WO2024250245A1 - Method for displaying expression level of spatial group - Google Patents

Abstract

The present disclosure relates to the technical field of molecular biology, and in particular relates to a method for displaying the expression level of a spatial group. The method for displaying the expression level of a spatial group provided by the present disclosure solves the problem that currently using a linear gray scale to display expression level data of spatial information results in an expression level diagram under a bin lacking fluctuation. In the method, an expression level matrix of samples is acquired by using a spatial transcriptome platform, and a bin expression level image is drawn; a nonlinear function is introduced, and UMI values corresponding to each coordinate in the expression level matrix are uniformly adjusted according to distribution characteristics of the UMI values; then a new bin expression level image is drawn, which can provide effective visual spatial information.

Description

A method for displaying spatial group expression Technical Field

The present invention relates to the field of bioinformatics, and in particular to a method for displaying expression levels based on a spatial group.

Background Art

Gene chip technology is used to retain the sample location information on the chip, and then the second-generation sequencing technology is used to sequence the RNA in the sample. The read content is superimposed back on the tissue image, thereby generating a complete gene expression image on the tissue section. This technology is called spatial transcriptome technology and is extremely valuable in clinical research and cancer diagnosis.

The current spatial transcriptome platform has an accuracy of nanometers.

Currently, the expression data with spatial information is displayed using linear grayscale, resulting in no fluctuations in the expression graph under the bin and failing to provide effective visual spatial information.

Therefore, there is an urgent need for a method that can accurately and effectively provide spatial visual information for spatial transcriptome data.

Summary of the invention

The present disclosure aims to solve one of the technical problems in the related art at least to a certain extent. To this end, the present disclosure provides a method for displaying the expression amount of a space group on the one hand. According to an embodiment of the present disclosure, the method comprises:

(A) The expression matrix of samples was obtained using the spatial transcriptome sequencing platform;

(B) introducing a nonlinear function, uniformly adjusting the UMI value corresponding to each coordinate in the expression matrix according to the distribution characteristics of the UMI value, and drawing an expression image;

Among them, the method of uniformly adjusting the UMI value includes directly adjusting the UMI value or adjusting the gene weight.

The method of adjusting directly according to the UMI value includes the following steps:

(C) calculating the curvature of the distribution function according to the distribution characteristics of the UMI values in step (B) to obtain an inflection point G;

(D) shifting the nonlinear function 1 to the left or right by G coordinates to obtain a transformation function, and using the transformation function to recalculate the UMI value in the expression matrix to draw an expression quantity image;

The gene weight adjustment method comprises the following steps:

(E) Counting the total UMI value of each gene in the expression matrix;

(F) Substituting the total UMI value of each gene into nonlinear function 2 to obtain the gene weight of each gene;

(G) According to the gene weight, the UMI value of each gene at the coordinate point is recalculated and an expression level image is drawn.

In the prior art, the expression data with spatial information is displayed using linear grayscale, resulting in no fluctuations in the expression graph below, and the difference in expression at each position is small, which cannot provide obvious visual differences and cannot provide effective visual spatial information. Based on this, the inventors of the present disclosure provide a method for displaying spatial group expression, firstly obtaining the expression matrix of the sample based on the spatial transcriptome sequencing platform, and drawing the expression image, then introducing a nonlinear function, according to the distribution characteristics of the UMI value, uniformly adjusting the UMI value corresponding to each coordinate in the expression image, and drawing a new expression image, thereby providing effective visual spatial information.

According to the implementation scheme of the present disclosure, the expression level image is a bin expression level image, and the bin can be bin1, bin2, bin70, etc. without restriction, and can be adjusted according to the required resolution, wherein the smaller the value after the bin, the higher the resolution. In the present invention, bin1 is preferred.

According to the embodiments of the present disclosure, the nonlinear function includes at least one selected from a power function, a logarithmic function, a Sigmoid function, and a Tanh function. It should be noted that any nonlinear function is applicable to the present invention.

According to an embodiment of the present disclosure, the nonlinear function 1 is the same as or different from the nonlinear function 2.

According to an embodiment of the present disclosure, the nonlinear function 1 includes at least one selected from a power function, a Sigmoid function, a Tanh function, and a logarithmic function.

According to an embodiment of the present disclosure, the nonlinear function 2 includes at least one selected from a logarithmic function and a Tanh function.

According to an embodiment of the present disclosure, the method for uniformly adjusting the UMI value includes a method of directly adjusting the UMI value or a method of redistributing the gene weight.

According to an embodiment of the present disclosure, the method of directly adjusting the UMI value includes the following steps:

(a) The spatial transcriptome platform was used to obtain the expression matrix of the samples and draw the bin1 expression image;

(b) Count all different UMI values in bin1 expression;

(c) Draw an inverted “L” shaped distribution graph of the UMI value, calculate the curvature of the distribution function, and obtain the inflection point G;

(d) The nonlinear function is shifted to the left or right by G coordinates to obtain a transformation function, and the UMI value in the bin1 expression image is recalculated using the transformation function to draw a new bin1 expression image.

According to an embodiment of the present disclosure, the gene weight adjustment method includes the following steps:

(e) Use the spatial transcriptome platform to obtain the expression matrix of the samples and draw the bin1 expression image;

(f) Count the total UMI value of each gene in the original expression graph;

(g) draw an inverted "L"-shaped distribution of log ₂ (total UMI) + N, and set the log ₂ (total UMI) + N value of the gene as the weight of the current gene, where "N" represents any natural number, such as 0, 1, 2, 3, etc., preferably N = 1;

(h) According to the weight of each gene, the UMI value of each gene at the coordinate point is recalculated and a new bin1 expression map is drawn.

According to an embodiment of the present disclosure, the spatial transcriptome sequencing platform includes a method selected from:

At least one of the Biotron S1000 spatial transcriptome sequencing platform, STOmics spatial transcriptome sequencing platform, and 10xgenomics spatial transcriptome sequencing platform.

According to an embodiment of the present disclosure, the sample comprises a tissue sample selected from an animal or a plant.

Another aspect of the present disclosure provides application of the above-mentioned method for displaying spatial group expression levels in spatial transcriptome visual imaging.

Another aspect of the present disclosure provides a system for displaying spatial group expression, the system comprising:

A data acquisition module, wherein the data acquisition module is used to obtain an expression matrix of a sample based on a spatial transcriptome sequencing platform;

An image drawing module, wherein the image drawing module is used to introduce a nonlinear function, and draw an expression image after uniformly adjusting the UMI value corresponding to each coordinate in the expression matrix according to the distribution characteristics of the UMI value;

Among them, the method of uniformly adjusting the UMI value includes directly adjusting the UMI value or adjusting the gene weight.

The method of adjusting directly according to the UMI value includes the following steps:

(I) Calculate the curvature of the distribution function according to the distribution characteristics of the UMI value in the image rendering module and obtain the inflection point G;

(II) shifting the nonlinear function 1 to the left or right by G logarithmic function coordinates to obtain a transformation function, using the transformation function to recalculate the UMI value in the expression matrix, and drawing an expression quantity image;

The gene weight adjustment method comprises the following steps:

(i) calculating the total UMI value of each gene in the expression matrix;

(ii) Substituting the total UMI value of each gene into nonlinear function 2 to obtain the gene weight of each gene;

(iii) Recalculate the UMI value of each gene at the coordinate point according to the weight and draw an expression level image.

According to the implementation scheme of the present disclosure, the expression level image is a bin expression level image, and the bin can be bin1, bin2, bin70, etc. without limitation, and can be adjusted according to the required resolution, wherein the smaller the value after the bin, the higher the resolution. In the present invention, bin1 is preferred.

According to the embodiments of the present disclosure, the nonlinear function includes at least one selected from a power function, a logarithmic function, a Sigmoid function, and a Tanh function. It should be noted that any nonlinear function is applicable to the present invention.

According to an embodiment of the present disclosure, the nonlinear function 1 is the same as or different from the nonlinear function 2.

According to an embodiment of the present disclosure, the nonlinear function 1 includes at least one selected from a power function, a Sigmoid function, a Tanh function, and a logarithmic function.

According to an embodiment of the present disclosure, the nonlinear function 2 includes at least one selected from a logarithmic function and a Tanh function.

According to an embodiment of the present disclosure, the method for uniformly adjusting the UMI value includes a method for directly adjusting the UMI value or a method for adjusting the gene weight by redistributing the gene weight;

The method of adjusting directly according to the UMI value includes the following steps:

(i) Using the spatial transcriptome platform to obtain the expression matrix of the samples and draw the bin1 expression image;

(j) Count all different UMI values in bin1 expression;

(k) Draw an inverted “L” shaped distribution graph of the UMI value, calculate the curvature of the distribution function, and obtain the inflection point G;

(1) Shifting the nonlinear function to the left or right by G coordinates to obtain a transformation function, using the transformation function to recalculate the UMI value in the bin1 expression image, and drawing a new bin1 expression image;

The gene weight adjustment method includes the following steps:

(m) Use the spatial transcriptome platform to obtain the expression matrix of the samples and draw the bin1 expression image;

(n) Count the total UMI value of each gene in the original expression graph;

(o) draw an inverted "L"-shaped distribution of log ₂ (total UMI) + N, and set the log ₂ (total UMI) + N value of the gene as the weight of the current gene, where "N" represents any natural number, such as 0, 1, 2, 3, etc., preferably N = 1;

(p) According to the weight of each gene, recalculate the UMI value of each gene at the coordinate point and draw a new bin1 expression map.

According to an embodiment of the present disclosure, the spatial transcriptome sequencing platform includes at least one selected from the group consisting of the Biotron S1000 spatial transcriptome sequencing platform, the STOmics spatial transcriptome sequencing platform, and the 10xgenomics spatial transcriptome sequencing platform.

According to an embodiment of the present disclosure, the nonlinear function includes at least one selected from a power function, a logarithmic function, a Sigmoid function, and a Tanh function.

According to an embodiment of the present disclosure, the sample comprises a tissue sample selected from an animal or a plant.

Another aspect of the present disclosure provides application of the aforementioned system for displaying spatial group expression levels in spatial transcriptome visual imaging.

Another aspect of the present disclosure provides an electronic device. According to an embodiment of the present disclosure, the electronic device includes a memory, a processor;

The processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to implement the method for displaying the spatial group expression quantity described above.

Another aspect of the present disclosure provides a computer-readable storage medium. According to an embodiment of the present disclosure, the computer-readable storage medium stores a computer program, and when the program is executed by a processor, the method for displaying the spatial group expression quantity described above is implemented.

Additional aspects and advantages of the present disclosure 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 disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

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

FIG1 shows a schematic diagram of a flow chart of a spatial expression quantity display method;

FIG2 shows the FB staining image of the heart-shaped embryo of the model plant Arabidopsis thaliana according to an embodiment of the present invention;

FIG3 shows an original bin1 expression level diagram of the heart-shaped embryo based on FIG2 according to an embodiment of the present invention (100% zoom);

FIG4 shows a distribution diagram of all different UMI points of the heart-shaped embryo based on FIG3 according to an embodiment of the present invention;

FIG5 shows sigmoid transformation functions with different intercepts according to an embodiment of the present invention;

FIG6 shows Tanh transformation functions with different intercepts according to an embodiment of the present invention;

FIG. 7 shows a bin1 expression graph after re-normalizing the UMI value using the y=Tanh(x-6) function in an embodiment of the present invention;

FIG8 shows a distribution diagram of the points of different UMIs after adjustment using the y=Tanh(x-6) function according to an embodiment of the present invention;

FIG9 shows the distribution of log2(total UMI)+1 of the total UMI value of each gene in an embodiment of the present invention;

FIG. 10 shows a bin1 expression graph drawn by weighting and reorganizing UMIs for each gene in an embodiment of the present invention.

DETAILED DESCRIPTION OF THE INVENTION

The embodiments of the present disclosure are described in detail below. The embodiments described below are exemplary and are only used to explain the present disclosure, and should not be understood as limiting the present disclosure.

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

The endpoints and any values of the ranges disclosed in this article are not limited to the precise ranges or values, and these ranges or values should be understood to include values close to these ranges or values. For numerical ranges, the endpoint values of each range, the endpoint values of each range and the individual point values, and the individual point values can be combined with each other to obtain one or more new numerical ranges, which should be considered as specifically disclosed in this article.

In order to make the present invention more easily understood, certain technical and scientific terms are specifically defined below. Unless expressly defined otherwise, all other technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

In this document, the terms “include” or “comprising” are open expressions, that is, including the contents specified in the present invention but not excluding other contents.

As used herein, the terms "optionally", "optional" or "optionally" generally mean that the subsequently described event or circumstance may but need not occur, and that the description includes instances where the event or circumstance occurs and instances where it does not.

In the present invention, the term "bin" refers to a data set at the resolution of a unit in the Stereo-seq spatial transcriptome sequencing results. For example, bin1 represents a sequencing data set in a single capture unit, and bin70 represents a sequencing data set formed by 70*70 capture units.

In the present invention, the term "bin1 expression map" refers to plotting the number of gene expressions in the data into a grayscale image according to their position coordinates.

In the present invention, the term "sigmoid transformation function" refers to an S-shaped function commonly seen in biology, also known as an S-shaped growth curve. In information science, due to its monotonic and inverse monotonic properties, the Sigmoid function is often used as an activation function of a neural network to map variables between 0 and 1.

In the present invention, the term "Tanh transformation function" refers to one of the hyperbolic functions, tanh being the hyperbolic tangent. In mathematics, the hyperbolic tangent "tanh" is derived from two basic hyperbolic functions, the hyperbolic sine and the hyperbolic cosine.

In the present invention, the term "FB staining" refers to calcium white staining, which can specifically bind to plant cell walls to achieve the purpose of displaying plant tissue morphology.

In the present invention, the term "UMI" stands for Unique Molecular Identifiers, also known as molecular barcode technology, which is to add a unique label sequence to each fragment after the original sample genome is interrupted, which is used to distinguish thousands of different fragments in the same sample. In subsequent data analysis, these label sequences can be used to eliminate errors introduced by DNA polymerase, amplification and sequencing processes. Molecular barcodes are usually composed of random sequences of about 10nt (such as NNNNNNN), or degenerate bases (NNNRNYN). Different from sample labels (sample index or sample barcode), molecular barcodes are label sequences added to different fragments in the same sample, while sample labels are label sequences added to distinguish different samples. Therefore, each sample can only have one identical sample label, but there can be thousands of molecular barcodes.

A method for displaying spatial group expression

Spatial transcriptome sequencing technology is a high-throughput spatiotemporal group sequencing technology. It uses two sequencings to confirm the spatial position and corresponding expression level of mRNA sequences respectively. This technology can be accurate to the cellular level or even higher resolution. This technology ① first deposits DNBs containing random barcode sequences onto a modified chip that has been etched by photolithography; ② Compared with the bead-based method, the random barcodes labeled with DNBs produced by rolling circle amplification are used to obtain a larger spatial barcode pool while maintaining sequence fidelity. The array is then microphotographed, incubated with primers and sequenced to obtain a data matrix containing the coordinate code (CID) of each etched DNB; ③ By hybridizing with the CID, the molecular code (MID) and the polyT sequence containing oligonucleotides are connected at each point; ④ The next step includes the capture of tissue polyA tail RNA by loading fresh nitrogen frozen tissue sections onto the chip surface, followed by fixation, permeation, and finally reverse transcription and amplification; ⑤ The amplified cDNA is collected as a template for library preparation and sequenced together with the CID; ⑥ Computational analysis of sequencing data can achieve spatially resolved transcriptomic research with a resolution of 500 or 715nm. Helping researchers identify the location of transcription within tissues, accurate to the cellular level or even higher resolution, will promote researchers' understanding and interpretation of single cells and cell populations within the entire tissue. It has now been applied in multiple biological and medical fields and has become one of the mainstream methods for spatial transcription sequencing.

According to some specific embodiments of the present invention, the present disclosure proposes a method for displaying the expression amount of a space group, as shown in FIG1 , comprising:

S100, using the spatial transcriptome sequencing platform to obtain the expression matrix of the samples;

S120, introducing a nonlinear function, uniformly adjusting the UMI value corresponding to each coordinate in the expression matrix according to the distribution characteristics of the UMI value, and drawing an expression image;

Among them, the method of uniformly adjusting the UMI value includes directly adjusting the UMI value or adjusting the gene weight.

The method of adjusting directly according to the UMI value includes the following steps:

S130, calculating the curvature of the distribution function according to the distribution characteristics of the UMI value in step S120, and obtaining an inflection point G;

S140, shifting the nonlinear function 1 to the left or right by G coordinates to obtain a transformation function, using the transformation function to recalculate the UMI value in the expression matrix, and drawing an expression quantity image;

The gene weight adjustment method comprises the following steps:

S150, counting the total UMI value of each gene in the expression matrix;

S160, substituting the total UMI value of each gene into nonlinear function 2 to obtain the gene weight of each gene;

S170: recalculate the UMI value of each gene at the coordinate point according to the gene weight, and draw an expression level image.

The spatial group expression quantity display method provided by the present disclosure is based on mathematical statistics and introduces a nonlinear model when converting the expression quantity into a grayscale image, which can accurately and effectively provide spatial visual information.

According to some specific embodiments of the present invention, the bin1 expression map is only related to the UMI value of each position. Those skilled in the art can adjust the UMI value by any known nonlinear function. Two methods are preferred here, including a method of adjusting directly according to the UMI value and a method of adjusting by redistributing the gene weight.

in,

The direct adjustment method based on the UMI value includes the following steps:

(a) The spatial transcriptome platform was used to obtain the expression matrix of the samples and draw the bin1 expression image;

(b) Count all different UMI values in bin1 expression;

(c) Draw an inverted “L” shaped distribution graph of the UMI value, calculate the curvature of the distribution function, and obtain the inflection point G;

(d) Shifting the nonlinear function to the left or right by G coordinates to obtain a transformation function, using the transformation function to recalculate the UMI value in the bin1 expression map, and drawing a new bin1 expression map;

The gene weight adjustment method includes the following steps:

(e) Use the spatial transcriptome platform to obtain the expression matrix of the samples and draw the bin1 expression image;

(f) Count the total UMI value of each gene in the original expression graph;

(g) Draw the inverted “L”-shaped distribution of log2(total UMI)+1, and set the log2(total UMI)+1 value of the gene as the weight of the current gene;

(h) According to the weight of each gene, the UMI value of each gene at the coordinate point is recalculated and a new bin1 expression map is drawn.

According to some specific embodiments of the present invention, the spatial transcriptome sequencing platform includes any spatial transcriptome technology platform known in the art.

According to some specific embodiments of the present invention, the nonlinear function includes but is not limited to nonlinear functions such as power function, logarithmic function, Sigmoid function, Tanh function, etc.

According to some specific embodiments of the present invention, the sample comprises a tissue sample selected from animals and plants.

If no specific techniques or conditions are specified in the examples, the techniques or conditions described in the literature in the field or the product instructions are used. If no manufacturer is specified for the reagents or instruments used, they are all conventional products that can be purchased commercially.

Example 1 Method for displaying the expression level of Arabidopsis heart-shaped embryo spatial group

Fresh Arabidopsis heart-shaped embryos were frozen and embedded in OCT embedding solution, and the embedded heart-shaped embryos were sliced and stained for FB imaging (Figure 2). The tissue slices were placed on a chip containing RNA-binding capture probes, fixed and permeabilized (to release the mRNA in the cells and bind to the corresponding capture probes to obtain gene expression information), and cDNA synthesis and sequencing library preparation were performed using the captured RNA as a template. The prepared library was sequenced (Stereo-seq spatial transcriptome sequencing platform) to obtain the expression data of the sample, and the expression matrix was drawn based on the data. The expression information was restored to the corresponding position in combination with the spatial position information carried in the data to obtain the original unprocessed bin1 expression image (Figure 3), and the expression level displayed by the original bin1 was viewed in the Fiji software. It was found that due to the excessive number of pixels in the bin1 image, the expression UMI value was relatively concentrated, and the clear tissue boundary and internal contour could not be observed. The original bin1 image was gray and it was difficult to identify effective information.

The inventors found that most of the UMI values were low, only a few points had high UMI values, and the noise was large (Figure 4). According to the distribution of the UMI and the spatial expression and display method proposed by the present invention, the transformation function can be adjusted.

Method 1: Directly adjust according to UMI value

(1) Count all different UMI values in bin1 expression;

(2) Draw an inverted “L”-shaped distribution graph of the UMI value, calculate the curvature of the distribution function, and obtain the inflection point G;

(3) The nonlinear function is shifted to the left or right by G coordinates to obtain a transformation function, and the UMI value in the bin1 expression map is recalculated using the transformation function to draw a new bin1 expression map.

FIG5 is a sigmoid transformation function with different intercepts, and FIG6 is a Tanh transformation function with different intercepts. The Tanh transformation function can effectively reduce the UMI points and increase the points with high UMI values.

During this period, the inventors also introduced different nonlinear functions to process the original bin1 image. The processing effect is shown in Table 1. It is found that different processing methods can highlight different key information in the bin1 image, such as highlighting areas with high expression, highlighting areas with low expression, or reducing background noise to highlight contour information, etc. Finally, the optimal function y=Tanh(x-G)+n (n is an optional hyperparameter) was selected. According to the statistics of the UMI of the original image, the inflection point G is calculated to be 6. Figure 7 is a new bin1 expression map generated after the y=Tanh(x-6) function reorganizes the UMI value. It can be seen that the background points are reduced and the tissue area is brightened. Figure 8 is a distribution diagram of the number of points of different UMIs after the y=Tanh(x-6) function is adjusted.

Table 1

Method 2: Transformation based on gene weights

(1) Count the total UMI value of each gene in the original expression map;

(2) Draw an inverted “L”-shaped distribution of log2(total UMI)+1 and set the log2(total UMI)+1 value of the gene as the weight of the current gene;

(3) According to the weight of each gene, recalculate the UMI value of each gene at the coordinate point and draw a new bin1 expression map.

The total UMI value of each gene is counted and the log2+1 distribution is taken. Log ₂ (total UMI)+1 is used as the weight of each gene. The weight of each gene is added to recalculate the UMI value of each coordinate point of each gene (Figure 9). The bin1 expression map generated by the new UMI is redrawn (Figure 10). It can be seen that although the image becomes darker as a whole, the background area is reduced, and highly expressed genes can be observed.

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 disclosure. 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.

Although the embodiments of the present disclosure 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 disclosure. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present disclosure.

Claims (18)

A method for displaying spatial group expression, wherein the method comprises: (A) The expression matrix of samples was obtained using the spatial transcriptome sequencing platform; (B) introducing a nonlinear function, uniformly adjusting the UMI value corresponding to each coordinate in the expression matrix according to the distribution characteristics of the UMI value, and drawing an expression image; Among them, the method of uniformly adjusting the UMI value includes directly adjusting the UMI value or adjusting the gene weight. The method of adjusting directly according to the UMI value includes the following steps: (C) calculating the curvature of the distribution function according to the distribution characteristics of the UMI values in step (B) to obtain an inflection point G; (D) shifting the nonlinear function 1 to the left or right by G coordinates to obtain a transformation function, and using the transformation function to recalculate the UMI value in the expression matrix to draw an expression quantity image; The gene weight adjustment method comprises the following steps: (E) Counting the total UMI value of each gene in the expression matrix; (F) Substituting the total UMI value of each gene into nonlinear function 2 to obtain the gene weight of each gene; (G) According to the gene weight, the UMI value of each gene at the coordinate point is recalculated and an expression level image is drawn. The method according to claim 1, wherein the expression level image is a bin expression level image, and the bin is preferably bin1. The method according to claim 1, wherein the nonlinear function includes at least one selected from a power function, a logarithmic function, a Sigmoid function, and a Tanh function. The method according to any one of claims 1 to 3, wherein the nonlinear function 1 is the same as or different from the nonlinear function 2. The method according to any one of claims 1 to 4, wherein the nonlinear function 1 comprises at least one selected from a power function, a Sigmoid function, a Tanh function, and a logarithmic function. The method according to any one of claims 1 to 5, wherein the nonlinear function 2 comprises at least one selected from a logarithmic function and a Tanh function. The method according to any one of claims 1 to 6, wherein the sample comprises a tissue sample selected from an animal or a plant. Application of the method for displaying spatial group expression levels as described in any one of claims 1 to 7 in spatial transcriptome visual imaging. A system for displaying spatial group expression, wherein the system comprises: A data acquisition module, wherein the data acquisition module is used to obtain an expression matrix of a sample based on a spatial transcriptome sequencing platform; An image drawing module, wherein the image drawing module is used to introduce a nonlinear function, and draw an expression image after uniformly adjusting the UMI value corresponding to each coordinate in the expression matrix according to the distribution characteristics of the UMI value; Among them, the method of uniformly adjusting the UMI value includes directly adjusting the UMI value or adjusting the gene weight. The method of adjusting directly according to the UMI value includes the following steps: (I) Calculate the curvature of the distribution function according to the distribution characteristics of the UMI value in the image rendering module and obtain the inflection point G; (II) shifting the nonlinear function 1 to the left or right by G logarithmic function coordinates to obtain a transformation function, using the transformation function to recalculate the UMI value in the expression matrix, and drawing an expression quantity image; The gene weight adjustment method comprises the following steps: (i) calculating the total UMI value of each gene in the expression matrix; (ii) Substituting the total UMI value of each gene into nonlinear function 2 to obtain the gene weight of each gene; (iii) Recalculate the UMI value of each gene at the coordinate point according to the weight and draw an expression level image. The system according to claim 9, wherein the expression image is a bin expression image, and the bin is preferably bin1. The system according to claim 9 or 10, wherein the nonlinear function includes at least one selected from a power function, a logarithmic function, a Sigmoid function, and a Tanh function. The system according to any one of claims 9 to 11, wherein the nonlinear function 1 is the same as or different from the nonlinear function 2. The system according to any one of claims 9 to 12, wherein the nonlinear function 1 comprises at least one selected from a power function, a Sigmoid function, a Tanh function, and a logarithmic function. The system according to any one of claims 9 to 13, wherein the nonlinear function 2 comprises at least one selected from a logarithmic function and a Tanh function. The system according to any one of claims 9 to 14, wherein the sample comprises a tissue sample selected from an animal or a plant. Application of the system for displaying spatial group expression levels as described in any one of claims 9 to 15 in spatial transcriptome visual imaging. An electronic device, comprising a memory and a processor; The processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to implement the method for displaying the spatial group expression amount described in any one of claims 1-7. A computer-readable storage medium storing a computer program, wherein the program, when executed by a processor, implements the method for displaying the spatial group expression amount described in any one of claims 1 to 7.