Method and system for carrying out region segmentation on sequencing result of spatial in-situ transcriptome

Abstract

The invention relates to the technical field of biological information, in particular to a method and a system for carrying out region segmentation on a space in-situ transcriptome sequencing result. The method provided by the invention comprises the following steps: acquiring spatial in-situ transcriptome sequencing data, wherein the spatial in-situ transcriptome sequencing data comprises a sequencing read length, spot coordinates corresponding to the sequencing read length and a UMI sequence; performing optimization processing on the spots based on the sequencing read length number corresponding to the spots so as to remove the spots with the sequencing read length number lower than a threshold value and obtain effective spots; segmenting the coordinates of the effective spots into a plurality of grids; determining a density value of each grid based on the number of effective spots contained in each grid; and performing region segmentation on the spatial in-situ transcriptome sequencing data based on the density value of each grid. According to the method, pseudo-cell segmentation of spatial in-situ transcriptome data is realized, the spatial in-situ transcriptome data is converted into data similar to single-cell RNA sequencing, and cell classification and identification of each spatial site / mRNA molecule are realized.

Description

Technical Field

[0001] The present invention relates to the field of bioinformatics, and in particular to a method and system for performing regional segmentation on spatial in situ transcriptome sequencing results. Background Art

[0002] Spatial in situ transcriptomics generates gene expression data with time or spatial location information (hereinafter referred to as spatial in situ transcriptomics data, or spatial in situ transcriptomics data). This type of data represents the types of genes (such as gene names) expressed by cells at various spatial locations in the studied tissue sample at various time points and the quantitative expression information of these genes (such as the number of transcript molecules). This type of research and data helps scientists identify the spatial expression patterns of various genes, identify the spatial distribution of different cell types, explore the spatial interaction relationship between different genes and different cells, and many other types of downstream analysis.

[0003] Cell segmentation is one of the important steps in the preprocessing of spatial in situ transcriptome data, and is also a step that some downstream analyses rely on. Cell segmentation is to identify the boundaries (cell membranes) of cells from spatial in situ transcriptome data (including images and expression data), so as to obtain a cell distribution state similar to that in real sample tissues, or to obtain single cell expression results similar to single cell RNA sequencing. The results of cell segmentation are conducive to studying the functional characteristics of individual cells in the tissue space and their position and function in the tissue.

[0004] Existing cell segmentation technologies include using the watershed algorithm to segment cell staining images. It is also possible to use artificially segmented cell boundary data to train a machine learning model, and then use the machine learning algorithm to segment cells. There is also a technology that is based on the cell type annotation results obtained from single-cell RNA sequencing data for training, and then uses the trained model to use a machine learning algorithm to optimize the cell boundaries of pre-segmented (preliminary segmentation by the watershed algorithm) cells.

[0005] Since it is difficult to accurately segment cells, some algorithms use a non-direct segmentation mode. First, the cell type and cell affiliation of the spatial sites of each gene expression are analyzed, and then the pseudo-cell boundaries are obtained through a clustering algorithm. For example, the published tool Baysor starts from the spatial in situ transcriptome data, uses the Markov random field (MRF) algorithm to calculate the probability that the genes at each spatial site belong to the cell region, and based on this probability, uses the Bayesian mixed model to cluster each spatial site to achieve cell segmentation. Another published method, SSAM, first calculates the density information of different types of RNA in the spatial in situ transcriptome data, and then annotates the cell type based on the area with the strongest signal in the superimposed RNA density map, and then uses correlation analysis to identify the cell type of each density area in the original density map, and finally uses a clustering method to divide the tissue region. However, the disadvantages of SSAM are: the method relies on the selection of marker genes, which can have a significant impact on the analysis results. This step is completed through artificial selection, relies on analysis experience, does not have good versatility, and may lose some gene information; the method does not use the expression level information of mRNA in each spatial site when calculating density; it only identifies cell types and does not segment cells, so it is impossible to divide the spatial attribution of each gene from the single-cell level and cannot conduct single-cell level research; it does not distinguish between the nucleus and cytoplasm and cannot conduct subcellular level research.

[0006] Therefore, there is an urgent need to develop a method for regional segmentation of spatial in situ transcriptome sequencing results that is independent of specific marker genes, integrates the spatial location information of mRNA and the expression level information in each spatial site, and can distinguish between cytoplasm and nucleus. Summary of the invention

[0007] The present invention aims to solve one of the technical problems in the related art at least to a certain extent. To this end, one object of the present invention is to propose a method for regional segmentation of spatial in situ transcriptome sequencing results, which realizes pseudo-cell segmentation of spatial in situ transcriptome data, converts spatial in situ transcriptome data into data similar to single-cell RNA sequencing, and realizes the cell or cell nucleus attribution of each spatial site / mRNA molecule.

[0008] To this end, in a first aspect of the present invention, the present invention provides a method for performing regional segmentation on spatial in situ transcriptome sequencing results. According to an embodiment of the present invention, the method comprises:

[0009] (1) obtaining spatial in situ transcriptome sequencing data, wherein the spatial in situ transcriptome sequencing data includes a sequencing read length, a spot coordinate corresponding to the sequencing read length, and a UMI sequence;

[0010] (2) optimizing the spots based on the number of sequencing reads corresponding to the spots, so as to remove the spots whose number of sequencing reads is lower than a threshold and obtain valid spots;

[0011] (3) dividing the coordinates of the effective spots into a plurality of grids;

[0012] (4) determining the density value of each grid based on the number of valid spots contained in each grid; and

[0013] (5) Based on the density value of each grid, the spatial in situ transcriptome sequencing data is segmented into regions.

[0014] The method for regional segmentation of spatial in situ transcriptome sequencing results provided by the present invention, using the spatial position information and expression information (i.e., single molecule number, Unique Molecular Identity count, UMI count) of all mRNA molecules in spatial in situ transcriptome data, calculating the weighted spatial density distribution result of mRNA (representative gene) in the spatial in situ transcriptome data of the sample under the same space. Based on this weighted density result, the spatial density contour information of mRNA is obtained. Then according to the density gradient change rate, the known cell diameter information, the contour line of the appropriate position is selected as the pseudo-cell boundary, the cell segmentation of spatial in situ transcriptome data is realized, and the spatial in situ transcriptome data is converted into the data similar to single cell RNA sequencing, and the cell or cell nucleus belonging to each spatial site / mRNA molecule is realized.

[0015] The regional segmentation method provided by the present invention does not rely on the selection of specific marker genes, reduces the possible deviation caused by artificial selection of marker genes, and improves accuracy to a certain extent. The available data of the spatial in situ transcriptome is fully utilized during the calculation process, including the spatial coordinate information of mRNA molecules (genes) and the molecular quantity (expression amount) information, reducing information loss. The method supports the segmentation of nuclear subcellular structures, and supports spatial in situ transcriptome sequencing data at the subcellular level compared to the prior art, which increases the versatility and usable range of the cell segmentation method.

[0016] A second aspect of the present invention provides a system for performing regional segmentation on spatial in situ transcriptome sequencing results. According to an embodiment of the present invention, the system comprises:

[0017] A sequencing data acquisition module, wherein the sequencing data acquisition module is used to acquire spatial in situ transcriptome sequencing data, wherein the spatial in situ transcriptome sequencing data includes sequencing read length, spot coordinates corresponding to the sequencing read length, and UMI sequence;

[0018] A filtering module, the filtering module is connected to the sequencing data acquisition module, and the filtering module is used to optimize the spots based on the number of sequencing read lengths corresponding to the spots, so as to remove the spots with sequencing read lengths lower than a threshold and obtain valid spots;

[0019] A grid division module, the grid division module is connected to the filtering module, and the grid division module is used to divide the coordinates of the effective spots into a plurality of grids;

[0020] A density value acquisition module, the density value acquisition module is connected to the grid division module, and the density value acquisition module is used to determine the density value of each grid based on the number of valid spots contained in each grid;

[0021] A region segmentation module, the region segmentation module is connected to the density value acquisition module, and the region segmentation module is used to perform region segmentation on the spatial in situ transcriptome sequencing data based on the density value of each grid.

[0022] The spatial in situ transcriptome sequencing result provided by the present invention is used to perform regional segmentation of the system, and the spatial position information and expression information of all mRNA molecules in the spatial in situ transcriptome data are used to calculate the weighted spatial density distribution result of mRNA (representative gene) in the spatial in situ transcriptome data of the sample under the same space. Based on this weighted density result, the spatial density contour information of mRNA is obtained. Then according to the density gradient change rate and the known cell diameter information, the contour line of the appropriate position is selected as the pseudo-cell boundary, and the cell segmentation of the spatial in situ transcriptome data is realized, and the spatial in situ transcriptome data is converted into the data similar to single-cell RNA sequencing, and the cell or cell nucleus belonging to each spatial site / mRNA molecule is realized.

[0023] The regional segmentation system provided by the present invention does not rely on the selection of specific marker genes, reduces the possible deviation caused by artificial selection of marker genes, and improves accuracy to a certain extent. The available data of the spatial in situ transcriptome is fully utilized during the calculation process, including the spatial coordinate information of mRNA molecules (genes) and the molecular quantity (expression amount) information, reducing information loss. The regional segmentation system supports the segmentation of nuclear subcellular structures, and supports spatial in situ transcriptome sequencing data at the subcellular level compared to the prior art, increasing the versatility and usable range of the cell segmentation method.

[0024] A third aspect of the present invention provides an electronic device. According to an embodiment of the present invention, the electronic device comprises:

[0025] A memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the method for performing regional segmentation on spatial in situ transcriptome sequencing results described in the first aspect is implemented.

[0026] The fourth aspect of the present invention provides a readable storage medium. According to an embodiment of the present invention, the readable storage medium stores computer-readable instructions for causing a computer to execute the method for performing regional segmentation on spatial in situ transcriptome sequencing results described in the first aspect.

[0027] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0029] Figure 1 A schematic flow chart of a method for performing regional segmentation on spatial in situ transcriptome sequencing results according to an embodiment of the present invention is shown;

[0030] Figure 2 A schematic diagram showing the structure of a system for performing regional segmentation on spatial in situ transcriptome sequencing results according to one embodiment of the present invention;

[0031] Figure 3 A schematic diagram showing the structure of an electronic device according to an embodiment of the present invention;

[0032] Figure 4 A hardware schematic diagram of an electronic device according to an embodiment of the present invention is shown;

[0033] Figure 5A is a frequency distribution histogram drawn according to the number of gene types of each pseudocell in Example 2 of the present invention;

[0034] Figure 5B is a frequency distribution histogram of the number of RNA molecules drawn according to the number of RNA molecules in each pseudocell in Example 2 of the present invention;

[0035] Figure 6 This is a result graph of Pearson correlation coefficient analysis of gene expression using the corr function of pandas according to Example 2 of the present invention;

[0036] Figure 7A-7B is a scatter plot of spatial in situ transcriptome data according to one embodiment of the present invention, wherein Fig. 7A This is a scatter plot of the original data of spatial in situ transcriptome data. Figure 7B It is a scatter plot of the spatial in situ transcriptome cell segmentation effect obtained by using the regional segmentation method of the present invention. DETAILED DESCRIPTION

[0037] Embodiments of the present invention are described in detail below, and the examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals represent the same or similar elements or elements with the same or similar functions from beginning to end. The embodiments described below by reference to the accompanying drawings are exemplary, are intended to be used to explain the present invention, and are not to be construed as limitations of the present invention. In the embodiments, if specific techniques or conditions are not indicated, the techniques or conditions described in the literature in this area or the product specifications are carried out. The reagents used or the instruments that are not indicated by the manufacturer are all conventional products that can be obtained commercially.

[0038] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "top", "bottom", "inside", "outside" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present invention.

[0039] In addition, the terms "first", "second", etc. 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. Thus, a feature defined as "first", "second", etc. may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0040] the term

[0041] "Spots" are also called "spots". They are holes with a certain diameter that are evenly distributed in the capture area of ​​the chip. There are millions of capture probes that can bind to mRNA. Each probe in the spot carries unique spatial barcodes to mark the spatial position of the captured mRNA.

[0042] "UMI" is the abbreviation of Unique Molecular Identifiers, which is a 10-20nt nucleotide sequence used to uniquely mark each molecule in the sample library. UMI is widely used in sequencing applications, mostly for PCR repeats of DNA and cDNA. UMI deduplication can also be used for RNA-seq gene expression analysis and other quantitative sequencing methods. In short: UMI is a uniquely identifiable short sequence. Since UMI is unique, it is possible to know which ones are amplified from the same DNA during PCR amplification. "Scatter plot" refers to a dot plot (dotplot) that shows the relative positions of each spot based on the spatial coordinate information of the spot, and each point in the figure represents a spot. The spatial coordinates of the spots are converted into integers, and the coordinate unit length is 1 (no specific physical length).

[0043] A "grid" refers to a network of sub-regions with specific side lengths (coordinate lengths) and no gaps in a specific spatial region (such as the spatial coordinate range of a spot), and each sub-region has the same shape.

[0044] The “density value” refers to a single value calculated by a specific density estimation algorithm based on the coordinate spacing and relative position information between all spots and the center point of the grid within a single grid area.

[0045] "Region-specific sequencing reads" refer to unique sequencing reads that exist in a specific region, such as the sequencing reads of pre-mRNA that exist in the cell nucleus.

[0046] For a typical pattern recognition system, it generally includes several parts such as data acquisition, pre-processing, regional segmentation, feature extraction, pattern classification, training and learning." Regional segmentation" refers to the data to be analyzed being divided into regions, and the data fragments of interest therein are extracted for further processing, while other data are discarded. The main purpose of regional segmentation is to reduce the amount of data for subsequent processing. The region in "regional segmentation" includes coordinate regions. The regional segmentation of the spatial in situ transcriptome sequencing results in the present invention refers to the coordinate region segmentation of the spatial in situ transcriptome sequencing results.

[0047] "Kernel density estimation (KDE)" is used in probability theory to estimate unknown density functions. It is one of the non-parametric test methods and was proposed by Rosenblatt (1955) and Emanuel Parzen (1962), also known as the Parzen window. Ruppert and Cline proposed a revised kernel density estimation method based on a data set density function clustering algorithm. The region (e.g., cell) segmentation method provided by the present invention can be based on a kernel density estimation algorithm, using the spatial position information and expression information of all mRNA molecules in the spatial in situ transcriptome data to calculate the weighted spatial density distribution results of mRNA (representative genes) in the spatial in situ transcriptome data of samples in the same space. Based on the weighted density results, cell segmentation is achieved.

[0048] A "contour" is a continuous area where cells with the same density value are connected to each other.

[0049] The “density gradient change rate” refers to the difference in density values ​​represented by two adjacent sets of contour lines.

[0050] "Read length" is synonymous with "reads" in the sequencing field. Sequencing read length refers to the base sequence information of a specific length in the sequencing results obtained by sequencing using a sequencer.

[0051] In conjunction with the accompanying drawings, the method for regional segmentation of spatial in situ transcriptome sequencing results, the system for regional segmentation of spatial in situ transcriptome sequencing results, the electronic device and the readable storage medium provided in the embodiments of the present application are described in detail through specific embodiments and their application scenarios.

[0052] The method for performing regional segmentation based on spatial in situ transcriptome sequencing results can be applied to a terminal, and can be specifically executed by hardware or software in the terminal.

[0053] The terminal includes, but is not limited to, a portable communication device such as a mobile phone or tablet computer with a touch-sensitive surface (e.g., a touch screen display and / or a touch pad). It should also be understood that in some embodiments, the terminal may not be a portable communication device, but a desktop computer with a touch-sensitive surface (e.g., a touch screen display and / or a touch pad); or a terminal including a display and a touch-sensitive surface; or a display and a terminal, wherein the terminal may include one or more other physical user interface devices such as a physical keyboard, a mouse, and a joystick.

[0054] The method for regional segmentation of spatial in situ transcriptome sequencing results provided in the embodiment of the present application can be an electronic device or a functional module or functional entity in the electronic device that can implement the method. The electronic devices mentioned in the implementation scheme of the present application include but are not limited to mobile phones, tablet computers, computers and wearable devices.

[0055] Figure 1 FIG. 1 is a flow chart of a method for performing regional segmentation on spatial in situ transcriptome sequencing results according to an embodiment of the present invention, as shown in FIG. Figure 1 As shown, according to a specific implementation method of the present invention, the present invention provides a method for performing regional segmentation on spatial in situ transcriptome sequencing results, comprising the following steps:

[0056] S100, obtaining spatial in situ transcriptome sequencing data.

[0057] According to a specific embodiment of the present invention, the spatial in situ transcriptome sequencing data includes sequencing read length, spot coordinates corresponding to the sequencing read length, and UMI sequence.

[0058] The method for obtaining spatial in situ transcriptome sequencing data provided by the present invention is not particularly limited, but it is required that the sequencing data obtained is a spatial in situ transcriptome, for example, it can be obtained using BGI's Stereo-seq spatial in situ transcriptome sequencing platform, or it can be obtained using other sequencing platforms.

[0059] S200, optimizing the spots based on the number of sequencing read lengths corresponding to the spots, removing spots with sequencing read lengths lower than a threshold and obtaining valid spots.

[0060] According to a specific embodiment of the present invention, the optimization process further includes selecting spots containing region-specific sequencing reads or a predetermined number of sequencing reads as the effective spots. The region-specific sequencing reads are selected from at least one of the cell nucleus-specific sequencing reads, the cytoplasm-specific sequencing reads, and the mitochondria-specific sequencing reads, such as the sequencing reads of pre-mRNA present in the cell nucleus.

[0061] According to a specific embodiment of the present invention, the predetermined number is determined according to at least one of the following:

[0062] (i) According to the spatial in situ transcriptome sequencing platform from which the spatial in situ transcriptome sequencing results are derived, the quantitative values ​​obtained based on the analysis purpose and analysis experience;

[0063] (ii) Re-analysis and optimization based on downstream results to obtain the best quantitative value;

[0064] (iii) Based on prior knowledge, the sequencing read length quantity distribution corresponding to the valid spots in the same type of data is obtained, and the minimum value is taken as the quantity value;

[0065] Optionally, reanalysis to optimize the optimal quantitative values ​​based on downstream results includes:

[0066] According to the results of the previous analysis by method (i), the parameters are readjusted to determine the impact of different values ​​on the final result in order to select the best parameters, wherein the criterion for selecting the best parameters is the parameter corresponding to the highest evaluation score when the evaluation method is used for evaluation.

[0067] According to some embodiments of the present invention, the following three methods can be used to evaluate the cell segmentation effect:

[0068] The first category is the physical characteristics after segmentation, such as the total number of cells, cell volume, number of genes in cells, and distribution of RNA molecules in cells. These values ​​themselves cannot quantitatively explain the quality of the segmentation effect, but can be qualitatively evaluated based on empirical values. Specifically, Baysor, PicSeq, Sparcle and other commonly used tools in the field can be used for evaluation.

[0069] The second category is based on marker gene features after cell segmentation. For example, the similarity (or correlation) of the composition and expression of marker genes in the annotated pseudo-cells and the real single-cell data of the same type can be evaluated using commonly used tools in this field, such as StereoCell, JSTA, and SSAMe.

[0070] The specific operation method is as follows:

[0071] (I) Use common single-cell analysis software, such as Scanpy and Seurat, to analyze spatial transcriptome data. Clustering is performed based on the expression data of each pseudocell and its internal genes. For each cell group obtained by clustering, the corresponding cell type is identified based on the marker genes in the same cell type;

[0072] (II) Use Scanpy's sc.tl.rank_genes_groups() function to analyze the top 50 or 100 genes with significant expression differences between different groups;

[0073] (III) Obtain a single-cell reference dataset for the same analysis and use it as the standard reference result. For some datasets that provide classification results, you can directly use the classification results as the standard reference result. Obtain the expression information of 50 or 100 genes;

[0074] (IV) By calculating the Pearson correlation coefficient of the analysis results of the spatial transcriptome sequencing data and the reference data set, quantitative evaluation is performed through the correlation coefficient value. Theoretically, for a better segmentation effect, the correlation coefficient of two types of cells of the same type usually needs to be greater than 0.7.

[0075] The third category is to evaluate based on simulated data of known components. For example, the methods mentioned in tools such as Baysor and SSAM. By comparing whether the indicators such as gene type and expression level in the pseudo cells obtained after segmentation are consistent with the real parameters obtained by simulation. This type of method is limited by the fact that there is no standardized and universal spatial transcriptome data simulation tool.

[0076] According to a specific embodiment of the present invention, based on the range value of the analysis purpose and analysis experience, taking Stereo-seq technology as an example, the spatial transcriptome sequencing data generated by it can usually use an integer between 2 and 8. For other technologies, the lower the resolution, the higher the corresponding value.

[0077] According to a specific embodiment of the present invention, re-analysis and optimization are performed based on downstream results, that is, parameters are readjusted according to the results of the previous empirical value analysis to determine the impact of different values ​​on the final results so as to select the best parameters. The ultimate goal is to achieve the highest evaluation score when evaluating using relevant evaluation methods in the field.

[0078] According to a specific embodiment of the present invention, based on prior knowledge (such as relevant literature), the distribution of sequencing read lengths corresponding to effective spots in the same type of data is obtained, and the minimum value thereof is used as the parameter value.

[0079] According to a specific embodiment of the present invention, the optimization process further includes data filtering and noise removal, which includes removing specific sequencing reads that are known to be irrelevant to downstream analysis, including removing spots (and their coordinate information) whose number of sequencing reads is less than a specified threshold.

[0080] S300, dividing the coordinates of the effective spots into a plurality of grids.

[0081] According to a specific embodiment of the present invention, each of the multiple grids has the same size, and there is no particular restriction on the number and width of the grids, which can be set by the user based on analysis accuracy, computing resources, and experience.

[0082] S400, determining the density value of each grid based on the number of valid spots contained in each grid.

[0083] According to a specific embodiment of the present invention, the density value of each grid can be determined by the following process:

[0084] According to the set grid parameters (grid number, or grid width), the coordinate matrix of the center point of the density distribution is constructed. The coordinate system of the center point is completely consistent with the original spatial in situ transcriptome data; the distribution density value of the filtered spatial in situ transcriptome data at each density distribution center point coordinate is calculated. During the calculation process, the parameters can be used to confirm whether the expression amount (mRNA molecule number) information is used as the weighting coefficient.

[0085] According to a specific embodiment of the present invention, a method for constructing a coordinate matrix of the center point of the density distribution according to the set grid parameters includes but is not limited to using the mgrid function provided in the Python module numpy to construct a coordinate matrix of the center point of the density distribution. Other methods known in the art that can be used to construct a coordinate matrix of the center point of the density distribution are also included in the scope of protection of the present invention.

[0086] According to a specific embodiment of the present invention, the method for calculating the distribution density value of the filtered spatial in situ transcriptome data at each density distribution center point coordinate includes but is not limited to implementing it by calling the density estimation algorithm gaussian_kde provided in the Python module scipy.stats.

[0087] It should be noted that there is no particular restriction on the algorithm used in determining the density value of each grid. It can be the above-mentioned density estimation algorithm, or other types of density or local clustering (such as point cloud clustering) algorithms. There is no particular restriction on the function used to construct the coordinate matrix of the center point of the density distribution. The algorithm and related functions used can determine the distribution density value of the spatial in situ transcriptome data at the coordinates of each density distribution center point.

[0088] According to a specific embodiment of the present invention, when determining the density value of each grid, the weighting coefficient of the effective spot is determined based on the number of sequencing reads corresponding to the effective spot.

[0089] S500, based on the density value of each grid, performs regional segmentation on spatial in situ transcriptome sequencing data.

[0090] According to a specific embodiment of the present invention, based on the density value of each grid, performing regional segmentation on the spatial in situ transcriptome sequencing data further comprises:

[0091] 1) based on the density value, drawing density value contour lines; and

[0092] 2) Based on the contour lines, the spatial in situ transcriptome sequencing data is segmented into regions.

[0093] According to a specific embodiment of the present invention, step 2) further includes using contour lines with predetermined density values ​​as region dividing lines among the drawn density value contour lines, and realizing region segmentation by using the closed areas enclosed by the region dividing lines, and the closed areas enclosed by the region dividing lines obtained after the region segmentation are discontinuous.

[0094] According to a specific embodiment of the present invention, the predetermined density value refers to the dividing boundary value obtained when each grid is divided into multiple uniform or non-uniform parts according to the density value in a linear or non-linear way. The specific number of parts is determined based on empirical values. Preferably, the multiple parts are 6-11.

[0095] According to a specific embodiment of the present invention, the predetermined density value refers to a segmentation boundary value obtained by re-analyzing and optimizing based on downstream analysis results to determine the impact of the number of density segmentations on the results.

[0096] According to a specific embodiment of the present invention, in step (5), based on the density value of each grid, performing regional segmentation on the spatial in situ transcriptome sequencing data further comprises:

[0097] According to at least one of the distribution of density values, the trend of density value changes, and the rate of density value changes, the region dividing lines are determined, and the region segmentation is achieved using the closed areas enclosed by the region dividing lines. The closed areas enclosed by the region dividing lines obtained after the region segmentation are discontinuous.

[0098] It should be noted that the method for determining the region dividing line and obtaining the closed area enclosed by the dividing line includes but is not limited to the contour lines based on the above-mentioned density values ​​or the distribution of density values, the density value change trend, the density value change rate, etc., and can also be determined by other methods. Other determination methods are also included in the protection scope of the segmentation method of the present invention.

[0099] According to a specific embodiment of the present invention, when determining the region segmentation line according to at least one of the density value contour line or the density value distribution, the density value change trend, and the density value change rate, it includes using prior knowledge and re-analysis optimization to determine the specific parameter range. The prior knowledge may be, for example, empirical value judgment, similar references, etc.

[0100] According to a specific embodiment of the present invention, step (5) further comprises: obtaining spatial in situ transcriptome sequencing data and spots within the closed areas enclosed by each area dividing line, individually numbering each of the spatially discontinuous closed areas, and using each of the spatially discontinuous closed areas as a candidate pseudocell area.

[0101] According to a specific embodiment of the present invention, the scatter plot is segmented into regions by the following method:

[0102] According to the coordinate matrix of the center point of the density distribution and the mRNA density value at each point coordinate, the density contour of the spatial density distribution matrix is ​​obtained, and a list of boundary coordinates of the contour is obtained, where the division range and number of divisions of the contour can be set and adjusted through parameters, and the coordinates of the contour are the boundary coordinates of the pseudo-cells after segmentation. For each set of pseudo-cell boundary coordinates, a number information is assigned as the cell number in the current spatial in situ transcriptome data; according to the coordinate information of the contour, the area enclosed by the contour is extracted to delineate the polygonal area in the target spatial area. Then, all mRNA data whose coordinates fall within the polygonal area in the original spatial in situ transcriptome data under the same coordinate system are determined, that is, data in the form of single-cell RNA sequencing.

[0103] According to a specific embodiment of the present invention, in the method for regional segmentation of spatial in situ transcriptome sequencing results provided by the present invention, the spatial position information and expression information of all mRNA molecules in the spatial in situ transcriptome data are used, wherein the transcriptome data will be converted into five columns of information (x coordinate, y coordinate, gene name, expression amount UMICount, gene type), and then the input data is directly used as input to generate the cell number (random number) of each valid spatial position. By presenting the target cell number in the form of a scatter plot, a scatter plot of the spatial in situ transcriptome cell segmentation effect obtained by the regional segmentation method of the present invention can be obtained.

[0104] The present invention proposes a new cell segmentation method for spatial in situ transcriptome data based on kernel density estimation (KDE). This method does not rely on the selection of specific marker genes, reduces the possible deviation caused by artificial selection of marker genes, and improves the accuracy to a certain extent. The available data of the spatial in situ transcriptome is fully utilized during the calculation process, including the spatial coordinate information of mRNA molecules (genes) and the molecular quantity (expression amount) information, reducing information loss. This method supports the segmentation of subcellular structures of the cell nucleus. Compared with the prior art, it supports spatial in situ transcriptome sequencing data at the subcellular level, which increases the versatility and usable range of the cell segmentation method.

[0105] Figure 2 This is a schematic diagram of the structure of a system for performing regional segmentation on spatial in situ transcriptome sequencing results according to an embodiment of the present invention. According to a specific implementation method of the present invention, the system includes:

[0106] A sequencing data acquisition module 1000, wherein the sequencing data acquisition module 1000 is used to acquire spatial in situ transcriptome sequencing data, wherein the spatial in situ transcriptome sequencing data includes a sequencing read length, a spot coordinate corresponding to the sequencing read length, and a UMI sequence;

[0107] A filtering module 2000, the filtering module 2000 is connected to the sequencing data acquisition module 1000, and the filtering module 2000 is used to optimize the spots based on the number of sequencing read lengths corresponding to the spots, so as to remove the spots with the number of sequencing read lengths lower than a threshold and obtain valid spots;

[0108] A grid division module 3000, the grid division module 3000 is connected to the filtering module 2000, and the grid division module 3000 is used to divide the coordinates of the effective spots into a plurality of grids;

[0109] A density value acquisition module 4000, the density value acquisition module 4000 is connected to the grid division module 3000, and the density value acquisition module 4000 is used to determine the density value of each grid based on the number of valid spots contained in each grid;

[0110] The region segmentation module 5000 is connected to the density value acquisition module 4000, and is used to perform region segmentation on the spatial in situ transcriptome sequencing data based on the density value of each grid.

[0111] The regional segmentation method and system provided by the present invention process spatial in situ transcriptome data through a density distribution algorithm to achieve cell segmentation. The method of the present invention: (1) does not rely on the selection of specific marker genes, and directly performs density analysis through a complete data set of spatial in situ transcriptome data; (2) the spatial position information of mRNA (representative gene) and the expression information (number of mRNA molecules) at each spatial site are integrated to perform density distribution analysis, and the available data of spatial in situ transcriptomics are fully utilized; based on the density distribution analysis, the spatial in situ transcriptome data is subjected to cell segmentation, as well as subcellular structure segmentation (for example, distinguishing between the nucleus and the cytoplasm, or distinguishing between other organelles, etc.); based on the segmentation results, the gene data in each segmented cell can be independently extracted, and the expression data of a single cell can be obtained, so as to achieve an effect similar to that of single-cell RNA sequencing; the nucleus and the cytoplasm are effectively distinguished.

[0112] In some embodiments, such as Figure 3 As shown, the present invention also provides an electronic device 800, including a processor 801, a memory 802, and a computer program stored in the memory 802 and executable on the processor 801. When the program is executed by the processor 801, each process of the above-mentioned method implementation scheme for regional segmentation of spatial in situ transcriptome sequencing results is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be described here.

[0113] Figure 4A schematic diagram of the hardware structure of an electronic device for implementing an embodiment of the present invention.

[0114] The electronic device 900 includes, but is not limited to, components such as an input unit 901 , a display unit 902 , a user input unit 903 , an interface unit 904 , a memory 905 , and a processor 906 .

[0115] Those skilled in the art will appreciate that the electronic device 900 may also include a power source (such as a battery) for supplying power to each component, and the power source may be connected to the processor 906 via a power management system, thereby implementing functions such as managing charging, discharging, and power consumption management through the power management system. Figure 4 The electronic device structure shown in the figure does not constitute a limitation of the electronic device. The electronic device may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently. For example, a network module, an audio output unit, etc. may be added, which will not be repeated here.

[0116] The input unit 901 is used to process the visualization results of the spatial in situ transcriptome sequencing data, and the input unit 901 may include a data processor 9011, which processes the image data of the static picture or video obtained by the image capture device in the image capture mode. The system for regional segmentation of the spatial in situ transcriptome sequencing results of the spatial transcriptome realizes the regional segmentation of the sequencing results through the data processor 9011.

[0117] The display unit 902 may include a display panel 9021, which may be configured in the form of a liquid crystal display, an organic light emitting diode, etc. The user input unit 903 includes a touch panel 9031 and at least one of other input devices 9032. The touch panel 9031 is also called a touch screen. The touch panel 9031 may include two parts: a touch detection device and a touch controller. Other input devices 9032 may include, but are not limited to, a physical keyboard, a function key (such as a volume control key, a switch key, etc.), a trackball, a mouse, and a joystick, which will not be repeated here.

[0118] The memory 905 can be used to store software programs and various data. The memory 905 may mainly include a first storage area for storing programs or instructions and a second storage area for storing data, wherein the first storage area may store an operating system, an application program or instructions required for at least one function (such as a sound playback function, an image playback function, etc.), etc. In addition, the memory 905 may include a volatile memory or a non-volatile memory, or the memory 905 may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. Volatile memory can be random access memory (Random Access Memory, RAM), static random access memory (Static RAM, SRAM), dynamic random access memory (Dynamic RAM, DRAM), synchronous dynamic random access memory (Synchronous DRAM, SDRAM), double data rate synchronous dynamic random access memory (Double Data Rate SDRAM, DDRSDRAM), enhanced synchronous dynamic random access memory (Enhanced SDRAM, ESDRAM), synchronous link dynamic random access memory (Synch link DRAM, SLDRAM) and direct memory bus random access memory (Direct Rambus RAM, DRRAM). The memory 905 in the implementation scheme of the present application includes but is not limited to these and any other suitable types of memory.

[0119] The processor 906 may include one or more processing units; the processor 906 integrates an application processor and a modem processor, wherein the application processor mainly processes operations related to the operating system, user interface, and application programs, and the modem processor mainly processes wireless communication signals, such as a baseband processor. It is understandable that the modem processor may not be integrated into the processor 906.

[0120] Electronic equipment is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, sequencers, gene sequencing systems, one-stop technology platforms for large population genomics, PCR instruments, spatiotemporal genomics integrated machines, laboratory automation systems, sample preparation equipment, packaging equipment, library production equipment, liquid handling equipment, magnetic bead detection equipment, nucleic acid purification equipment and other suitable computers. Electronic equipment can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices and other similar computing devices. The components shown in this application, their connections and relationships, and their functions are only used as examples, and are not intended to limit the realization of the application described in this application and / or required.

[0121] A specific implementation scheme of the present application also provides a readable storage medium, on which computer-readable instructions are stored. When the computer-readable instructions are executed by a processor, the various processes of the above-mentioned method embodiment for regional segmentation of spatial in situ transcriptome sequencing results are implemented, and the same technical effect can be achieved. To avoid repetition, they are not repeated here.

[0122] The processor is the processor in the electronic device described in the above embodiment. The readable storage medium includes a computer readable storage medium, such as a computer read-only memory ROM, a random access memory RAM, a magnetic disk or an optical disk.

[0123] The scheme of the present disclosure will be explained below in conjunction with the examples. Those skilled in the art will appreciate that the following examples are only used to illustrate the present disclosure and should not be considered to limit the scope of the present disclosure. Where specific techniques or conditions are not indicated in the examples, the techniques or conditions described in the literature in this area or the product instructions are used. Where the manufacturers of reagents or instruments are not indicated, they are all conventional products that can be obtained commercially.

[0124] Example 1: Spatial in situ transcriptome data cell segmentation method

[0125] A method for spatial in situ transcriptome data cell segmentation comprises the following steps:

[0126] (1) The spatial in situ transcriptome sequencing data of the samples were obtained using the spatial in situ transcriptome sequencing platform (data source: https: / / doi.org / 10.1016 / j.cub.2018.07.062);

[0127] (2) basic filtering of the spatial in situ transcriptome data according to the number of mRNA molecules or gene annotation information (gene name) in each coordinate. The basic filtering specifically includes filtering the region-specific reads of the specified category and filtering the spots whose number of reads exceeds the threshold;

[0128] (3) Obtain the coordinate range of the spatial in situ transcriptome data, and construct a coordinate matrix of the center point of the density distribution according to the set grid parameters (number of grids, or grid width). The coordinate system of the center point is completely consistent with the original spatial in situ transcriptome data;

[0129] (4) Calculate the distribution density value of the filtered spatial in situ transcriptome data at the coordinates of each density distribution center point. During the calculation process, the parameters can be used to confirm whether the expression amount (mRNA molecule number) information is used as the weighting coefficient;

[0130] (5) According to the coordinate matrix of the center point of the density distribution and the mRNA density value at each point coordinate, the density contour of the spatial density distribution matrix is ​​obtained, and a list of boundary coordinates of the contour line is obtained. The division range and number of the contour line can be adjusted by setting parameters;

[0131] (6) The coordinates of the contour lines are the coordinates of the pseudo-cell boundaries after segmentation. For each set of pseudo-cell boundary coordinates, a number is assigned as the cell number in the current spatial in situ transcriptome data;

[0132] (7) According to the coordinate information of the contour lines, the area enclosed by the contour lines is extracted, and all mRNA data whose coordinates fall within the polygonal area in the original spatial in situ transcriptome data under the same coordinate system are determined. That is, the data in the form of single-cell RNA sequencing is extracted to extract pseudo-cell single-cell data;

[0133] (8) Finally, the corresponding polygon (pseudo-cell) number is added to the original spatial in situ transcriptome data to achieve the cell attribution corresponding to each valid coordinate of the spatial in situ transcriptome data (unfiltered spatial in situ transcriptome data coordinates).

[0134] The sample in step (1) can be a tissue slice of a specific animal or plant. After obtaining the frozen slice, the slice is attached to the sequencing chip, and after fixation, staining, photography, permeabilization, reverse transcription, library construction, sequencing and other processes, the spatial in situ transcriptome sequencing data is finally obtained. For the specific process, please refer to the experimental method section in the reference https: / / doi.org / 10.1016 / j.cub.2018.07.062.

[0135] Example 2: Accuracy evaluation of segmentation results

[0136] The accuracy of the segmentation results obtained using steps (1)-(8) of Example 1 is evaluated by the following two methods:

[0137] Method 1:

[0138] The number of genes and the number of RNA / UMI molecules of the cells obtained by segmentation according to the method of Example 1 are counted to determine whether the distribution of the number (or: log-transformed value) conforms to the normal distribution.

[0139] Specifically, the accuracy is evaluated through the following steps:

[0140] 1. Based on the spatial transcriptome sequencing data of the sample, pseudo cells are segmented using the above method, and each pseudo cell is numbered independently;

[0141] 2. For each independent pseudocell, use its number information to obtain the number of gene types (gene number) of each pseudocell. Draw a frequency distribution histogram based on the number of gene types of each pseudocell ( Figure 5A ). The horizontal axis is the number of gene types, the vertical axis is the number of pseudocells, and the black curve in the figure is a normal distribution curve fitted based on the sample data characteristics (mean and variance);

[0142] 3. For each independent pseudocell, use its number information to obtain the number of RNA molecules in each pseudocell (the sum of UMI_count in the area with the same pseudocell number). Then draw a frequency distribution histogram of the number of RNA molecules based on this number of molecules ( Figure 5B ). The horizontal axis is the number of RNA molecules, the vertical axis is the number of pseudocells, and the black curve in the figure is a normal distribution curve fitted based on the sample data characteristics (mean and variance);

[0143] 4. Check whether the fitting curve and the frequency distribution histogram of the data are consistent. If necessary, the Shapiro-Wilk normality test (p-value>0.05) can also be used to evaluate whether the number of genes and RNA molecules conforms to the normal distribution.

[0144] Method 2:

[0145] Based on the literature: https: / / doi.org / 10.15252 / msb.202010108, segmentation quality was evaluated by correlation with gene expression profiles.

[0146] (1) Download and preprocess single-cell transcriptome data as a reference dataset.

[0147] The E16 / E17 mouse embryonic cerebellum single-cell dataset used in this example comes from the literature Carter, RA, Bihannic, L., Rosencrance, C., Hadley, JL, Tong, Y., Phoenix, TN, ... & Gawad, C. (2018). A single-cell transcriptional atlas of the developing murine cerebellum. Current Biology, 28(18), 2910-2920. The raw data were aligned, cell called, clustered and annotated using the standard process of 10X Cell ranger and Scanpy to obtain 12 types of cells, including Astrocytes, Erthrocytes, Microglia, and the top 50 characteristic genes of each type of cell relative to other types were obtained.

[0148] (2) Spatial transcriptome cell annotation after segmentation.

[0149] This example uses Python's pandas, numpy, and scanpy modules to convert the results of cell segmentation into h5ad files, and clusters the data according to scanpy's standard spatial transcriptome analysis process https: / / scanpy-tutorials.readthedocs.io / en / latest / spatial / basic-analysis.html. And refer to the marker genes in the references in 1) to annotate different cell groups. Finally, two types of cells are annotated, namely roof plate and Erythrocytes.

[0150] (3) Correlation analysis of gene expression profiles.

[0151] In this example, all cells of the Erythrocytes group in the single cell data set in (1) are separated and the top 50 characteristic genes and their corresponding expression levels are extracted. At the same time, the gene expression levels of the above 50 genes in the Erythrocytes cells of the spatial transcriptome segmentation data are extracted, and the Pearson correlation coefficient analysis of the gene expression levels is performed using the corr function of pandas. Figure 6 Results shown.

[0152] Image display of segmentation effect:

[0153] Fig. 7A This is a scatter plot of the original data of the in situ transcriptome data of the above samples. Figure 7BThe scatter plot of the spatial in situ transcriptome cell segmentation effect obtained by the regional segmentation method of the present invention (each pseudocell in the adjacent area can be represented by a different color, and the color of the non-adjacent area can be repeated). It shows that the method of the present invention achieves the cell segmentation of the spatial transcriptome sequencing results.

[0154] 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 invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more 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.

[0155] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A method for regional segmentation of spatial in situ transcriptome sequencing results, characterized in that: include: (1) obtaining spatial in situ transcriptome sequencing data, wherein the spatial in situ transcriptome sequencing data includes a sequencing read length, a spot coordinate corresponding to the sequencing read length, and a UMI sequence; (2) optimizing the spots based on the number of sequencing reads corresponding to the spots, so as to remove the spots whose number of sequencing reads is lower than a threshold and obtain valid spots; (3) dividing the coordinates of the effective spots into a plurality of grids; (4) Determine the density value of each grid based on the number of valid spots contained in each grid; and (5) Based on the density value of each grid, the spatial in situ transcriptome sequencing data is segmented into regions.

2. The method according to claim 1, characterized in that The optimization process further includes selecting spots containing region-specific sequencing reads or containing a predetermined number of sequencing reads as the effective spots.

3. The method according to claim 2, characterized in that The region-specific sequencing read length is selected from at least one of a nucleus-specific sequencing read length, a cytoplasm-specific sequencing read length, and a mitochondrial-specific sequencing read length.

4. The method according to claim 2, characterized in that: The predetermined number is determined according to at least one of the following: (i) According to the spatial in situ transcriptome sequencing platform from which the spatial in situ transcriptome sequencing results are derived, the quantitative values ​​obtained based on the analysis purpose and analysis experience; (ii) Re-analysis and optimization based on downstream results to obtain the best quantitative value; (iii) Based on prior knowledge, the sequencing read length quantity distribution corresponding to the valid spots in the same type of data is obtained, and the minimum value is taken as the quantity value; Optionally, reanalysis to optimize the optimal quantitative values ​​based on downstream results includes: According to the results of the previous analysis by method (i), the parameters are readjusted to determine the impact of different values ​​on the final result in order to select the best parameters, wherein the criterion for selecting the best parameters is the parameter corresponding to the highest evaluation score when the evaluation method is used for evaluation.

5. The method according to claim 1, characterized in that In step (4), when determining the density value of each grid, the weighting coefficient of the effective spot is determined based on the number of sequencing reads corresponding to the effective spot.

6. The method according to claim 1, characterized in that The region segmentation includes determining at least one of a cell boundary, a cell nucleus region, and a cell organelle.

7. The method according to claim 1, characterized in that The density value of each grid is calculated using a density estimation algorithm.

8. The method according to claim 1, characterized in that In step (5), based on the density value of each grid, performing regional segmentation on the spatial in situ transcriptome sequencing data further includes: 1) based on the density value, drawing density value contour lines; and 2) Based on the contour lines, the spatial in situ transcriptome sequencing data is segmented into regions.

9. The method according to claim 8, characterized in that Step 2) further includes using contour lines with predetermined density values ​​as region segmentation lines among the drawn density value contour lines, and realizing region segmentation by using closed areas surrounded by each region segmentation line. Optionally, the predetermined density value refers to a segmentation boundary value obtained by segmenting each grid into multiple uniform or non-uniform portions according to the density value in a linear or non-linear manner; Optionally, the predetermined density value refers to a segmentation boundary value obtained by reanalyzing and optimizing the impact of the number of density segmentation on the result based on the downstream analysis result; Optionally, the closed areas enclosed by the area segmentation lines obtained after the area segmentation are discontinuous.

10. The method according to claim 1, characterized in that In step (5), based on the density value of each grid, performing regional segmentation on the spatial in situ transcriptome sequencing data further includes: Determine the region segmentation lines according to at least one of the distribution of density values, the trend of density value changes, and the rate of density value changes, and implement region segmentation using the closed areas enclosed by the region segmentation lines; Optionally, the closed areas enclosed by the area segmentation lines obtained after the area segmentation are discontinuous.

11. The method according to claim 9 or 10, characterized in that: When determining the region segmentation line according to at least one of the density value contour line or the density value distribution, the density value change trend, and the density value change rate, it includes using prior knowledge and re-analysis optimization to determine the specific parameter range.

12. The method according to claim 9 or 10, characterized in that: Step (5) further comprises: The spatial in situ transcriptome sequencing data and spots in the closed area surrounded by each area dividing line are obtained, each of the spatially discontinuous closed areas is individually numbered, and each of the spatially discontinuous closed areas is used as a candidate pseudocell area.

13. A system for performing regional segmentation on spatial in situ transcriptome sequencing results, characterized in that: include: A sequencing data acquisition module, wherein the sequencing data acquisition module is used to acquire spatial in situ transcriptome sequencing data, wherein the spatial in situ transcriptome sequencing data includes sequencing read length, spot coordinates corresponding to the sequencing read length, and UMI sequence; A filtering module, the filtering module is connected to the sequencing data acquisition module, and the filtering module is used to optimize the spots based on the number of sequencing read lengths corresponding to the spots, so as to remove the spots with sequencing read lengths lower than a threshold and obtain valid spots; A grid division module, the grid division module is connected to the filtering module, and the grid division module is used to divide the coordinates of the effective spots into a plurality of grids; A density value acquisition module, the density value acquisition module is connected to the grid division module, and the density value acquisition module is used to determine the density value of each grid based on the number of valid spots contained in each grid; A region segmentation module, the region segmentation module is connected to the density value acquisition module, and the region segmentation module is used to perform region segmentation on the spatial in situ transcriptome sequencing data based on the density value of each grid.

14. The system according to claim 13, characterized in that The optimization process further includes selecting spots containing region-specific sequencing reads or spots containing a predetermined number of sequencing reads as the effective spots; Optionally, the region-specific sequencing read length is selected from at least one of a nucleus-specific sequencing read length, a cytoplasm-specific sequencing read length, and a mitochondrion-specific sequencing read length; Optionally, the predetermined number is determined according to at least one of the following: (i) According to the spatial in situ transcriptome sequencing platform from which the spatial in situ transcriptome sequencing results are derived, the quantitative values ​​obtained based on the analysis purpose and analysis experience; (ii) Re-analysis and optimization based on downstream results to obtain the best quantitative value; (iii) Based on prior knowledge, the sequencing read length quantity distribution corresponding to the valid spots in the same type of data is obtained, and the minimum value is taken as the quantity value; Optionally, reanalysis to optimize the optimal quantitative values ​​based on downstream results includes: According to the results of the previous analysis by method (i), readjust the parameters to determine the impact of different values ​​on the final results, so as to select the best parameters, wherein the selection criteria of the best parameters are the parameters corresponding to the highest evaluation score when the evaluation method is used for evaluation; Optionally, when determining the density value of each grid, a weighting coefficient of the effective spot is determined based on the number of sequencing reads corresponding to the effective spot; Optionally, the region segmentation includes determining at least one of a cell boundary, a cell nuclear region, and a cell organelle; Optionally, calculating a density value for each grid using a density estimation algorithm; Optionally, based on the density value of each grid, performing regional segmentation on the spatial in situ transcriptome sequencing data further comprises: 1) based on the density value, drawing density value contour lines; and 2) performing regional segmentation on the spatial in situ transcriptome sequencing data based on the contour lines; Optionally, among the contour lines of the drawn density values, contour lines with predetermined density values ​​are used as region segmentation lines, and region segmentation is achieved using closed areas surrounded by the region segmentation lines. Optionally, the predetermined density value refers to a segmentation boundary value obtained by segmenting each grid into multiple uniform or non-uniform portions according to the density value in a linear or non-linear manner; Optionally, the predetermined density value refers to a segmentation boundary value obtained by reanalyzing and optimizing the impact of the number of density segmentation on the result based on the downstream analysis result; Optionally, the closed areas enclosed by the area segmentation lines obtained after the area segmentation are discontinuous.

15. The system according to claim 13, characterized in that The region segmentation module is used to perform region segmentation on the spatial in situ transcriptome sequencing data based on the density value of each grid, further comprising: Determine the region segmentation lines according to at least one of the distribution of density values, the trend of density value changes, and the rate of density value changes, and implement region segmentation using the closed areas enclosed by the region segmentation lines; Optionally, the closed areas enclosed by the area segmentation lines obtained after the area segmentation are discontinuous; Optionally, when determining the region segmentation line according to at least one of the density value contour line or the density value distribution, the density value change trend, and the density value change rate, it includes using prior knowledge and re-analysis optimization to determine the specific parameter range; Optionally, the region segmentation module is used to obtain spatial in situ transcriptome sequencing data and spots in the closed areas enclosed by each region segmentation line, and to individually number each of the spatially discontinuous closed areas, and to use each of the spatially discontinuous closed areas as a candidate pseudocell area.

16. 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 when the processor executes the program, the method for performing regional segmentation on spatial in situ transcriptome sequencing results according to any one of claims 1 to 12 is implemented.

17. A readable storage medium, characterized in that: The readable storage medium stores computer-readable instructions, and the computer-readable instructions are used to enable a computer to execute the method for performing regional segmentation on spatial in situ transcriptome sequencing results according to any one of claims 1 to 12.

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