Method and device for analyzing immune metabolism and AD (Alzheimer's disease) related genes by integrating multiple omics

Abstract

The invention discloses a method and a device for analyzing immune metabolism and AD (Alzheimer's Disease) related genes by integrating multiple omics. The method comprises the following steps: acquiring single cell sequencing data of an AD patient; acquiring GWAS data of immune cells and metabolites of the AD patient; screening out an independent single nucleotide polymorphism (SNPS); performing Mendel randomization causal inference to obtain causal-associated immune cell characteristics and metabolite factor characteristics; analyzing the causal-associated immune cells and metabolites, and identifying the immune cells and metabolites which have important influence on AD generation, so as to obtain a metabolic pathway which affects AD; obtaining gene expression data of expression differences of metabolic pathways in different cell populations; performing differential gene analysis to obtain a gene analysis result; and screening out the gene related to the AD disease. According to the method, key biomarkers related to the Alzheimer's disease are identified by using an advanced machine learning technology, and the risk of an individual in disease development is accurately predicted.

Description

TECHNICAL FIELD

[0001] The present application relates to the cross-application field of bioinformatics, genomics and machine learning in the biomedical field, and in particular to a method for comprehensive multi-omics analysis based on immune metabolism crosstalk and AD disease-related genes. BACKGROUND

[0002] Gene screening is a process of identifying key genes related to a specific biological phenotype (such as disease, trait, etc.) from massive gene data, which is of great significance for revealing biological mechanisms, disease diagnosis and therapeutic target discovery.

[0003] At present, gene screening methods include gene screening methods based on transcriptomics data, gene screening methods based on genomics data, gene screening methods based on proteomics data, and gene screening methods based on epigenomics data, etc.

[0004] The above-mentioned existing gene screening methods rely on single-omics data (such as GWAS or differential gene expression analysis), lack multi-dimensional data integration, and are difficult to analyze the correlation between gene function and metabolic pathways.

[0005] In addition, due to the lack of integration of multi-omics data (such as transcriptome and single-cell sequencing), key genes related to immune metabolism may be missed. SUMMARY

[0006] The present application provides a method for comprehensive multi-omics analysis based on immune metabolism crosstalk and AD disease-related genes. The method can integrate multi-omics data, analyze AD disease-related genes, and obtain immune cells and metabolites associated with AD.

[0007] In a first aspect, a method for comprehensive multi-omics analysis of immune metabolism and AD disease-related genes is provided, comprising: obtaining single-cell sequencing data of AD patients; obtaining AD patient immune cell and metabolite genome-wide association study (GWAS) data; based on the GWAS data, screening out independent single nucleotide polymorphisms (SNPs); through Mendelian randomization causal inference, obtaining causally related immune cell features and metabolite factor features; classifying the immune cells with causal correlation and performing related metabolite enrichment analysis to identify immune cells and metabolites that have important effects on AD occurrence, and further obtaining metabolic pathways affecting AD; wherein the identified immune cells include immune cells with protective factors and risk factors for AD, and the identified metabolites include metabolites with protective factors and risk factors for AD; analyzing single-cell sequencing data of AD patients, identifying cell populations related to metabolic pathways, obtaining gene expression data of expression differences of metabolic pathways in different cell populations; differential gene analysis, obtaining gene analysis results; According to the expression difference results of different metabolic pathways in different cell populations in the gene analysis results, genes related to AD disease are screened out.

[0008] In one possible implementation, the GWAS data is used to screen out independent single nucleotide polymorphisms SNPs; Mendelian randomization causal inference is used to obtain causally related immune cell characteristics and metabolite factor characteristics, including: AD-related exposure factor instrumental variable screening is performed using GWAS data to screen out candidate single nucleotide polymorphisms SNPs; Based on the candidate SNPs, SNPs related to AD are screened out, linkage disequilibrium calculation is performed, and independent SNPs are obtained; Mendelian randomization method causal inference is performed based on the independent SNPs to obtain causally related immune cell characteristics and metabolite factor characteristics; The causally related immune cell characteristics and metabolite factor characteristics are respectively subjected to sensitivity analysis, and consistency test, heterogeneity test and level pleiotropy test are used to evaluate the stability and reliability of the analysis results; and stable and reliable causally related characteristics are retained.

[0009] In one possible implementation, the analysis of causally related immune cells and metabolites identifies immune cells and metabolites that have important effects on the occurrence of AD, and further obtains metabolic pathways that affect AD, including: The analysis of causally related immune cells and metabolites identifies immune cells and metabolites that have important effects on the occurrence of AD; By classifying the identified immune cells, immune cells with protective factors and immune cells with risk factors for AD are obtained; Statistics are performed on the identified metabolites to obtain metabolites with risk factors and protective factors for AD; enrichment analysis is performed to obtain metabolic pathways that affect AD; and the metabolic pathways that affect AD include protective pathways and risk pathways.

[0010] In one possible implementation, the analysis of single-cell sequencing data of AD patients, the identification of cell populations related to metabolic pathways, and the obtaining of gene expression data of expression differences of metabolic pathways in different cell populations; differential gene analysis, obtaining gene analysis results, include: The single-cell sequencing data of AD patients is analyzed to obtain preprocessed data; Dimensionality reduction and clustering analysis are performed on the pretreated data to obtain the main eigenvectors, the optimal principal component number, and the clustering analysis results of uniform manifold approximation and projection analysis; According to the clustering analysis results, cell clustering and annotation are performed to identify cell populations that have an important impact on the occurrence of AD; Cell population subclustering and gene expression level: combining the immune cell and metabolite information obtained by S400, the cell population is subclustered; and the gene expression level of the metabolite pathway in different cell populations is analyzed; The cell populations of T cells, B cells and monocytes are extracted from the subclustered cell populations; and according to the gene expression level of the metabolite pathway, the cells are divided into high-metabolism and low-metabolism groups; Differential gene analysis is performed to obtain the gene analysis results.

[0011] In one possible implementation, according to the expression difference results of different metabolic pathways in different cell populations in the gene analysis results, genes related to AD disease are screened out, including: The high and low metabolism group differential gene lists of various types of cells obtained are subjected to differential expression analysis using a statistical analysis method; a screening standard is determined for screening to obtain genes related to AD disease.

[0012] Further, the genes screened out in the genes related to AD disease include: High nucleotide metabolism monocyte-high amino acid metabolism monocyte differential gene, protective T cell-high amino acid T cell differential gene, and protective B cell-high amino acid B cell differential gene.

[0013] In a second aspect, a device for comprehensively analyzing immune metabolism and AD disease-related genes is provided, including: A first acquisition module is configured to acquire single-cell sequencing data of AD patients; A second acquisition module is configured to acquire whole genome association study (GWAS) data of immune cells and metabolites of AD patients; An inference module is configured to screen out independent single nucleotide polymorphisms (SNPs) based on the GWAS data; and obtain causally related immune cell features and metabolite factor features through Mendelian randomization causal inference; A first analysis module is configured to classify immune cells with causal relationships and perform related metabolite enrichment analysis, identify immune cells and metabolites that have an important impact on the occurrence of AD, and further obtain metabolic pathways that affect AD; wherein the identified immune cells include immune cells that have protective factors and risk factors for AD, and the identified metabolites include metabolites that have protective factors and risk factors for AD; The second analysis module is configured to analyze single-cell sequencing data of AD patients, identify cell populations related to metabolic pathways, and obtain gene expression data of expression differences of metabolic pathways in different cell populations; and the differential gene analysis obtains a gene analysis result. The screening module is configured to screen genes related to AD disease according to expression difference results of different metabolic pathways in different cell populations in the gene analysis result.

[0014] In a third aspect, an electronic device is provided, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the method for comprehensively analyzing multiple omics to determine genes related to immune metabolism and AD disease according to the first aspect.

[0015] In a fourth aspect, a non-transitory computer-readable storage medium is provided, which stores a computer program, and the computer program is executable on a processor to implement the method for comprehensively analyzing multiple omics to determine genes related to immune metabolism and AD disease according to the first aspect.

[0016] In a fifth aspect, a computer program product is provided, which includes a computer program, and the computer program is executable on a processor to implement the method for comprehensively analyzing multiple omics to determine genes related to immune metabolism and AD disease according to the first aspect.

[0017] The beneficial effects of the present application are as follows: (1) The present application provides a comprehensive multi-omics Alzheimer's disease prediction method combined with machine learning, which aims to integrate genetic, transcriptional, and single-cell transcriptomic data, and use advanced machine learning techniques to identify key biomarkers related to Alzheimer's disease and accurately predict individual risk in disease development.

[0018] (2) The method described in the present application combines a machine learning model and a prediction model generated by a training data set, which can capture complex nonlinear relationships and high-dimensional data features, significantly improving the prediction accuracy of Alzheimer's disease risk compared to traditional statistical methods. The method comprehensively considers multiple biological data (genome, transcriptome), allowing the model to analyze the biological mechanisms of the disease from a global perspective and provide more comprehensive biological explanations.

[0019] (3) The method provided in the present application analyzes and ultimately determines immune cells and metabolites associated with AD using single-cell sequencing GSE181279 data sets of peripheral blood from normal elderly and AD patients in the GEO database.

[0020] (4) The method provided in the present application provides technical support for further exploring the characteristics of related differential genes in AD patient populations related to immune cell metabolism.

[0021] (5) The method provided by the application provides strong technical support for in-depth research on the role of differentially expressed genes (DEGs) in Alzheimer's Disease (AD) population, especially the correlation with immune cell metabolism. BRIEF DESCRIPTION OF DRAWINGS

[0022] Figure 1 To identify SNPs-based AD susceptibility factors related to immune cells and plasma metabolites for Mendelian randomization study; wherein, Figure 1 (a, e) are: (a) immune cell features (731 phenotypes; screening threshold: P < 5 x 10 -6 ; nSNP = 8,926) and (e) plasma metabolites (1,400 compounds; screening threshold: P < 5 x 10 -6 ; nSNP = 5,804) for the process diagram of Mendelian randomization (MR) analysis of Alzheimer's Disease (AD) related factors. Both analyses are based on AD genome-wide association study (GWAS) data (cases = 954; controls = 487,331; total nSNP = 12,321,875), with inverse variance weighted (IVW) regression as the main analysis method, supplemented by sensitivity analysis (MR-Egger, weighted median method).

[0023] Figure 1 (b, f) are: volcano plots of immune cell features (b) and metabolite factors (f) that have causal association with AD. Risk increasing factors (red) and protective factors (blue) are plotted according to the effect size (odds ratio) and statistical significance (-log10P value).

[0024] Figure 1 (c, d) are: bubble plots of AD-related immune cell types, with bubble size reflecting relative cell abundance. Orange represents risk-related cell types, and blue indicates protective cell types.

[0025] Figure 1 (g, h) are: KEGG pathway enrichment analysis of AD-related metabolites, with orange indicating risk-related pathways and blue representing protective pathways. The enrichment ratio and corresponding P value are shown in the figure.

[0026] Figure 2 To identify SNPs-based AD susceptibility factors related to immune cells and plasma metabolites for Mendelian randomization study; wherein, Figure 2 (a) immune cells in the blood of AD patients and NC were clustered using UMAP technology; Figure 2 (b) Percentage changes of immune cell types in NC and AD patients; Figure 2 (c) Locations and distributions of AD risk metabolic pathways in immune cells; the cells with more distribution of red arrow are monocytes; Figure 2 (d) Locations and distributions of AD protective metabolic pathways in immune cells; the cells with more distribution of green arrow are T cells and B cells; Figure 2 (e) Volcano plot showing significant differences in monocyte gene expression between high and low NamIis; Figure 2 (f) Bar plot showing the results of gene function enrichment analysis of up- and down-regulated genes in high NamIis monocytes; dark red bars represent up-regulated gene functions, and light red bars represent down-regulated gene functions; Figure 2 (g) and (i) Volcano plots showing significant differences in T cell and B cell gene expression between high and low Aam, respectively; Figure 2 (h) and (j) Bar plots showing the results of gene function enrichment analysis of up- and down-regulated genes in high Aam T cells and B cells, respectively; dark green bars represent up-regulated gene functions, and light green bars represent down-regulated gene functions.

[0027] Figure 3 Clustered and annotated subpopulation analysis plots for monocytes in AD patients' blood; wherein, Figure 3 (a) RNA signatures of different cell populations before removing batch effects; Figure 3 (b) UMAP dimensionality reduction plot before removing batch effects; Figure 3 (c) RNA signatures of different cell populations after removing batch effects; Figure 3 (d) UMAP plot after removing batch effects using the Harmony algorithm; Figure 3 (e) Relationship between nCount_RNA and percent.mt, and relationship between nCount_RNA and nFeature_RNA; Figure 3 (f) Hierarchical clustering dendrogram of cell populations; Figure 3 (g) Relationship between standard deviation and principal components (PCs) in principal component analysis (PCA); Figure 3(h) UMAP dimensionality reduction plot of NC and AD samples using parameters such as number of neighbors (n_neighbours), minimum distance (min_dist) and metric (metric); Figure 3 (i) Heatmap of different cell populations, including marker gene expression of cell populations and proportion of cell populations.

[0028] Figure 4 Schematic diagram for determining immune metabolism and AD disease related genes; wherein, Figure 4 (a) determining differential genes in AD blood single cells; Figure 4 (b) determining differential genes in AD blood transcriptome; Figure 4 (c) determining three final characteristic gene sets.

[0029] Figure 5 is the structural schematic diagram of the device provided by the application for comprehensively analyzing immune metabolism and AD disease related genes by multi-omics.

[0030] Figure 6 is the structural schematic diagram of the electronic device provided by the application. DETAILED DESCRIPTION

[0031] The content of the application will be further described below in combination with specific embodiments, and the content of the application is not limited thereto.

[0032] The embodiment of the application provides a method for comprehensively analyzing AD disease related genes based on immune metabolism crosstalk, which comprises the following steps: S100, obtaining single cell sequencing data of AD patients.

[0033] In a possible implementation, the single cell sequencing data is obtained by searching AD patient single cell sequencing data GSE181279 dataset, and contains single cell RNA sequencing data of peripheral blood mononuclear cells (PBMCs) of AD patients. The single cell sequencing data aims to reveal AD related immune characteristics and potential biomarkers through single cell level analysis.

[0034] S200, obtaining GWAS data of immune cells and metabolites of AD patients.

[0035] S300, screening independent single nucleotide polymorphisms (SNPS) based on GWAS data; through Mendelian randomization causal inference, immune cell characteristics and metabolite factor characteristics with causal correlation are obtained.

[0036] Exemplarily, 731 GWAS summary statistics related to immune cells were obtained from the GWAS Catalog. The GWAS IDs of these data ranged from GCST90001391 to GCST90002121, covering a wide range of immune cell-related genetic variation information, which helps to reveal the potential role of the immune cell system in the pathogenesis of AD.

[0037] In addition, the embodiments of the present application also downloaded data of a large-scale plasma metabolite GWAS cohort from peer-reviewed publications. This cohort includes 8,299 participants, 1,091 metabolites and 309 metabolite ratios, providing important data support for studying the relationship between metabolites and AD. These factors affect the risk of Alzheimer's disease.

[0038] In a possible implementation, the S300 includes the following sub-steps: S310, using GWAS data to perform AD-related exposure factor instrumental variable screening to screen out candidate single nucleotide polymorphisms SNPs.

[0039] Further, the exposure factors include immune cell features and metabolite features.

[0040] Further, using the functions in the TwoSampleMR package, single nucleotide polymorphisms (SNPs) are screened out from the GWAS data. These SNPs will serve as potential instrumental variables (IVs) to systematically identify features that have a significant causal relationship with Alzheimer's disease, with a view to providing a solid theoretical basis and data support for future research and clinical applications.

[0041] S320, based on the candidate SNPs, screening out SNPs related to AD, performing linkage disequilibrium (LD) calculation to obtain independent SNPs.

[0042] In a possible implementation, S320 includes: S321, based on the candidate SNPs, screening out SNPs related to AD.

[0043] Specifically, based on the candidate SNPs, in the process of instrumental variable screening, a strict screening standard is set relying on the MR Base database, that is, selecting those single nucleotide polymorphisms (SNPs) related to Alzheimer's disease (AD) with a significance level lower than 5e-8.

[0044] S322, linkage disequilibrium calculation is performed on SNPs related to AD to identify and retain SNPs that are genetically independent. This step is crucial because it ensures that there is no significant correlation between the selected SNPs, thereby forming a carefully screened independent SNP dataset.

[0045] It can be understood that through S320, the embodiments of the present application can effectively reduce the influence of multiple collinearity problems, thereby significantly improving the accuracy and reliability of subsequent analysis. In the process of LD independence verification, first, those redundant single nucleotide polymorphisms (SNPs) with a value (determination coefficient) greater than or equal to 0.001 are excluded to ensure that all retained are independent instrumental variables. 2 2 It can be understood that through S320, the embodiments of the present application can effectively reduce the influence of multiple collinearity problems, thereby significantly improving the accuracy and reliability of subsequent analysis. In the process of LD independence verification, first, those redundant single nucleotide polymorphisms (SNPs) with a value (determination coefficient) greater than or equal to 0.001 are excluded to ensure that all retained are independent instrumental variables.

[0046] S330, Mendelian randomization (MR) method causal inference is performed based on independent SNPs to obtain causally related immune cell characteristics and metabolite factor characteristics.

[0047] In one possible implementation manner, the Mendelian randomization method includes: inverse variance weighting (IVW), MR-Egger regression, weighted median, and weighted mode.

[0048] It should be noted that the MR method aims to reveal the causal relationship between exposure factors and diseases and provide corresponding statistical significance and effect size estimates. Multiple methods are used to ensure that the embodiments of the present application can obtain reliable results from different perspectives and methodologies, thereby deeply understanding the complex relationship between exposure factors and diseases.

[0049] S340, sensitivity analysis is performed on the causally related immune cell characteristics and metabolite factor characteristics, respectively, and consistency test, heterogeneity test, and level pleiotropy test are used to evaluate the stability and reliability of the analysis results; and causally related characteristics that are stable and reliable are retained.

[0050] In one possible implementation manner, the S340 includes: S341, for causally related immune cell characteristics and metabolite factor characteristics, respectively, using the instrumental variable exclusion method, each single nucleotide polymorphism (SNP) is excluded one by one, and the effect value is recalculated on this basis to ensure the comprehensiveness and accuracy of the results.

[0051] S342, performing multi-method consistency test on the immune cell features and metabolite factor features of the causal relationship obtained in S341 to verify whether the results under different methods remain consistent; the multi-method consistency test comprises comparing the consistency of the results of IVW (inverse variance weighting method), MR-Egger regression and weighted median method.

[0052] S343, performing heterogeneity test on the SNP effect size of each exposure factor.

[0053] In a possible implementation manner, the method of heterogeneity test comprises: adopting Cochran's Q test, setting p value greater than 0.05 as an acceptable standard, and combining I 2 The statistical quantity is evaluated, and if the value is less than 50%, it indicates that there is low heterogeneity. This method helps to determine the reliability of the results.

[0054] S344, performing horizontal pleiotropy test on the immune cell features and metabolite factor features of the causal relationship obtained in S344, adopting MR-Egger intercept test, and if the p value is greater than 0.05, it indicates that there is no significant pleiotropy.

[0055] S345, eliminating the related features of the causal relationship that do not meet the consistency test, heterogeneity test and horizontal pleiotropy test, and retaining the stable and reliable features.

[0056] Through the analysis steps of S340, the present application strives to ensure the robustness and scientificity of the research results, and provides a solid foundation for subsequent research.

[0057] S400, performing classification analysis on the immune cells and metabolites having causal relationship in the features obtained in S345, identifying immune cells and metabolites that have important influence on AD occurrence, and further obtaining metabolic pathways affecting AD; wherein the identified immune cells comprise immune cells having protective factors and immune cells having risk factors for AD, and the identified metabolites comprise metabolites having factor risk and protective factors for AD.

[0058] In a possible implementation manner, the step S400 comprises: S410, performing analysis on the immune cells and metabolites having causal relationship, and identifying immune cells and metabolites that have important influence on AD occurrence; S420, obtaining immune cells having protective factors and immune cells having risk factors for AD by classifying the identified immune cells.

[0059] S430, count the identified metabolites to obtain metabolites related to risk factors and protective factors of AD; perform enrichment analysis by accessing the MetaboAnalyst website to obtain metabolic pathways affecting AD; wherein the metabolic pathways affecting AD include protective pathways and risk pathways.

[0060] Further, the S430 includes the following sub-steps: S431, count the identified metabolites and divide them into two categories: risk factors and protective factors; S432, perform enrichment analysis on the metabolites: Open the MetaboAnalyst website (https: / / www.metaboanalyst.ca / ).

[0061] Select the enrichment analysis module: click the "Pathway Analysis" module.

[0062] Upload the metabolite list: upload the sorted metabolite name list (including risk factors and protective factors) to MetaboAnalyst. Select "Compound Name" as the input type.

[0063] Match metabolites: the system will automatically match the uploaded metabolite names with the metabolites in the database. Unmatched metabolites will be displayed in yellow, and you can click "View" to view detailed information and manually match them.

[0064] Ensure that all metabolites are correctly matched. For metabolites that cannot be matched, refer to databases such as HMDB for further confirmation.

[0065] Select analysis algorithm: in the enrichment analysis, you can choose the Fishers Exact Test method.

[0066] Select species database: according to the research object (such as humans), select the corresponding species database (Homosapiens).

[0067] Set parameters: set other parameters as needed, such as p-value cutoff.

[0068] Run analysis: click the "Proceed" button to start the enrichment analysis. The analysis results will display metabolic pathways related to AD, including risk pathways and protective pathways.

[0069] The results are usually displayed in table form, including pathway names, p-values, adjusted p-values, and other information.

[0070] Visualize the pathway: click the pathway name to view the corresponding pathway diagram, which helps understand the position and role of metabolites in the pathway.

[0071] S433、based on the enrichment analysis result, obtaining the metabolic pathway affecting AD.

[0072] S500, analyzing single-cell sequencing data of AD patients, identifying cell populations related to metabolic pathways, and obtaining gene expression data of expression differences of metabolic pathways in different cell populations; differential gene analysis, obtaining gene analysis results.

[0073] In one possible implementation, S500 includes the following sub-steps: S510, analyzing single-cell sequencing data of AD patients to obtain preprocessed data.

[0074] In one possible implementation, the single-cell sequencing data of the AD patient includes sample clinical information and gene expression profiles.

[0075] In one possible implementation, preprocessing includes reading single-cell expression profile data, filtering low-expression genes, standardizing, and normalizing.

[0076] Specifically, find the GSE181279 dataset in the GEO database (download link at https: / / www.ncbi.nlm.nih.gov / geo / query / acc.cgi?acc=GSE181279), click to download Series Matrix File. After downloading and decompressing, obtain the file containing sample clinical information and gene expression profiles, download barcodes, features and matrix data, import the downloaded matrix data into the R software system using R packages such as Seurat, harmony, etc. Read single-cell expression profile data, filter low-expression genes, standardize and normalize to obtain preprocessed data.

[0077] Standardize the data: standardize the data to ensure comparability between different samples.

[0078] Normalize the data: normalize the data to eliminate technical bias.

[0079] S520, dimension reduction and clustering analysis on the preprocessed data, obtaining clustering analysis results including main feature vectors, optimal principal component number, and uniform manifold approximation and projection analysis.

[0080] Principal component analysis (PCA) analysis: perform principal component analysis (PCA) on the preprocessed data to extract main feature vectors.

[0081] Determine the optimal principal component number: use the tool ElbowPlot to observe the optimal principal component (pcs) number.

[0082] Uniform Manifold Approximation and Projection (UMAP) analysis: Based on the PCA results, UMAP analysis is performed to visualize the clustering results and observe the positions between each cluster.

[0083] S530, according to the clustering analysis results, cell clustering and annotation are performed to identify the cell population that has important influence on AD occurrence.

[0084] Clustering analysis: According to the UMAP analysis results, the cells are clustered.

[0085] Cell annotation: The clusters are annotated using the Seurat package to identify the cell population that has important influence on AD occurrence.

[0086] S540, cell population re-clustering and gene expression level: Based on the immune cell and metabolite information obtained in S400, the cell population is re-clustered; the gene expression level of the metabolite pathway in different cell populations is analyzed.

[0087] S550, extract the cell population of T cells, B cells and monocytes from the re-clustered cell population; according to the gene expression level of the metabolite pathway, the cells are divided into high-metabolism group and low-metabolism group.

[0088] It should be noted that the T cells include: T cells of amino acid metabolism obtained by enrichment of protective metabolite pathways and T cells of amino acid metabolism obtained by enrichment of dangerous metabolite pathways; the B cells include: B cells of amino acid metabolism obtained by enrichment of protective metabolite pathways and B cells of amino acid metabolism obtained by enrichment of dangerous metabolite pathways; the monocytes include monocytes of amino acid metabolism obtained by enrichment of protective metabolite pathways and monocytes of amino acid metabolism obtained by enrichment of dangerous metabolite pathways.

[0089] S560, differential gene analysis, to obtain gene analysis results.

[0090] Compare the expression difference genes of high and low metabolism groups: use the FindMarkers function of Seurat to compare the expression difference genes between high and low metabolism groups.

[0091] Visualize the differential genes: the expression of the differential genes is displayed through Volcano Plot and Heatmap.

[0092] S600, according to the expression difference results of different metabolic pathways in different cell populations in the gene analysis results, screen out genes related to AD disease.

[0093] In one possible implementation, the S600 includes: The high and low metabolic group difference gene lists of each cell type (T / B / monocytes) obtained are subjected to differential expression analysis using statistical analysis methods; screening criteria are determined for screening, and AD disease-related genes are obtained.

[0094] Specifically, the screening criteria are: significance threshold: adjusted p value (adj. P. Val) <0.05; expression change threshold: average log2 fold change >0.5.

[0095] The above-described method is described below in conjunction with detailed examples.

[0096] The method for comprehensively analyzing AD disease-related genes based on immune metabolic crosstalk includes the following steps: S1, obtaining single-cell sequencing data of AD patients.

[0097] Downloading of AD patient blood single-cell sequencing data: The publicly available blood single-cell sequencing data related to AD is downloaded from the GEO (Gene Expression Omnibus) database (https: / / www.ncbi.nlm.nih.gov / geo / info / datasets.html), which includes: AD patient blood single-cell data GSE181279, which includes 2 healthy patients and 3 AD patients complete single-cell expression profile sample data for single-cell analysis.

[0098] The specific method for obtaining the transcriptome data of the blood of the AD patient is as follows: (1) Access the GEO database: open the GEO database website (<https: / / www.ncbi.nlm.nih.gov / geo / info / datasets.html>). Enter the dataset number (such as GSE63060, GSE63061, etc.) in the search box. Click the search button to find the corresponding dataset. After entering the dataset page, click the "Download" button. Select the download format (such as SOFT file or MINiML file). Save the data to the local computer. Find the "Annotation SOFT file" link in the dataset page. Download the annotation file (such as GPL6947).

[0099] (2) Data preprocessing - reading data: read the downloaded SOFT file using R. Filter low-expression genes: filter out low-expression genes to reduce noise. Standardization and normalization: standardize and normalize the data to ensure comparability between different samples.

[0100] S2, obtaining AD patient immune cell and metabolite genome-wide association study GWAS data.

[0101] GWAS data of immune cells and metabolites in AD patients are downloaded: GWAS data of AD patients blood are downloaded from GWAS summary datasets (https: / / gwas.mrcieu.ac.uk / ) (GWAS ID: ieu-b-5067, including 488,285 samples, 12,321,875 Number of SNPs).

[0102] Summary statistics of 731 immune cell-related GWASs are also obtained from GWAS catalog (GWAS ID: GCST90001391 to GCST90002121).

[0103] And GWAS cohort of large serum metabolites are downloaded, including 1,091 metabolites and 309 metabolite ratios of 8,299 individuals, all of which are from European population, ensuring consistency in the analysis of this study.

[0104] Data preprocessing: The downloaded GWAS data is read using the R package "TwoSampleMR". Low-quality SNPs are filtered out.

[0105] S3, based on whole genome association study data, screening independent single nucleotide polymorphism SNPS; through Mendelian randomization causal inference, obtain causal correlation immune cell characteristics and metabolite factor characteristics.

[0106] Specifically, S3 includes: S3.1, screening related SNPs: SNPs associated with AD are screened according to p value.

[0107] S3.2, construct instrumental variables: use IVW (Inverse Variance Weighted) method to construct instrumental variables.

[0108] S3.3, Mendelian randomization analysis: use MR analysis tools (such as MR Base) for analysis, find out the immune cells and metabolites related to AD.

[0109] S3.4, result verification: use sensitivity analysis (such as MR-PRESSO) to verify the reliability of the results.

[0110] Among them, S3.3 Mendelian randomization (Mendelian Randomization, MR) analysis includes: The MR Base database (http: / / app.mrbase.org / ) was opened to access a large number of summary statistics containing hundreds of GWAS studies. The result ID screened out through the MR Base database was extracted from the GWAS summary data (https: / / gwas.mrcieu.ac.uk / ), matching the significant threshold (P<1e-5) of each gene in the whole genome as potential IVs (Instrumental variables), calculating the linkage disequilibrium (LD, linkage disequilibrium) between SNPs in R 2 <0.001 (clumping window size=10,000kb) SNPs, only P 2 <5e-8 SNPs. In turn, inverse variance weighting (IVW, using meta-analysis methods combined with the Wald estimate of each SNP to calculate the causal effect), MR-Egger (based on the assumption that the strength of the tool is independent of the direct effect (InSIDE)) uses MR-Egger regression method to estimate the causal effect. MR-Egger allows estimation in the presence of horizontal pleiotropy. Weighted median (weighted median method allows correct estimation of causal relationship in the presence of 50% IV invalidity) This method is robust to outliers. Weighted mode (weighted model estimation has stronger ability to detect causal relationship).

[0111] Among them, S3.4 sensitivity analysis includes: The embodiments of the present application use Mendelian randomization (MR) sensitivity analysis to evaluate the influence of specific genetic variations on the risk of Alzheimer's disease (AD). By excluding each SNP one by one and recalculating the effect, identify variations that have a significant impact on the overall estimate, generate new estimate points and their 95% confidence intervals, to evaluate the unique contribution and robustness of each SNP. The estimate after excluding a single SNP is compared with the overall estimate after including all SNPs to check the robustness of the analysis. In addition, the statistical heterogeneity between SNPs is evaluated by Mendelian heterogeneity test, and the Q value is calculated. If the P value is greater than 0.05, it is considered that the SNP effect is consistent. MR-Egger regression is used to test horizontal pleiotropy, and if the P value is greater than 0.05, it is considered that there is no significant horizontal pleiotropy.

[0112] The results show that there is no heterogeneity (Q test P> 0.05) and horizontal pleiotropy (MR-Egger P> 0.05) for the 31 immune cell phenotypes used in AD MR analysis, demonstrating the robustness of the causal relationship (Table 2). The results of leave-one-out method and funnel plot analysis also show that the data is reliable.

[0113] S4, classifying immune cells with causal relationship and performing enrichment analysis on related metabolites, identifying immune cells and metabolites that have important influence on AD occurrence, and further obtaining metabolic pathways affecting AD; wherein the identified immune cells include immune cells with protective factors and dangerous factors for AD, and the identified metabolites include metabolites with protective factors and dangerous factors for AD.

[0114] Specifically, S4 includes the following sub-steps: S4.1, analyzing immune cells and metabolites with causal relationship, and identifying immune cells and metabolites that have important influence on AD occurrence; S4.2, obtaining immune cells with protective factors and immune cells with dangerous factors for AD by classifying the identified immune cells.

[0115] S4.3, performing statistics on the identified metabolites to obtain metabolites with dangerous factors and protective factors for AD; performing enrichment analysis by accessing the MetaboAnalyst website to obtain metabolic pathways affecting AD; wherein the metabolic pathways affecting AD include protective pathways and dangerous pathways.

[0116] Specifically, MSEA of metabolites in S4.3 is performed using MetaboAnalyst 6.0.

[0117] Metabolic enrichment analysis (MSEA) is performed using the MetaboAnalyst 6.0 platform, which is a network-based comprehensive tool designed for analyzing and interpreting metabolomics data. The MSEA module of MetaboAnalyst 6.0 provides a variety of enrichment analysis methods. The over-represented analysis (ORA) is selected by the embodiments of the present application to determine the metabolite group that has significant association with the data set. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Small Molecule Pathway Database (SMPDB) are used as reference libraries for pathway-based enrichment 34.

[0118] Specifically, the method of S4.3 includes the following sub-steps: S4.3.1, classifying metabolites related to Alzheimer's disease (AD) into two categories of dangerous factors and protective factors. Ensure that the metabolite name is accurate and correct for subsequent matching.

[0119] S4.3.2, data uploading S4.3.2a, access the MetaboAnalyst website: open the MetaboAnalyst official website (<https: / / www.metaboanalyst.ca / >).

[0120] S4.3.2b, Select enrichment analysis module: Click the "Pathway Analysis" module to enter the enrichment analysis page.

[0121] S4.3.2c, Upload metabolite list: Upload the sorted metabolite name list (including risk factors and protective factors) to MetaboAnalyst. Select "Compound Name" as the input type.

[0122] S4.3.3, Data matching Automatic matching of metabolites: The system will automatically match the uploaded metabolite names with the metabolites in the database. Unmatched metabolites will be displayed in yellow.

[0123] Manual matching of metabolites: Click "View" to view the detailed information of the unmatched metabolites. For metabolites that cannot be automatically matched, refer to databases such as HMDB for manual confirmation and matching.

[0124] S4.3.4, Set analysis parameters S4.3.4a, Select analysis algorithm: In the enrichment analysis, select the Fisher's Exact Test method.

[0125] S4.3.4b, Select species database: According to the research object (such as human), select the corresponding species database (Homosapiens).

[0126] S4.3.4c, Set other parameters: Set the p-value cutoff (such as p<0.05) and other required parameters.

[0127] S4.3.5, Run enrichment analysis S4.3.5a, Start analysis: Click the "Proceed" button to start the enrichment analysis.

[0128] S4.3.5c, View results: The analysis results will be displayed in table form, including pathway name, p-value, adjusted p-value, etc.

[0129] The results will show the metabolic pathways related to AD, including risk pathways and protective pathways.

[0130] S5, Analyze single-cell sequencing data of AD patients, identify cell populations related to metabolic pathways, and obtain gene expression data of metabolic pathways in different cell populations; differential gene analysis to obtain gene analysis results.

[0131] Specifically, S5 includes: First, read the expression profile using the Seurat package and filter out low-expression genes. Then, standardize, normalize, PCA, and UMAP analyze the data in turn, observe the optimal pcs number by ElbowPlot, and get the position between each cluster by UMAP analysis. Next, use the Seurat package to annotate the cluster, identify some cell groups that have important influence on the occurrence of AD, combine the immune cells and metabolites obtained by Mendelian randomization analysis to re-cluster the cell groups, and explore the expression of metabolite pathways in the cell groups. Finally, extract specific cell groups and divide them into high-metabolism and low-metabolism groups according to the expression of metabolite pathway genes, and compare the differential genes of the two groups of cells to obtain the gene analysis results.

[0132] S5 includes the following sub-steps: S5.1, data quality control: quality control of single cell RNA sequencing data, filter out cells with gene feature number exceeding the specified range or high proportion of mitochondrial genes. Use the `subset` function to filter the data to ensure data quality.

[0133] S5.2, standardization: standardize the filtered data, use logarithmic standardization method to ensure data comparability.

[0134] S5.3, dimensionality reduction, clustering and batch effect correction: S5.3a, find high variable genes: use the `FindVariableFeatures` function to identify high variable genes.

[0135] S5.3b, data standardization: standardize high variable genes.

[0136] S5.3c, principal component analysis (PCA): use the `RunPCA` function to perform PCA analysis and extract main feature vectors.

[0137] S5.3d, visualize PCA results: use the `DimPlot` function to visualize PCA results and observe the distribution of different samples.

[0138] S5.3e, use the `Harmony` package to correct batch effects to ensure comparability of data from different batches.

[0139] S5.4, clustering analysis S5.4a, determine the optimal number of principal components: use the `ElbowPlot` function to determine the optimal number of principal components.

[0140] S5.4b, construct adjacency graph: use the `FindNeighbors` function to construct an adjacency graph.

[0141] S5.4c, Clustering analysis: Use the `FindClusters` function for clustering analysis.

[0142] S5.4d, UMAP dimensionality reduction: Use the `RunUMAP` function for UMAP dimensionality reduction and visualize the clustering results.

[0143] S5.5, Cell type annotation S5.5a, Load reference data: Use the `SingleR` package to load reference data.

[0144] S5.5b, Cell type prediction: Use the `SingleR` function to predict cell types for the test data.

[0145] S5.5c, Save prediction results: Save the prediction results as a CSV file.

[0146] S5.5d, Annotate cell types: Annotate the predicted cell types to the metadata.

[0147] S5.5e, Use the `plotScoreHeatmap` function to plot a heatmap showing the cell type prediction results. S5.6, Metabolic-related differential gene identification: S5.6a, Risk-M (high nucleotide metabolism monocytes) - screening of high amino acid metabolism monocytes differential genes: multiple R packages are loaded in this step, including dplyr, hdf5r, Seurat, data.table, SeuratDisk, ggplot2, tidyr, patchwork, clustree and clusterProfiler, for data processing, visualization and analysis. First, the Seurat package is used to extract the monocyte population with scRNA_M = subset(scRNA, celltype =='monocyte'), then the `read.gmt` function is used to read the Pathways of nucleic acid metabolism and innate immune sensing.gmt gene set file, and the `DotPlot` function is used to show the expression of these genes in different cell clusters (`seurat_clusters`). The `AddModuleScore` function is used to calculate the module score for each gene set, and the score results are added to the Seurat object. The module score is used to evaluate the activity of a specific gene set in each cell. The data generated by `DotPlot` is extracted and visualized using `ggplot2` to create a bubble chart showing the expression of different gene sets in cell clusters. Select a specific cell type (monocyte) from the Seurat object and evaluate the activity of a specific metabolic pathway (such as nucleotide metabolism). According to the module score, the cells are divided into high activity and low activity groups, and the `FindMarkers` function is used to find the differentially expressed genes between the two groups. The found differential genes are screened, and screening conditions such as expression percentage, p value and average log2 fold change are set. Finally, the high nucleotide metabolism monocyte differential genes are obtained.

[0148] S5.6b, Protective T (High Amino Acid T Cell) - Screening of High Amino Acid T Cell Differential Genes, multiple R packages are loaded, including dplyr, hdf5r, Seurat, data.table, SeuratDisk, ggplot2, tidyr, patchwork, clustree and clusterProfiler, for data processing, visualization and analysis. First, use the Seurat package to extract the T cell population with scRNA_T = subset(scRNA, celltype == T-cell ), then read the Pathways of nucleic acid metabolism and innate immune sensing.gmt gene set file using the `read.gmt` function, and use the `DotPlot` function to show the expression of these genes in different cell clusters (`seurat_clusters`). Use the `AddModuleScore` function to calculate the module score for each gene set, and add the score result to the Seurat object. The module score is used to evaluate the activity of a specific gene set in each cell. Extract the data generated by `DotPlot` and use `ggplot2` for visualization, creating a bubble chart showing the expression of different gene sets in cell clusters. Select a specific cell type (T cell) from the Seurat object and evaluate the activity of a specific metabolic pathway (such as amino acid metabolism). According to the module score, the cells are divided into high activity and low activity groups, and the `FindMarkers` function is used to find the differentially expressed genes between the two groups. The found differential genes are screened, and screening conditions such as expression percentage, p value and average log2 fold change are set. Finally, the high amino acid metabolism T cell differential genes are obtained.

[0149] S5.6c, Protective B (High Amino Acid B Cell) - Screening of high amino acid B cell differential genes, multiple R packages are loaded in this step, including dplyr, hdf5r, Seurat, data.table, SeuratDisk, ggplot2, tidyr, patchwork, clustree and clusterProfiler, which are used for data processing, visualization and analysis. First, use the Seurat package to extract the B cell population with scRNA_B = subset(scRNA, celltype == Bcell ), then read the Amino acid metabolism.gmt gene set file using the `read.gmt` function, and use the `DotPlot` function to show the expression of these genes in different cell clusters (`seurat_clusters`). Use the `AddModuleScore` function to calculate the module score for each gene set, and add the score result to the Seurat object. The module score is used to evaluate the activity of a specific gene set in each cell. Extract the data generated by `DotPlot` and use `ggplot2` for visualization, create a bubble chart to show the expression of different gene sets in cell clusters. Select a specific cell type (B cell) from the Seurat object and evaluate the activity of a specific metabolic pathway (amino acid metabolism). According to the module score, the cells are divided into high activity and low activity groups, and the `FindMarkers` function is used to find the differentially expressed genes between the two groups. The found differential genes are screened, and the screening conditions such as expression percentage, p value and average log2 fold change are set. Finally, the high amino acid metabolism B cell differential genes are obtained.

[0150] The detailed implementation steps are introduced above, and the related test results are introduced below.

[0151] 1. Blood cells and plasma metabolites have statistical causal relationship with AD The present application embodiment uses Mendelian randomization (MR) to eliminate sensitivity analysis to evaluate the effect of specific genetic variations on the risk of AD. The genetic prediction of immune cells for AD is obtained by forward IVW method, and the results are shown in Figure 1 b and Table 1, and the volcano plot result shows that 22 kinds of immune cells are positively correlated with the occurrence of AD. Further, the above 22 kinds of immune cells are classified, and it is found that the cells positively correlated with the risk of AD are mainly: myeloid cells (Myeloid cell) and monocytes (Monocyte) and T cells (T cell) Figure 1d). In addition, 12 types of immune cells were negatively correlated with the incidence of AD, among which T cells and B cells accounted for the most Figure 1 e). It is indicated that the volume change of immune cells has a certain causal relationship or accompanying effect with the onset of AD. The genetic prediction of 1400 plasma metabolites on AD is obtained by the positive IVW method, and the results are shown in Figure 1 c and Table 1, a total of 42 metabolites are associated with AD, among which 19 are risk-related metabolites and 23 are protective factor metabolites. After enrichment analysis of the classified metabolites, it is found that the AD risk factor metabolite enrichment analysis results are shown in Figure 1 f), which indicates that these metabolites are mainly enriched in the pathways of nucleic acid metabolism and innate immune sensing (NamIis), and other pathways and diseases related to nucleic acid metabolism; and the AD protective factor metabolites are enriched in the amino acid metabolism pathway. The above results show that the plasma metabolites indeed have a certain causal relationship or accompanying effect with the onset of AD.

[0152]

[0153] 2. Blood tissue single cell data processing and cell type analysis of AD patients Plasma metabolites reflect the overall metabolic characteristics of the body, based on which it can be known whether immune cells are also related to the AD-sensitive metabolite characteristics. Therefore, the blood single cell transcriptome data of the AD patients and the healthy control group are analyzed in the embodiments of the present application, and the metabolic state of immune cells under the pathological condition of AD is focused. First, the quality control of single cells of two different types of tissues is performed. The blood single cell transcriptome data retains 36750 cells for analysis Figure 3 a, b). The mRNA / UMI / particle / rRNA content distribution of the two types of cells is uniform, and the normalization processing is performed to reduce the batch effect Figure 3 b, d).

[0154] The blood single cell transcriptome clustering analysis determines four types of cells: NK cells, T cells, B cells and monocytes Figure 3 e-i). In AD patients, the proportion of T cells increases by 12.1%, and the proportion of NK cells decreases by 52.5% Figure 2 a, b). In addition, the risk metabolic pathways sensitive to AD are mainly expressed in monocytes Figure 2 c), and the protective metabolic pathways are mainly expressed in T cells and B cells Figure 2 d).

[0155] 3. Analysis of the relationship between metabolic state and monocytes in AD risk In this application, cells were divided into a high-metabolic group and a low-metabolic group based on the average expression level of NamIis in the risk metabolic pathway. The differences in gene expression and function between the two groups were explored, and differentially expressed genes in nucleotide metabolism and immune pathways were displayed using volcano plots. Figure 2 e), and GO enrichment analysis was performed on these differentially regulated genes. The results showed that genes enriched in the high-nucleotide-metabolism monocyte group were mainly related to viral genome replication and regulation of innate immune responses; while genes enriched in the high-nucleotide-metabolism monocyte group were mainly involved in biological processes such as regulation of neuronal apoptosis and cytoplasmic translation. Figure 2 f). The homology between monocytes and Micro / Macro cells suggests that these cells play a crucial role in the pathogenesis of AD, which is also consistent with... Figure 1 The presence of monocytes in the middle basal cells is consistent with the fact that AD is a risk factor.

[0156] 4. Analysis of the relationship between AD protective metabolic state and B and T immune cells This application's embodiments tested the expression of the AD protective metabolic pathway in human blood cells. It was found that the high amino acid metabolism (Aam) group was mainly concentrated in T cells and B cells. After dividing the cells into high Aam and low Aam groups, the gene expression differences of T cells and B cells in the two groups were displayed using volcano plots. Figure 2 g, i). GO enrichment analysis of T cell Aam metabolism showed that high Aam levels in T cells were related to amino acid metabolism and ribosome synthesis, while low Aam levels in T cells involved regulation of immune cell function. Figure 2 h). Gene ontology analysis of B cell Aam metabolism genes showed that high Aam processes in B cells are related to amino acid metabolism and ribosome synthesis, while low Aam processes in B cells involve immune cell function (h). Figure 2 j). These findings suggest that the immune function of T cells and B cells is antagonized when they are in a protective metabolic state against Alzheimer's disease (AD). Figure 1 The protective effects of T cells and B cells in AD are not truly beneficial because their own immune function is antagonized.

[0157] 5. Screening of key genes in Alzheimer's disease (AD) The embodiments of this application are based on Figure 2The results of e involved the identification of differentially expressed genes in the blood transcriptome. Considering the genetic differences between European and Asian populations, this application included blood transcriptome data from Chinese clinical AD patients and controls to reduce inter-racial variations. By merging the GSE63060 and GSE63061 Chinese self-test datasets, differential analysis was performed on genes related to risk metabolic status and monocytes, identifying a total of 8 differentially expressed genes between AD blood samples and normal blood samples. The same method was used to perform differential analysis on AD-related genes in high-Aam and low-Aam T cells and B cells, identifying 60 differentially expressed genes in ADAam B cells and 840 differentially expressed genes in AD Aam T cells.

[0158] See Figure 4 The optimal differential gene set was selected by analyzing the intersection of the two gene sets. Figure 4 (a) Identification of differentially expressed genes in blood single cells for AD: Differential analysis of blood single cells yielded genes related to metabolic state (high metabolic activity group and low metabolic activity group in blood single cells). Figure 4 (b) Identification of differentially expressed genes in blood transcriptomes for AD: differential expression genes in AD and normal control groups were identified by differential analysis of blood transcriptome data. Figure 4 (c) To ultimately obtain three characteristic gene sets, the following methods are used: Figure 4 (a) and Figure 4 The results in (b) were subjected to intersection analysis, and the optimal differential gene set was finally selected: (Risk-M, high NamIis monocytes), protective T cell group (Protective-T, high AamT cells) and protective B cell group (Protective-B, high AamB cells).

[0159] The apparatus for comprehensively analyzing immune metabolism and AD-related genes provided by the present invention will be described below. The apparatus for comprehensively analyzing immune metabolism and AD-related genes described below can be referred to in correspondence with the method for comprehensively analyzing immune metabolism and AD-related genes described above.

[0160] Figure 5 This is a schematic diagram of the device for comprehensively analyzing immune metabolism and AD-related genes provided in an embodiment of the present invention, as shown below. Figure 5 As shown, it includes: a first acquisition module 51, a second acquisition module 52, an inference module 53, a first analysis module 54, a second analysis module 55, and a filtering module 56, wherein: The first acquisition module 51 is used to acquire single-cell sequencing data from AD patients; The second acquisition module 52 is used to acquire genome-wide association study (GWAS) data of immune cells and metabolites in AD patients. The inference module 53 is used for screening independent single nucleotide polymorphisms SNPs based on GWAS data; and obtaining causally related immune cell characteristics and metabolite factor characteristics through Mendelian randomization causal inference. The first analysis module 54 is used for classifying immune cells with causal correlation and performing related metabolite enrichment analysis, identifying immune cells and metabolites that have important influence on AD occurrence, and further obtaining metabolic pathways affecting AD; wherein the identified immune cells include immune cells having protective factors and risk factors for AD, and the identified metabolites include metabolites having protective factors and risk factors for AD. The second analysis module 55 is used for analyzing single-cell sequencing data of AD patients, identifying cell populations related to metabolic pathways, and obtaining gene expression data of expression differences of metabolic pathways in different cell populations; and performing differential gene analysis to obtain gene analysis results. The screening module 56 is used for screening genes related to AD disease according to expression difference results of different metabolic pathways in different cell populations in the gene analysis results.

[0161] Figure 6 An example of an entity structure diagram of an electronic device is shown in Figure 6 As shown in the figure, the electronic device can include a processor 610, a communications interface 620, a memory 630, and a communications bus 640, wherein the processor 610, the communications interface 620, and the memory 630 complete mutual communication through the communications bus 640. The processor 610 can invoke logical instructions in the memory 630 to execute the method for comprehensively analyzing immune metabolism and AD disease-related genes of multiple omics.

[0162] In addition, the logical instructions in the memory 630 described above can be implemented in the form of a software function unit and sold or used as an independent product, which can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or part of the technical solutions can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a plurality of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the method described in various embodiments of the present application. The foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes.

[0163] In another aspect, the present application also provides a computer program product comprising a computer program, the computer program being stored in a non-transitory computer-readable storage medium, and the computer program being capable of executing the method for comprehensively analyzing AD disease-related genes by integrating multi-omics analysis of immune metabolism when executed by a processor.

[0164] In yet another aspect, the present application also provides a non-transitory computer-readable storage medium having stored thereon a computer program, the computer program being capable of implementing the method for comprehensively analyzing AD disease-related genes by integrating multi-omics analysis of immune metabolism when executed by a processor.

[0165] The device embodiments described above are merely illustrative, wherein the units described as separate components can or can not be physically separate, and the components displayed as units can or can not be physical units, i.e., can be located in one place, or can be distributed on multiple network units. Part or all of the modules can be selected to achieve the purpose of the present embodiment scheme according to actual needs. Those skilled in the art can understand and implement without creative labor.

[0166] From the above description of the embodiments, those skilled in the art can clearly understand that the embodiments can be realized by means of software plus necessary general hardware platforms, and of course can also be realized by hardware. Based on such understanding, the above technical solutions, essentially or in other words, the part that contributes to the prior art, can be embodied in the form of a software product, which can be stored in a computer-readable storage medium, such as a ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the methods described in each embodiment or some parts of the embodiments.

[0167] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacements for some technical features; and these modifications or replacements do not make the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for comprehensively analyzing immune metabolism and AD disease-related genes by multi-omics, characterized in that, The application relates to a method for identifying AD-related metabolic pathways. The method comprises the following steps: Obtaining single-cell sequencing data of AD patients; Obtaining genome-wide association study (GWAS) data of immune cells and metabolites of AD patients; Based on the GWAS data, independent single nucleotide polymorphisms (SNPs) are screened out; through Mendelian randomization causal inference, immune cell characteristics and metabolite factor characteristics with causal correlation are obtained; The immune cells with causal correlation are classified, and related metabolites are enriched, so that immune cells and metabolites with important influence on AD are identified, and metabolic pathways affecting AD are obtained; wherein the identified immune cells include immune cells with protective factors and risk factors for AD, and the identified metabolites include metabolites with protective factors and risk factors for AD; The single-cell sequencing data of the AD patients is analyzed, cell populations related to metabolic pathways are identified, and gene expression data of expression differences of the metabolic pathways in different cell populations are obtained; through differential gene analysis, gene analysis results are obtained; 2. The method of claim 1, wherein, According to the expression difference results of different metabolic pathways in different cell populations in the gene analysis results, AD disease-related genes are screened out. The method comprises the following steps: Based on the GWAS data, independent single nucleotide polymorphisms (SNPs) are screened out; Through Mendelian randomization causal inference, immune cell characteristics and metabolite factor characteristics with causal correlation are obtained, including: AD-related exposure factor instrumental variable screening is performed by using the GWAS data, and candidate single nucleotide polymorphisms (SNPs) are screened out; Based on the candidate SNPs, AD-related SNPs are screened out, linkage disequilibrium calculation is performed, and independent SNPs are obtained; 3. The method of claim 1, wherein, Based on the independent SNPs, Mendelian randomization method causal inference is performed, and immune cell characteristics and metabolite factor characteristics with causal correlation are obtained; The immune cell characteristics and metabolite factor characteristics with causal correlation are respectively subjected to sensitivity analysis, and consistency test, heterogeneity test and level pleiotropy test are used to evaluate the stability and reliability of the analysis results; and stable and reliable causal correlation related characteristics are reserved. The method comprises the following steps: The immune cell characteristics and metabolite factor characteristics with causal correlation are analyzed, and immune cells and metabolites with important influence on AD are identified; 4. The method of claim 1, wherein, Through classification of the identified immune cells, immune cells with protective factors and immune cells with risk factors for AD are obtained; Through statistics of the identified metabolites, metabolites with risk factors and protective factors for AD are obtained; through enrichment analysis, metabolic pathways affecting AD are obtained; wherein the metabolic pathways affecting AD include protective pathways and risk pathways. The method comprises the following steps: The single-cell sequencing data of the AD patients is analyzed, cell populations related to metabolic pathways are identified, and gene expression data of expression differences of the metabolic pathways in different cell populations are obtained; Differential gene analysis is performed to obtain gene analysis results, including: The single-cell sequencing data of the AD patients is analyzed, and preprocessed data is obtained; Dimensionality reduction and clustering analysis are performed on the pretreated data to obtain the main eigenvectors, the optimal principal component number, and the clustering analysis results of uniform manifold approximation and projection analysis; According to the clustering analysis results, cell clustering and annotation are performed to identify cell populations that have an important impact on the occurrence of AD; Cell population subclustering and gene expression level: combining the immune cell and metabolite information obtained by S400, the cell population is subclustered; the gene expression level of the metabolite pathway in different cell populations is analyzed; The cell populations of T cells, B cells, and monocytes are extracted from the subclustered cell populations; according to the gene expression level of the metabolite pathway, the cells are divided into high-metabolism and low-metabolism groups; Differential gene analysis is performed to obtain gene analysis results.

5. The method of claim 1, wherein, According to the expression difference results of different metabolic pathways in different cell populations in the gene analysis results, genes related to AD disease are screened out, including: The high and low metabolism group differential gene lists of various types of cells obtained are subjected to differential expression analysis using statistical analysis methods; a screening standard is determined for screening to obtain genes related to AD disease.

6. The method of claim 5, wherein, The genes screened out in the genes related to AD disease include: High nucleotide monocyte-high amino acid monocyte differential genes, protective T cell-high amino acid T cell differential genes, and protective B cell-high amino acid B cell differential genes.

7. The device for comprehensively analyzing immune metabolism and AD disease-related genes based on multi-omics, characterized in that, Including: A first acquisition module for acquiring single-cell sequencing data of AD patients; A second acquisition module for acquiring whole genome association study (GWAS) data of immune cells and metabolites of AD patients; An inference module for screening independent single nucleotide polymorphisms (SNPS) based on the GWAS data; through Mendelian randomization causal inference, immune cell features and metabolite factor features with causal associations are obtained; A first analysis module for classifying immune cells with causal associations and related metabolite enrichment analysis to identify immune cells and metabolites that have an important impact on the occurrence of AD, and further obtain metabolic pathways that affect AD; wherein the identified immune cells include immune cells with protective factors and risk factors for AD, and the identified metabolites include metabolites with protective factors and risk factors for AD; A second analysis module for analyzing the single-cell sequencing data of AD patients to identify cell populations related to metabolic pathways and obtain gene expression data of metabolic pathways in different cell populations; differential gene analysis is performed to obtain gene analysis results; A screening module for screening genes related to AD disease according to the expression difference results of different metabolic pathways in different cell populations in the gene analysis results.

8. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the program to implement the method for comprehensively analyzing immune metabolism and AD disease-related genes according to any one of claims 1 to 6. 9.A non-transitory computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the method for comprehensively analyzing immune metabolism and AD disease-related genes according to any one of claims 1 to 6.

10. A computer program product comprising a computer program, characterized in that, The computer program is executed by a processor to implement the method for comprehensively analyzing immune metabolism and AD disease-related genes by multiple omics according to any one of claims 1 to 6.

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