Method and device for detecting accessibility of spatial chromatin by utilizing intercellular correlation

Abstract

The invention relates to the technical field of spatial sequencing, in particular to a method and a device for detecting accessibility of spatial chromatin by utilizing intercellular correlation, and the method comprises the following steps: acquiring spatial position data and chromatin openness data of cells; identifying the spatial position of each point in the slice features in the spatial position data, and constructing a spatial diagram based on the spatial position of each point in the slice features; identifying chromatin with a plurality of slice characteristics in the chromatin open data, and constructing a chromatin accessibility map based on the chromatin with the plurality of slice characteristics; generating an intercellular correlation map according to the space map and the chromatin accessibility map, sorting the slice features based on the autocorrelation of each slice feature in the intercellular correlation map, and iteratively updating the chromatin accessibility map according to the sorting result of the slice features. Therefore, the problems of high data sparsity, discrete feature accessibility mode, low processing efficiency, lack of data customized modeling and the like in the prior art are solved.

Description

Technical Field

[0001] The present application relates to the field of spatial sequencing technology, and in particular to a method and device for detecting spatial chromatin accessibility using inter-cell correlation. Background Art

[0002] Spatial molecular analysis technology has revolutionized the way biomolecules are measured, allowing for direct quantification of biomolecules in intact tissue sections, helping to build high spatial resolution cell maps, analyze intercellular communications, and explore cancer tumor microenvironments. Spatial sequencing technology, especially the combination of spatial barcoding and transposase-accessible chromatin sequencing, has successfully captured spatial epigenetic information at the tissue level. Spatial multi-omics sequencing has further enhanced this capability, revealing the spatial association between chromatin accessibility and gene expression, providing important data support for understanding the gene regulation mechanisms of complex tissues.

[0003] However, although the analysis methods for spatial RNA sequencing data are relatively mature, the analysis methods for spATAC-seq (Spatial Assay for Transposase-Accessible Chromatin using sequencing) data are still in the development stage. Compared with spRNA-seq (Spatial RNA sequencing) data, spATAC-seq data is more sparse, and the accessibility patterns of features in a single slice are more discrete, which makes the assumptions adopted by methods based on spRNA-seq data no longer applicable to spATAC-seq data. In addition, the dimension of spATAC-seq data is an order of magnitude higher than the number of genes, resulting in the inefficiency of methods designed for spRNA-seq data on spATAC-seq. At the same time, the methods used to identify features with high heterogeneity in single-cell ATAC sequencing data ignore spatial information and cannot capture spatial changes. Summary of the invention

[0004] The present application provides a method and device for detecting spatial chromatin accessibility using inter-cell correlation to solve the problems of high data sparsity, discrete feature accessibility patterns, low processing efficiency, and lack of customized modeling of data in the prior art.

[0005] The first aspect of the present application provides a method for detecting spatial chromatin accessibility using inter-cell correlation, comprising the following steps: obtaining spatial position data and chromatin openness data of cells; identifying the spatial position of each point in a slice feature in the spatial position data, and constructing a spatial map based on the spatial position of each point in the slice feature; identifying chromatin of multiple slice features in the chromatin openness data, and constructing a chromatin accessibility map based on the chromatin of multiple slice features; generating an inter-cell correlation map based on the spatial map and the chromatin accessibility map, sorting the slice features based on the autocorrelation of each slice feature in the inter-cell correlation map, and iteratively updating the chromatin accessibility map based on the sorting results of the slice features.

[0006] Optionally, a spatial graph is constructed based on the spatial position of each point in the slice feature, including: identifying the spatial position of each point and calculating the average Euclidean distance between each point and surrounding points; determining the neighbor points and edge weights of each point based on the average Euclidean distance; and constructing a spatial graph based on the neighbor points and edge weights of each point.

[0007] Optionally, the edge weights of the spatial graph are:

[0008]

[0009] Among them, d s is the average Euclidean distance between a point and its nearest neighbor, is the edge weight set W of the spatial graph (spatial) An element in Dist(x. i ,x. j ) represents the Euclidean distance between point i and point j in space.

[0010] Optionally, a chromatin accessibility map is constructed based on the chromatin of multiple slice features, including: performing principal component analysis transformation on some slice features based on the chromatin of multiple slice features; and constructing the chromatin accessibility map according to the embedded representation matrix of the points obtained from the principal component analysis transformation.

[0011] Optionally, the edge weights of the chromatin accessibility graph are:

[0012]

[0013] Among them, Cosine(p i ,p j ) represents the vector p i and p j The pre-similarity between is the edge weight set W (ATAC-seq) An element in .

[0014] Optionally, the edge weight between point i and point j in the cell-to-cell correlation graph is:

[0015]

[0016] Among them, α is the factor that balances the two types of weights, w ij is an element in the edge weight set W.

[0017] Optionally, the slice features are sorted based on the autocorrelation of each slice feature in the intercellular correlation map, including: identifying the peak value of each slice feature in the intercellular correlation map; evaluating the importance score of the peak value of each slice feature by autocorrelation; and sorting the slice features according to the importance score.

[0018] A second aspect of the present application provides a device for detecting spatial chromatin accessibility using inter-cell correlation, including: an acquisition module, used to acquire spatial position data and chromatin openness data of cells; a first construction module, used to identify the spatial position of each point in a slice feature in the spatial position data, and construct a spatial map based on the spatial position of each point in the slice feature; a second construction module, used to identify the chromatin of multiple slice features in the chromatin openness data, and construct a chromatin accessibility map based on the chromatin of multiple slice features; an update module, used to generate an inter-cell correlation map based on the spatial map and the chromatin accessibility map, sort the slice features based on the autocorrelation of each slice feature in the inter-cell correlation map, and iteratively update the chromatin accessibility map based on the sorting results of the slice features.

[0019] The third aspect of the present application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement a method for detecting spatial chromatin accessibility using inter-cell correlation as in the above-mentioned embodiment.

[0020] The fourth aspect of the present application provides a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to implement a method for detecting spatial chromatin accessibility using inter-cell correlation as described in the above embodiment.

[0021] Therefore, this application includes the following beneficial effects:

[0022] The embodiment of the present application constructs a spatial map and a chromatin accessibility map by fusing the spatial position of the cell and the chromatin openness data, and integrates and generates a cell-to-cell correlation map. Through feature sorting and iterative updating, the spatial chromatin accessibility pattern is effectively identified, revealing cell heterogeneity and tissue structure information, which not only supports data enhancement, peak open pattern recognition and gene-peak interaction detection, but also significantly improves the computational efficiency and provides an efficient tool for gene regulatory network research. Thus, technical problems such as high data sparsity, discrete feature accessibility patterns, low processing efficiency and lack of data customized modeling in the prior art are solved.

[0023] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0025] Figure 1 A flowchart of a method for detecting spatial chromatin accessibility using intercellular correlation according to an embodiment of the present application;

[0026] Figure 2 A flowchart of a method for detecting spatial chromatin accessibility using inter-cell correlation according to one embodiment of the present application;

[0027] Figure 3 This is an exemplary diagram of a device for detecting spatial chromatin accessibility using inter-cell correlation according to an embodiment of the present application;

[0028] Figure 4 Schematic diagram of the structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0029] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0030] The following is a description of the method and device for detecting spatial chromatin accessibility using intercellular correlation according to an embodiment of the present application with reference to the accompanying drawings. In view of the problem of low computational efficiency mentioned in the above background technology, the present application provides a method for detecting spatial chromatin accessibility using intercellular correlation. In this method, the embodiment of the present application constructs a spatial map and a chromatin accessibility map by fusing the spatial position of the cell and the chromatin openness data, and integrates and generates an intercellular correlation map. Through feature sorting and iterative updating, the spatial chromatin accessibility pattern is effectively identified, and the cell heterogeneity and tissue structure information are revealed. It not only supports data enhancement, peak open pattern recognition and gene-peak interaction detection, but also significantly improves the computational efficiency, and provides an efficient tool for gene regulatory network research. As a result, the problems of high data sparsity, discrete feature accessibility patterns, low processing efficiency and lack of customized modeling of data in the prior art are solved.

[0031] Specifically, Figure 1A schematic diagram of the process of the method for detecting spatial chromatin accessibility using inter-cell correlation provided in an embodiment of the present application.

[0032] like Figure 1 As shown, the method for detecting spatial chromatin accessibility using inter-cell correlation includes the following steps:

[0033] In step S101, the spatial position data and chromatin accessibility data of the cells are obtained.

[0034] Among them, spatial location data can refer to the physical location information of cells in tissue sections, and chromatin openness data can be a high-throughput sequencing technology used to study the openness and accessibility of chromatin.

[0035] It can be understood that the embodiments of the present application can more accurately identify spatial chromatin accessibility patterns with cell-to-cell correlation through spatial position data and chromatin openness data, thereby facilitating subsequent detection and analysis of spatial chromatin accessibility patterns.

[0036] In step S102, the spatial position of each point in the slice feature in the spatial position data is identified, and a spatial graph is constructed based on the spatial position of each point in the slice feature.

[0037] It is understandable that the embodiments of the present application construct a spatial map, which realizes the accurate description of the relative position and neighboring relationship of cells in two-dimensional or three-dimensional space, not only improving the accuracy of chromatin accessibility analysis and spatial variable feature identification, but also enhancing the reliability and efficiency of data analysis by integrating spatial position information. In addition, the spatial map promotes the identification of spatial variable features, reveals the spatial pattern of chromatin accessibility, and helps to understand the spatial heterogeneity of gene expression regulation mechanisms and disease occurrence. In terms of data enhancement and downstream analysis, the spatial map provides strong support, and improves data quality and computational efficiency by optimizing the data processing process.

[0038] In an embodiment of the present application, a spatial graph is constructed based on the spatial position of each point in the slice feature, including: identifying the spatial position of each point and calculating the average Euclidean distance between each point and the surrounding points; determining the neighbor points and edge weights of each point based on the average Euclidean distance; and constructing a spatial graph based on the neighbor points and edge weights of each point.

[0039] It is understandable that the embodiment of the present application accurately captures the spatial data position relationship by calculating the average Euclidean distance, and realizes adaptive neighbor selection and edge weight assignment. The constructed spatial graph efficiently expresses the data structure, supports efficient algorithm processing and deep data analysis, provides a strong foundation for advanced methods such as graph neural networks, and promotes the in-depth development of spatial data applications.

[0040] In the embodiment of the present application, the edge weight of the spatial graph is:

[0041]

[0042] Among them, d s is the average Euclidean distance between a point and its nearest neighbor, is the edge weight set W of the spatial graph (spatial) An element in Dist(x. i ,x. j ) represents the Euclidean distance between point i and point j in space.

[0043] Specifically, Descartes builds a map based on the spatial position of the spot to capture the spatial accessibility pattern of each peak. Assume X = (x.1,…,x. N ) represents the spatial position of N spots in a single slice, and Descartes calculates the average Euclidean distance between each point and its nearest neighbor:

[0044]

[0045] Connect each point to its five times d s Neighbors within the range to obtain the spatial graph. The edge weight of the spatial graph is inversely proportional to the Euclidean distance between the points, that is, the edge weight between point i and point j is:

[0046]

[0047] in, is the edge weight set W of the spatial graph (spatial) An element in Dist(x. i ,x. j ) represents the Euclidean distance between point i and point j in space.

[0048] In step S103, chromatin with multiple slice features in the chromatin accessibility data is identified, and a chromatin accessibility map is constructed based on the chromatin with multiple slice features.

[0049] Among them, the slicing feature can be the segmentation or partitioning of chromatin openness data on the genome, and the chromatin accessibility map can be a graphical representation used to display the open state of chromatin across the entire genome.

[0050] It is understandable that the embodiments of the present application construct a chromatin accessibility map by identifying the open state of chromatin in different genomic regions and its multi-slice characteristics, thereby achieving a comprehensive analysis of chromatin structure. This not only helps to deeply reveal the regulatory mechanism of gene expression, but also provides a powerful tool for the identification of disease-related markers, further promoting the early diagnosis of diseases and the discovery of therapeutic targets. At the same time, it improves the analysis accuracy and enhances the visualization of data, thereby accelerating the research process and improving research efficiency.

[0051] In an embodiment of the present application, a chromatin accessibility map is constructed based on chromatin of multiple slice features, including: performing principal component analysis transformation on some slice features based on chromatin of multiple slice features; and constructing a chromatin accessibility map according to an embedded representation matrix of points obtained from the principal component analysis transformation.

[0052] Among them, principal component analysis transformation refers to the process of reducing the dimension of chromatin data of multiple slice features through the PCA method.

[0053] It can be understood that the embodiment of the present application performs dimension reduction processing on the chromatin data of multiple slice features through principal component analysis transformation, extracts key features and constructs a chromatin accessibility map, which not only improves the accuracy and reliability of data analysis, but also enhances the visualization of the data, making complex chromatin structure information intuitive and easy to understand.

[0054] Specifically, due to the high dimensionality, sparsity, and noise of the ATAC-seq matrix, it is impractical to build a map directly from the raw count matrix. represents the original peak-spot count matrix, where the number of spots is N and the number of peaks is M. Descartes first applies the same term frequency-inverse document frequency transformation as in Signac to obtain a continuous value matrix. Descartes selects a subset of peaks and performs PCA transformation to obtain a spot-spot matrix

[0055] In the present embodiment, the edge weight of the chromatin accessibility graph is:

[0056]

[0057] Among them, Cosine(p i ,p j ) represents the vector p i and p j The pre-similarity between is the edge weight set W (ATAC-seq) An element in .

[0058] Specifically, Descartes directly selects the peak based on the descending order of openness, and in subsequent iterations, it is selected based on the ranking of the previous iteration. Using the spot-spot matrix P, Descartes connects each point to its nearest 20 neighbors and assigns the edge weight between point i and point j to be:

[0059]

[0060] Among them, Cosine(p i ,p j ) represents the vector p i and p j The pre-similarity between is the edge weight set W (ATAC-seq) An element in .

[0061] In step S104, a cell-to-cell correlation map is generated according to the spatial map and the chromatin accessibility map, slice features are sorted based on the autocorrelation of each slice feature in the cell-to-cell correlation map, and the chromatin accessibility map is iteratively updated according to the sorting results of the slice features.

[0062] Among them, the intercellular correlation map is a graphical representation of the similarities or differences between different cells, and the autocorrelation refers to the similarity or correlation between a sequence and itself at different time points.

[0063] It is understandable that the embodiments of the present application achieve a comprehensive integration of the spatial position of cells and the open state of chromatin by combining the spatial map with the chromatin accessibility map, significantly enhancing the understanding of the complex interactions between cells and the dynamic changes of chromatin structure. The generated intercellular correlation map intuitively shows the similarities and differences between cells, revealing the multidimensional complexity of intercellular relationships. At the same time, the sorting strategy based on the autocorrelation of slice features optimizes feature selection and improves analysis accuracy. In addition, through the iterative update mechanism, the continuous optimization and accuracy of the chromatin accessibility map is ensured, providing strong data support and insights for fields such as biological research, disease diagnosis and treatment.

[0064] In the embodiment of the present application, the edge weight between point i and point j in the intercellular correlation graph is:

[0065]

[0066] Among them, α is the factor that balances the two types of weights, w ij is an element in the edge weight set W.

[0067] In an embodiment of the present application, slice feature sorting is performed based on the autocorrelation of each slice feature in the intercellular correlation map, including: identifying the peak value of each slice feature in the intercellular correlation map; evaluating the importance score of the peak value of each slice feature by autocorrelation; and sorting the slice features according to the importance score.

[0068] It is understandable that the embodiment of the present application accurately locates the most important information points that affect cell-to-cell interactions or feature changes by identifying the peak values ​​of slice features in the cell-to-cell correlation map. The importance score of each peak feature is quantified using the autocorrelation evaluation method to ensure that the contribution of these features in the overall data structure is accurately reflected, which not only improves the accuracy of data analysis, but also significantly optimizes the allocation of research resources.

[0069] Specifically, the Descartes pair matrix Perform a z-score transformation to obtain the matrix Descartes identified peaks with regional continuity as informative features, while peaks with different levels of accessibility between two points connected by most graph edges were considered uninformative features. Descartes evaluated the importance score of each peak by autocorrelation:

[0070]

[0071] in, Representation Matrix represents the row vector of peak-k, s k is the peak-k score, and W is the edge weight set.

[0072] Based on the importance scores, Descartes ranks all peaks and feeds the ranking back into constructing the chromatin accessibility map until the ranking stabilizes.

[0073] Descartes can identify characteristic patterns and calculate the peak-peak similarity matrix s based on the mapping results. (peak-peak) :

[0074]

[0075] Descartes converts the similarity matrix into a distance matrix D (peak-peak) , and hierarchical clustering is performed to obtain different peak opening patterns.

[0076] It should be noted that for spatial multi-omics data, Descartes preprocesses the data. The processed matrix is ​​then combined with the peak-spot matrix of the z-score conversion to obtain a feature-spot matrix The features include all genes and peaks. Descartes then derived a correlation matrix between all features, showing the strength of gene-peak or peak-gene interactions.

[0077] According to the method of detecting spatial chromatin accessibility using intercellular correlation proposed in the embodiment of the present application, a spatial map and a chromatin accessibility map are constructed by fusing the spatial position of the cell and the chromatin openness data, and the intercellular correlation map is integrated to generate. Through feature sorting and iterative updating, the spatial chromatin accessibility pattern is effectively identified, and the cell heterogeneity and tissue structure information are revealed. It not only supports data enhancement, peak open pattern recognition and gene-peak interaction detection, but also significantly improves the computational efficiency, and provides an efficient tool for gene regulatory network research. As a result, the problems of high data sparsity, discrete feature accessibility patterns, low processing efficiency and lack of data customized modeling in the prior art are solved.

[0078] The following is a specific example of a method for detecting spatial chromatin accessibility using intercellular correlation, as follows:

[0079] S1, build a spatial map based on the spatial position of the spot;

[0080] S2, perform principal component analysis transformation on some peaks to obtain the embedded representation of the spot;

[0081] S3, constructing a chromatin accessibility map based on the embedded representation and integrating it with the spatial map to obtain a cell-to-cell correlation map;

[0082] S4. Calculate the importance score using the autocorrelation of each peak in the graph;

[0083] S5, evaluate and rank all peaks based on importance scores, and then feed the current ranking back to S2 until the obtained feature ranking undergoes minimal change;

[0084] Specifically, Figure 2 As shown, the method for detecting spatial chromatin accessibility using intercellular correlation includes a data input module, a graph construction module, an integration module, and a score calculation module; wherein the data input module contains spatial position data and chromatin openness data; the graph construction module constructs a spatial graph and a chromatin openness graph according to spatial information and ATAC-seq data, respectively; the integration module integrates the spatial graph and the chromatin openness graph into an intercellular interaction graph according to weights; the score calculation module calculates the importance score of the feature and feeds the result back to the graph construction module to complete an iteration.

[0085] In summary, the SV feature recognition method designed for spATAC-seq data in the embodiment of the present application accurately captures the neighborhood relationship between cells and shows excellent performance in benchmark tests; the neighbor relationship enhancement data in the constructed graph structure is used to enhance the accuracy and reliability of downstream analysis, including the identification of peak open patterns and in-depth analysis of gene-peak interactions. For spatial multi-omics data, a gene-peak correlation matrix can be generated, which provides a powerful tool for detecting the interaction between genes and peaks and constructing gene regulatory networks. In addition, the significant improvement in computational efficiency further reduces the time cost of research.

[0086] Next, a device for detecting spatial chromatin accessibility using intercellular correlation proposed in an embodiment of the present application will be described with reference to the accompanying drawings.

[0087] Figure 3 It is a schematic block diagram of a device for detecting spatial chromatin accessibility using inter-cell correlation according to an embodiment of the present application.

[0088] like Figure 3 As shown, the device 10 for detecting spatial chromatin accessibility using inter-cell correlation includes: an acquisition module 100, a first construction module 200, a second construction module 300 and an update module 400.

[0089] Among them, the acquisition module 100 is used to obtain the spatial position data and chromatin accessibility data of the cell; the first construction module 200 is used to identify the spatial position of each point in the slice feature in the spatial position data, and construct a spatial map based on the spatial position of each point in the slice feature; the second construction module 300 is used to identify the chromatin of multiple slice features in the chromatin accessibility data, and construct a chromatin accessibility map based on the chromatin of multiple slice features; the update module 400 is used to generate a cell-to-cell correlation map based on the spatial map and the chromatin accessibility map, sort the slice features based on the autocorrelation of each slice feature in the cell-to-cell correlation map, and iteratively update the chromatin accessibility map according to the sorting results of the slice features.

[0090] It should be noted that the aforementioned explanation of the embodiment of the method for detecting spatial chromatin accessibility using inter-cell correlation is also applicable to the device for detecting spatial chromatin accessibility using inter-cell correlation in this embodiment, and will not be repeated here.

[0091] According to the device for detecting spatial chromatin accessibility using intercellular correlation proposed in the embodiment of the present application, a spatial map and a chromatin accessibility map are constructed by fusing the spatial position of the cell and the chromatin openness data, and the intercellular correlation map is integrated and generated. Through feature sorting and iterative updating, the spatial chromatin accessibility pattern is effectively identified, and the cell heterogeneity and tissue structure information are revealed. It not only supports data enhancement, peak open pattern recognition and gene-peak interaction detection, but also significantly improves the computational efficiency, and provides an efficient tool for gene regulatory network research. As a result, the problems of high data sparsity, discrete feature accessibility patterns, low processing efficiency and lack of customized modeling of data in the prior art are solved.

[0092] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include:

[0093] Memory 401 , processor 402 , and a computer program stored in the memory 401 and executable on the processor 402 .

[0094] When the processor 402 executes the program, the method for detecting spatial chromatin accessibility using inter-cell correlation provided in the above embodiment is implemented.

[0095] Furthermore, the electronic device further comprises:

[0096] The communication interface 403 is used for communication between the memory 401 and the processor 402 .

[0097] The memory 401 is used to store computer programs that can be executed on the processor 402 .

[0098] The memory 401 may include a high-speed RAM (Random Access Memory) memory, and may also include a non-volatile memory, such as at least one disk memory.

[0099] If the memory 401, the processor 402 and the communication interface 403 are implemented independently, the communication interface 403, the memory 401 and the processor 402 can be connected to each other through a bus and communicate with each other. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0100] Optionally, in a specific implementation, if the memory 401, the processor 402 and the communication interface 403 are integrated on a chip, the memory 401, the processor 402 and the communication interface 403 can communicate with each other through an internal interface.

[0101] The processor 402 may be a CPU (Central Processing Unit), or an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present application.

[0102] An embodiment of the present application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above method for detecting spatial chromatin accessibility using inter-cell correlation.

[0103] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms are not necessarily directed to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0104] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0105] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0106] It should be understood that the various parts of the present application can be implemented in hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, the steps or methods can be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array, a field programmable gate array, etc.

[0107] A person of ordinary skill in the art may understand that all or part of the steps carried by the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the above-mentioned program may be stored in a computer-readable storage medium, which, when executed, includes one of the steps of the method embodiment or a combination thereof.

[0108] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for detecting spatial chromatin accessibility using intercellular correlation, characterized in that: The following steps are involved: Obtain the spatial location data and chromatin accessibility data of cells; Identifying the spatial position of each point in the slice feature in the spatial position data, and constructing a spatial map based on the spatial position of each point in the slice feature; Identifying chromatin of multiple slice features in the chromatin openness data, and constructing a chromatin accessibility map based on the chromatin of the multiple slice features; A cell-to-cell correlation map is generated according to the spatial map and the chromatin accessibility map, slice features are sorted based on the autocorrelation of each slice feature in the cell-to-cell correlation map, and the chromatin accessibility map is iteratively updated according to the sorting results of the slice features.

2. The method for detecting spatial chromatin accessibility using intercellular correlation according to claim 1, characterized in that: The step of constructing a spatial graph based on the spatial position of each point in the slice feature comprises: Identify the spatial location of each point and calculate the average Euclidean distance between each point and surrounding points; Determine the neighbor points and edge weights of each point according to the average Euclidean distance; The spatial graph is constructed according to the neighbor points and edge weights of each point.

3. The method for detecting spatial chromatin accessibility using intercellular correlation according to claim 2, characterized in that: The edge weights of the spatial graph are: Among them, d s is the average Euclidean distance between a point and its nearest neighbor, is the edge weight set W of the spatial graph (spatial) An element in Dist(x ·i ,x ·j ) represents the Euclidean distance between point i and point j in space.

4. The method for detecting spatial chromatin accessibility using intercellular correlation according to claim 1, characterized in that: The step of constructing a chromatin accessibility map based on the chromatin of the multiple slice features comprises: Performing principal component analysis transformation on some slice features based on the chromatin of the multiple slice features; The chromatin accessibility map is constructed based on the embedded representation matrix of the points obtained through principal component analysis transformation.

5. The method for detecting spatial chromatin accessibility using intercellular correlation according to claim 4, characterized in that: The edge weights of the chromatin accessibility graph are: Among them, Cosine(p i ,p j ) represents the vector p i and p j The pre-similarity between is the edge weight set W (ATAC-seq) An element in .

6. The method for detecting spatial chromatin accessibility using intercellular correlation according to claim 1, characterized in that: The edge weight between point i and point j in the intercellular correlation graph is: Among them, α is the factor that balances the two types of weights, w ij is an element in the edge weight set W.

7. The method for detecting spatial chromatin accessibility using intercellular correlation according to claim 1, characterized in that: The step of sorting slice features based on the autocorrelation of each slice feature in the inter-cell correlation map comprises: The peak of each slice feature within the intercellular correlation map was identified; Autocorrelation evaluates the importance score of the peak of each slice feature; Slice features are sorted according to the importance scores.

8. A device for detecting spatial chromatin accessibility using intercellular correlation, characterized in that: include: An acquisition module is used to obtain the spatial location data and chromatin accessibility data of cells; A first construction module is used to identify the spatial position of each point in the slice feature in the spatial position data, and to construct a spatial graph based on the spatial position of each point in the slice feature; A second construction module is used to identify chromatin with multiple slice features in the chromatin openness data, and construct a chromatin accessibility map based on the chromatin with multiple slice features; An updating module is used to generate an inter-cell correlation map based on the spatial map and the chromatin accessibility map, sort slice features based on the autocorrelation of each slice feature in the inter-cell correlation map, and iteratively update the chromatin accessibility map according to the sorting results of the slice features.

9. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for detecting spatial chromatin accessibility using intercellular correlation as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program or instruction stored thereon, characterized in that: When the computer program or instructions are executed, the method for detecting spatial chromatin accessibility using intercellular correlation as described in any one of claims 1 to 7 is implemented.

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