has developed a variety of methods based on different cell capture and cell library establishment strategies, and with the gradual deepening of stem cell throughput research, it is possible to process hundreds or even thousands of cells; for example, droplet stem cell sequencing and indexed droplet sequencing technologies improve the sequencing throughput by combining droplets and unique barcodes; in the sequencing data analysis stage, the mixed cell population method is used to analyze stem cell experimental data; in particular, through unbiased clustering and differential gene expression analysis methods, different cell types and subpopulations and their marker genes are identified from stem cell experimental data sets. However, existing stem cell experimental data analysis methods are insufficient in studying the state of stem cell differentiation process, especially when studying the specific period of stem cell differentiation, the gene expression of the differentiation process is not analyzed based on the multidimensional characteristic information of stem cells, which makes it difficult to fully present the gene expression of stem cells in specific periods. Summary of the invention

[0003] Based on this, it is necessary to provide a stem cell experimental data analysis method and system to solve at least one of the above technical problems.

[0004] To achieve the above object, a stem cell experimental data analysis method is provided, the method comprising the following steps:

[0005] Step S1: obtaining an original experimental data set of stem cells; performing standardization processing on the original experimental data set of stem cells to obtain experimental data of stem cells; performing multidimensional feature recognition on the experimental data of stem cells to obtain multidimensional feature data of stem cells; performing multidimensional differentiation information extraction on the multidimensional feature data of stem cells to obtain multidimensional differentiation information of stem cells;

[0006] Step S2: performing differentiation marker identification on the multidimensional differentiation information of stem cells to obtain differentiation marker characteristics; performing chromatin feature marking on the multidimensional differentiation information of stem cells based on the differentiation marker characteristics to generate chromatin feature data; performing chromatin accessibility detection on the chromatin feature data to obtain chromatin accessibility data;

[0007] Step S3: performing cell expression feature detection on the multidimensional differentiation information of stem cells to obtain cell expression feature data; performing gene function analysis on the cell expression feature data to obtain stem cell gene function data; dividing the stem cell experimental data into stem cell growth cycles based on the stem cell gene function data to generate stem cell growth cycle data; performing transcription factor change footprint tracking on the stem cell growth cycle data to generate transcription factor change footprint information;

[0008] Step S4: Based on the transcription factor change footprint information, the intermediate state of stem cell differentiation is identified for the multidimensional differentiation information of stem cells to generate the intermediate state information of stem cell differentiation; according to the chromatin accessibility data, the gene expression changes during the differentiation process of the intermediate state information of stem cell differentiation are analyzed to obtain the gene expression data of the differentiation process; the gene expression data of the differentiation process is analyzed and visualized to generate a stem cell differentiation expression report.

[0009] The present invention obtains stem cell experimental data by obtaining the original experimental data set of stem cells and performing standardization processing, which helps to eliminate the deviation and noise in the data and improve the accuracy of subsequent analysis; multidimensional feature recognition and stem cell multidimensional differentiation information extraction enable a more comprehensive understanding of the differentiation process of stem cells, and provide an accurate data basis for subsequent differentiation marker identification and chromatin feature labeling. Differentiation marker identification is performed on the multidimensional differentiation information of stem cells to obtain differentiation marker characteristics, which helps to identify key molecules in the differentiation process of stem cells and provide data indicators for the study of stem cell differentiation mechanism; chromatin feature labeling and chromatin accessibility detection can reveal the changes in chromatin structure during stem cell differentiation, and then understand gene expression regulation and cell cycle conditions. Cell expression feature detection and gene function analysis can deeply understand the gene expression pattern and functional changes in the process of stem cell differentiation, and provide data support for the molecular mechanism of stem cell differentiation; stem cell growth cycle division and transcription factor change footprint tracking help to reveal the dynamic changes of key transcription factors in the process of stem cell differentiation, so as to understand the regulation of cell differentiation. The identification of intermediate states of stem cell differentiation can reveal the intermediate states in the process of stem cell differentiation, making it clear that the intermediate state characteristics of stem cell differentiation; the analysis of gene expression changes in the differentiation process can describe in detail the dynamic changes of gene expression in the process of stem cell differentiation, and provide detailed data for the molecular regulation of stem cell differentiation; the visualization of stem cell differentiation expression reports makes complex data information more intuitive and easy to understand, which is convenient for researchers to quickly grasp the key information of stem cell differentiation. Therefore, the present invention uses data processing technology, pattern recognition technology and machine learning technology to realize the comprehensive analysis of gene expression status in the differentiation process of multidimensional characteristic information of stem cells, so that the gene expression of stem cells in a specific period of time can be fully presented.

[0010] Preferably, step S2 comprises the following steps:

[0011] Step S21: performing feature enhancement on the multidimensional differentiation information of stem cells to obtain multidimensional differentiation enhancement features; performing differentiation marker feature recognition on the multidimensional differentiation enhancement features to obtain differentiation marker features;

[0012] Step S22: performing marker type identification on the differentiation marker characteristics to obtain differentiation marker type data; performing stem cell differentiation region detection on the stem cell multidimensional differentiation information based on the differentiation marker type data to generate stem cell differentiation region information;

[0013] Step S23: performing regional chromatin marking on the stem cell differentiation region information to obtain regional chromatin marking information; performing chromatin feature extraction on the stem cell differentiation region information according to the regional chromatin marking information to obtain chromatin feature data;

[0014] Step S24: Perform chromatin accessibility detection on the chromatin feature data to obtain chromatin accessibility data.

[0015] The present invention performs feature enhancement on multidimensional differentiation information of stem cells, which can highlight the key information in the process of stem cell differentiation and enhance the discrimination and information content of the data; differentiation marker type identification can distinguish different categories of differentiation markers; stem cell differentiation region detection based on differentiation marker type data is helpful to accurately locate the key region of stem cell differentiation; regional chromatin markers can provide chromatin-level markers for stem cell differentiation region information, and chromatin feature extraction guided by regional chromatin marker information can reveal changes in chromatin structure in stem cell differentiation regions; chromatin accessibility detection can evaluate chromatin open regions in chromatin feature data, reflecting active regions of gene expression regulation, and helping to identify key transcription factor binding sites that regulate stem cell differentiation.

[0016] Preferably, step S24 includes the following steps:

[0017] Step S241: performing chromosome edge detection on the chromatin feature data to obtain chromosome edge data; performing chromosome contour recognition on the chromosome edge data to generate chromosome contour information;

[0018] Step S242: determining the chromosome structure of the chromatin feature data according to the chromosome outline information to obtain the chromosome structure feature; performing chromosome morphology detection on the chromosome structure feature to generate chromosome morphology information;

[0019] Step S243: performing chromosome state recognition on the chromosome morphology information to obtain chromosome state data; extracting chromatin state parameters from the multidimensional differentiation information of stem cells based on the chromosome state data to generate chromatin state parameters;

[0020] Step S244: performing cell nucleosome positioning identification on the multidimensional differentiation information of stem cells to obtain cell nucleosome features; performing cell nucleosome parameter extraction on the cell nucleosome features to generate cell nucleosome parameters;

[0021] Step S245: Calculate the occupancy degree of cell nucleosome parameters and chromatin body parameters to obtain chromatin occupancy degree data; classify the chromatin occupancy degree data by chromatin accessibility to obtain chromatin accessibility data.

[0022] The chromosome edge detection and chromosome outline recognition of the present invention enhance the spatial structural understanding of chromatin feature data and provide morphological data for further chromosome structure analysis; chromosome structure determination and morphological detection based on chromosome outline information help to deeply understand the spatial organization and functional state of chromosomes; chromosome state recognition and chromatin body parameter extraction enable the extraction of key chromatin structure parameters from multidimensional differentiation information of stem cells; cell nucleosome positioning recognition and parameter extraction provide accurate distribution characteristics of cell nucleosomes on chromatin, thereby understanding gene expression regulation and chromatin dynamic changes; by calculating the occupancy degree of cell nucleosome parameters and chromatin body parameters, and classifying chromatin accessibility, the relationship between the physical state of chromatin and gene expression regulation is revealed, providing key information for the gene regulatory network analysis of stem cell differentiation.

[0023] Preferably, step S3 comprises the following steps:

[0024] Step S31: performing cell expression feature detection on the multidimensional differentiation information of stem cells to obtain cell expression feature data;

[0025] Step S32: performing gene function analysis on the cell expression feature data to obtain stem cell gene function data;

[0026] Step S33: performing gene function characterization characteristics recognition on stem cell gene function data to obtain gene function characterization characteristics; performing characterization characteristic time series division on the gene function characterization characteristics to generate characterization characteristic time series division data;

[0027] Step S34: dividing the stem cell experimental data into stem cell growth cycles according to the characteristic time series division data to obtain stem cell growth cycle data;

[0028] Step S35: Tracking the transcription factor change footprints of stem cell growth cycle data to generate transcription factor change footprint information.

[0029] The cell expression feature detection of the present invention can accurately capture the changes in gene expression during stem cell differentiation, providing basic data for understanding the molecular mechanism of stem cell differentiation; gene function analysis further resolves the gene function in the cell expression feature data, and reveals the role and interaction of genes in the stem cell differentiation process; gene function characterization feature identification and characterization feature time series division provide time series information for the dynamic changes of gene function during stem cell differentiation; stem cell growth cycle division based on characterization feature time series division data helps to identify the key stages of stem cell differentiation, and provides important time nodes for further biological research and application; transcription factor change footprint tracking can monitor the dynamic changes of transcription factors in the stem cell growth cycle, thereby revealing changes in the stem cell differentiation process.

[0030] Preferably, step S31 includes the following steps:

[0031] Step S311: performing single cell feature recognition on the multidimensional differentiation information of stem cells to obtain single cell feature data;

[0032] Step S312: extracting protein information from the single-cell characteristic data to obtain single-cell protein information; calculating the protein ratio of the single-cell protein information to generate single-cell protein ratio data; estimating the stem cell protein abundance based on the single-cell protein ratio data to obtain the stem cell protein abundance;

[0033] Step S313: performing single-cell metabolic feature recognition on the multidimensional differentiation information of stem cells to obtain single-cell metabolic features; performing single-cell metabolic quantity calculation on the single-cell metabolic features to generate single-cell metabolic quantity; and evaluating the degree of stem cell metabolism based on the single-cell metabolic quantity to obtain the degree of stem cell metabolism;

[0034] Step S314: merging the cell expression characteristics of stem cell protein abundance and stem cell metabolism degree to obtain cell expression characteristic data.

[0035] The present invention provides individual cell characteristics and physical property data of stem cells during the differentiation process through single-cell feature recognition and single-cell volume parameter extraction; protein information extraction and protein proportion measurement reveal the protein composition and relative abundance at the single-cell level, and stem cell protein abundance estimation provides quantitative protein expression information for evaluating the differentiation state of stem cells; single-cell metabolic feature recognition and metabolic quantity calculation provide key indicators for evaluating the energy metabolism state and differentiation potential of stem cells, and stem cell metabolic degree assessment helps to identify metabolic changes during stem cell differentiation; cell expression features combine and integrate protein abundance and metabolic degree information, so as to reflect the molecular and metabolic state during stem cell differentiation, and provide comprehensive data for the study of stem cell differentiation mechanism.

[0036] Preferably, step S32 includes the following steps:

[0037] Step S321: performing protein abundance expression feature recognition on the protein abundance of stem cells to obtain protein abundance expression features; performing time series expression parameter extraction on the protein abundance expression features to obtain protein time series expression parameters;

[0038] Step S322: determining the protein expression level parameters of the cell expression feature data according to the protein temporal expression parameters to generate protein expression level change data; mapping the cell expression feature data to gene expression activity functions based on the protein expression level change data to obtain gene expression activity function data;

[0039] Step S323: performing metabolic type identification on the metabolic degree of stem cells to obtain metabolic type data of stem cells; performing estimation of the amount of each metabolic type based on the metabolic type data of stem cells to generate the number of each type of metabolism; performing metabolic feature enrichment statistics on the number of each type of metabolism to obtain metabolic feature enrichment data;

[0040] Step S324: performing enrichment expression feature annotation on the metabolic feature enrichment data to obtain stem cell enrichment feature annotation data; performing gene regulation function matching on the cell expression feature data based on the stem cell enrichment feature annotation data to obtain gene regulation function data;

[0041] Step S325: Perform gene function integration on the gene expression activity function data and the gene regulation function data to obtain stem cell gene function data.

[0042] The protein abundance expression feature identification and time series expression parameter extraction of the present invention can reveal the changing pattern of protein expression over time, and provide quantitative parameters for understanding the dynamic changes of proteins during stem cell differentiation; protein expression level parameter determination and gene expression activity function mapping can link the changes in protein expression levels with gene activity, and reveal the role of genes in stem cell differentiation; stem cell metabolic type identification and metabolic feature enrichment statistics can identify and quantify the main metabolic pathways in the process of stem cell differentiation, and provide information for understanding stem cell metabolic regulation; stem cell enrichment feature annotation and gene regulatory function matching can link metabolic features with gene regulatory networks, thereby revealing the impact of metabolic changes on gene expression; gene function integration combines gene expression activity and gene regulatory function data, thereby providing a comprehensive perspective for a comprehensive understanding of the gene regulatory network in the process of stem cell differentiation.

[0043] Preferably, step S35 includes the following steps:

[0044] Step S351: extracting single-cell transcription factor features from multi-dimensional differentiation information of stem cells to obtain single-cell transcription factor features; locating the position of stem cell transcription factors according to the single-cell transcription factor features to generate transcription factor position information;

[0045] Step S352: segmenting the transcription factor position information into overlapping chromatin segments to obtain overlapping chromatin segment data; identifying the transcription factor binding sites on the overlapping chromatin segment data to generate transcription factor binding site data;

[0046] Step S353: extracting the periodic transcription factor expression amount from the stem cell growth cycle data according to the transcription factor binding point data to obtain the periodic transcription factor expression amount; performing periodic node expression amount statistics on the periodic transcription factor expression amount to generate the periodic node transcription factor expression amount;

[0047] Step S354: evaluating the transcription factor expression change state based on the transcription factor expression amount at the periodic node to obtain the transcription factor expression level state; detecting the transcription factor expression level state change in the stem cell region where the transcription factor is located to obtain the transcription factor region state change data;

[0048] Step S355: performing stem cell transcription factor expression footprint identification on the transcription factor region state change data to generate transcription factor expression footprint data; tracking the changes of stem cell transcription factors based on the transcription factor expression footprint data to obtain transcription factor change footprint information.

[0049] The present invention can accurately identify the active areas of transcription factors in stem cells through single-cell transcription factor feature extraction and transcription factor position positioning, and provide spatial information for understanding the role of transcription factors in stem cell differentiation; chromatin overlapping segment segmentation and transcription factor binding site identification can reveal the binding mode of transcription factors on chromatin; periodic transcription factor expression extraction and periodic node expression statistics can capture the dynamic changes of transcription factors in the stem cell growth cycle, and provide quantitative data for analyzing the role of transcription factors in the stem cell differentiation cycle; transcription factor expression change state evaluation and transcription factor regional state change detection can monitor the expression and position changes of transcription factors during stem cell differentiation, which is helpful to reveal the role of transcription factors in stem cell regional state changes; stem cell transcription factor expression footprint identification and transcription factor change tracking can obtain transcription factor expression footprint data, thereby providing a detailed record for tracking the dynamic changes of transcription factors during stem cell differentiation.

[0050] Preferably, step S4 comprises the following steps:

[0051] Step S41: measuring the duration of the change state of the transcription factor change footprint information to obtain the duration of the change state; performing cycle time mapping on the duration of the change state according to the stem cell growth cycle data to generate a cycle change state time; dividing the cycle change state time into cycle time periods to obtain cycle time period division data;

[0052] Step S42: performing a period expression mean detection on the transcription factor change footprint information according to the period division data to obtain the period expression mean of the transcription factor; marking the period division data with the mean mid-period point based on the period expression mean of the transcription factor to generate the mean mid-period point marking information;

[0053] Step S43: positioning the intermediate state of stem cell differentiation based on the mean intermediate time point mark information of the stem cell multidimensional differentiation information to generate the intermediate state information of stem cell differentiation;

[0054] Step S44: analyzing gene expression changes during differentiation of stem cell differentiation intermediate states according to the chromatin accessibility data to obtain gene expression data during differentiation;

[0055] Step S45: Visualize and graph the gene expression data of the differentiation process to obtain a gene expression graph of the differentiation process; compile a visualized report of the gene expression graph of the differentiation process to obtain a stem cell differentiation expression report.

[0056] The present invention can quantify the duration of transcription factor changes through changing state duration measurement and cycle time mapping, and provides a time frame for analyzing the dynamic changes of transcription factor activities during the stem cell growth cycle; the time period expression mean detection and mean intermediate time period point marking can identify the typical pattern and key time points of transcription factor expression, and the mean intermediate time period point marking information provides an intermediate time period reference for further analysis of transcription factor activities during stem cell differentiation; the positioning of stem cell differentiation intermediate states can clarify the key intermediate states in the stem cell differentiation process, which helps to understand the continuity and stage characteristics of the differentiation process; the analysis of gene expression changes during the differentiation process can reveal the gene expression pattern of the intermediate state of stem cell differentiation, and provide detailed information for studying the molecular mechanism of stem cell differentiation; the visualization and charting of gene expression data during the differentiation process and the compilation of reports make complex gene expression data more intuitive and easy to understand, and the stem cell differentiation expression report provides researchers with a clear view of the differentiation process.

[0057] Preferably, step S44 includes the following steps:

[0058] Step S441: classifying chromatin regions according to the chromatin accessibility data to generate chromatin region classification data; identifying chromatin expression states on the chromatin region classification data to obtain chromatin expression state characteristics;

[0059] Step S442: performing chromatin differentiation state matching on the stem cell differentiation intermediate state information according to the chromatin expression state characteristics to generate chromatin differentiation intermediate state data;

[0060] Step S443: extracting the chromatin intermediate state accessible parameters from the chromatin differentiation intermediate state data to obtain the chromatin intermediate state accessible parameters; sorting the chromatin intermediate state accessible parameters by parameter quantity to generate accessible parameter sorting information;

[0061] Step S444: Arrange the accessible parameter sorting information in descending order to obtain parameter descending order data; perform stem cell gene quantity mapping extraction on the parameter descending order data to obtain stem cell gene quantity data; perform gene expression level measurement on the stem cell gene quantity data to obtain gene expression level data;

[0062] Step S445: performing expression level change pattern recognition on the gene expression level data to obtain the gene expression level change pattern; performing differentiation process gene expression change determination on the gene expression level change pattern to obtain differentiation process gene expression data.

[0063] The present invention can distinguish the functional states of different chromatin regions through chromatin region classification and chromatin expression state recognition; chromatin differentiation state matching can correspond the chromatin state to the intermediate state of stem cell differentiation, and reveal the changes in chromatin state during stem cell differentiation; chromatin intermediate state accessible parameter extraction and parameter quantity sorting can identify the key parameters of chromatin accessibility, and provide a quantitative basis for analyzing chromatin state changes; parameter descending order arrangement and stem cell gene quantity mapping extraction can highlight key areas with high chromatin accessibility, and further provide quantitative information for evaluating gene activity; gene expression level change pattern recognition and differentiation process gene expression change determination can reveal the changing rules of gene expression during stem cell differentiation, and the obtained differentiation process gene expression data provides important information for understanding the molecular regulatory mechanism of stem cell differentiation.

[0064] In this specification, a stem cell experiment data analysis system is provided, which is used to perform the above-mentioned stem cell experiment data analysis method. The stem cell experiment data analysis system includes:

[0065] The stem cell experiment data acquisition module is used to obtain the original stem cell experiment data set; standardize the original stem cell experiment data set to obtain stem cell experiment data; perform multi-dimensional feature recognition on the stem cell experiment data to obtain stem cell multi-dimensional feature data; extract stem cell multi-dimensional differentiation information from the stem cell multi-dimensional feature data to obtain stem cell multi-dimensional differentiation information;

[0066] The chromatin accessibility detection module is used to identify differentiation markers for multidimensional differentiation information of stem cells to obtain differentiation marker characteristics; perform chromatin feature marking on multidimensional differentiation information of stem cells based on differentiation marker characteristics to generate chromatin feature data; perform chromatin accessibility detection on the chromatin feature data to obtain chromatin accessibility data;

[0067] The transcription factor change footprint tracking module is used to detect cell expression characteristics of stem cell multidimensional differentiation information to obtain cell expression characteristic data; perform gene function analysis on cell expression characteristic data to obtain stem cell gene function data; divide stem cell experimental data into stem cell growth cycles based on stem cell gene function data to generate stem cell growth cycle data; perform transcription factor change footprint tracking on stem cell growth cycle data to generate transcription factor change footprint information;

[0068] The stem cell gene expression change analysis module is used to identify the intermediate states of stem cell differentiation based on the transcription factor change footprint information of stem cell multidimensional differentiation information, and generate stem cell differentiation intermediate state information; analyze the gene expression changes in the differentiation process of the stem cell differentiation intermediate state information according to the chromatin accessibility data to obtain the gene expression data of the differentiation process; and visualize the analysis report of the gene expression data of the differentiation process to generate a stem cell differentiation expression report.

[0069] The present invention obtains the original experimental data set of stem cells and performs standardization through the stem cell experimental data acquisition module to obtain stem cell experimental data, which helps to eliminate the deviation and noise in the data and improve the accuracy of subsequent analysis; multidimensional feature recognition and stem cell multidimensional differentiation information extraction enable a more comprehensive understanding of the differentiation process of stem cells, and provide an accurate data basis for subsequent differentiation marker identification and chromatin feature marking. Through the chromatin accessibility detection module, differentiation markers are identified for the multidimensional differentiation information of stem cells to obtain differentiation marker features, which helps to identify key molecules in the differentiation process of stem cells and provide data indicators for the study of stem cell differentiation mechanism; chromatin feature marking and chromatin accessibility detection can reveal the changes in chromatin structure during stem cell differentiation, and then understand gene expression regulation and cell cycle conditions. Through the transcription factor change footprint tracking module, it is used for cell expression feature detection and gene function analysis to deeply understand the gene expression pattern and functional changes in the differentiation process of stem cells, and provide data support for the molecular mechanism of stem cell differentiation; stem cell growth cycle division and transcription factor change footprint tracking help to reveal the dynamic changes of key transcription factors in the differentiation process of stem cells, so as to understand the regulation of cell differentiation. Through the stem cell gene expression change analysis module, the intermediate state identification of stem cell differentiation can reveal the intermediate state in the process of stem cell differentiation, so as to clarify the intermediate state characteristics of stem cell differentiation; the analysis of gene expression changes in the differentiation process can describe in detail the dynamic changes of gene expression in the process of stem cell differentiation, and provide detailed data for the molecular regulation of stem cell differentiation; the visualization of stem cell differentiation expression report makes complex data information more intuitive and easy to understand, which is convenient for researchers to quickly grasp the key information of stem cell differentiation. Therefore, the present invention realizes the comprehensive analysis of gene expression state in the differentiation process of stem cell multidimensional feature information through data processing technology, pattern recognition technology and machine learning technology, so that the gene expression of stem cells in a specific period of time can be fully presented. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 A schematic diagram of the steps of a stem cell experimental data analysis method;

[0071] Figure 2 for Figure 1 Detailed implementation steps of step S4 in FIG.

[0072] Figure 3 for Figure 2 Detailed implementation steps of step S44 are shown in the flowchart;

[0073] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0074] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0075] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0076] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0077] To achieve this, please refer to Figures 1 to 3 , a stem cell experimental data analysis method, the method comprising the following steps:

[0078] Step S1: obtaining an original experimental data set of stem cells; performing standardization processing on the original experimental data set of stem cells to obtain experimental data of stem cells; performing multidimensional feature recognition on the experimental data of stem cells to obtain multidimensional feature data of stem cells; performing multidimensional differentiation information extraction on the multidimensional feature data of stem cells to obtain multidimensional differentiation information of stem cells;

[0079] Step S2: performing differentiation marker identification on the multidimensional differentiation information of stem cells to obtain differentiation marker characteristics; performing chromatin feature marking on the multidimensional differentiation information of stem cells based on the differentiation marker characteristics to generate chromatin feature data; performing chromatin accessibility detection on the chromatin feature data to obtain chromatin accessibility data;

[0080] Step S3: performing cell expression feature detection on the multidimensional differentiation information of stem cells to obtain cell expression feature data; performing gene function analysis on the cell expression feature data to obtain stem cell gene function data; dividing the stem cell experimental data into stem cell growth cycles based on the stem cell gene function data to generate stem cell growth cycle data; performing transcription factor change footprint tracking on the stem cell growth cycle data to generate transcription factor change footprint information;

[0081] Step S4: Based on the transcription factor change footprint information, the intermediate state of stem cell differentiation is identified for the multidimensional differentiation information of stem cells to generate the intermediate state information of stem cell differentiation; according to the chromatin accessibility data, the gene expression changes during the differentiation process of the intermediate state information of stem cell differentiation are analyzed to obtain the gene expression data of the differentiation process; the gene expression data of the differentiation process is analyzed and visualized to generate a stem cell differentiation expression report.

[0082] The present invention obtains stem cell experimental data by obtaining the original experimental data set of stem cells and performing standardization processing, which helps to eliminate the deviation and noise in the data and improve the accuracy of subsequent analysis; multidimensional feature recognition and stem cell multidimensional differentiation information extraction enable a more comprehensive understanding of the differentiation process of stem cells, and provide an accurate data basis for subsequent differentiation marker identification and chromatin feature labeling. Differentiation marker identification is performed on the multidimensional differentiation information of stem cells to obtain differentiation marker characteristics, which helps to identify key molecules in the differentiation process of stem cells and provide data indicators for the study of stem cell differentiation mechanism; chromatin feature labeling and chromatin accessibility detection can reveal the changes in chromatin structure during stem cell differentiation, and then understand gene expression regulation and cell cycle conditions. Cell expression feature detection and gene function analysis can deeply understand the gene expression pattern and functional changes in the process of stem cell differentiation, and provide data support for the molecular mechanism of stem cell differentiation; stem cell growth cycle division and transcription factor change footprint tracking help to reveal the dynamic changes of key transcription factors in the process of stem cell differentiation, so as to understand the regulation of cell differentiation. The identification of intermediate states of stem cell differentiation can reveal the intermediate states in the process of stem cell differentiation, making it clear that the intermediate state characteristics of stem cell differentiation; the analysis of gene expression changes in the differentiation process can describe in detail the dynamic changes of gene expression in the process of stem cell differentiation, and provide detailed data for the molecular regulation of stem cell differentiation; the visualization of stem cell differentiation expression reports makes complex data information more intuitive and easy to understand, which is convenient for researchers to quickly grasp the key information of stem cell differentiation. Therefore, the present invention uses data processing technology, pattern recognition technology and machine learning technology to realize the comprehensive analysis of gene expression status in the differentiation process of multidimensional characteristic information of stem cells, so that the gene expression of stem cells in a specific period of time can be fully presented.

[0083] In the embodiment of the present invention, reference Figure 1FIG. 1 is a schematic diagram of a step flow chart of a stem cell experiment data analysis method of the present invention. In this example, the stem cell experiment data analysis method includes the following steps:

[0084] Step S1: obtaining an original experimental data set of stem cells; performing standardization processing on the original experimental data set of stem cells to obtain experimental data of stem cells; performing multidimensional feature recognition on the experimental data of stem cells to obtain multidimensional feature data of stem cells; performing multidimensional differentiation information extraction on the multidimensional feature data of stem cells to obtain multidimensional differentiation information of stem cells;

[0085] In the embodiment of the present invention, a stem cell original experimental data set is obtained from a specific bioinformatics database through a data interface, and the stem cell original experimental data set is standardized, specifically using a Z-score standardization method; for example, for a stem cell data set containing gene expression, in a Python compilation environment, the StandardScaler class in the scikit-learn library is used to perform a Z-score standardization method; multidimensional feature recognition is performed on the stem cell experimental data, and a principal component analysis (PCA) method is used to convert the data into a new coordinate system through an orthogonal transformation, so that the first largest variance of any projection of the data is on the first coordinate (the first principal component), the second largest variance is on the second coordinate, and so on. ; In the specific Python compilation environment, the PCA class in the scikit-learn library is used to implement this process; first, the standardized data is divided into a training set and a test set, and then the StandardScaler is used for normalization. Then, the fit_transform method of the PCA object is used to map the data to the principal component space to obtain a multidimensional feature data set; a support vector machine (SVM) is used to extract the multidimensional differentiation information of stem cells from the multidimensional feature data set. Specifically, the SVM is used to find the best hyperplane to distinguish different categories. In the Python compilation environment, the predict method is used to classify the multidimensional feature data set to obtain the multidimensional differentiation information of stem cells, which is recorded as the differentiation information data set.

[0086] Step S2: performing differentiation marker identification on the multidimensional differentiation information of stem cells to obtain differentiation marker characteristics; performing chromatin feature marking on the multidimensional differentiation information of stem cells based on the differentiation marker characteristics to generate chromatin feature data; performing chromatin accessibility detection on the chromatin feature data to obtain chromatin accessibility data;

[0087] In an embodiment of the present invention, the identification of differentiation markers of multidimensional differentiation information of stem cells is achieved by integrating machine learning methods. Specifically, the support vector machine (SVM) algorithm is applied to compare the expression data of stem cells in different differentiation states to identify key genes or protein markers that can distinguish different differentiation states. The core of the SVM algorithm is to find a hyperplane that maximizes the separation of data points of different categories, that is, to maximize the interval between two types of data points; for example, through the SVM algorithm, genes with significant expression changes during the differentiation process are screened out from the gene expression data of stem cells. These genes are used as differentiation markers for subsequent chromatin feature marking; chromatin feature marking is achieved by ChIP-seq technology. ChIP-seq is a high-throughput sequencing technology used to study protein-DNA interactions. The specific steps include: formaldehyde cross-linking the entire cell line (tissue) to link the target protein to chromatin; isolating genomic DNA and breaking it into small fragments of a certain length with ultrasound; adding antibodies specific to the target protein to form an immunoprecipitation immune binding complex; removing the cross-linking and purifying the DNA to obtain a chromatin immunoprecipitated DNA sample for sequencing; deep sequencing the prepared samples; for example, for the identified differentiation markers, antibodies against these markers can be designed, and the binding sites of these markers on chromatin can be determined through ChIP-seq experiments to generate chromatin feature data; chromatin accessibility detection can be achieved through a variety of technologies, including DNase-seq, FAIRE-seq, MNase-seq and ATAC-seq. Taking ATAC-seq as an example, this technology uses Tn5 transposase to cut and insert sequencing adapters into cellular DNA, thereby mapping super-accessible regions; the specific steps of ATAC-seq include cell lysis to release nuclei, nuclei are digested with DNase I, DNase I-digested DNA is embedded in low-melting point agarose plugs to reduce additional random shearing, DNA (still in the plug) is blunt-ended, extracted and connected to biotin-labeled linker 1, excess linkers are removed by gel purification, biotin-labeled fragments (linker 1 plus 20 genomic DNA bases) are digested with MmeI and captured by streptavidin-coated Dynal beads, linker 2 is connected to the 2-base overhang generated by MmeI, amplified by PCR and sequenced by Illumina / Solexa; in actual operation, ATAC-seq experiments are first performed on stem cells, and then quantitative analysis is performed using specialized data analysis software (such as New Element celescope_ATAC) to determine the open areas of chromatin. The DNA sequences in these open regions are more easily bound by transcription factors or other regulatory proteins, thereby affecting gene expression and cell differentiation status.

[0088] Step S3: performing cell expression feature detection on the multidimensional differentiation information of stem cells to obtain cell expression feature data; performing gene function analysis on the cell expression feature data to obtain stem cell gene function data; dividing the stem cell experimental data into stem cell growth cycles based on the stem cell gene function data to generate stem cell growth cycle data; performing transcription factor change footprint tracking on the stem cell growth cycle data to generate transcription factor change footprint information;

[0089] In the embodiments of the present invention, RNA-seq technology is generally used for detecting cell expression characteristics; specifically, images of the cell differentiation process are collected in real time by live cell imaging technology; machine learning technology is used to accurately identify the differentiated cell state from bright field images at different stages, and the induction efficiency of induced differentiation of pluripotent stem cells into cardiomyocytes is non-invasively monitored in real time; and special data analysis software (such as HTSeq or featureCounts) is used to count reads to obtain cell expression feature data; RNA-seq technology is used to perform quality control on the cell expression feature data, remove low-quality reads, and then use alignment tools (such as HISAT2) to align the reads to the reference genome; DESeq2 or edgeR and other packages are used to perform differentially expressed gene analysis to identify genes whose expression levels change significantly during stem cell differentiation; Gene Ontology (GO) and KEGG pathway analysis tools such as DAVID or clusterProfiler are used. R package, functional enrichment analysis of differentially expressed genes was performed to determine the biological functions of these genes and the metabolic pathways involved; GSEA software was used to evaluate the change pattern of gene expression in specific biological processes through preset gene sets, thereby identifying the core gene set; by analyzing the expression changes of cell cycle-related genes, such as cyclin-dependent kinases (CDKs) and cell cycle proteins (Cyclins), and identifying the expression peaks of these genes, the growth cycle of stem cells was divided; using the known knowledge of cell cycle checkpoints, such as R point and S point, combined with the expression data of cell cycle proteins and CDKs, the expression peaks of cell cycle proteins and CDKs were determined Various stages of the cell cycle; using high-content imaging technology, such as FUCCI (CA) probes, combined with TruAI deep learning technology, to perform long-term dynamic monitoring of the G1, S and G2 phases of single embryonic stem cells (ESCs), evaluate the movement trajectory of each cell, and obtain quantitative dynamic information on cell cycle changes at the single-cell level; using dynamic module software, such as scanR, to analyze the collected cell cycle data, establish a mathematical model to simulate the dynamic changes of the cell cycle, and thus more accurately divide the growth cycle of stem cells; tracking of transcription factor changes can be achieved through ChIP-seq technology. The ChIP-seq experimental process includes cell fixation, chromatin shearing, antibody immunoprecipitation, DNA reverse crosslinking and purification, construction of sequencing libraries and high-throughput sequencing; using Bowtie2 or BWA to align sequences to the reference genome and generate BAM files. BWA's BWA-MEM algorithm is suitable for high-quality long-read sequences, while Bowtie2 is suitable for data analysis of large genomes; peak recognition uses the MACS2 tool, and control samples are used to correct background noise. MACS2 generates a score after sorting and normalizing the signal to indicate the intensity or significance of the peak. By comparing stem cells at different differentiation stages, it tracks the changes in transcription factor binding sites and generates transcription factor change footprint information.

[0090] Step S4: Based on the transcription factor change footprint information, the intermediate state of stem cell differentiation is identified for the multidimensional differentiation information of stem cells to generate the intermediate state information of stem cell differentiation; according to the chromatin accessibility data, the gene expression changes during the differentiation process of the intermediate state information of stem cell differentiation are analyzed to obtain the gene expression data of the differentiation process; the gene expression data of the differentiation process is analyzed and visualized to generate a stem cell differentiation expression report.

[0091] In an embodiment of the present invention, cell analysis technology is used, specifically Signac and Seurat packages are used to perform footprint analysis on single-cell data; first, Motif information needs to be added to the analysis object, including the precise location of each Motif; for example, the JASPAR2020 database is used to obtain the binding motif of the transcription factor, and then the TFBSTools package is used to perform footprint analysis to identify changes in the transcription factor binding sites; by comparing the changes in transcription factor binding sites in stem cells at different differentiation stages, the intermediate state of stem cell differentiation is identified; ATAC-seq data is used to identify the activity of transcription factors, which is corrected by ATACorrect, ScoreBigwig identifies the transcription factor footprint region, and BINDetect is used to bind the transcription factor to obtain the activity of each transcription factor; the gene expression data obtained by RNA-seq technology is used to perform differentially expressed gene analysis by software such as DESeq2 or edgeR to identify genes whose expression levels change significantly during stem cell differentiation; ggplot2 is used to draw scatter plots and bar graphs to display the relationship between genes and logarithmic change folds (Fold Change); use the limma package to perform linear model analysis to analyze the effects of different conditions on gene expression; perform principal component analysis (PCA) and draw a dual principal component score graph to show the distribution of gene expression data in the two principal component directions; draw a volcano map for gene expression data analysis, with Fold Change as the x-axis and negative logarithmic P value (-Log10 P-value) as the y-axis to distinguish significant and non-significant genes; use the pheatmap package to draw a heat map of gene expression data, annotate colors according to conditions, and show the gene expression pattern and the similarity between samples; draw a box plot to show the distribution of gene expression under different conditions, compare the expression differences between the two groups, and finally generate a stem cell differentiation expression report.

[0092] Preferably, step S2 comprises the following steps:

[0093] Step S21: performing feature enhancement on the multidimensional differentiation information of stem cells to obtain multidimensional differentiation enhancement features; performing differentiation marker feature recognition on the multidimensional differentiation enhancement features to obtain differentiation marker features;

[0094] Step S22: performing marker type identification on the differentiation marker characteristics to obtain differentiation marker type data; performing stem cell differentiation region detection on the stem cell multidimensional differentiation information based on the differentiation marker type data to generate stem cell differentiation region information;

[0095] Step S23: performing regional chromatin marking on the stem cell differentiation region information to obtain regional chromatin marking information; performing chromatin feature extraction on the stem cell differentiation region information according to the regional chromatin marking information to obtain chromatin feature data;

[0096] Step S24: Perform chromatin accessibility detection on the chromatin feature data to obtain chromatin accessibility data.

[0097] In the embodiment of the present invention, the kNN (k-Nearest Neighbors) algorithm is used for feature enhancement. By calculating the distance between each sample and its k nearest neighbor samples, the relative position relationship of the sample in the feature space is captured, thereby generating an enhanced feature vector; the support vector machine (SVM) machine learning technology is used to analyze the multidimensional differentiation enhancement features to identify key genes or protein markers that can distinguish different differentiation states; by analyzing the expression pattern and function of the differentiation markers, the markers are divided into different types using K-means or hierarchical clustering algorithms to obtain differentiation marker type data; the scNanoSeq-CUT&Tag technology is used in combination with single-molecule sequencing technology to detect chromatin modifications in complex regions of the single-cell genome, identify key regions in the stem cell differentiation process, and generate stem cell differentiation region information; Using scNanoSeq-CUT&Tag technology, precise chromatin modification detection of stem cell differentiation regions was performed to obtain regional chromatin mark information; based on the regional chromatin mark information, chromatin features were extracted, including the distribution patterns of histone modifications and chromatin-binding proteins, which reflect the specific state and function of chromatin; using ATAC-seq technology, the sequencing adapters were cut and inserted into the cell DNA by Tn5 transposase, and a map of super-accessible regions was drawn to obtain chromatin accessibility data; using MACS2 data analysis software, ATAC-seq data were peak-identified and analyzed to determine the open regions of chromatin. The DNA sequences in these regions are more easily bound by transcription factors or other regulatory proteins, thereby affecting gene expression and the differentiation state of cells.

[0098] Preferably, step S24 includes the following steps:

[0099] Step S241: performing chromosome edge detection on the chromatin feature data to obtain chromosome edge data; performing chromosome contour recognition on the chromosome edge data to generate chromosome contour information;

[0100] Step S242: determining the chromosome structure of the chromatin feature data according to the chromosome outline information to obtain the chromosome structure feature; performing chromosome morphology detection on the chromosome structure feature to generate chromosome morphology information;

[0101] Step S243: performing chromosome state recognition on the chromosome morphology information to obtain chromosome state data; extracting chromatin state parameters from the multidimensional differentiation information of stem cells based on the chromosome state data to generate chromatin state parameters;

[0102] Step S244: performing cell nucleosome positioning identification on the multidimensional differentiation information of stem cells to obtain cell nucleosome features; performing cell nucleosome parameter extraction on the cell nucleosome features to generate cell nucleosome parameters;

[0103] Step S245: Calculate the occupancy degree of cell nucleosome parameters and chromatin body parameters to obtain chromatin occupancy degree data; classify the chromatin occupancy degree data by chromatin accessibility to obtain chromatin accessibility data.

[0104] In an embodiment of the present invention, three-dimensional chromatin interaction data generated by Hi-C technology is used to identify chromosome edges through the SDOC algorithm; the SDOC algorithm calculates the spatial density of the chromatin region and identifies topologically associated domains (TADs) whose SDOC index significantly decreases during the differentiation process, thereby obtaining chromosome edge data; based on the chromosome edge data, the DTCI (developmental TAD clustering index) indicator is used to measure the degree of change in the contour of the gradual aggregation of TADs in space, thereby generating chromosome contour information; according to the chromosome contour information, the chromosome structural characteristics are determined by analyzing the three-dimensional structure of chromatin; specifically, the chromosome structural characteristics are obtained by analyzing the chromatin interaction pattern in the Hi-C data; the chromosome structural characteristics are analyzed, and a genome browser such as IGV or UCSC Genome Browser observes the morphological changes of chromosomes and generates chromosome morphological information; uses chromatin state annotation tools, such as ChromHMM, to identify different chromatin states based on chromatin marks and gene expression data; extracts chromatin body parameters, such as chromatin openness and histone modification levels, based on chromosome state data; uses MNase-seq technology to digest chromatin with micrococcal nuclease (MNase) to identify nucleosome positioning, and is used to detect nucleosome positioning and nucleosome occupancy; extracts cell nucleosome parameters, such as nucleosome occupancy and DNA methylation status, based on MNase-seq data; calculates the occupancy degree of cell nucleosome parameters and chromatin body parameters, and the specific calculation formula is: Among them, O represents the degree of chromatin occupancy; N represents the nucleosome occupied area; C represents the chromatin open area; T represents the total chromatin area; specifically, the nucleosome occupied area refers to the nucleosome positioning area identified by MNase-seq technology; the chromatin open area refers to the chromatin open area identified by ATAC-seq technology; the total chromatin area refers to the sum of the chromatin areas considered in the entire genome. According to the chromatin occupancy data, the MACS2 software is used to classify chromatin accessibility; specifically, MACS2 is used to identify open chromatin areas, and the DNA sequences in these areas are easily bound by transcription factors or other regulatory proteins, thereby affecting gene expression and cell differentiation.

[0105] Preferably, step S3 comprises the following steps:

[0106] Step S31: performing cell expression feature detection on the multidimensional differentiation information of stem cells to obtain cell expression feature data;

[0107] Step S32: performing gene function analysis on the cell expression feature data to obtain stem cell gene function data;

[0108] Step S33: performing gene function characterization characteristics recognition on stem cell gene function data to obtain gene function characterization characteristics; performing characterization characteristic time series division on the gene function characterization characteristics to generate characterization characteristic time series division data;

[0109] Step S34: dividing the stem cell experimental data into stem cell growth cycles according to the characteristic time series division data to obtain stem cell growth cycle data;

[0110] Step S35: Tracking the transcription factor change footprints of stem cell growth cycle data to generate transcription factor change footprint information.

[0111] In an embodiment of the present invention, the gene expression level of stem cells at different differentiation stages is detected by RNA-seq technology using the Illumina NovaSeq high-throughput sequencing platform; first, total RNA is extracted from the stem cell sample, and a cDNA library is constructed. Then, the read segments obtained by sequencing are aligned to the reference genome using the HISAT2 alignment tool to ensure the accuracy and efficiency of the alignment; then, HTSeq or featureCounts is used to count the read segments to obtain the expression level of each gene to form a cell expression feature data set; functional enrichment analysis is performed using gene expression data in combination with gene ontology (GO) and KEGG pathway analysis tools, such as DAVID or clusterProfiler R packages; GSEA analysis is used to identify core gene sets in specific biological processes, thereby obtaining a stem cell gene function data set; by analyzing gene expression patterns and functions, genes are divided into different functional characterization types using the K-means clustering algorithm to obtain gene function characterization characteristics; then, time series analysis methods, such as Dynamic Time Warping (Dynamic Time Warping) algorithm is used to divide the gene function characterization characteristics into time series and generate characterization characteristic time series division data; based on the characterization characteristic time series division data, combined with the expression changes of cell cycle-related genes, such as CDKs and Cyclins, the stem cell growth cycle is divided to generate a stem cell growth cycle data set; ChIP-seq technology is used, especially in combination with ATAC-seq technology, to track the changes in transcription factor binding sites in the stem cell growth cycle data; by comparing stem cells at different differentiation stages, the changes in transcription factor binding sites are tracked to generate transcription factor change footprint information; MACS2 tool is used for peak recognition to determine the open areas of chromatin. The DNA sequences in these areas are easily bound by transcription factors or other regulatory proteins, thereby affecting gene expression and cell differentiation status.

[0112] Preferably, step S31 includes the following steps:

[0113] Step S311: performing single cell feature recognition on the multidimensional differentiation information of stem cells to obtain single cell feature data;

[0114] Step S312: extracting protein information from the single-cell characteristic data to obtain single-cell protein information; calculating the protein ratio of the single-cell protein information to generate single-cell protein ratio data; estimating the stem cell protein abundance based on the single-cell protein ratio data to obtain the stem cell protein abundance;

[0115] Step S313: performing single-cell metabolic feature recognition on the multidimensional differentiation information of stem cells to obtain single-cell metabolic features; performing single-cell metabolic quantity calculation on the single-cell metabolic features to generate single-cell metabolic quantity; and evaluating the degree of stem cell metabolism based on the single-cell metabolic quantity to obtain the degree of stem cell metabolism;

[0116] Step S314: merging the cell expression characteristics of stem cell protein abundance and stem cell metabolism degree to obtain cell expression characteristic data.

[0117] In the embodiment of the present invention, single-cell sequencing technology, such as the 10X Genomics platform, is used to detect single-cell gene expression of stem cells at different differentiation stages to obtain single-cell characteristic data; flow cytometry technology is used in combination with fluorescently labeled antibodies to measure single-cell metabolic profiles to obtain single-cell volume parameters; protein enzymatic hydrolysis and mass spectrometry analysis are performed on single cells by a single-cell proteomics analysis method based on mass spectrometry to obtain single-cell protein information; protein proportion is calculated for single-cell protein information, and the specific calculation formula is: Among them, R represents the protein ratio; C p Indicates the content of a specific protein; C sumRepresents the sum of all protein contents; by mass spectrometry, all proteins in a single cell are enzymatically hydrolyzed and analyzed, and the signal intensities of all identified proteins are accumulated to obtain the sum of all protein contents; for example, by using capillary electrophoresis-nanoelastic spray-high-resolution mass spectrometry, a detection limit as low as 700zmol is obtained, and about 800 proteins are identified in 5ng of protein hydrolysate; by mass spectrometry, the signal intensity of a specific protein in a single cell is measured to obtain the content of the specific protein. The SCENITH method was used to measure the overall cellular energy metabolism level by measuring the PS level and identify the metabolic characteristics of single cells. The mathematical evaluation method was used to estimate the protein abundance of stem cells based on the single-cell protein ratio data to obtain the stem cell protein abundance. Droplet microextraction combined with pico-electrospray ionization (Pico-ESI) mass spectrometry workflow was used to obtain more than 600 MS2 metabolite information from single cells. This method can identify more than 300 phospholipid molecules, revealing that the ratio of unsaturated to saturated phosphatidylcholines (PCs) in glioblastoma cells is significantly higher than that in normal cells. Mass spectrometry techniques such as MALDI imaging mass spectrometry combined with fluorescence signal The single-cell, high-throughput, in situ metabolome analysis was achieved by combining the number and morphological space characteristics; the R package scMetabolism was used to infer the flux of intracellular metabolites from single-cell RNA sequencing data, thereby calculating the single-cell metabolic activation score; the scFEA tool was used to infer the flux of intracellular metabolites from scRNA-seq data; the degree of stem cell metabolism was assessed based on the number of single-cell metabolism to obtain the degree of stem cell metabolism; the stem cell protein abundance and stem cell metabolism degree were integrated, and the Seurat object creation and data merging methods were used, specifically the merge function in the Seurat package, to merge the data of multiple samples to obtain cell expression feature data.

[0118] Preferably, step S32 includes the following steps:

[0119] Step S321: performing protein abundance expression feature recognition on the protein abundance of stem cells to obtain protein abundance expression features; performing time series expression parameter extraction on the protein abundance expression features to obtain protein time series expression parameters;

[0120] Step S322: determining the protein expression level parameters of the cell expression feature data according to the protein temporal expression parameters to generate protein expression level change data; mapping the cell expression feature data to gene expression activity functions based on the protein expression level change data to obtain gene expression activity function data;

[0121] Step S323: performing metabolic type identification on the metabolic degree of stem cells to obtain metabolic type data of stem cells; performing estimation of the amount of each metabolic type based on the metabolic type data of stem cells to generate the number of each type of metabolism; performing metabolic feature enrichment statistics on the number of each type of metabolism to obtain metabolic feature enrichment data;

[0122] Step S324: performing enrichment expression feature annotation on the metabolic feature enrichment data to obtain stem cell enrichment feature annotation data; performing gene regulation function matching on the cell expression feature data based on the stem cell enrichment feature annotation data to obtain gene regulation function data;

[0123] Step S325: Perform gene function integration on the gene expression activity function data and the gene regulation function data to obtain stem cell gene function data.

[0124] In an embodiment of the present invention, a mass spectrometry technique, such as liquid chromatography-tandem mass spectrometry (LC-MS / MS), is used to analyze the single-cell proteome and identify the protein abundance expression characteristics. For example, by using a droplet-loaded chip technology at a nanoliter air-oil interface, cell lysis, enzymatic hydrolysis and other processes are completed in a droplet, and analyzed by nanoLC-MS / MS, thereby obtaining protein abundance expression characteristics; a time series analysis method, such as a dynamic time warping algorithm, is used to perform a time series analysis on the protein abundance expression characteristics and extract protein time series expression parameters; and specifically process the expansion and contraction changes in the time series data, and record the time series applicable to the single-cell proteome data; according to the protein time series expression parameters, a statistical method such as linear regression or a nonlinear model is used to determine the change trend of the protein expression level and generate protein expression level change data. For example, by comparing the protein abundance of embryonic stem cells and in vitro differentiated motor neurons, differentially expressed proteins can be identified; using gene expression data, combined with protein expression level change data, by methods such as gene set enrichment analysis (GSEA), the gene expression activity function is mapped to obtain gene expression activity function data. For example, by comparing the differences between gene expression levels and protein abundance, information about post-transcriptional regulation, such as changes in mRNA stability and translation efficiency, can be provided; by single-cell metabolomics analysis, such as MALDI imaging mass spectrometry, stem cell metabolic types can be identified to obtain stem cell metabolic type data; for example, single-cell detection can be performed using the SpaceM platform in combination with MALDI-MS and optical microscopy to generate a spatial molecular matrix for each cell, which contains standardized metabolic features and microscope-derived phenotypic attributes; based on stem cell metabolic type data, mass spectrometry technology is used to perform quantitative analysis of metabolites, estimate the amount of each metabolic type, and generate the number of each type of metabolism; enrichment statistics are performed on the number of each type of metabolism, and metabolic pathway analysis tools such as MetaboAnalyst are used to obtain metabolic feature enrichment data; using metabolic feature enrichment data, by enriching expression feature annotation, stem cell enrichment feature annotation data is obtained; based on stem cell enrichment feature annotation data, combined with gene expression data, gene regulatory network analysis tools such as GeneMANIA are used to match gene regulatory functions to obtain gene regulatory function data; gene expression activity function data and gene regulatory function data are integrated, and data fusion technology is used to obtain stem cell gene function data.

[0125] Preferably, step S35 includes the following steps:

[0126] Step S351: extracting single-cell transcription factor features from multi-dimensional differentiation information of stem cells to obtain single-cell transcription factor features; locating the position of stem cell transcription factors according to the single-cell transcription factor features to generate transcription factor position information;

[0127] Step S352: segmenting the transcription factor position information into overlapping chromatin segments to obtain overlapping chromatin segment data; identifying the transcription factor binding sites on the overlapping chromatin segment data to generate transcription factor binding site data;

[0128] Step S353: extracting the periodic transcription factor expression amount from the stem cell growth cycle data according to the transcription factor binding point data to obtain the periodic transcription factor expression amount; performing periodic node expression amount statistics on the periodic transcription factor expression amount to generate the periodic node transcription factor expression amount;

[0129] Step S354: evaluating the transcription factor expression change state based on the transcription factor expression amount at the periodic node to obtain the transcription factor expression level state; detecting the transcription factor expression level state change in the stem cell region where the transcription factor is located to obtain the transcription factor region state change data;

[0130] Step S355: performing stem cell transcription factor expression footprint identification on the transcription factor region state change data to generate transcription factor expression footprint data; tracking the changes of stem cell transcription factors based on the transcription factor expression footprint data to obtain transcription factor change footprint information.

[0131] In an embodiment of the present invention, single-cell transcriptome sequencing technology (scRNA-seq) is used in combination with bioinformatics tools such as SIMBA to embed single cells and their characteristics (such as genes, chromatin accessible regions, and transcription factor binding sequences) into a common latent space, thereby extracting single-cell transcription factor characteristics; transcription factors with phase separation ability are identified in embryonic stem cells (mESCs) through ACC-Seq technology. These transcription factors are called dual-action transcription factors (dual-action TFs), including ETV5; based on the single-cell transcription factor characteristics, CUT&RUN technology is used to detect protein-DNA interactions on a genome-wide scale to determine the binding sites of transcription factors on the genome and generate transcription factor position information; the transcription factor position information is analyzed using bioinformatics tools such as ChromHMM; the chromatin state is annotated, and the chromatin overlapping segments are identified through the transition of the chromatin state to obtain the chromatin overlapping segment data; the chromatin overlapping segment data is further analyzed using SELEX technology or Protein Binding Microarray (PBM) technology is used to identify transcription factor binding sites; based on the transcription factor binding site data, combined with the single-cell transcriptome data, the expression levels of transcription factors related to the stem cell growth cycle are extracted; statistical analysis is performed on the expression levels of cycle transcription factors, and the key nodes in the stem cell differentiation process are determined using lineage tracing technology; based on the expression levels of transcription factors at cycle nodes, Regulon regulator analysis is used to evaluate the changing state of transcription factor expression levels; the state of transcription factor expression levels is analyzed, and the changes in the state of the stem cell region where the transcription factors are located are detected using CUT&Tag technology; the data on changes in the regional state of transcription factors are analyzed, and the LARRY barcoding library is used to identify the expression footprint of stem cell transcription factors; based on the transcription factor expression footprint data, single-cell multi-omics technology is used to track the changes in stem cell transcription factors and obtain transcription factor change footprint information.

[0132] As an example of the present invention, refer to Figure 2 As shown, in this example, step S4 includes:

[0133] Step S41: measuring the duration of the change state of the transcription factor change footprint information to obtain the duration of the change state; performing cycle time mapping on the duration of the change state according to the stem cell growth cycle data to generate a cycle change state time; dividing the cycle change state time into cycle time periods to obtain cycle time period division data;

[0134] Step S42: performing a period expression mean detection on the transcription factor change footprint information according to the period division data to obtain the period expression mean of the transcription factor; marking the period division data with the mean mid-period point based on the period expression mean of the transcription factor to generate the mean mid-period point marking information;

[0135] Step S43: positioning the intermediate state of stem cell differentiation based on the mean intermediate time point mark information of the stem cell multidimensional differentiation information to generate the intermediate state information of stem cell differentiation;

[0136] Step S44: analyzing gene expression changes during differentiation of stem cell differentiation intermediate states according to the chromatin accessibility data to obtain gene expression data during differentiation;

[0137] Step S45: Visualize and graph the gene expression data of the differentiation process to obtain a gene expression graph of the differentiation process; compile a visualized report of the gene expression graph of the differentiation process to obtain a stem cell differentiation expression report.

[0138] In the embodiment of the present invention, the single-cell multi-omics technology HiRES is used to detect the transcriptome and the three-dimensional genome, and the duration of the change state of the transcription factor is determined by analyzing the changes in the binding sites of the transcription factors; specifically, according to the stem cell growth cycle data, the flow cytometer technology is used in combination with DNA binding dyes (such as propidium iodide) to mark the DNA content, and cells in different cell cycle phases are identified, so that the duration of the change state is mapped to the corresponding cell cycle time, and the cycle change state time is generated; the cycle change state time is analyzed, and the imaging-based fluorescence detection method (such as FUCCI technology) is used to observe the cell cycle Different stages are formed, and the period division is carried out to obtain the period division data; according to the period division data, the single-cell transcriptome sequencing technology (scRNA-seq) is used to detect the expression of transcription factors in different periods, and the period expression mean is calculated to obtain the period expression mean of transcription factors; specifically, the scatter plot and principal component analysis (PCA) are used to visualize the transcription factor expression data to show the expression changes in different periods; based on the transcription factor period expression mean, the period division data is analyzed, and the mean mid-period point is determined by linear model analysis, and the mean mid-period point marking information is generated; using LARRY The barcoding library is used for precise lineage tracing, and pseudo-time analysis is achieved in combination with time flow to locate the intermediate states of stem cell differentiation. Based on the chromatin accessibility data, the open regions on the chromatin are detected using ATAC-seq technology, and the changes in gene expression during the differentiation process are further analyzed to obtain gene expression data of the differentiation process. The gene expression data of the differentiation process are visualized and graphed, and gene expression heat maps are used to display gene expression levels, such as red for high expression and blue for low expression. Genes or samples with similar expression patterns are clustered together through cluster analysis to form cluster blocks in the heat map. Based on the gene expression chart of the differentiation process, a visual report is compiled to integrate gene expression patterns, sample information and other relevant data to explain the patterns and relationships in the heat map to obtain a stem cell differentiation expression report.

[0139] As an example of the present invention, refer to Figure 3 As shown, in this example, step S44 includes:

[0140] Step S441: classifying chromatin regions according to the chromatin accessibility data to generate chromatin region classification data; identifying chromatin expression states on the chromatin region classification data to obtain chromatin expression state characteristics;

[0141] Step S442: performing chromatin differentiation state matching on the stem cell differentiation intermediate state information according to the chromatin expression state characteristics to generate chromatin differentiation intermediate state data;

[0142] Step S443: extracting the chromatin intermediate state accessible parameters from the chromatin differentiation intermediate state data to obtain the chromatin intermediate state accessible parameters; sorting the chromatin intermediate state accessible parameters by parameter quantity to generate accessible parameter sorting information;

[0143] Step S444: Arrange the accessible parameter sorting information in descending order to obtain parameter descending order data; perform stem cell gene quantity mapping extraction on the parameter descending order data to obtain stem cell gene quantity data; perform gene expression level measurement on the stem cell gene quantity data to obtain gene expression level data;

[0144] Step S445: performing expression level change pattern recognition on the gene expression level data to obtain the gene expression level change pattern; performing differentiation process gene expression change determination on the gene expression level change pattern to obtain differentiation process gene expression data.

[0145] In an embodiment of the present invention, ATAC-seq technology is used to directly insert sequencing adapters into exposed regions of DNA through Tn5 transposase to generate chromatin region classification data; the ChromHMM tool is used to identify and characterize chromatin states based on a hidden Markov model (HMM); specifically, the pattern of chromatin states is automatically learned from ChIP-seq data, and chromatin expression state characteristics associated with different gene expression activities, histone modifications, and DNA binding proteins are identified; according to the chromatin expression state characteristics, chromatin differentiation state matching is performed on intermediate state information of stem cell differentiation; specific stages of stem cells in the differentiation process are identified by analyzing changes in chromatin states; chromatin differentiation intermediate state data are analyzed to extract chromatin intermediate state accessible parameters; specifically, MNase-seq technology is used to draw super-accessible regions or nucleosome positions according to enzyme dosage; the chromatin intermediate state accessible parameters are sorted by parameter quantity to generate accessible parameter sorting information, specifically by identifying each The accessibility parameter values ​​of the chromatin region are sorted in descending order for the accessible parameter sorting information to obtain the parameter descending order data, and the sorted parameters are processed by programming scripts to identify the most important chromatin regions; stem cell gene quantity mapping and extraction are performed on the parameter descending order data, and the influence of specific chromatin regions on gene expression is identified by combining the chromatin accessibility data with the gene expression data; gene expression levels are calculated for the stem cell gene quantity data, and this is achieved by quantitative RT-PCR or RNA-seq technology to measure the expression levels of specific genes; gene expression level data are analyzed to identify the pattern of gene expression level changes; by comparing gene expression data at different differentiation stages, statistical methods such as t-test or ANOVA are used to identify significant changes in expression levels; based on the gene expression level change pattern, the gene expression data of the differentiation process is determined; by linking gene expression changes with specific stages of stem cell differentiation, the role of gene expression in the stem cell differentiation process is revealed.

[0146] In this specification, a stem cell experiment data analysis system is provided, which is used to perform the above-mentioned stem cell experiment data analysis method. The stem cell experiment data analysis system includes:

[0147] The stem cell experiment data acquisition module is used to obtain the original stem cell experiment data set; standardize the original stem cell experiment data set to obtain stem cell experiment data; perform multi-dimensional feature recognition on the stem cell experiment data to obtain stem cell multi-dimensional feature data; extract stem cell multi-dimensional differentiation information from the stem cell multi-dimensional feature data to obtain stem cell multi-dimensional differentiation information;

[0148] The chromatin accessibility detection module is used to identify differentiation markers for multidimensional differentiation information of stem cells to obtain differentiation marker characteristics; perform chromatin feature marking on multidimensional differentiation information of stem cells based on differentiation marker characteristics to generate chromatin feature data; perform chromatin accessibility detection on the chromatin feature data to obtain chromatin accessibility data;

[0149] The transcription factor change footprint tracking module is used to detect cell expression characteristics of stem cell multidimensional differentiation information to obtain cell expression characteristic data; perform gene function analysis on cell expression characteristic data to obtain stem cell gene function data; divide stem cell experimental data into stem cell growth cycles based on stem cell gene function data to generate stem cell growth cycle data; perform transcription factor change footprint tracking on stem cell growth cycle data to generate transcription factor change footprint information;

[0150] The stem cell gene expression change analysis module is used to identify the intermediate states of stem cell differentiation based on the transcription factor change footprint information of stem cell multidimensional differentiation information, and generate stem cell differentiation intermediate state information; analyze the gene expression changes in the differentiation process of the stem cell differentiation intermediate state information according to the chromatin accessibility data to obtain the gene expression data of the differentiation process; and visualize the analysis report of the gene expression data of the differentiation process to generate a stem cell differentiation expression report.

[0151] The present invention obtains the original experimental data set of stem cells and performs standardization through the stem cell experimental data acquisition module to obtain stem cell experimental data, which helps to eliminate the deviation and noise in the data and improve the accuracy of subsequent analysis; multidimensional feature recognition and stem cell multidimensional differentiation information extraction enable a more comprehensive understanding of the differentiation process of stem cells, and provide an accurate data basis for subsequent differentiation marker identification and chromatin feature marking. Through the chromatin accessibility detection module, differentiation markers are identified for the multidimensional differentiation information of stem cells to obtain differentiation marker features, which helps to identify key molecules in the differentiation process of stem cells and provide data indicators for the study of stem cell differentiation mechanism; chromatin feature marking and chromatin accessibility detection can reveal the changes in chromatin structure during stem cell differentiation, and then understand gene expression regulation and cell cycle conditions. Through the transcription factor change footprint tracking module, it is used for cell expression feature detection and gene function analysis to deeply understand the gene expression pattern and functional changes in the differentiation process of stem cells, and provide data support for the molecular mechanism of stem cell differentiation; stem cell growth cycle division and transcription factor change footprint tracking help to reveal the dynamic changes of key transcription factors in the differentiation process of stem cells, so as to understand the regulation of cell differentiation. Through the stem cell gene expression change analysis module, the intermediate state identification of stem cell differentiation can reveal the intermediate state in the process of stem cell differentiation, so as to clarify the intermediate state characteristics of stem cell differentiation; the analysis of gene expression changes in the differentiation process can describe in detail the dynamic changes of gene expression in the process of stem cell differentiation, and provide detailed data for the molecular regulation of stem cell differentiation; the visualization of stem cell differentiation expression report makes complex data information more intuitive and easy to understand, which is convenient for researchers to quickly grasp the key information of stem cell differentiation. Therefore, the present invention realizes the comprehensive analysis of gene expression state in the differentiation process of stem cell multidimensional feature information through data processing technology, pattern recognition technology and machine learning technology, so that the gene expression of stem cells in a specific period of time can be fully presented.

[0152] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0153] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for analyzing stem cell experimental data, characterized in that: The following steps are involved: Step S1: obtaining an original experimental data set of stem cells; performing standardization processing on the original experimental data set of stem cells to obtain experimental data of stem cells; performing multidimensional feature recognition on the experimental data of stem cells to obtain multidimensional feature data of stem cells; performing multidimensional differentiation information extraction on the multidimensional feature data of stem cells to obtain multidimensional differentiation information of stem cells; Step S2: Identifying differentiation markers on the multidimensional differentiation information of stem cells to obtain differentiation marker characteristics; Based on the characteristics of differentiation markers, chromatin feature marking is performed on the multidimensional differentiation information of stem cells to generate chromatin feature data; chromatin accessibility detection is performed on the chromatin feature data to obtain chromatin accessibility data; Step S3: performing cell expression feature detection on the multidimensional differentiation information of stem cells to obtain cell expression feature data; performing gene function analysis on the cell expression feature data to obtain stem cell gene function data; dividing the stem cell experimental data into stem cell growth cycles based on the stem cell gene function data to generate stem cell growth cycle data; performing transcription factor change footprint tracking on the stem cell growth cycle data to generate transcription factor change footprint information; Step S4: Based on the transcription factor change footprint information, the stem cell multidimensional differentiation information is used to identify the intermediate state of stem cell differentiation, and the stem cell differentiation intermediate state information is generated; based on the chromatin accessibility data, the stem cell differentiation intermediate state information is used to analyze the gene expression changes during the differentiation process, and the differentiation process gene expression data is obtained; The gene expression data of the differentiation process is analyzed and reported visually to generate a stem cell differentiation expression report.

2. The stem cell experimental data analysis method according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: performing feature enhancement on the multidimensional differentiation information of stem cells to obtain multidimensional differentiation enhancement features; performing differentiation marker feature recognition on the multidimensional differentiation enhancement features to obtain differentiation marker features; Step S22: performing marker type identification on the differentiation marker characteristics to obtain differentiation marker type data; performing stem cell differentiation region detection on the stem cell multidimensional differentiation information based on the differentiation marker type data to generate stem cell differentiation region information; Step S23: performing regional chromatin marking on the stem cell differentiation region information to obtain regional chromatin marking information; performing chromatin feature extraction on the stem cell differentiation region information according to the regional chromatin marking information to obtain chromatin feature data; Step S24: Perform chromatin accessibility detection on the chromatin feature data to obtain chromatin accessibility data.

3. The stem cell experimental data analysis method according to claim 2, characterized in that: Step S24 includes the following steps: Step S241: performing chromosome edge detection on the chromatin feature data to obtain chromosome edge data; performing chromosome contour recognition on the chromosome edge data to generate chromosome contour information; Step S242: determining the chromosome structure of the chromatin feature data according to the chromosome outline information to obtain the chromosome structure feature; performing chromosome morphology detection on the chromosome structure feature to generate chromosome morphology information; Step S243: performing chromosome state recognition on the chromosome morphology information to obtain chromosome state data; extracting chromatin state parameters from the multidimensional differentiation information of stem cells based on the chromosome state data to generate chromatin state parameters; Step S244: performing cell nucleosome positioning identification on the multidimensional differentiation information of stem cells to obtain cell nucleosome features; performing cell nucleosome parameter extraction on the cell nucleosome features to generate cell nucleosome parameters; Step S245: Calculate the occupancy degree of cell nucleosome parameters and chromatin body parameters to obtain chromatin occupancy degree data; classify the chromatin occupancy degree data by chromatin accessibility to obtain chromatin accessibility data.

4. The stem cell experimental data analysis method according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: performing cell expression feature detection on the multidimensional differentiation information of stem cells to obtain cell expression feature data; Step S32: performing gene function analysis on the cell expression feature data to obtain stem cell gene function data; Step S33: performing gene function characterization characteristics recognition on stem cell gene function data to obtain gene function characterization characteristics; performing characterization characteristic time series division on the gene function characterization characteristics to generate characterization characteristic time series division data; Step S34: dividing the stem cell experimental data into stem cell growth cycles according to the characteristic time series division data to obtain stem cell growth cycle data; Step S35: Tracking the transcription factor change footprints of stem cell growth cycle data to generate transcription factor change footprint information.

5. The stem cell experimental data analysis method according to claim 4, characterized in that: Step S31 includes the following steps: Step S311: performing single cell feature recognition on the multidimensional differentiation information of stem cells to obtain single cell feature data; Step S312: extracting protein information from the single-cell characteristic data to obtain single-cell protein information; calculating the protein ratio of the single-cell protein information to generate single-cell protein ratio data; estimating the stem cell protein abundance based on the single-cell protein ratio data to obtain the stem cell protein abundance; Step S313: performing single-cell metabolic feature recognition on the multidimensional differentiation information of stem cells to obtain single-cell metabolic features; performing single-cell metabolic quantity calculation on the single-cell metabolic features to generate single-cell metabolic quantity; and evaluating the degree of stem cell metabolism based on the single-cell metabolic quantity to obtain the degree of stem cell metabolism; Step S314: merging the cell expression characteristics of stem cell protein abundance and stem cell metabolism degree to obtain cell expression characteristic data.

6. The stem cell experimental data analysis method according to claim 4, characterized in that: Step S32 includes the following steps: Step S321: performing protein abundance expression feature recognition on the protein abundance of stem cells to obtain protein abundance expression features; performing time series expression parameter extraction on the protein abundance expression features to obtain protein time series expression parameters; Step S322: determining the protein expression level parameters of the cell expression feature data according to the protein temporal expression parameters to generate protein expression level change data; mapping the cell expression feature data to gene expression activity functions based on the protein expression level change data to obtain gene expression activity function data; Step S323: performing metabolic type identification on the metabolic degree of stem cells to obtain metabolic type data of stem cells; performing estimation of the amount of each metabolic type based on the metabolic type data of stem cells to generate the number of each type of metabolism; performing metabolic feature enrichment statistics on the number of each type of metabolism to obtain metabolic feature enrichment data; Step S324: performing enrichment expression feature annotation on the metabolic feature enrichment data to obtain stem cell enrichment feature annotation data; performing gene regulation function matching on the cell expression feature data based on the stem cell enrichment feature annotation data to obtain gene regulation function data; Step S325: Perform gene function integration on the gene expression activity function data and the gene regulation function data to obtain stem cell gene function data.

7. The stem cell experimental data analysis method according to claim 4, characterized in that: Step S35 includes the following steps: Step S351: extracting single-cell transcription factor features from multi-dimensional differentiation information of stem cells to obtain single-cell transcription factor features; locating the position of stem cell transcription factors according to the single-cell transcription factor features to generate transcription factor position information; Step S352: segmenting the transcription factor position information into overlapping chromatin segments to obtain overlapping chromatin segment data; identifying the transcription factor binding sites on the overlapping chromatin segment data to generate transcription factor binding site data; Step S353: extracting the periodic transcription factor expression amount from the stem cell growth cycle data according to the transcription factor binding point data to obtain the periodic transcription factor expression amount; performing periodic node expression amount statistics on the periodic transcription factor expression amount to generate the periodic node transcription factor expression amount; Step S354: evaluating the transcription factor expression change state based on the transcription factor expression amount at the periodic node to obtain the transcription factor expression level state; detecting the transcription factor expression level state change in the stem cell region where the transcription factor is located to obtain the transcription factor region state change data; Step S355: performing stem cell transcription factor expression footprint identification on the transcription factor region state change data to generate transcription factor expression footprint data; tracking the changes of stem cell transcription factors based on the transcription factor expression footprint data to obtain transcription factor change footprint information.

8. The stem cell experimental data analysis method according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: measuring the duration of the change state of the transcription factor change footprint information to obtain the duration of the change state; performing cycle time mapping on the duration of the change state according to the stem cell growth cycle data to generate a cycle change state time; dividing the cycle change state time into cycle time periods to obtain cycle time period division data; Step S42: performing a period expression mean detection on the transcription factor change footprint information according to the period division data to obtain the period expression mean of the transcription factor; marking the period division data with the mean mid-period point based on the period expression mean of the transcription factor to generate the mean mid-period point marking information; Step S43: positioning the intermediate state of stem cell differentiation based on the mean intermediate time point mark information of the stem cell multidimensional differentiation information to generate the intermediate state information of stem cell differentiation; Step S44: analyzing gene expression changes during differentiation of stem cell differentiation intermediate states according to the chromatin accessibility data to obtain gene expression data during differentiation; Step S45: Visualize and graph the gene expression data of the differentiation process to obtain a gene expression graph of the differentiation process; compile a visualized report of the gene expression graph of the differentiation process to obtain a stem cell differentiation expression report.

9. The stem cell experimental data analysis method according to claim 8, characterized in that: Step S44 includes the following steps: Step S441: classifying chromatin regions according to the chromatin accessibility data to generate chromatin region classification data; identifying chromatin expression states on the chromatin region classification data to obtain chromatin expression state characteristics; Step S442: performing chromatin differentiation state matching on the stem cell differentiation intermediate state information according to the chromatin expression state characteristics to generate chromatin differentiation intermediate state data; Step S443: extracting the chromatin intermediate state accessible parameters from the chromatin differentiation intermediate state data to obtain the chromatin intermediate state accessible parameters; sorting the chromatin intermediate state accessible parameters by parameter quantity to generate accessible parameter sorting information; Step S444: Arrange the accessible parameter sorting information in descending order to obtain parameter descending order data; perform stem cell gene quantity mapping extraction on the parameter descending order data to obtain stem cell gene quantity data; perform gene expression level measurement on the stem cell gene quantity data to obtain gene expression level data; Step S445: performing expression level change pattern recognition on the gene expression level data to obtain the gene expression level change pattern; performing differentiation process gene expression change determination on the gene expression level change pattern to obtain differentiation process gene expression data.

10. A stem cell experiment data analysis system, characterized in that: Used to execute the stem cell experiment data analysis method as claimed in claim 1, the stem cell experiment data analysis system comprises: The stem cell experiment data acquisition module is used to obtain the original stem cell experiment data set; standardize the original stem cell experiment data set to obtain stem cell experiment data; perform multi-dimensional feature recognition on the stem cell experiment data to obtain stem cell multi-dimensional feature data; extract stem cell multi-dimensional differentiation information from the stem cell multi-dimensional feature data to obtain stem cell multi-dimensional differentiation information; The chromatin accessibility detection module is used to identify differentiation markers for multidimensional differentiation information of stem cells to obtain differentiation marker characteristics; perform chromatin feature marking on multidimensional differentiation information of stem cells based on differentiation marker characteristics to generate chromatin feature data; perform chromatin accessibility detection on the chromatin feature data to obtain chromatin accessibility data; The transcription factor change footprint tracking module is used to detect cell expression characteristics of stem cell multidimensional differentiation information to obtain cell expression characteristic data; perform gene function analysis on cell expression characteristic data to obtain stem cell gene function data; divide stem cell experimental data into stem cell growth cycles based on stem cell gene function data to generate stem cell growth cycle data; perform transcription factor change footprint tracking on stem cell growth cycle data to generate transcription factor change footprint information; The stem cell gene expression change analysis module is used to identify the intermediate states of stem cell differentiation based on the transcription factor change footprint information of stem cell multidimensional differentiation information, and generate stem cell differentiation intermediate state information; analyze the gene expression changes in the differentiation process of the stem cell differentiation intermediate state information according to the chromatin accessibility data to obtain the gene expression data of the differentiation process; and visualize the analysis report of the gene expression data of the differentiation process to generate a stem cell differentiation expression report.

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