Multi-omics analysis method and device, electronic equipment and storage medium

Abstract

The invention discloses a multi-omics analysis method and device, electronic equipment and a storage medium, and relates to the technical field of biological information, and the method comprises the following steps: obtaining a spatial single cell function map set based on multi-omics data, the multiple omics data at least comprise single cell transcriptome data, spatial transcriptome data, single cell chromatin transposase accessibility sequencing ATAC data and whole genome association research GWAS data, and the spatial single cell function map set comprises a plurality of maps used for indicating spatial distribution of different omics data; and establishing a mapping relationship from the spatial transcriptome data to the single cell transcriptome data, wherein the mapping relationship indicates spatial position information of each cell in the single cell transcriptome data. By utilizing the method, the flexibility during multi-omics analysis is improved, and the data integration capability is improved.

Description

Technical Field

[0001] The present application relates to the field of bioinformatics, and in particular to a multi-omics analysis method, device, electronic device and storage medium. Background Art

[0002] With the development of biological technology, single-cell transcriptome data can be obtained through single-cell transcriptome sequencing technology. The location distribution information of sample gene expression can be obtained through spatial transcriptome technology.

[0003] By integrating the processed data of these two technologies, it is possible to analyze and mine the spatial distribution characteristics of multi-omics data at the single-cell level. However, the current integration method is to cluster cells and define cell types based on the data of single-cell transcriptome sequencing technology, obtain the gene expression characteristics of cell types, and then analyze the cell composition of each point in spatial transcriptomics, and further map the cell type or cell type composition factors to the spatial distribution to obtain the spatial distribution information of cell types.

[0004] The above data integration methods can only achieve a one-way mapping from single-cell transcriptome data to spatial transcriptome data, and cannot integrate the genome, epigenome, transcriptome, proteome and other data obtained by single-cell transcriptome sequencing technology, as well as the derived data of the above data. Therefore, the above integration solutions have poor flexibility and poor data integration capabilities. Summary of the invention

[0005] In order to solve the above-mentioned technical problems existing in the prior art, the present application provides a multi-omics analysis method, device, electronic device and storage medium, which improve the flexibility of multi-omics analysis and the ability to integrate data.

[0006] In a first aspect, the present application provides a multi-omics analysis method, the method comprising: obtaining a set of spatial single-cell functional maps based on multi-omics data, wherein the multi-omics data at least includes single-cell transcriptome data, spatial transcriptome data, single-cell chromatin transposase accessibility sequencing ATAC data, and whole-genome association study GWAS data, and the spatial single-cell functional map set includes multiple maps for indicating the spatial distribution of different omics data; establishing a mapping relationship from spatial transcriptome data to single-cell transcriptome data, wherein the mapping relationship indicates the spatial position information of each cell in the single-cell transcriptome data.

[0007] The method provided by the present application, in multi-omics analysis, not only includes the integration of single-cell transcriptome and spatial transcriptome data, but also realizes the integration of single-cell ATAC and GWAS data, and realizes the integration of more dimensional data. And the analysis content is more comprehensive, and the spatial single-cell functional atlas set includes multiple atlases, which can indicate the distribution of different omics data in space. In addition, not only the mapping of cell type definition data based on single-cell transcriptome to spatial transcriptome data is realized in the spatial single-cell functional atlas set, but also the mapping relationship from spatial transcriptome data to single-cell transcriptome data is established, and the mapping relationship indicates the spatial position information of each cell in the single-cell transcriptome data, so that the single-cell transcriptome data has position coordinate information, and realizes the two-way mapping between spatial transcriptome data and single-cell transcriptome data, so that data integration and analysis are more flexible.

[0008] In a possible implementation, before obtaining a set of spatial single-cell functional maps based on multi-omics data, the method further includes: when the initial spatial transcriptome data has a first spatial resolution, converting the initial spatial transcriptome data into spatial transcriptome data with a second spatial resolution, wherein the second spatial resolution is greater than the first spatial resolution.

[0009] In a possible implementation, initial spatial transcriptome data are converted into spatial transcriptome data with a second spatial resolution, specifically including: splitting each point in the initial spatial transcriptome data into a first number of sub-points according to the corresponding area; using the expression information of each point as the initial expression information of each sub-point corresponding to the point; using the Metropolis-Hastings algorithm to iterate the points to obtain spatial transcriptome data with a second spatial resolution, wherein the expression information of each sub-point of the next generation during iteration is the sum of the expression information of the sub-point of the previous generation and a correction value, the correction value obeys a normal distribution with a mean of 0, the sum of the correction values ​​of all sub-points is 0, and the average expression information of the first number of sub-points after the iteration is completed is the expression information of the point.

[0010] In a possible implementation, the bidirectional mapping relationship between the spatial transcriptome data and the single-cell transcriptome data is the correspondence between each cell in the single-cell transcriptome data and the spatial position information. The bidirectional mapping relationship between the spatial transcriptome data and the single-cell transcriptome data is established, specifically including: using the Seurat canonical association analysis CCA algorithm to embed the spatial transcriptome data and the single-cell transcriptome data into a common reduced dimensionality space; inserting multiple points based on the coordinates of the original points and the principal component set in the reduced dimensionality space, and the data of each inserted point includes the spatial coordinates and the principal component information PCs; based on the spatial coordinates of all points and the P after common dimensionality reduction Cs, a multivariate random forest RF model is obtained through machine learning training, and multiple decision trees are constructed by randomly extracting point samples and PCs of different dimensions to train and learn the relationship between PCs and spatial coordinates; the distance matrix is ​​obtained using single-cell transcriptome data, the spatial coordinates of all points, and the RF model, and the distance matrix measures the expression similarity between points in the data in the spatial transcriptome and cells in the single-cell transcriptome data; the distance matrix is ​​used to generate a point-cell network diagram through the mutual nearest neighbor MNN algorithm, and according to the neighboring relationship, the spatial coordinates of the points in the data after inserting multiple points are assigned to their neighboring cells to obtain the spatial position information of each cell in the single-cell transcriptome data.

[0011] In one possible implementation, the set of spatial single-cell functional maps includes at least one or more of the following: a spatial expression map, which is used to reflect the spatial expression of genes and / or proteins; a cell type abundance map, which is used to reflect the number of cell types and the proportion of each type of cells; a spatial cell state map, which is used to reflect the spatial distribution of cell type characteristic gene set scores; a cell type co-localization map, which is used to reflect the association between different types of cells when they appear in space; a spatial pathway activity map, which is used to reflect signal pathways and signal pathways that have undergone key changes in spatial distribution; a spatial pathway synergy map, which is used to reflect the synergistic effect of signal pathways; a spatial transcription factor binding map, which is used to reflect the spatial distribution characteristics of transcription factor chromatin accessibility; a spatial transcription factor regulation map, which is used to reflect the spatial distribution characteristics of regulator activity; a spatial GWAS phenotype relationship map, which is used to reflect the distribution of realized diseases or phenotypes in spatial transcriptome slices.

[0012] In a possible implementation, the spatial single-cell functional map set includes a spatial cell state map, and the spatial single-cell functional map set is obtained based on multi-omics data, including: using the marker gene set of each cell type as the characteristic gene set of the corresponding cell type, or using the gene expression data and cell type annotation data in the single-cell transcriptome data, selecting each cell type and all the remaining cells for gene differential expression analysis in turn, and using the first second number of genes sorted in reverse order by Log2FC as the characteristic gene set of the corresponding cell type; for each cell type, determining the characteristic statistics of the characteristic gene set in this cell type by the following method: taking the cumulative sum of the Log2FC products of the second number of genes under the cell type as an overall score of the genes included in the characteristic gene set, and at the same time as the characteristic statistics of the characteristic gene set in the cell type; using the following formula to determine the characteristic statistics of each point in the spatial transcriptome data in each characteristic gene set:

[0013]

[0014] Among them, ACTij represents the characteristic statistic under the j-th characteristic gene set in the i-th point; k is the k-th cell type, K is the total number of cell types, k is less than or equal to K, K and k are positive integers; Proportion ik is the component proportion or abundance value of the kth cell type in the ith point, ACT kj is the characteristic statistic of the jth characteristic gene set of the kth cell type.

[0015] In the second aspect, the present application also provides a multi-omics analysis device, which includes an acquisition unit and an establishment unit; the acquisition unit is used to obtain a set of spatial single-cell functional maps based on multi-omics data, wherein the multi-omics data at least includes single-cell transcriptome data, spatial transcriptome data, single-cell chromatin transposase accessibility sequencing ATAC data, and whole-genome association study GWAS data, and the spatial single-cell functional map set includes multiple maps for indicating the spatial distribution of different omics data; the establishment unit is used to establish a bidirectional mapping relationship between the spatial transcriptome data and the single-cell transcriptome data, wherein the bidirectional mapping relationship includes the spatial position information of each cell in the single-cell transcriptome data.

[0016] In a possible implementation, the device further includes: a precision conversion unit. The precision conversion unit is used to convert the initial spatial transcriptome data into spatial transcriptome data with a second spatial resolution when the initial spatial transcriptome data has a first spatial resolution, wherein the second spatial resolution is greater than the first spatial resolution.

[0017] In a third aspect, the present application further provides an electronic device, which includes a processor and a memory. The memory is used to store computer programs or computer instructions; the processor is used to execute the computer programs or computer instructions stored in the memory, so that the electronic device performs the multi-omics analysis method described in the first aspect and any one of the time modes of the first aspect.

[0018] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the multi-omics analysis method described in the first aspect and any one of the time modes of the first aspect. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A flowchart of a multi-omics analysis method provided in an embodiment of the present application;

[0020] Figure 2 A schematic diagram of a system for implementing a multi-omics analysis method provided in an embodiment of the present application;

[0021] Figure 3 A schematic diagram of a spatial single-cell functional map set provided in an embodiment of the present application;

[0022] Figure 4 A schematic diagram of the spatial distribution characteristics of a cell type provided in an embodiment of the present application;

[0023] Figure 5 A schematic diagram of the results of spatial co-localization analysis of cell types provided in the embodiments of the present application;

[0024] Figure 6 A spatial distribution diagram of the signaling pathway activity score provided in the embodiments of the present application;

[0025] Figure 7 A spatial distribution diagram of the transcription factor regulatory ability score provided in the embodiment of the present application;

[0026] Figure 8 The spatial distribution of chromatin accessibility scores of transcription factors provided in the embodiments of the present application;

[0027] Fig. 9 The spatial distribution map of the GWAS clinical phenotype scores provided in the embodiments of the present application;

[0028] Fig.10 A cell cluster distribution map before improving the spatial resolution provided in an embodiment of the present application;

[0029] Fig.11 The cell cluster distribution map after improving the spatial resolution provided in the embodiment of the present application;

[0030] Fig.12 A schematic diagram of a multi-omics analysis device provided in an embodiment of the present application;

[0031] Fig.13 A schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0032] In order to enable persons skilled in the art to more clearly understand the present application, the following first explains the relevant terms involved in the technical solution of the present application.

[0033] Omics usually refers to the systematic study of a collection of various research objects (generally biological molecules) in biology, such as genomics, proteomics, and metabolomics, and the collection of these research objects is called a "group".

[0034] Transcription is the process by which genetic information is copied from DNA to RNA (especially mRNA) under the catalysis of RNA polymerase. As the first step in protein biosynthesis, transcription is the pathway for synthesizing mRNA and non-coding RNA (tRNA, rRNA, etc.).

[0035] Transcriptome, also known as "transcriptome", broadly refers to the sum of all RNAs that can be transcribed in a cell or a group of cells under the same environment (or physiological conditions), including messenger RNA (mRNA), ribosomal RNA (rRNA), transfer RNA (tRNA) and other types of non-coding RNA; in a narrow sense, it refers to all messenger RNA (mRNA) that can be transcribed by cells.

[0036] Single-cell sequencing is a technology that amplifies and sequences DNA or RNA at the level of a single cell, which is used to obtain single-cell transcriptome data. The current single-cell sequencing platform can obtain transcriptome data of up to 128 samples and about 1 million cells at a time.

[0037] With the development and popularization of single-cell sequencing technology, single-cell RNA sequencing (scRNA-seq) technology has been used to derive multiple platform applications such as single-cell protein antibody-derived tags (ADT), Assay for Transposase Accessible Chromatin with high-throughput sequencing (ATAC-seq or ATAC for short), DNA methylation (DNA Methylation), chromatin immunoprecipitation (ChIP) and genome. Similarly, high-throughput spatial transcriptome sequencing technology has also developed from the original 100um resolution level to single-cell and subcellular levels.

[0038] In terms of single-cell epigenomics (also known as epigenome), some current technology platforms can obtain chromatin open region data at the single-cell level, and related technologies can also achieve the acquisition of histone modification data and transcription factor and DNA interaction data at the single-cell level. In addition, related technologies have also achieved the detection of DNA methylation data at the single-cell level.

[0039] In terms of single-cell proteomics, on the one hand, due to the requirement of mass spectrometer for protein sample volume, it is impossible to perform full-spectrum protein detection in a single cell, and nearly 100 cells of the same type need to be sequenced together through cell sorting; on the other hand, the related technology can achieve quantitative detection of about 500 proteins at the single-cell level based on the specific Tag sequence corresponding to the targeted protein, and can also obtain transcription data at the single-cell level at the same time. Single-cell genome sequencing cannot yet achieve full-genome detection at the single-cell level, and it is necessary to first target and capture a specific set of genes, and then perform single-cell level detection on this captured DNA data.

[0040] Through the brief description above, single-cell sequencing technology has been applied to multiple levels of single-cell genome, epigenome, transcriptome and proteome.

[0041] Spatial transcriptomics technology prepares fixed barcoded oligonucleotides on glass slides. These barcodes not only contain the spatial position correspondence information of tissue sections, but also can capture the expression of messenger RNA from the sections. For solid tissues, spatial transcriptomics slice images can be further aligned with high-resolution microscopic images such as immunofluorescence / pathology, and spatial distribution information and expression information of different cell types can be obtained without cell dissociation and sorting. The diameter of a single cell of ordinary eukaryotic organisms is usually 5 to 30um. Taking the development of 10x Genomics Spatial Gene Expression (ST) to HD Spatial Gene Expression (HD) as an example, the spatial position resolution has been reduced from the distance between two adjacent points of about 55um to the distance between two adjacent points of about 2um. The spatial position of the ST platform has developed from the transcriptome data of one point containing the gene expression data of 5 to 10 cells to the gene expression at the subcellular level. With spatial transcriptome sequencing technology refined to subcellular resolution, combined with image recognition and cutting of cell nuclei and cell boundary extension algorithms, some platforms can now obtain transcriptome data with spatial location information at the single-cell level.

[0042] As explained above, the core of single-cell transcriptome sequencing technology is to solve the problem of allocating a sample's transcriptome data to the single-cell level. The core of spatial transcriptome sequencing technology is to provide location information of gene expression in a sample, that is, spatial transcriptome sequencing can obtain the location distribution information of sample gene expression. At present, the integration idea of ​​the above two technologies is: based on the data of single-cell transcriptome sequencing technology, cell clustering and cell type definition are performed to obtain the gene expression characteristics of cell types, and then the cell composition of each point in spatial transcriptomics is analyzed, and the cell type or cell type composition factors are further mapped to the spatial distribution to obtain the spatial distribution information of cell types.

[0043] However, the current integration scheme cannot integrate the genome, epigenome, transcriptome, and proteome data obtained by single-cell sequencing technology, nor can it integrate the spatial distribution analysis of features such as transcription factor expression, signaling pathway activity scores, epigenetic regulation, and transcription factor accessibility derived from these data. Secondly, the existing integration scheme does not include the integration of public research results based on bulk samples, such as genome-wide association studies (GWAS) association analysis of conventional genomes and clinical characteristics, analysis of cell types related to clinical phenotypes, and spatial location distribution characteristics of clinical phenotypes. In addition, the current integration scheme cannot realize the transmission of spatial information to single-cell data, that is, to predict the spatial location information corresponding to cells in single-cell transcriptome data by integrating spatial transcriptome data. In addition, although biologically, spatially adjacent cells tend to belong to the same cell type, for low-resolution spatial transcriptome data, each point contains 5 to 10 cells. Existing methods will obtain each point as a weighted combination of various cell types. This method is not conducive to studying the spatial distribution characteristics of cell types, especially the spatial interaction analysis of different cell types in biological processes such as tumor occurrence and development, and immune infiltration of tumor boundaries.

[0044] In order to solve the above problems, the embodiments of the present application provide a multi-omics analysis method, device, electronic device and storage medium, which improve the flexibility of multi-omics analysis and the ability to integrate data.

[0045] In order to enable persons skilled in the art to more clearly understand the solution of the present application, the technical solution in the embodiments of the present application will be described below in conjunction with the drawings in the embodiments of the present application.

[0046] The terms "first", "second", etc. in the description of this application are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features.

[0047] The embodiment of the present application provides a multi-omics analysis method, which is described in detail below with reference to the accompanying drawings.

[0048] See also Figure 1 and Figure 2 .in, Figure 1 A flowchart of a multi-omics analysis method provided in an embodiment of the present application; Figure 2 A schematic diagram of a system for implementing a multi-omics analysis method provided in an embodiment of the present application.

[0049] Figure 2The system shown includes a data input module 21 and an analysis module 22. The data input by the data input module 21 may include: transcriptome data obtained by single-cell transcriptome sequencing technology, proteome data obtained by single-cell transcriptome sequencing technology, single-cell epigenetic data obtained by single-cell transcriptome sequencing technology, GWAS data, spatial transcriptome data obtained by spatial transcriptome sequencing technology, and proteome data obtained by spatial transcriptome sequencing technology.

[0050] The functions of the analysis module mainly include three parts:

[0051] Part 1: Integrate the spatial functional map parts of spatial transcriptome, single-cell transcriptome, single-cell ATAC and GWAS data to obtain a set of spatial single-cell functional maps. Furthermore, the integrated data can include genome, epigenome, transcriptome and other data as well as the derivative data of the above data.

[0052] Part 2: Split the spatial transcriptome data with a spatial resolution greater than the cell diameter to obtain spatial transcriptome data with subspot resolution, that is, obtain high-precision spatial transcriptome data. Among them, Spot is the smallest basic detection unit of the spatial transcriptome. For example, for the ST platform, the transcriptome data of a Spot at a spatial position may contain gene expression data of 5 to 10 cells. At this time, it is necessary to further obtain the spatial transcriptome data of the Subspot of the Spot.

[0053] The third part is the bidirectional mapping of spatial transcriptome data and single-cell transcriptome data.

[0054] The following is a detailed description of the method, which includes the following steps:

[0055] S11: Integrate and obtain a collection of spatial single-cell functional maps.

[0056] For the first part of the system, see Figure 3 , which is a schematic diagram of a spatial single-cell functional map set provided in an embodiment of the present application.

[0057] The spatial single-cell functional map set includes: spatial expression map 31, cell type abundance map 32, spatial cell state map 33, cell type co-localization map 34, spatial pathway activity map 35, spatial pathway synergy map 36, spatial transcription factor binding map 37, spatial transcription factor regulation map 38, spatial GWAS phenotype association map 39. The spatial single-cell functional map set realizes the integration of multi-dimensional data, including the spatial distribution map of different omics data, that is, the spatial distribution of different omics data is indicated in the spatial single-cell functional map set. It is understandable that the spatial single-cell functional map set may also include only part of the maps in the above maps. Usually, at least maps 31, 32, and 33 are included.

[0058] The following describes in detail how to obtain each graph.

[0059] For the spatial expression map 31, which reflects the spatial expression of genes and / or proteins, the scheme of the present application supports different schemes such as Seurat and BayesSpace to perform spatial point (Spot) clustering analysis on spatial transcriptome expression data, and displays the spatial distribution characteristics of genes based on the spatial clustering information results.

[0060] Among them, Seurat is a single-cell RNA sequencing (scRNA-seq) data analysis package that can process and analyze scRNA-seq data from different platforms. Seurat also supports the analysis of spatial transcriptome data. Seurat clusters spatial transcriptome data only based on the expression similarity of genes between Spots, without considering the location information of Spots.

[0061] BayesSpace is a clustering method based on a spatial transcriptome model. It models the low-dimensional representation of the gene expression matrix and encourages adjacent points in space to belong to the same cluster through prior spatial location information to achieve final clustering. Specifically, the BayesSpace method first initializes the cluster label of a given spot using the non-spatial clustering method k-means or mclust, and uses the prior idea that adjacent spots belong to the same cluster as the Markov random field prior model to calculate the prior probability; then calculate the likelihood probability of the cluster label that the spot may belong to based on the spot principal component; finally, calculate the posterior probability based on the prior probability and likelihood probability, and use the Markov Chain Monte Carlo method (MarkovChain Monte Carlo, MCMC) to iteratively sample to obtain the optimal solution that satisfies the posterior probability. The cluster with the maximum posterior probability is the final cluster to which the spot belongs.

[0062] The cell type abundance map 32 is used to reflect the number of cell types and the proportion of each type of cells.

[0063] In the examples of the present application, based on the cell type annotation results of the single-cell transcriptome, two deconvolution methods, SPOTlight and Cell2Location, are provided to annotate the spatial transcriptome.

[0064] Among them, for Cell2location, it is based on negative binomial regression and hierarchical Bayesian model, and combines the marker information provided by single-cell sequencing to perform deconvolution analysis on Spot, with higher sensitivity and resolution, to obtain the proportion abundance value of each cell type in Spot, and estimate the approximate distribution of cell types in tissue areas, and finally realize the spatial distribution map of cell types.

[0065] The spatial cell state atlas 33 is used to reflect the spatial distribution of the cell type characteristic gene set score. The spatial cell state atlas 33 can indicate the distribution of a gene set on a spatial slice, and the gene set is a characteristic gene set of a cell type. In one possible implementation, the marker gene set of each cell type can be directly used as the characteristic gene set of the corresponding cell type. In another possible implementation, if the marker gene set is not pre-sorted, the gene expression data and cell type annotation data in the single-cell transcriptome data can also be used to select each cell type and all the remaining cells for gene differential expression analysis in turn. After Log2FC is sorted in reverse order, the largest top 20 genes are used as the characteristic gene set of the cell type. It can be understood that 20 is the second number, and the second number can also be replaced by other values. The embodiment of the present application is not specifically limited.

[0066] Among them, the cell type marker gene set is used to characterize the gene set of a cell type, which may come from reports in databases or published literature, such as CD3D and CD4, which are often used as the marker gene set of CD4-positive T cells.

[0067] In the embodiment of the present application, a characteristic gene set may include multiple genes. In the embodiment of the present application, multiple genes can be used to accumulate as the expression of this characteristic gene set. For a gene in the characteristic gene set of a cell type, it is not limited that this gene is expressed only in this cell type, but generally refers to that the expression of this gene under the current cell type is higher relative to other cell types. For example, assuming that 1 point in the current space includes two cell types A and B, each of A and B accounts for 50%. The characteristic gene set of cell type A is a, and the characteristic gene set of cell type B is bar b. The characteristic gene set a corresponds to the first score value S1 under cell type A, and the characteristic gene set a corresponds to the second score value S2 under cell type B, and the second score value is usually less than the first score value. Then, the proportion of cell composition is used as a weight, and a weighted accumulation is performed as the score of the characteristic gene set a under this point, and the score of the characteristic gene set a is 0.5S1+0.5S2 at this time.

[0068] For a selected cell type, the cumulative sum of the Log2FC (if not defined as 0) products of the 20 genes in the cell type is calculated as an overall score for the genes included in the feature gene set, and also as the feature statistic of the feature gene set in the cell type. The following formula is used to determine the feature statistics of each point in the spatial transcriptome data in each feature gene set:

[0069]

[0070] Among them, ACT ij Represents the characteristic statistics of the j-th characteristic gene set in the i-th Spot. Here, the module score of the cell type characteristic gene set is calculated based on Seurat, which can reflect the spatial distribution of the cell type characteristic gene set.

[0071] k represents the kth cell type, K represents the total number of cell types. K is a positive integer, k=1, 2, ..., K.

[0072] Proportion ik Represents the component proportion or abundance value of the kth cell type in the ith Spot, which can be calculated using the result of Cell2location.

[0073] ACT kj Represents the feature statistics of the j-th feature gene set of the k-th cell type.

[0074] It can be understood that the above formula (1) is not only applicable to cell types, signal pathway activity scores, transcription factors, and GWAS association scores, but can also be extended to other attribute data of cell types, such as GeneOntology gene set scores, KEGGPathway scores, single-cell metabolic pathway scores, and pseudo-time.

[0075] The cell type co-localization map 34 is used to reflect the correlation between different types of cells when they appear in space. Normally, different types of cells do not appear randomly in spatial positions. The positioning of cells in space is affected by themselves and other cell types. There is also a certain preference for the positioning of different types of cells in space, and there may be a synergistic relationship between them. The embodiment of the present application uses a spatial transcriptome cell interaction analysis method, such as the MISTy analysis method, to evaluate the distribution characteristics of Spot features and surrounding Spot feature cell types, and model and construct a spatial relationship view. In addition, the scheme of the present application can be further combined with a machine learning method to obtain the optimal solution for the cell distribution relationship, that is, to obtain the dependence strength of different types of cells in spatial position, and to explore the co-localization relationship between different cell types.

[0076] Spatial pathway activity maps 35 are used to reflect signal pathways and signal pathways that have undergone key changes in spatial distribution. Among them, signal pathways are biochemical processes by which cells perceive and respond to external stimuli, and are related to cell growth, survival, and differentiation.

[0077] Pathway analysis is essential for studying cell function. Pathway enrichment analysis methods such as GSVA and ClusterProfiler do not consider the difference in contribution weights between each gene in the pathway gene set, as well as the effects of post-transcriptional regulation and post-translational modification on changes in gene function, resulting in inferred pathway activity that is not accurate enough. The scheme of the present application adds the PROGENy module, which builds a regression model for selected multiple signaling pathways based on the gene expression data of a large number of perturbation experiments collected, to evaluate the contribution of different genes in the same pathway, and then screens the 100 genes with the largest contribution in each pathway to form the core gene set (Pathway RespOnsive GENes) of PROGENy, it is understandable that 100 can also be replaced by other numerical values, and the present application embodiment is not specifically limited. Finally, based on the PROGENy signaling pathway core gene set, the signaling pathway score of each Spot in the space is calculated, and the subsequent combination of the Spot clustering results in the space, as well as the distribution differences of the signaling pathway scores between the clusters, can be quickly located to the signaling pathway where key changes occur in the spatial distribution of the research object.

[0078] The spatial pathway synergy map 36 is used to reflect the synergy of signaling pathways.

[0079] Biological functions are achieved through the coordinated regulation of different genes, proteins, cell types, and signaling pathways through complex interactions, similar to the spatial co-localization analysis of cell types. Based on the PROGENy pathway activity score of spatial Spot, the MISTy method is used to screen signal pathway relationship pairs with consistent score trends in spatial Spot to achieve the evaluation of the synergistic relationship between different pathways.

[0080] The spatial transcription factor binding map 37 is used to reflect the spatial distribution characteristics of the chromatin accessibility of transcription factors (TFs). Transcription factors are an important class of proteins that can regulate gene expression and play a key role in biological processes such as cell development and differentiation, immune response, and tumor development.

[0081] Epigenetic modifications at the DNA level and the degree of chromatin openness will affect the transcriptional expression of genes. This analysis requires that the sample must have spatial transcriptome, single-cell transcriptome and single-cell ATAC data (scATAC). First, scATAC and scRNA data are jointly analyzed, and the cell type annotation of scATAC data is performed using the transfer label method. Then, ChromVAR is used to calculate the chromatin accessibility Deviations Score of each transcription factor motif in each cell of each cell type, and the average chromatin accessibility Deviations Score of the transcription factor motif of each cell type is further obtained. According to the average chromatin accessibility score of each transcription factor in different cell types, the spatial distribution characteristics of the chromatin accessibility of the transcription factor are calculated by the above formula (1). Among them, DeviationsScore is used to characterize the degree of development of the chromatin accessibility of the transcription factor.

[0082] The spatial transcription factor regulation map 38 is used to reflect the spatial distribution characteristics of the activity of the regulator (Regulon).

[0083] Regulon is a collection of genes regulated by the same regulatory element (transcription factor). Each regulon can be defined as a collection of a transcription factor and its target genes.

[0084] The scheme of the embodiment of the present application includes a SCENIC analysis module, which first predicts and screens the direct co-expression relationship between transcription factors and target genes through the SECNIC algorithm, mines the Regulon regulon, and then uses the AUCell algorithm to score the Regulon regulon activity of each group, generates a Regulon-Barcodes / Spot activity matrix, and calculates the regulon specificity score of each cell type based on the JS (Jensen-Shannon) divergence algorithm.

[0085] Specifically, the solution of the embodiment of the present application provides two strategies: one is to directly perform SCENIC analysis based on spatial transcription data to analyze the Regulon activity on each Spot; the other is to first perform SCENIC analysis on the single-cell transcriptome data, based on the cell's regulator activity score matrix and cell type annotation results, and also apply the above formula (1) to obtain the spatial distribution characteristics of Regulon activity.

[0086] Spatial GWAS phenotype association maps39 are used to characterize the distribution of diseases or phenotypes in spatial transcriptome slices.

[0087] GWAS can provide a better understanding of the genetic basis of a disease or phenotype, and there are a large number of public GWAS research resources. The scheme of the embodiment of the present application supports the use of gChromVar to perform co-localization and enrichment analysis on the clinical phenotype GWAS analysis results and scATAC chromatin accessibility regions, and then uses SCAVENGE to screen cells whose scores meet the set range from the results as seed cells, and performs network propagation iterations in the cell adjacency network. When the network reaches a steady state, the enrichment score of each cell and each GWAS phenotype is obtained. Finally, combined with the cell type annotation results of scATAC, the average enrichment score of each cell type and each GWAS phenotype is calculated, and the phenotypic correlation score in each Spot is calculated by the method of formula (1), so as to realize the distribution analysis of clinical phenotype correlation scores in spatial transcriptome slices.

[0088] The implementation of the second part of the system is described below.

[0089] At present, the transcriptome data of a point in the spatial position of the ST platform contains gene expression data of about 5 to 10 cells. When integrating the spatial transcriptome and the single-cell transcriptome and other omics data, if the precision of the spatial position information can be improved, it is more conducive to aligning the data of different platforms to the single-cell level, thereby realizing the spatial distribution feature analysis and mining of multi-omics data at the single-cell level. Therefore, the scheme of the embodiment of the present application is in S11 when integrating the spatial single-cell function atlas set, if the initial acquisition is low-precision spatial transcriptome data, it can be converted into high-precision spatial transcriptome data first. That is, when the initial spatial transcriptome data adopts the first spatial resolution, it is converted into the spatial transcriptome data of the second spatial resolution, wherein the second spatial resolution is greater than the first spatial resolution.

[0090] The following describes in detail how to obtain high-precision spatial transcriptome data.

[0091] In order to obtain transcriptome data with higher spatial resolution, the scheme of the embodiment of the present application can split the spatial transcriptome Spot level data into Subsplot levels by integrating the BayesSpace analysis module. Each Spot can be split into a first number of Subsplots, and the embodiment of the present application does not specifically limit the first number. In the following description, the first number is 6 as an example for explanation.

[0092] Taking the ST platform as an example, the spot spacing is 55um, and the area of ​​each spot is about 2500um. 2 After a single spot is divided into 6 subspots, the area of ​​each subspot is about 400um. 2 , when the cell area is 400um 2 When the diameter of an ideal round cell is about 23um, the diameter of a eukaryotic cell is generally between 5um and 30um. At this time, the area of ​​a single subsplot is basically equal to the area of ​​a single cell, achieving high-precision conversion.

[0093] The spatial enhanced resolution clustering model used by the BayesSpace analysis module is designed to solve the principal component (PC) of the subspot.

[0094] The solution of the embodiment of the present application uses the MCMC algorithm to model it as a new variable. The expression of each Spot is used as the initial expression information of each Subspot under the Spot. The Metropolis-Hastings algorithm is used to sample and update. For Spot iteration, the expression information of each Subspot under it can always be obtained by adding the previous generation expression information and a correction value (prediction value iteration jitter), and the correction value obeys a normal distribution with a mean of 0. At the same time, the sum of the correction values ​​of all Subspots is kept to be 0. Based on the Gaussian prior idea, the iteration jitter value will not be too far away from the Spot expression. Since the Subspot is not only related to the other 5 Subspots under the current Spot, but also to the Subspots of the adjacent Spot, the optimal Subspot PC combination is obtained through iteration, and finally the average expression information of the 6 Subspots after the splitting and iteration is completed is the expression information of the original Spot. In addition, in order to improve the accuracy of the expression information of the Subspot, the expression information of the Subspots from different original Spots with adjacent spatial distribution after the splitting should be as consistent as possible.

[0095] After obtaining spatial data close to the single-cell resolution, on the one hand, Cell2location analysis and spatial functional map analysis can be performed; on the other hand, the spatial transcriptome data at the Subspot level is close to the single-cell resolution level. The Seurat Co-embedding and CCA (Canonical Correlation Analysis) methods are used to establish a one-to-one mapping between spatial transcriptome data and single-cell transcriptome data to achieve the integration of the two platforms. For the 10x Genomics HD Spatial Gene Expression platform, the transcriptome data at the 20um level can be selected for subsequent joint analysis.

[0096] S12: Establish a mapping relationship from spatial transcriptome data to single-cell transcriptome data.

[0097] The above mapping relationship indicates the spatial location information of each cell in the single-cell transcriptome data.

[0098] Calculate the spatial location information of single-cell transcriptome. Integrate the CellTrek tool module. First, based on the SeuratCCA (Canonical Correlation Analysis) integration algorithm, merge the single-cell data and the spatial data and embed them into a common dimensionality reduction space. A single spot on the 10X visium platform contains 1-10 cells. In order to improve the resolution, a two-dimensional spatial interpolation prediction algorithm is used to interpolate 5,000 spot data based on the original spot coordinates and PCs (where PC refers to the principal component, and PCs is the plural form of the principal component) in the dimensionality reduction space. Each spot data includes spatial coordinates and PCs (PCs can represent principal component information, where multiple principal components are indicated) to improve spatial resolution. Then, for spatial data, based on the spot coordinates and the PCs after common dimensionality reduction (for example, the top 30 PCs in the sorting are selected), a multivariate random forest (RF) model is trained through machine learning. A large number of decision trees are constructed by randomly extracting spots (samples) and PCs of different dimensions with replacement, and finally the relationship between PCs and spatial coordinates is learned through training. The 5000 in the above description may also be replaced by other values.

[0099] Then the trained RF (Random Forest) model is applied to the integrated data (including single-cell transcriptome data and interpolated Spot), and the distance matrix based on the RF model is calculated to measure the expression similarity between Spot in the spatial transcriptome data and cells in the single-cell transcriptome data. In the decision tree of the model, assuming that there is a cell and a Spot, when the distance from the root node is the same, the fewer the number of edges from their nearest shared ancestor node, the smaller the RF distance, that is, the higher the similarity of the expression patterns of the two cells, the above matrix is ​​generated by the mutual nearest neighbors (MNN) algorithm. The Spot-cell network diagram is a sparse Spot-cell network diagram. According to the neighbor relationship, all interpolated Spot coordinates are assigned to neighboring cells, and finally the spatial position information of each cell in the single-cell transcriptome data is obtained, that is, the corresponding relationship between each cell and the spatial position information in the single-cell transcriptome data is obtained.

[0100] It can be understood that when the spatial position information of a single cell is obtained, a set of spatial single-cell data is formed, and the spatial single-cell data indicates the spatial position information of each cell in the single-cell transcriptome data, that is, the single-cell transcriptome data has specific position coordinate information.

[0101] In addition, after obtaining the spatial location information of each cell in the single-cell transcriptome data, the spatial single-cell functional map set analysis in Part 1 of the analysis module 22 can be performed by further integrating the single-cell protein expression, single-cell ATAC, transcription factor, signaling pathway and GWAS data.

[0102] In summary, the solution provided by the embodiment of the present application has at least the following technical effects:

[0103] Supports the integration of data in more dimensions, including not only the integration of single-cell transcriptome and spatial transcriptome data, but also the integration of single-cell ATAC and GWAS data;

[0104] The analysis content is more comprehensive. In addition to realizing the spatial mapping of cell types, new cell type co-localization maps, spatial pathway activity maps, spatial pathway synergy maps, spatial cell state maps, spatial transcription factor regulation maps, spatial transcription factor binding maps, and spatial GWAS phenotype association maps have been added.

[0105] Based on the existing spatial transcriptome data, it is possible to generate new transcriptome data with higher spatial resolution. It not only supports the BayesSpace method of splitting Spot, but also supports the CellTrek method of inserting Spot on the original platform. The high-precision spatial data is closer to the single-cell resolution, so that the original single-cell transcriptome cell type annotation tool can be directly applied to spatial transcriptome data, which is convenient for advanced analysis of spatial transcriptome data.

[0106] Not only does it realize the mapping of cell type definition data based on single-cell transcriptome to spatial transcriptome data in the spatial single-cell functional atlas collection, it also realizes the transmission of spatial location information of spatial transcriptome data to single-cell transcriptome data, so that single-cell transcriptome data has location coordinate information, thus realizing the bidirectional mapping between spatial transcriptome data and single-cell transcriptome data, making data integration and analysis more flexible;

[0107] The solution of the embodiments of the present application provides richer analysis content and solutions. For example, for spatial transcriptome clustering, it supports both conventional clustering based on expression patterns and clustering methods that combine spatial location information. For another example, two modules, Cell2location and SPOTlight, are provided for predicting the cell composition of spatial sites based on single-cell transcriptomes. For another example, the modules for improving the resolution of spatial transcriptomes include BayesSpace and CellTrek, and Seurat CCA and CellTrek are used for one-to-one mapping of high-precision spatial transcriptome data and single-cell transcriptome data. The embodiments of the present application provide richer analysis modules for the same analysis content, and can obtain multiple analysis results at the same time, which is helpful to quickly obtain better analysis results after comparison.

[0108] It is understandable that the division of the above steps is only to better illustrate the implementation method of the technical solution of the present application, and does not constitute a limitation on the technical solution of the present application. In practical applications, the above steps can be adjusted according to actual conditions. For example, if the spatial transcriptome data is high-definition spatial gene expression, high-precision spatial transcriptome data may not be further obtained. Alternatively, only one of steps S11 or S12 may be implemented.

[0109] In order to make those skilled in the art more clearly understand the implementation of the technical solution of the present application, the following is explained in conjunction with specific examples. It is understandable that the solution provided in the embodiment of the present application is not used for diagnosis and treatment of diseases, but for data processing and analysis of relevant data in diagnosis and treatment of diseases.

[0110] Tumors, especially primary solid tumors, usually show spatial distribution specificity, and in the process of tumor occurrence and development, they are affected by expression regulation, and immune cells and tumor cells also interact. For the same human colorectal cancer FFPE sample, 10x Genomics spatial gene expression was used for spatial transcriptome sequencing, Chromium single cell Gene Expression V3 reagent was used to construct a single cell transcriptome library, and Chromium single cellATAC V2 reagent was used for cell nuclear extraction to construct a single cell ATAC library.

[0111] First, data preprocessing was performed, and spatial transcriptome data were analyzed using spacerangerV2.1 and 10x Under the visualization software, the areas with unclear boundaries and containing stomata were removed. After the single-cell transcriptome data were routinely analyzed by cellranger V6.1, Seurat was used to perform quality control according to the proportion of mitochondria less than <25%, the number of genes in the cell>100, and the molecular labels (Unique Molecular Identifiers, UMI)>200. The top 2000 highly variable genes were screened for principal component analysis (Principal Component Analysis, PCA) dimensionality reduction clustering. Based on the cell marker genes of the colorectum in CellMarker, scCATCH and manual annotation were used to complete the definition of cell types. After the single-cell ATAC data were routinely analyzed by cellranger-atac V2.1, Signac was used to perform quality control according to the TSS enrichment score>3 and the number of fragments between 1000 and 50000. The LSI algorithm was used for dimensionality reduction clustering, and then the Seurat CCA transfer class label method was used to annotate the cell type of scATAC data.

[0112] According to the method for obtaining the spatial expression map 31 in the above embodiment, the spatial transcriptome data were clustered using both Seurat and BayesSpace methods. It was found that the clustering result of the BayesSapce method considering the location information of the Spot point in space had clearer boundaries and was significantly better than the Seurat clustering result.

[0113] According to the above method of obtaining cell type abundance atlas 32, based on the default parameters of Cell2location software, the spatial transcriptome data Spot is deconvoluted to obtain the cell type composition information of each Spot point. Each Spot is the weight accumulation of all cell types. If a cell type has a larger weight in this Spot, it means that this Spot is mainly composed of this cell type. Therefore, after Cell2location analysis, the weight matrix of all Spots and all cell types is obtained.

[0114] See also Figure 4 , which is a schematic diagram of the spatial distribution characteristics of a cell type provided in an embodiment of the present application.

[0115] As attached Figure 4 As shown, the spatial distribution characteristics of helper T cells (CD4T), cytotoxic T lymphocytes (CD8T), epithelial cells (Epitheliums) and fibroblasts (Fibroblasts) are respectively displayed.

[0116] Based on the above method for obtaining the cell type co-localization map 34, when obtaining the cell type abundance map 32, the weight matrix results of all Spots and all cell types are obtained after Cell2location analysis, and the default parameters of the MISTy analysis module are used to obtain the co-localization analysis results of the cell types in space.

[0117] See also Figure 5 , which is a schematic diagram of the spatial co-localization analysis results of cell types provided in the examples of the present application.

[0118] The darker the color in the figure, the stronger the correlation between the two corresponding types of cells in spatial distribution. Figure 5 The co-localization relationship between CD4T, CD8T, Epitheliums, Fibroblasts, Goblet cells (Goblets), Granulocytes (Granulocytes), Macrophages (Macrophages), Myofibroblasts (Myofibroblasts), etc. is illustrated.

[0119] For example, it can be observed that epithelial-derived tumor cells have a strong dependence and interaction with CD4 T and CD8 T.

[0120] Based on the above method of obtaining spatial pathway synergy maps36, the MISTy analysis module was also used to perform synergy analysis of signal pathways. The spatial distribution map of the signal pathway activity score obtained is shown in Figure 6 Among them, the epidermal growth factor receptor (EGFR) pathway, hypoxia pathway, JAK-STAT pathway, NF-kB signaling pathway ( Figure 6 NFkB), p53 pathway, Wnt signaling pathway ( Figure 6 WNT in the literature).

[0121] See also Figure 7 , which is a spatial distribution diagram of the transcription factor regulatory ability score provided in the embodiment of the present application.

[0122] Based on the above method for obtaining the spatial transcription factor regulation map 38, the single-cell transcriptome data of the colorectum were combined with the cell type annotation results to perform SCENIC transcription factor regulation ability analysis. According to the discreteness JS, the top five transcription factors with the highest ranking for each cell type were screened out. According to the formula (1) used when obtaining the spatial cell state map 33, the spatial distribution map of the transcription factor regulation ability score of each Spot point was calculated as shown in the following figure: Figure 7 As shown, the spatial distribution of nuclear factor of activated T cells 4 (NFATC4) and ETS variant transcription factor 4 (ETV4) is schematically shown.

[0123] See also Figure 8 , this figure shows the spatial distribution of transcription factor chromatin accessibility scores provided in the examples of the present application.

[0124] Based on the above spatial transcription factor binding map acquisition method37, based on the vertebrate core transcription factor motif data in the public JASPAR, ChromVAR was used to calculate the chromatin accessibility Deviations Score of each transcription factor motif in each cell type, and it was mapped to the spatial transcriptome data through formula (1), and the spatial distribution characteristics of transcription factor chromatin accessibility were calculated as follows: Figure 8 shown. Figure 8 The figure shows the spatial distribution of chromatin accessibility scores of ETS1 in the Ets transcription factor (E26transformation specific, Ets) and the SNAI2 transcription factor.

[0125] See also Fig. 9 , which is a spatial distribution diagram of the GWAS clinical phenotype scores provided in the embodiments of the present application.

[0126] Based on the acquisition method of the above spatial GWAS phenotype association map39, gChromVar was used to perform co-localization and enrichment analysis on the clinical phenotype GWAS results and scATAC chromatin accessibility regions for single-cell ATAC data. Then, the SCAVENGE default parameters were used to calculate the average enrichment score TRS for each cell type and each GWAS phenotype. Finally, the correlation score between each Spot and the clinical phenotype was calculated using formula (1). The spatial distribution map of the GWAS clinical phenotype score is shown in the figure below. Fig. 9 shown.

[0127] Through the above example analysis, using the spatial multi-omics analysis method provided in the embodiments of the present application, we can better capture the spatial distribution characteristics of cell type maker genes, the spatial distribution characteristics of cell types, spatial cell co-localization information, signal pathway distribution and signal pathway interaction information, clinical phenotype spatial distribution correlation, and the spatial distribution of transcription factors in three dimensions: expression, regulatory ability, and chromatin accessibility.

[0128] The following is a specific example to illustrate how the present invention achieves high-precision spatial transcriptome data.

[0129] See also Fig.10 and Fig.11 ,in, Fig.10 A cell cluster distribution map before improving the spatial resolution provided in an embodiment of the present application; Fig.11 This is a cell cluster distribution map after improving the spatial resolution provided in an embodiment of the present application.

[0130] Fig.10 and Fig.11 The corresponding spatial transcriptome data and single-cell transcriptome data of the experimental mouse brain are both 10x The spatial transcriptome data is from the Visium Spatial Gene Expression platform, and the single-cell transcriptome data is from the Chromium single cell Gene Expression platform.

[0131] For the spatial transcriptome data of the experimental mouse brain, the BayesSpace method was used to split the Spot to obtain more accurate Subspot transcriptome data. The original Spot spacing was 55um, and the Subspot spacing after splitting was about 20um. The spatial position information of Spot points and Subspots was clustered according to the relevant method when obtaining the spatial expression map31.

[0132] Fig.10 is the clustering of the original spatial data based on the BayesSpace method. Fig.11 This is a cluster distribution diagram of Subspot with higher resolution accuracy. Fig.11 It can be seen that the spatial resolution has been significantly improved, and the spatial distribution of the experimental mouse brain is more delicate. Next, take Cluster 10 as an example Fig.10 Only a few Spots are distributed in other Clusters. Fig.11 It can be found that more Subspots belonging to Cluster 10 are dispersed in other Clusters. The split structure is closer to the actual state of cell distribution and is more conducive to studying the communication and interaction between different cell types in spatial distribution.

[0133] Based on the multi-omics analysis method provided in the above embodiments, the embodiments of the present application further provide a multi-omics analysis device, which is described in detail below with reference to the accompanying drawings.

[0134] See also Fig.12 , which is a schematic diagram of a multi-omics analysis device provided in an embodiment of the present application.

[0135] The device includes: an acquisition unit 41 and an establishment unit 42 .

[0136] The acquisition unit 41 is used to acquire a spatial single-cell functional map set based on multi-omics data, wherein the multi-omics data at least includes single-cell transcriptome data, spatial transcriptome data, single-cell chromatin transposase accessibility sequencing ATAC data, and whole-genome association study GWAS data.

[0137] The spatial single-cell functional atlas collection includes multiple maps that indicate the spatial distribution of different omics data.

[0138] The establishing unit 42 is used to establish a bidirectional mapping relationship between the spatial transcriptome data and the single-cell transcriptome data, wherein the bidirectional mapping relationship includes the spatial position information of each cell in the single-cell transcriptome data.

[0139] In a possible implementation, the device may further include an accuracy conversion unit, which is used to convert the initial spatial transcriptome data into spatial transcriptome data with a second spatial resolution when the initial spatial transcriptome data has a first spatial resolution, and the second spatial resolution is greater than the first spatial resolution. In this implementation, when the acquisition unit 41 integrates the spatial single-cell function map set, if the initially acquired spatial transcriptome data is low-precision, it can be converted into high-precision spatial transcriptome data first.

[0140] It is understandable that the above device is only an example and does not constitute a limitation on the technical solution of the present application. In practical applications, if the spatial transcriptome data acquired by the integration unit 41 is high-definition spatial gene expression, high-precision spatial transcriptome data may not be further acquired, that is, the device may not include a precision conversion unit.

[0141] The multi-omics analysis device includes a processor and a memory. The above acquisition unit 41 and establishment unit 42 are stored in the memory as program units, and the processor executes the above program units stored in the memory to realize corresponding functions.

[0142] The processor includes a kernel, which calls the corresponding program unit from the memory. One or more kernels can be set, and the above multi-omics analysis methods can be implemented by adjusting kernel parameters.

[0143] An embodiment of the present invention provides a storage medium on which a program is stored. When the program is executed by a processor, a multi-omics analysis method is implemented.

[0144] An embodiment of the present invention provides a processor, which is used to run a program, wherein the multi-omics analysis method is executed when the program is run.

[0145] An embodiment of the present invention provides a device, which is described in detail below with reference to the accompanying drawings.

[0146] See also Fig.13 , which is a schematic diagram of an electronic device provided in an embodiment of the present application.

[0147] The electronic device includes at least one processor 701, at least one memory 702 and a bus 703 connected to the processor 701. The processor 701 and the memory 702 communicate with each other through the bus 703; the processor 701 is used to call the program instructions in the memory 702 to execute the above-mentioned multi-omics analysis method. The electronic device in the embodiment of the present application can be a server, a server cluster, a PC, etc.

[0148] The present application also provides a computer program product, which, when executed on a data processing device, is suitable for executing a program for initiating the relevant steps of a multi-omics analysis method.

[0149] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0150] In a typical configuration, the device includes one or more processors (CPU), memory and bus. The device may also include input / output interface, network interface and the like.

[0151] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip. The memory is an example of a computer-readable medium.

[0152] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0153] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single items or plural items.

[0154] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. The device embodiments described above are merely schematic, wherein the units and modules described as separate components may or may not be physically separated. In addition, some or all of the units and modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Those of ordinary skill in the art can understand and implement it without paying creative work.

[0155] The above is only a specific implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A multi-omics analysis method, characterized in that: The method comprises: Based on multi-omics data, a spatial single-cell functional map set is obtained, wherein the multi-omics data at least includes single-cell transcriptome data, spatial transcriptome data, single-cell chromatin transposase accessibility sequencing ATAC data, and genome-wide association study GWAS data, and the spatial single-cell functional map set includes multiple maps for indicating the spatial distribution of different omics data; A mapping relationship from spatial transcriptome data to single-cell transcriptome data is established, wherein the mapping relationship indicates the spatial position information of each cell in the single-cell transcriptome data.

2. The method according to claim 1, characterized in that Before obtaining a spatial single-cell functional atlas set based on multi-omics data, the method further comprises: When the initial spatial transcriptome data has a first spatial resolution, the initial spatial transcriptome data is converted into spatial transcriptome data with a second spatial resolution, where the second spatial resolution is greater than the first spatial resolution.

3. The method according to claim 2, characterized in that The converting the initial spatial transcriptome data into spatial transcriptome data with a second spatial resolution specifically includes: Splitting each point in the initial spatial transcriptome data into a first number of sub-points on average according to the corresponding area; Using the expression information of each point as the initial expression information of each sub-point corresponding to the point; The points are iterated using the Metropolis-Hastings algorithm to obtain spatial transcriptome data with the second spatial resolution, wherein the expression information of each of the sub-points in the next generation during iteration is the sum of the expression information of the sub-points in the previous generation and a correction value, the correction value obeys a normal distribution with a mean of 0, the sum of the correction values ​​of all the sub-points is 0, and the average expression information amount of the first number of sub-points after the iteration is completed is the expression information amount of the point.

4. The method according to claim 1, characterized in that: The bidirectional mapping relationship between the spatial transcriptome data and the single-cell transcriptome data is the correspondence between each cell in the single-cell transcriptome data and the spatial position information. The establishment of the bidirectional mapping relationship between the spatial transcriptome data and the single-cell transcriptome data specifically includes: The spatial transcriptome data and the single-cell transcriptome data are co-embedded into a common dimensionality reduction space using the Seurat canonical association analysis (CCA) algorithm; On the basis of the coordinates of the original points and the principal component set in the dimension reduction space, multiple points are inserted, and the data of each inserted point includes the spatial coordinates and principal component information PCs; Based on the spatial coordinates of all points and the PCs after dimensionality reduction, a multivariate random forest RF model is obtained through machine learning training. By randomly extracting point samples and PCs of different dimensions, multiple decision trees are constructed to train and learn the relationship between PCs and spatial coordinates. Using the single-cell transcriptome data, the spatial coordinates of all points, and the RF model, a distance matrix is ​​obtained, wherein the distance matrix measures the expression similarity between the points in the spatial transcriptome data and the cells in the single-cell transcriptome data; The distance matrix is ​​used to generate a point-cell network diagram through the mutual nearest neighbor MNN algorithm, and the spatial coordinates of the data points after inserting multiple points are assigned to their neighboring cells based on the neighboring relationship to obtain the spatial position information of each cell in the single-cell transcriptome data.

5. The method according to claim 1, characterized in that The spatial single-cell functional atlas set includes at least one or more of the following: Spatial expression maps, used to reflect the spatial expression of genes and / or proteins; Cell type abundance map, which reflects the number of cell types and the proportion of each type of cells; Spatial cell state maps, which are used to reflect the spatial distribution of cell type characteristic gene set scores; Cell type co-localization map, used to reflect the association between different types of cells when they appear in space; Spatial pathway activity maps are used to reflect signaling pathways and signaling pathways that have undergone key changes in spatial distribution; Spatial pathway synergy maps are used to reflect the synergy of signaling pathways; Spatial transcription factor binding map, which is used to reflect the spatial distribution characteristics of transcription factor chromatin accessibility; Spatial transcription factor regulation map, used to reflect the spatial distribution characteristics of regulator activity; Spatial GWAS phenotype relationship maps are used to reflect the distribution of diseases or phenotypes in spatial transcriptome slices.

6. The method according to claim 5, characterized in that The spatial single-cell functional map set includes the spatial cell state map, and the method of obtaining the spatial single-cell functional map set based on multi-omics data includes: Using the marker gene set of each cell type as the characteristic gene set of the corresponding cell type, or using the gene expression data and cell type annotation data in the single-cell transcriptome data, selecting each cell type and all the remaining cells for gene differential expression analysis in turn, and using the first second number of genes sorted in reverse order by Log2FC method as the characteristic gene set of the corresponding cell type; For each cell type, the characteristic statistics of the characteristic gene set in this cell type are determined by: The cumulative sum of the Log2FC products of the second number of genes in the cell type is used as an overall score of the genes included in the characteristic gene set, and also as the characteristic statistic of the characteristic gene set in the cell type; The characteristic statistics of each point in the spatial transcriptome data in each characteristic gene set are determined by the following formula: Wherein, ACTij represents the characteristic statistic under the j-th characteristic gene set in the i-th point; k is the k-th cell type, K is the total number of cell types, k is less than or equal to K, K and k are positive integers; Proportion ik is the component proportion or abundance value of the kth cell type in the ith point, ACT kj is the characteristic statistic of the jth characteristic gene set of the kth cell type.

7. A multi-omics analysis device, characterized in that: The device comprises an acquisition unit and an establishment unit; The acquisition unit is used to acquire a spatial single-cell functional map set based on multi-omics data, wherein the multi-omics data at least includes single-cell transcriptome data, spatial transcriptome data, single-cell chromatin transposase accessibility sequencing ATAC data, and genome-wide association study GWAS data, and the spatial single-cell functional map set includes multiple maps for indicating the spatial distribution of different omics data; The establishing unit is used to establish a bidirectional mapping relationship between the spatial transcriptome data and the single-cell transcriptome data, wherein the bidirectional mapping relationship includes the spatial position information of each cell in the single-cell transcriptome data.

8. The device according to claim 7, characterized in that The device further comprises: a precision conversion unit; The precision conversion unit is used to convert the initial spatial transcription group data into spatial transcription group data with a second spatial resolution when the initial spatial transcription group data has a first spatial resolution, and the second spatial resolution is greater than the first spatial resolution.

9. An electronic device, characterized in that: The electronic device comprises a processor and a memory; The memory is used to store computer programs or computer instructions; The processor is configured to execute a computer program or computer instructions stored in the memory, so that the electronic device performs the multi-omics analysis method according to any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the program is executed by a processor, the multi-omics analysis method according to any one of claims 1 to 6 is implemented.

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