Method and system for generating interpolation results of different resolutions by single-cell Hi-C

Abstract

The embodiments of the present application provide a method and system for generating interpolation results of different resolutions through single-cell Hi-C, which belongs to the technical field of artificial intelligence and bioinformatics. The present invention obtains several single-cell Hi-C data files; pre-processes the single-cell Hi-C data files to obtain a contact matrix; interpolates the contact matrix to obtain an interpolated contact matrix; sums the interpolated contact matrix to obtain an updated contact matrix; and obtains low-resolution interpolation information based on the updated contact matrix. The present invention solves the problem of needing to perform multiple interpolations of the original single-cell Hi-C data at different resolutions, improves the interpolation efficiency of single-cell Hi-C data, provides more flexible options for downstream analysis, has broad application prospects in fields such as genomics and transcriptomics, and provides a powerful tool for exploring the relationship between genome structure and function.

Description

Technical Field

[0001] The present application relates to the technical field of artificial intelligence and bioinformatics, and in particular to a method and system for generating interpolation results of different resolutions through single-cell Hi-C. Background Art

[0002] In single-cell Hi-C experiments, due to the limited amount of chromatin captured per cell, the contact matrix obtained is usually sparse, that is, contact information between many genomic regions is missing. The interpolation process infers or estimates these missing data points by using computational methods or algorithms (such as statistical methods, machine learning techniques, etc.). This processing method can improve the integrity of the data, making it closer to the actual chromatin structure, thereby improving the accuracy and effectiveness of downstream analysis. However, existing methods for scHi-C data interpolation interpolate the original scHi-C data according to different resolutions, which results in low efficiency of data interpolation. Summary of the invention

[0003] The main purpose of the embodiments of the present application is to provide a method and system for generating interpolation results of different resolutions through single-cell Hi-C.

[0004] The technical solution adopted by the present invention is:

[0005] In one aspect, an embodiment of the present invention provides a method for generating interpolation results of different resolutions by single-cell Hi-C, the method comprising the following steps:

[0006] Obtain several single-cell Hi-C data files;

[0007] Preprocessing the single cell Hi-C data file to obtain a contact matrix;

[0008] interpolating the contact matrix to obtain an interpolated contact matrix;

[0009] Performing a summation operation on the interpolated contact matrix to obtain an updated contact matrix;

[0010] According to the updated contact matrix, low-resolution interpolation information is obtained.

[0011] Furthermore, the obtaining of several single cell Hi-C data files comprises the following steps:

[0012] Set each single-cell Hi-C data file to include the following data columns: cell ID, first chromosome, first gene locus, second chromosome, second gene locus.

[0013] Furthermore, the single cell Hi-C data file is preprocessed to obtain a contact matrix, comprising the following steps:

[0014] According to the single-cell Hi-C data file, removing the data rows that do not match the first chromosome and the second chromosome to obtain first data;

[0015] Add 1 to the first gene locus and the second gene locus of the first data and divide the result by a set resolution to obtain second data;

[0016] The same data rows in the second data are merged, new columns are generated by counting, the values ​​of the first gene sites are used as row coordinates, and the values ​​of the second gene sites are used as column coordinates to obtain a contact matrix.

[0017] Furthermore, the interpolating the contact matrix to obtain an interpolated contact matrix comprises the following steps:

[0018] The contact matrix is ​​interpolated using the scHiCluster algorithm to obtain an interpolated contact matrix.

[0019] Furthermore, the step of performing a summation operation on the interpolated contact matrix to obtain an updated contact matrix comprises the following steps:

[0020] Dividing the interpolation contact matrix into a plurality of small matrices along the direction of the main diagonal;

[0021] Each value in each of the small matrices is summed up to obtain an updated contact matrix.

[0022] Further, obtaining low-resolution interpolation information according to the updated contact matrix comprises the following steps:

[0023] The updated contact matrix is ​​stored in a sparse matrix format to obtain low-resolution interpolation information.

[0024] Furthermore, the method for generating interpolation results of different resolutions by single-cell Hi-C further comprises the following steps:

[0025] The low-resolution interpolation information is subjected to dimension reduction, and the adjusted Rand index is calculated to obtain clustering effect information.

[0026] On the other hand, an embodiment of the present invention further provides a system for generating interpolation results of different resolutions by single-cell Hi-C, the system comprising:

[0027] The first module is used to obtain several single-cell Hi-C data files;

[0028] The second module is used to preprocess the single cell Hi-C data file to obtain a contact matrix;

[0029] The third module is used to interpolate the contact matrix to obtain an interpolated contact matrix;

[0030] A fourth module is used to perform a sum operation on the interpolation contact matrix to obtain an updated contact matrix;

[0031] The fifth module is used to obtain low-resolution interpolation information according to the updated contact matrix.

[0032] On the other hand, an embodiment of the present invention also provides a device for generating interpolation results of different resolutions through single-cell Hi-C, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for generating interpolation results of different resolutions through single-cell Hi-C as described above is implemented.

[0033] On the other hand, an embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to enable a computer to execute the method described above.

[0034] The embodiments of the present application include at least the following beneficial effects: The present application provides a method and system for generating interpolation results of different resolutions through single-cell Hi-C. The present invention obtains several single-cell Hi-C data files; pre-processes the single-cell Hi-C data files to obtain a contact matrix; interpolates the contact matrix to obtain an interpolated contact matrix; sums the interpolated contact matrix to obtain an updated contact matrix; and obtains low-resolution interpolation information based on the updated contact matrix. The present invention solves the problem of needing to perform multiple interpolations of the original single-cell Hi-C data at different resolutions, improves the interpolation efficiency of single-cell Hi-C data, provides more flexible options for downstream analysis, has broad application prospects in fields such as genomics and transcriptomics, and provides a powerful tool for exploring the relationship between genome structure and function. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 is a flow chart of a method for generating interpolation results of different resolutions through single-cell Hi-C provided by an embodiment of the present invention;

[0036] Figure 2 is a schematic diagram of merging interpolation contact matrices provided by an embodiment of the present invention;

[0037] Figure 3 It is a flow chart of the technical solution provided by an embodiment of the present invention;

[0038] Figure 4 is a sample diagram of the content format in the scHi-C data file provided by an embodiment of the present invention;

[0039] Figure 5 This is a cell clustering diagram provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0040] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is further described in detail below in conjunction with the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of the present application. They are only examples of devices and methods consistent with some aspects of the embodiments of the present application as detailed in the attached claims.

[0041] It is understood that the terms "first", "second", etc. used in this application can be used to describe various concepts in this article, but unless otherwise specified, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another concept. For example, without departing from the scope of the embodiment of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if" and "if" as used herein can be interpreted as "at the time of" or "when" or "in response to determination".

[0042] The terms "at least one", "multiple", "each", "any", etc. used in this application, at least one includes one, two or more, multiple includes two or more, each refers to each of the corresponding multiple, and any refers to any one of the multiple.

[0043] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0044] Before describing the embodiments of the present application in detail, some nouns and terms involved in the embodiments of the present application are first described. The nouns and terms involved in the embodiments of the present application are subject to the following explanations.

[0045] 1) Hi-C technology, the full name of which is High-throughput Chromosome Conformation Capture, is a high-throughput genomics technology used to study the three-dimensional structure of chromatin. It can capture the physical contact between different regions in the genome and reveal the spatial organization and topological structure of the genome.

[0046] 2) Single-cell Hi-C (scHi-C), a technology used to study the three-dimensional structure of chromatin at the single-cell level. It is based on traditional Hi-C technology and reveals the spatial proximity of different regions in the genome by capturing and sequencing the physical interactions between DNA fragments.

[0047] 3) ARI, also known as the Adjusted Rand Index, is an indicator used to measure the consistency between clustering results and true labels. In cluster analysis, ARI is used to evaluate the accuracy and reliability of classification or clustering results.

[0048] 4) Resolution refers to the fineness of measurement or the fineness of data between different regions in the genome. Specifically, resolution refers to the size of the smallest genomic region that can be distinguished when detecting or analyzing chromatin structure. The smaller the gene region corresponding to high resolution, the larger the gene region corresponding to low resolution. Commonly used resolutions are (arranged from high to low): 10,000, 25,000, 100,000, 500,000, 1000,000.

[0049] 5) Interpolation refers to filling or supplementing missing information caused by data sparsity or insufficient sequencing depth.

[0050] 6) scHiCluster algorithm, an algorithm for clustering analysis of single-cell Hi-C data. It identifies and groups cells with similar chromatin structural features by analyzing chromatin interaction data in single cells.

[0051] The embodiments of the present invention are further described below in conjunction with the accompanying drawings.

[0052] On the one hand, the embodiment of the present invention provides a method for generating interpolation results of different resolutions by single-cell Hi-C, referring to Figure 1 , the method comprises the following steps:

[0053] S100, obtaining several single-cell Hi-C data files;

[0054] S200, preprocessing the single-cell Hi-C data file to obtain the contact matrix;

[0055] S300, interpolating the contact matrix to obtain an interpolated contact matrix;

[0056] S400, performing a sum operation on the interpolation contact matrix to obtain an updated contact matrix;

[0057] S500: Obtain low-resolution interpolation information according to the updated contact matrix.

[0058] The embodiment of the present invention discloses step S100 of acquiring a plurality of single cell Hi-C data files, including the following steps:

[0059] S110 , setting each single-cell Hi-C data file to include the following data columns: cell ID, first chromosome, first gene locus, second chromosome, second gene locus.

[0060] S100 of the embodiment of the present invention involves obtaining multiple single-cell Hi-C data files from the experiment. These data files contain high-resolution contact information of the genome, usually formatted as a table, each table column contains specific information, such as cell ID, chromosome number and gene site location. The detailed process of this step includes:

[0061] S110: Set each single cell Hi-C data file to include the following data columns:

[0062] Cell ID: A unique identifier for each cell, ensuring that data from different cells can be distinguished.

[0063] Chromosome 1: The number of the first chromosome in the genome.

[0064] Primary locus: A specific gene locus on chromosome 1.

[0065] Chromosome II: The number of the second chromosome in the genome.

[0066] Secondary locus: A specific genetic locus on chromosome 2.

[0067] These data columns are the basis for further data processing, ensuring accurate contact analysis between gene loci.

[0068] The embodiment of the present invention discloses that step S200 pre-processes the single cell Hi-C data file to obtain a contact matrix, including the following steps:

[0069] S210, according to the single-cell Hi-C data file, removing the data rows that do not match the first chromosome and the second chromosome to obtain first data;

[0070] S220, adding 1 to the first gene locus and the second gene locus of the first data and dividing the result by a set resolution to obtain second data;

[0071] S230, merging identical data rows in the second data, counting and generating new column numbers, using the values ​​of the first gene loci as row coordinates, and using the values ​​of the second gene loci as column coordinates, to obtain a contact matrix.

[0072] S200 of the embodiment of the present invention involves processing the original single-cell Hi-C data file into a contact matrix that can be used for subsequent analysis. The specific steps include:

[0073] S210 removes the data rows that do not match the first chromosome and the second chromosome according to the single-cell Hi-C data file to obtain first data, including:

[0074] Data filtering: Filter out records with no contact between different chromosomes and retain data rows with only contact between gene loci on the same chromosome.

[0075] Data cleaning: Ensure that all retained data rows are useful and consistent with subsequent analysis.

[0076] S220 adds 1 to the first gene locus and the second gene locus of the first data and divides the result by the set resolution to obtain the second data, including:

[0077] Position adjustment: Gene loci are processed by adding 1 to avoid the bias caused by starting the index from 0.

[0078] Resolution setting: The gene locus positions are divided to meet the set resolution standard, such as 10,000, to facilitate the construction of the contact matrix.

[0079] S230 merges the same data rows in the second data, counts and generates new column numbers, uses the value of the first gene site as the row coordinate, uses the value of the second gene site as the column coordinate, and obtains a contact matrix, including:

[0080] Row merging: Count duplicate pairs of loci and generate contact frequencies for each pair of loci.

[0081] Matrix construction: The processed data are converted into a contact matrix, where rows and columns correspond to gene loci, and each element of the matrix represents the contact frequency between a pair of gene loci.

[0082] The embodiment of the present invention discloses that step S300 interpolates the contact matrix to obtain an interpolated contact matrix, including the following steps:

[0083] S310, using the scHiCluster algorithm to interpolate the contact matrix to obtain an interpolated contact matrix.

[0084] S300 of the embodiment of the present invention involves performing an interpolation operation on the contact matrix to fill in the missing data that may exist. The specific steps include:

[0085] S310 uses the scHiCluster algorithm to interpolate the contact matrix to obtain the interpolated contact matrix, including:

[0086] Algorithm selection: The scHiCluster algorithm is used, which is suitable for processing the interpolation of scHi-C data and filling the missing values ​​in the matrix.

[0087] Interpolation process: The contact matrix is ​​interpolated through an algorithm to generate a complete interpolation contact matrix that can more accurately reflect the contact relationship between gene loci.

[0088] The embodiment of the present invention discloses that step S400 performs a summation operation on the interpolated contact matrix to obtain an updated contact matrix, including the following steps:

[0089] S410, dividing the interpolation contact matrix into a plurality of small matrices along the direction of the main diagonal;

[0090] S420, performing a sum operation on each value in each small matrix to obtain an updated contact matrix.

[0091] S400 of the embodiment of the present invention involves further processing the interpolated contact matrix to generate a matrix of target resolution. The specific steps include:

[0092] S410 divides the interpolation contact matrix into several small matrices along the main diagonal direction, including:

[0093] Matrix division: According to the ratio of target resolution to interpolation resolution, the interpolation contact matrix is ​​divided into several small matrices to ensure that the size of each small matrix corresponds to the new resolution requirement.

[0094] Matrix adjustment: handle small matrices of insufficient size and fill them with 0 to ensure that all small matrices are of the same size.

[0095] S420 performs a sum operation on each value in each small matrix to obtain an updated contact matrix, including:

[0096] Summation operation: Sum the elements in each small matrix to obtain an element of the corresponding new contact matrix.

[0097] Update matrix: Generate a new contact matrix by merging the summation results of small matrices to represent the contact information at the target resolution.

[0098] The embodiment of the present invention discloses that step S500 obtains low-resolution interpolation information according to the updated contact matrix, including the following steps:

[0099] S510, storing the updated contact matrix in a sparse matrix format to obtain low-resolution interpolation information.

[0100] S500 of the embodiment of the present invention involves storing the updated contact matrix as the final low-resolution interpolation result. The specific steps include:

[0101] S510 stores the updated contact matrix in a sparse matrix format to obtain low-resolution interpolation information, including:

[0102] Format conversion: Convert the updated contact matrix to a sparse matrix format to optimize storage space and improve computational efficiency.

[0103] Data storage: Store the converted sparse matrix as a file or database as the final low-resolution interpolation result.

[0104] The method for generating interpolation results of different resolutions by single-cell Hi-C disclosed in an embodiment of the present invention further includes the following steps:

[0105] S600, reducing the dimension of the low-resolution interpolation information, calculating and adjusting the Rand index, and obtaining clustering effect information.

[0106] S600 of the embodiment of the present invention reduces the dimension of the low-resolution interpolation information, calculates and adjusts the Rand index, and obtains clustering effect information, including:

[0107] Dimensionality reduction: The low-resolution interpolation information is reduced in dimension to facilitate cluster analysis. Commonly used dimensionality reduction methods include principal component analysis (PCA) or t-SNE.

[0108] Cluster analysis: Cluster the data after dimensionality reduction and calculate the Adjusted Rand Index (ARI) to evaluate the clustering effect. The higher the ARI index, the better the clustering effect of the data.

[0109] Result analysis: The clustering effects of the imputed data generated by different methods were compared and evaluated by ARI index and visual charts.

[0110] As an optional embodiment, the steps of the present invention include:

[0111] The first step is to preprocess the scHi-C data. First, remove the data in which the two gene loci in a group of data are located on different chromosomes; second, add 1 to the two gene loci of all data and divide them by the set resolution; finally, count the number of occurrences of each group of data, and create a contact matrix with the two gene loci as the row and column coordinates of the matrix and the number as the value of the coordinate in the matrix.

[0112] In the second step, the contact matrix in the first step is interpolated using the interpolation algorithm to obtain the interpolated contact matrix.

[0113] The third step is to interpolate the contact matrix after interpolation according to the ratio of the target resolution (the target resolution must be an integer multiple of the resolution used for interpolation) to the resolution used for interpolation. Figure 2By merging them in a way, the interpolated contact matrix of the target resolution can be obtained. The obtained contact matrix is ​​stored in the format of a sparse matrix to obtain the final data. Among them, Figure 2 The ratio of the target resolution to the resolution during interpolation is 2:1 (the left picture shows the result after high-resolution interpolation, and the right picture shows the target result). The merging method is to add each element in each 2*2 small matrix in the left picture, and the result of the addition of each small matrix is ​​an element in the matrix in the right picture. The red in the left picture represents the data to be merged, and the red in the right picture represents the new data generated after merging the red parts in the left picture. The light red in both the left and right pictures represent data that has not been processed.

[0114] The above process reference Figure 3 .

[0115] As an optional implementation, the data set of the embodiment herein adopts the data set of Lee 2019, the interpolation algorithm adopts scHiCluster, the resolution value is set to 10,000, and the target resolution value is 100,000.

[0116] Since the Lee2019 dataset contains 4,238 cell data files, the invention needs to preprocess the data of these 4,238 cell data files one by one; each cell file format of this dataset is a text file with 5 columns (cell id, chromosome 1, gene site 1, chromosome 2, gene site 2) and multiple lines, and each line represents a set of data. Since the format of all cell data files is the same, here we process one cell data file (such as Figure 4 ) as an example.

[0117] Step 1: Remove the different data rows of chromosome 1 and chromosome 2 in the cell data file, then add 1 to the data on gene locus 1 and gene locus 2 and divide it by 10,000; then merge and count the rows with the same 5 columns of data in each row to generate a new column count; finally, let the value of gene locus 1 be the row coordinate, the value of gene locus 2 be the column coordinate, and count be the value to generate a contact matrix.

[0118] Step 2: Use the scHiCluster algorithm to interpolate the contact matrix in the first step to obtain the interpolated contact matrix.

[0119] Step 3: The ratio of the target resolution to the set resolution is 10:1. The interpolated contact matrix obtained in the second step is divided into 10*10 small matrices along the main diagonal (if the matrix is ​​less than 10*10, it is padded with 0). Each value in each small matrix is ​​summed up, and each value obtained is a new contact matrix. This new contact matrix is ​​the interpolated contact matrix with a resolution of 100,000. The target matrix is ​​stored in the sparse matrix format to obtain the final data.

[0120] Finally, the data obtained by the above method is compared with the data directly interpolated with a resolution of 100,000. The same dimensionality reduction method is used to reduce the dimensionality of the two data, and then the ARI index is calculated for the reduced dimensionality data. The clustering effect of the two data is compared by comparing the size of the ARI index (the higher the ARI index, the better the clustering effect of the data). The ARI index of the former is 0.922061414630775, and the ARI index of the latter is 0.9126750256424924. The ARI index of the former is about 0.01 higher than that of the latter. Then, the same drawing method is used to present the reduced dimensionality data of the two (such as Figure 5 The left picture is the former, and the right picture is the latter. The left picture is a cell clustering diagram made by merging the interpolation data with a resolution of 10,000 into interpolation data of 100,000 using the present invention, and the right picture is a cell clustering diagram made by directly using the data obtained by interpolation with a resolution of 100,000). Figure 5 It can be seen that the clustering effects of the two are basically the same. Through the above two verification methods, it can be concluded that the data results obtained by using the interpolation data with a resolution of 10,000 to generate the interpolation data with a resolution of 100,000 are basically consistent with those obtained by directly using the interpolation data with a resolution of 100,000.

[0121] The present invention can generate low-resolution interpolation results by using high-resolution interpolation results, solving the problem that the related art can only generate corresponding interpolation results by one resolution. In subsequent test experiments, the present invention compares the low-resolution interpolation data generated by the present invention with the low-resolution interpolation data generated by directly using the related art under low-resolution conditions. By comparing the clustering effects of the two, it can be found that the clustering effect of the interpolation data obtained by the present invention is roughly the same as the clustering effect of the interpolation data obtained by the prior art; and in the comparison of the ARI index of the two, the ARI index of the present invention is about 0.01 higher than that of the prior art. More importantly, when the present invention requires interpolation results of multiple resolutions, the present invention only interpolates the original scHi-C data under high-resolution conditions, and then generates low-resolution interpolation results through high-resolution interpolation results; while the related art needs to interpolate the original scHi-C data one by one at different resolutions to obtain interpolation results of different resolutions. From this point, it can be seen that the present invention can greatly save the interpolation time of scHi-C data, improve the interpolation efficiency of scHi-C, provide more flexible options for downstream analysis, facilitate classification and clustering analysis at the single-cell level, have broad application prospects in the fields of genomics and transcriptomics, and provide a powerful tool for exploring the relationship between genome structure and function.

[0122] On the other hand, an embodiment of the present invention further provides a system for generating interpolation results of different resolutions through single-cell Hi-C, the system comprising:

[0123] The first module is used to obtain several single-cell Hi-C data files;

[0124] The second module is used to preprocess the single-cell Hi-C data files to obtain the contact matrix;

[0125] The third module is used to interpolate the contact matrix to obtain an interpolated contact matrix;

[0126] The fourth module is used for performing a sum operation on the interpolation contact matrix to obtain an updated contact matrix;

[0127] The fifth module is used to obtain low-resolution interpolation information according to the updated contact matrix.

[0128] On the other hand, an embodiment of the present invention further provides a device for generating interpolation results of different resolutions through single-cell Hi-C, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for generating interpolation results of different resolutions through single-cell Hi-C as described above is implemented.

[0129] The device for generating interpolation results of different resolutions by single-cell Hi-C according to an embodiment of the present invention includes a memory and a processor.

[0130] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0131] The memory may include various types of storage units, such as system memory, read-only memory (ROM), and permanent storage. Among them, ROM can store static data or instructions required by the processor or other modules of the computer. The permanent storage device may be a readable and writable storage device. The permanent storage device may be a non-volatile storage device that does not lose the stored instructions and data even after the computer is powered off. In some embodiments, the permanent storage device uses a large-capacity storage device (such as a magnetic or optical disk, flash memory) as a permanent storage device. In some other embodiments, the permanent storage device may be a removable storage device (such as a floppy disk, optical drive). The system memory may be a readable and writable storage device or a volatile readable and writable storage device, such as a dynamic random access memory. The system memory may store some or all instructions and data required by the processor at run time. In addition, the memory may include any combination of computer-readable storage media, including various types of semiconductor memory chips (DRAM, SRAM, SDRAM, flash memory, programmable read-only memory), and disks and / or optical disks may also be used. In some embodiments, the memory may include a readable and / or writable removable storage device, such as a laser disc (CD), a read-only digital versatile disc (such as a DVD-ROM, a double-layer DVD-ROM), a read-only Blu-ray disc, an ultra-density optical disc, a flash memory card (such as an SD card, a mini SD card, a Micro-SD card, etc.), a magnetic floppy disk, etc. The computer-readable storage medium does not include carrier waves and transient electronic signals transmitted wirelessly or wired.

[0132] The memory stores executable codes, and when the executable codes are processed by the processor, the processor can execute part or all of the methods described above.

[0133] On the other hand, an embodiment of the present invention further provides a computer-readable storage medium, which stores computer-executable instructions, and the computer-executable instructions are used to enable a computer to execute the above method.

[0134] It will be appreciated by those skilled in the art that all or some of the steps and systems in the disclosed method above may be implemented as software, firmware, hardware and appropriate combinations thereof. Some physical components or all physical components may be implemented as software executed by a processor, such as a central processing unit, a digital signal processor or a microprocessor, or may be implemented as hardware, or may be implemented as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or a non-transitory medium) and a communication medium (or a temporary medium). As known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, disk storage or other magnetic storage devices, or any other medium that may be used to store desired information and may be accessed by a computer. Furthermore, it is well known to those skilled in the art that communication media typically embodies computer readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0135] The preferred embodiments of the present application are described above with reference to the accompanying drawings, but the scope of the rights of the present application is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present application should be within the scope of the rights of the present application.

Claims

1. A method for generating interpolation results of different resolutions by single-cell Hi-C, characterized in that: The method comprises the following steps: Obtain several single-cell Hi-C data files; Preprocessing the single cell Hi-C data file to obtain a contact matrix; interpolating the contact matrix to obtain an interpolated contact matrix; Performing a summation operation on the interpolated contact matrix to obtain an updated contact matrix; According to the updated contact matrix, low-resolution interpolation information is obtained; The step of obtaining a plurality of single cell Hi-C data files comprises the following steps: Set each single-cell Hi-C data file to include the following data columns: cell ID, first chromosome, first gene locus, second chromosome, second gene locus; The preprocessing of the single cell Hi-C data file to obtain a contact matrix comprises the following steps: According to the single-cell Hi-C data file, removing the data rows that do not match the first chromosome and the second chromosome to obtain first data; Add 1 to the first gene locus and the second gene locus of the first data and divide the result by a set resolution to obtain second data; Merging the same data rows in the second data, counting and generating new column numbers, using the values ​​of the first gene loci as row coordinates, and using the values ​​of the second gene loci as column coordinates, to obtain a contact matrix; The step of summing the interpolated contact matrix to obtain an updated contact matrix comprises the following steps: Dividing the interpolation contact matrix into a plurality of small matrices along the direction of the main diagonal; Each value in each of the small matrices is summed up to obtain an updated contact matrix.

2. The method according to claim 1, characterized in that The interpolation of the contact matrix to obtain an interpolated contact matrix comprises the following steps: The contact matrix is ​​interpolated using the scHiCluster algorithm to obtain an interpolated contact matrix.

3. The method according to claim 1, characterized in that The step of obtaining low-resolution interpolation information according to the updated contact matrix comprises the following steps: The updated contact matrix is ​​stored in a sparse matrix format to obtain low-resolution interpolation information.

4. The method according to claim 1, characterized in that: The method for generating interpolation results of different resolutions by single-cell Hi-C further comprises the following steps: The low-resolution interpolation information is subjected to dimension reduction, and the adjusted Rand index is calculated to obtain clustering effect information.

5. A system for generating interpolation results of different resolutions by single-cell Hi-C, characterized in that: The system comprises: The first module is used to obtain several single-cell Hi-C data files; The second module is used to preprocess the single cell Hi-C data file to obtain a contact matrix; The third module is used to interpolate the contact matrix to obtain an interpolated contact matrix; A fourth module is used to perform a sum operation on the interpolation contact matrix to obtain an updated contact matrix; A fifth module is used to obtain low-resolution interpolation information according to the updated contact matrix; The step of obtaining a plurality of single cell Hi-C data files comprises the following steps: Set each single-cell Hi-C data file to include the following data columns: cell ID, first chromosome, first gene locus, second chromosome, second gene locus; The preprocessing of the single cell Hi-C data file to obtain a contact matrix comprises the following steps: According to the single-cell Hi-C data file, removing the data rows that do not match the first chromosome and the second chromosome to obtain first data; Add 1 to the first gene locus and the second gene locus of the first data and divide the result by a set resolution to obtain second data; Merging the same data rows in the second data, counting and generating new column numbers, using the values ​​of the first gene loci as row coordinates, and using the values ​​of the second gene loci as column coordinates, to obtain a contact matrix; The step of summing the interpolated contact matrix to obtain an updated contact matrix comprises the following steps: Dividing the interpolation contact matrix into a plurality of small matrices along the direction of the main diagonal; Each value in each of the small matrices is summed up to obtain an updated contact matrix.

6. A device for generating interpolation results of different resolutions by single-cell Hi-C, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for generating interpolation results of different resolutions by single-cell Hi-C as claimed in any one of claims 1 to 4 is implemented.

7. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to enable a computer to execute the method according to any one of claims 1 to 4.

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