Modeling method and device for m6A methylation level visual model

Abstract

The invention discloses an m6A methylation level visual model modeling method and device, and relates to the field of biomedicines.The m6A methylation level visual model modeling method comprises the steps that expression quantities of three gene sets, obtained after genes related to m6A methylation are standardized, in single cells of a sample to be detected are mapped to three coordinate axes of a ternary phase diagram; the distribution condition of the gene set expression quantity in the ternary phase diagram after the gene related to m6A methylation in the single cell of the sample to be detected is standardized is obtained, and the m6A methylation level of the single cell of the sample to be detected can be visually seen through the corresponding distribution area of the ternary phase diagram; expression of multiple genes which have different functions and are related to single cell m6A methylation can be unified under the coordinate axis of the same ternary space, and a reasonable, quantifiable and explainable model is formed, so that the cell m6A methylation degree is indicated.

Description

Technical Field

[0001] The present application relates to the field of biomedicine technology, and in particular to a method and device for modeling a visualization model of m6A methylation levels. Background Art

[0002] Single-cell sequencing can reveal intercellular heterogeneity, providing an unprecedented perspective for understanding cellular complexity. As one of the most abundant RNA modifications in eukaryotic cells, m6A methylation plays a crucial role in physiological and pathological processes, including gene expression regulation, cell differentiation, and immune responses. However, current research in interdisciplinary fields primarily focuses on the development of single-cell m6A methylation sequencing technology. With the continuous advancement of epigenetic research, single-cell m6A methylation sequencing has attracted considerable attention. Some studies have utilized single-cell m6A methylation sequencing in fields such as oncology. However, single-cell m6A methylation sequencing technology is still in its infancy and has numerous limitations. Firstly, the technical process is complex, requiring extremely high sample quality and operational skills, and the accuracy and reproducibility of the data need to be further improved. Secondly, and more critically, existing single-cell m6A methylation sequencing technologies differ from single-cell transcriptome sequencing, with distinct sequencing principles and procedures. Therefore, it is impossible to simultaneously obtain both single-cell transcriptome and single-cell m6A methylation data from the same sample, significantly limiting the comprehensive understanding of complex intracellular regulatory networks. Summary of the Invention

[0003] This application provides a method and device for modeling a visualization model of m6A methylation levels to solve the existing problem of being unable to simultaneously obtain single-cell transcriptome and single-cell m6A methylation data from the same sample.

[0004] In a first aspect, the present application provides a method for modeling a visualization model of m6A methylation levels, comprising the following steps:

[0005] Obtain the normalized expression values of genes related to m6A methylation in single cells of the sample to be tested;

[0006] According to the normalized expression values of genes related to m6A methylation in the single cells of the sample to be tested, the normalized gene set expression values of genes related to m6A methylation in the single cells of the sample to be tested are calculated respectively;

[0007] The normalized gene set expression levels of genes related to m6A methylation in the single cells of the sample to be tested are mapped to the three coordinate axes of the ternary phase diagram respectively, and the distribution of the normalized gene set expression levels of genes related to m6A methylation in the single cells of the sample to be tested in the ternary phase diagram is obtained;

[0008] Based on the normalized gene set expression levels of genes related to m6A methylation in the single cells of the sample to be tested and the angles between them and the three coordinate axes, the reciprocal of the standard deviation of the cosine values between the normalized gene set expression levels of genes related to m6A methylation in the single cells of the sample to be tested and the three coordinate axes is calculated to obtain the WRE score of the single cells of the sample to be tested;

[0009] The m6A methylation level of the sample to be tested is evaluated based on the distribution of the normalized gene set expression levels of the single cell of the sample to be tested in the ternary phase diagram and / or the WRE score of the single cell of the sample to be tested.

[0010] Due to the complexity and poor reproducibility of single-cell m6A methylation sequencing technology, directly measuring the m6A methylation level of cells is inconvenient. However, in order to study the m6A methylation level of cells, it is necessary to study the m6A methylation level of cells. Single-cell transcriptome sequencing is a common and mature sequencing method. Using high-throughput sequencing methods, it can simultaneously measure the expression levels of all target genes, which is convenient, fast, and reproducible. Therefore, on the basis of the inconvenience of directly measuring single-cell m6A methylation sequencing, by measuring the gene expression values related to single-cell m6A methylation, the degree of cellular m6A methylation can be indicated by the gene expression values related to single-cell m6A methylation. Since the genes related to single-cell m6A methylation are not single genes, but are jointly regulated by multiple genes with different functions, the normalized gene set expression quantities of genes related to m6A methylation in single cells of the sample to be tested are corresponded to the three coordinate axes of the ternary phase diagram, and the distribution of the normalized gene set expression quantities of genes related to m6A methylation in single cells of the sample to be tested in the ternary phase diagram is obtained. The corresponding distribution area of the ternary phase diagram can intuitively see the single-cell m6A methylation level of the sample to be tested, and the expression of multiple genes related to single-cell m6A methylation with different functions can be unified under the same three-dimensional coordinate axis, turning it into a reasonable, quantifiable and interpretable model, thereby indicating the degree of cellular m6A methylation. The WRE score of the single cell of the sample to be tested was obtained by calculating the reciprocal of the standard deviation of the cosine value between the normalized gene set expression levels of the genes related to m6A methylation in the single cell of the sample to be tested and the angles between them and the three coordinate axes. The WRE score was then used to further evaluate the m6A methylation level of the sample to be tested.

[0011] In some embodiments, genes related to m6A methylation include writing factors, reading factors, and erasing factors. The m6A methylation modification process involves three key genes: writer (writing), reader (reading), and eraser (erasing), and the relationship between them is dynamically balanced and complex. In different cell types and under different physiological and pathological conditions, writer, reader, and eraser genes may work together or mutually exclusive to finely regulate the m6A methylation level of RNA. For example, in some cells, high expression of writer genes may be accompanied by low expression of eraser genes, thereby maintaining the stability of m6A methylation in cells; while in other cells, the expression level of reader genes may be jointly affected by the expression of writer and eraser genes. This complex interaction relationship enables m6A methylation modification to form a fine regulatory network in cells, which has a profound impact on gene expression and cell function. Therefore, in-depth exploration of the interaction mechanism between writer, reader, and eraser genes is crucial to understanding the role of m6A methylation.

[0012] In some embodiments, the write factor includes at least one of METTL3, METTL14, RBM15 and WTAP; and / or,

[0013] The read factor includes at least one of YTHDF1, YTHDF2, YTHDF3, YTHDC1, YTHDC2, IGF2BP1, IGF2BP2, IGF2BP3 and EIF3A; and / or,

[0014] The erasure factor includes at least one of FTO and ALKBH5.

[0015] In some embodiments, the three coordinate axes are a write factor coordinate axis, a read factor coordinate axis, and an erase factor coordinate axis. By establishing the write factor coordinate axis, the read factor coordinate axis, and the erase factor coordinate axis, and mapping the expression levels of the write factor gene, the read factor gene, and the erase factor of the test cell to the factor coordinate axis, the read factor coordinate axis, and the erase factor coordinate axis to form a ternary phase diagram, the m6A methylation level of the cell can be comprehensively evaluated.

[0016] In some embodiments, the three coordinate axes form a triangular coordinate axis, with the three sides of the triangular coordinate axis representing the expression levels of the write factor, the read factor, and the erase factor, respectively. Each of the three vertices of the ternary phase diagram represents a molecular signature of a cellular m6A methylation gene set. These three gene groups are interrelated and constrained, conforming to the coordinate system characteristics of the ternary phase diagram.

[0017] In some embodiments, the method for calculating the normalized expression levels of the gene set related to m6A methylation includes:

[0018] G1 = Geneset1 mean / (Geneset1 mean + Geneset2 mean + Geneset3 mean);

[0019] G2 = Geneset2 mean / (Geneset1 mean + Geneset2 mean + Geneset3 mean);

[0020] G3 = Geneset3 mean / (Geneset1 mean + Geneset2 mean + Geneset3 mean);

[0021]

[0022] Among them, G1 represents the expression level of the writing factor gene set;

[0023] G2 represents the expression level of the readout factor gene set;

[0024] G3 represents the expression level of the erasure factor gene set;

[0025] X i is the expression value of the i-th gene associated with the write factor;

[0026] Y i is the expression value of the i-th gene associated with the read factor;

[0027] Z i is the expression value of the i-th gene associated with the erasure factor.

[0028] In some embodiments, the method for calculating the cosine value of the angle between the expression value of the m6A methylation-related gene set and the three coordinate axes includes:

[0029]

[0030] in:

[0031] cosθ1 is the cosine of the angle between the expression value of the write factor gene set and the write factor coordinate axis;

[0032] cosθ2 is the cosine of the angle between the expression value of the read factor gene set and the read factor coordinate axis;

[0033] cosθ3 is the cosine of the angle between the expression value of the erasure factor gene set and the erasure factor coordinate axis;

[0034] θ1 is the angle between the expression value of the m6A methylation writing factor gene set and the writing factor coordinate axis;

[0035] θ2 is the angle between the expression value of the m6A methylation readout factor gene set and the readout factor coordinate axis;

[0036] θ3 is the angle between the expression value of the m6A methylation eraser gene set and the eraser coordinate axis.

[0037] For each single cell, calculate the angles θ1, θ2, and θ3 relative to the three vertices in the ternary graph. These angles reflect the differences in expression between m6A-related gene sets within the single cell. A cosine value closer to 1 indicates a higher expression level of the m6A methylated gene set type represented by that vertex in the single cell.

[0038] In some embodiments, the method for calculating the WRE score of the sample to be tested includes:

[0039]

[0040] In some embodiments, if WRE ≤ a first threshold, the m6A methylation level of the cell to be tested is in an inactive state;

[0041] If WRE>the first threshold, the m6A methylation level of the tested cell is in an active state.

[0042] In general, the higher the WRE score, the more balanced the expression of the three m6A methylation-related gene sets, and the more active the m6A methylation activity. The first threshold is usually the mean or median of the WRE score.

[0043] If WRE ≤ WRE mean or WRE median, the m6A methylation level is closer to a state where one or two m6A-related gene sets (writing factors, reading factors, or erasing factors) are dominant, that is, the m6A methylation level of the tested cell is inactive.

[0044] If WRE>WRE mean or WRE median, the expression levels of the three types of m6A methylation-related gene sets are more balanced, the m6A methylation activity is more active, and the m6A methylation level of the tested cell is active.

[0045] The mean or median WRE score is usually the mean or median of the WRE scores calculated for all cells in a single sequencing run.

[0046] In a second aspect, the present application provides a device for modeling a visualization model of m6A methylation levels, comprising:

[0047] The first acquisition unit is used to obtain the normalized expression value of genes related to m6A methylation in a single cell of the sample to be tested;

[0048] The first calculation unit is used to calculate the normalized gene set expression amount of the genes related to m6A methylation in the single cell of the sample to be tested according to the normalized expression value of the genes related to m6A methylation in the single cell of the sample to be tested;

[0049] The first execution unit is used to map the normalized gene set expression levels of genes related to m6A methylation in the single cell of the sample to be tested to the three coordinate axes of the ternary phase diagram, respectively, to obtain the distribution of the normalized gene set expression levels of genes related to m6A methylation in the single cell of the sample to be tested in the ternary phase diagram;

[0050] The second calculation unit is used to calculate the reciprocal of the standard deviation of the cosine value between the normalized gene set expression amount of the gene related to m6A methylation in the single cell of the sample to be tested and the angle between the gene set expression amount and the three coordinate axes according to the normalized gene set expression amount of the gene related to m6A methylation in the single cell of the sample to be tested and the angle between the gene set expression amount and the three coordinate axes to obtain the WRE score of the single cell of the sample to be tested;

[0051] The second execution unit is used to evaluate the m6A methylation level of the sample to be tested based on the distribution of the normalized gene set expression levels of the single cell of the sample to be tested in the ternary phase diagram and / or the WRE score of the single cell of the sample to be tested.

[0052] Due to the complexity and poor reproducibility of single-cell m6A methylation sequencing technology, directly measuring the m6A methylation level of cells is inconvenient. However, in order to study the m6A methylation level of cells, it is necessary to study the m6A methylation level of cells. Single-cell transcriptome sequencing is a common and mature sequencing method. Using high-throughput sequencing methods, it can simultaneously measure the expression levels of all target genes, which is convenient, fast, and reproducible. Therefore, on the basis of the inconvenience of directly measuring single-cell m6A methylation sequencing, by measuring the gene expression values related to single-cell m6A methylation, the degree of cellular m6A methylation can be indicated by the gene expression values related to single-cell m6A methylation. Since the genes related to single-cell m6A methylation are not single genes, but are jointly regulated by multiple genes with different functions, the normalized gene set expression quantities of genes related to m6A methylation in single cells of the sample to be tested are corresponded to the three coordinate axes of the ternary phase diagram, and the distribution of the normalized gene set expression quantities of genes related to m6A methylation in single cells of the sample to be tested in the ternary phase diagram is obtained. The corresponding distribution area of the ternary phase diagram can intuitively see the single-cell m6A methylation level of the sample to be tested, and the expression of multiple genes related to single-cell m6A methylation with different functions can be unified under the same three-dimensional coordinate axis, turning it into a reasonable, quantifiable and interpretable model, thereby indicating the degree of cellular m6A methylation. At the same time, the reciprocal of the standard deviation of the cosine value between the normalized gene set expression levels of genes related to m6A methylation in the single cells of the sample to be tested and the angles between them and the three coordinate axes was calculated to obtain the WRE score of the single cell of the sample to be tested. The WRE score was used to further evaluate the m6A methylation level of the sample to be tested. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0054] Figure 1 Flowchart of a method for modeling a visualization model of m6A methylation levels according to an embodiment of the present application.

[0055] Figure 2 This is a logic diagram of the m6A methylation level visualization modeling method according to one embodiment of the present application.

[0056] Figure 3 Schematic diagram of a device for modeling a visualization model of m6A methylation levels according to one embodiment of the present application.

[0057] Figure 4 This is a result diagram of the m6A methylation level visualization model modeling method of Example 1 of the present application.

[0058] Figure 5 This is a triangular coordinate axis partition diagram of the m6A methylation level visualization modeling method according to an embodiment of the present application. DETAILED DESCRIPTION

[0059] To make the objectives, technical solutions, and advantages of this application more clear, the technical solutions of this application will be clearly and completely described below in conjunction with the embodiments of this application. Obviously, the embodiments described are part of the embodiments of this application, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of this application without making any creative efforts shall fall within the scope of protection of this application.

[0060] Single-cell sequencing can reveal intercellular heterogeneity, providing unprecedented insights into the complexity of tumors. As one of the most abundant RNA modifications in eukaryotic cells, m6A methylation plays a crucial role in physiological and pathological processes, including gene expression regulation, cell differentiation, and immune responses. However, current research in interdisciplinary fields primarily focuses on the development of single-cell m6A methylation sequencing technology. With the continuous advancement of epigenetic research, single-cell m6A methylation sequencing has attracted considerable attention. Several studies have explored the application of single-cell m6A methylation sequencing in the field of oncology. However, single-cell m6A methylation sequencing technology is still in its infancy and has numerous limitations. Firstly, the technical process is complex, requiring extremely high sample quality and operational skills, and the accuracy and reproducibility of the data need to be further improved. Secondly, and more critically, existing single-cell m6A methylation sequencing technologies differ from single-cell transcriptome sequencing, with distinct sequencing principles and procedures. Therefore, it is impossible to simultaneously obtain both single-cell transcriptome and single-cell m6A methylation data, significantly limiting the comprehensive understanding of complex intracellular regulatory networks.

[0061] In view of this, the present application provides a method and device for modeling a visualization model of m6A methylation levels to solve the existing problem of being unable to simultaneously obtain single-cell transcriptome and single-cell m6A methylation data.

[0062] First, as Figure 1 As shown, the present application provides a method for modeling a visualization model of m6A methylation levels, comprising the following steps:

[0063] S100, obtaining the normalized expression value of genes related to m6A methylation in single cells of the sample to be tested;

[0064] S200, calculating the normalized gene set expression amounts of the genes related to m6A methylation in the single cells of the sample to be tested, based on the normalized expression values of the genes related to m6A methylation in the single cells of the sample to be tested;

[0065] S300, mapping the normalized gene set expression levels of genes related to m6A methylation in the single cell of the sample to be tested to the three coordinate axes of the ternary phase diagram, respectively, to obtain the distribution of the normalized gene set expression levels of genes related to m6A methylation in the single cell of the sample to be tested in the ternary phase diagram;

[0066] S400, calculating the reciprocal of the standard deviation of the cosine value between the normalized gene set expression amount of the gene related to m6A methylation in the single cell of the sample to be tested and the angle between the gene set expression amount and the three coordinate axes according to the normalized gene set expression amount of the gene related to m6A methylation in the single cell of the sample to be tested and the angle between the gene set expression amount and the three coordinate axes to obtain the WRE score of the single cell of the sample to be tested;

[0067] S500. Evaluate the m6A methylation level of the sample to be tested based on the distribution of the normalized gene set expression levels of the single cell of the sample to be tested in the ternary phase diagram and / or the WRE score of the single cell of the sample to be tested.

[0068] Due to the complexity and poor reproducibility of single-cell m6A methylation sequencing technology, directly measuring the m6A methylation level of cells is inconvenient. However, in order to study the m6A methylation level of cells, it is necessary to study the m6A methylation level of cells. Single-cell transcriptome sequencing is a common and mature sequencing method. Using high-throughput sequencing methods, it can simultaneously measure the expression levels of all target genes, which is convenient, fast, and reproducible. Therefore, on the basis of the inconvenience of directly measuring single-cell m6A methylation sequencing, by measuring the gene expression values related to single-cell m6A methylation, the degree of cell m6A methylation can be indicated by the gene expression values related to single-cell m6A methylation. Since the genes related to single-cell m6A methylation are not single genes, but are jointly regulated by multiple genes with different functions, the normalized gene set expression amounts of genes related to m6A methylation in the single cells of the sample to be tested are corresponding to the three coordinate axes of the ternary phase diagram, and the distribution of the normalized gene set expression amounts of genes related to m6A methylation in the single cells of the sample to be tested in the ternary phase diagram is obtained. The corresponding distribution area of the ternary phase diagram can intuitively see the single-cell m6A methylation level of the sample to be tested, such as Figure 2As shown, the expression of multiple genes with different functions related to single-cell m6A methylation can be unified under the same three-dimensional coordinate axis to form a reasonable, quantifiable and interpretable model, thereby indicating the degree of cellular m6A methylation. At the same time, the reciprocal of the standard deviation of the cosine value between the normalized gene set expression of genes related to m6A methylation in the single cell of the test sample and the three coordinate axes is calculated by calculating the reciprocal of the standard deviation of the cosine value between the normalized gene set expression of genes related to m6A methylation in the single cell of the test sample and the three coordinate axes, and the WRE score of the single cell of the test sample is obtained. The WRE score is used to further evaluate the m6A methylation level of the test sample.

[0069] In conjunction with the first aspect, in some embodiments provided in the present application, genes related to m6A methylation include writing factors, reading factors, and erasing factors. The m6A methylation modification process involves three key genes: writer (writing), reader (reading), and eraser (erasing), and the relationship between them is dynamically balanced and complex. In different cell types and under different physiological and pathological conditions, writer, reader, and eraser genes may work together or mutually exclusive to finely regulate the m6A methylation level of RNA. For example, in some cells, high expression of writer genes may be accompanied by low expression of eraser genes, thereby maintaining the stability of m6A methylation in cells; while in other cells, the expression level of reader genes may be affected by the expression of writer and eraser genes. This complex interaction relationship enables m6A methylation modification to form a fine regulatory network in cells, which has a profound impact on gene expression and cell function. Therefore, in-depth exploration of the interaction mechanism between writer, reader, and eraser genes is crucial to understanding the role of m6A methylation.

[0070] In combination with the first aspect, in some embodiments provided in the present application, the write factor includes at least one of METTL3, METTL14, RBM15 and WTAP.

[0071] In combination with the first aspect, in some embodiments provided in the present application, the read factor includes at least one of YTHDF1, YTHDF2, YTHDF3, YTHDC1, YTHDC2, IGF2BP1, IGF2BP2, IGF2BP3 and EIF3A.

[0072] In combination with the first aspect, in some embodiments provided in the present application, the erasure factor includes at least one of FTO and ALKBH5.

[0073] In conjunction with the first aspect, in some embodiments provided herein, the three coordinate axes are respectively a write factor coordinate axis, a read factor coordinate axis, and an erase factor coordinate axis. By establishing the write factor coordinate axis, the read factor coordinate axis, and the erase factor coordinate axis, and mapping the expression levels of the write factor gene, the read factor gene, and the erase factor of the cell to be tested to the ternary phase diagram formed by the factor coordinate axis, the read factor coordinate axis, and the erase factor coordinate axis, the m6A methylation level of the cell can be comprehensively evaluated.

[0074] In conjunction with the first aspect, in some embodiments provided herein, three coordinate axes form a triangular coordinate axis, with the three sides of the triangular coordinate axis representing the expression levels of the write factor, the read factor, and the erase factor, respectively. Each of the three vertices of the ternary phase diagram represents a molecular signature of a cellular m6A methylation gene set. These three gene types are interrelated and constrained, conforming to the coordinate system characteristics of the ternary phase diagram.

[0075] In conjunction with the first aspect, in some embodiments provided herein, a method for calculating the normalized expression levels of a gene set related to m6A methylation includes:

[0076] G1 = Geneset1 mean / (Geneset1 mean + Geneset2 mean + Geneset3 mean);

[0077] G2 = Geneset2 mean / (Geneset1 mean + Geneset2 mean + Geneset3 mean);

[0078] G3 = Geneset3 mean / (Geneset1 mean + Geneset2 mean + Geneset3 mean);

[0079]

[0080] Among them, G1 represents the expression level of the writing factor gene set;

[0081] G2 represents the expression level of the readout factor gene set;

[0082] G3 represents the expression level of the erasure factor gene set;

[0083] X i is the expression value of the i-th gene associated with the write factor;

[0084] Y i is the expression value of the i-th gene associated with the read factor;

[0085] Z i is the expression value of the i-th gene associated with the erasure factor.

[0086] In conjunction with the first aspect, in some embodiments provided herein, a method for calculating the cosine value of the angle between the m6A methylation-related gene set expression value and the three coordinate axes includes:

[0087]

[0088]

[0089] in:

[0090] cosθ1 is the cosine of the angle between the expression value of the write factor gene set and the write factor coordinate axis;

[0091] cosθ2 is the cosine of the angle between the expression value of the read factor gene set and the read factor coordinate axis;

[0092] cosθ3 is the cosine of the angle between the expression value of the erasure factor gene set and the erasure factor coordinate axis;

[0093] θ1 is the angle between the expression value of the m6A methylation writing factor gene set and the writing factor coordinate axis;

[0094] θ2 is the angle between the expression value of the m6A methylation readout factor gene set and the readout factor coordinate axis;

[0095] θ3 is the angle between the expression value of the m6A methylation erasure factor gene set and the erasure factor coordinate axis. For each single cell, the angles θ1, θ2, and θ3 relative to the three vertices in the ternary graph are calculated. These angles reflect the differences in the expression patterns of the m6A-related gene sets within the single cell. A cosine value closer to 1 indicates a higher expression of the m6A methylation gene set type represented by that vertex in the single cell.

[0096] It should be noted that at the single-cell level, multiple genes associated with m6A methylation will calculate a set of G1, G2, and G3 values, and correspondingly, a set of cosθ1, cosθ2, and cosθ3 will be obtained. Due to the high-throughput nature of single-cell sequencing, a single sequencing run can generate data from up to 10,000 cells, resulting in the calculation of multiple sets of G1, G2, and G3 values and corresponding sets of cosθ1, cosθ2, and cosθ3.

[0097] In conjunction with the first aspect, in some embodiments provided herein, a method for calculating the WRE score of a sample to be tested includes:

[0098]

[0099] In combination with the first aspect, in some embodiments provided herein, if WRE ≤ a first threshold, the m6A methylation level of the cell to be tested is in an inactive state;

[0100] If WRE>the first threshold, the m6A methylation level of the tested cell is in an active state.

[0101] In general, the higher the WRE score, the more balanced the expression of the three m6A methylation-related gene sets, and the more active the m6A methylation activity. The first threshold is usually the mean or median of the WRE score.

[0102] If WRE ≤ WRE mean or WRE median, the m6A methylation level is closer to a state where one or two m6A-related gene sets (writing factors, reading factors, or erasing factors) are dominant, that is, the m6A methylation level of the tested cell is inactive.

[0103] If WRE>WRE mean or WRE median, the expression levels of the three types of m6A methylation-related gene sets are more balanced, the m6A methylation activity is more active, and the m6A methylation level of the tested cell is active.

[0104] The mean or median WRE score is usually the mean or median of the WRE scores calculated for all cells in a single sequencing run.

[0105] Second, as Figure 3 As shown, the present application provides a device for modeling a visualization model of m6A methylation levels, comprising:

[0106] The first acquisition unit is used to obtain the normalized expression value of genes related to m6A methylation in a single cell of the sample to be tested;

[0107] The first calculation unit is used to calculate the normalized gene set expression amount of the genes related to m6A methylation in the single cell of the sample to be tested according to the normalized expression value of the genes related to m6A methylation in the single cell of the sample to be tested;

[0108] The first execution unit is used to map the normalized gene set expression levels of genes related to m6A methylation in the single cell of the sample to be tested to the three coordinate axes of the ternary phase diagram, respectively, to obtain the distribution of the normalized gene set expression levels of genes related to m6A methylation in the single cell of the sample to be tested in the ternary phase diagram;

[0109] The second calculation unit is used to calculate the reciprocal of the standard deviation of the cosine value between the normalized gene set expression amount of the gene related to m6A methylation in the single cell of the sample to be tested and the angle between the gene set expression amount and the three coordinate axes according to the normalized gene set expression amount of the gene related to m6A methylation in the single cell of the sample to be tested and the angle between the gene set expression amount and the three coordinate axes to obtain the WRE score of the single cell of the sample to be tested;

[0110] The second execution unit is used to evaluate the m6A methylation level of the sample to be tested based on the distribution of the normalized gene set expression levels of the single cell of the sample to be tested in the ternary phase diagram and / or the WRE score of the single cell of the sample to be tested.

[0111] Due to the complexity and poor reproducibility of single-cell m6A methylation sequencing technology, directly measuring the m6A methylation level of cells is inconvenient. However, in order to study the m6A methylation level of cells, it is necessary to study the m6A methylation level of cells. Single-cell transcriptome sequencing is a common and mature sequencing method. Using high-throughput sequencing methods, it can simultaneously measure the expression levels of all target genes, which is convenient, fast, and reproducible. Therefore, on the basis of the inconvenience of directly measuring single-cell m6A methylation sequencing, by measuring the gene expression values related to single-cell m6A methylation, the degree of cellular m6A methylation can be indicated by the gene expression values related to single-cell m6A methylation. Since the genes related to single-cell m6A methylation are not single genes, but are jointly regulated by multiple genes with different functions, the normalized gene set expression quantities of genes related to m6A methylation in single cells of the sample to be tested are corresponded to the three coordinate axes of the ternary phase diagram, and the distribution of the normalized gene set expression quantities of genes related to m6A methylation in single cells of the sample to be tested in the ternary phase diagram is obtained. The corresponding distribution area of the ternary phase diagram can intuitively see the single-cell m6A methylation level of the sample to be tested, and the expression of multiple genes related to single-cell m6A methylation with different functions can be unified under the same three-dimensional coordinate axis, turning it into a reasonable, quantifiable and interpretable model, thereby indicating the degree of cellular m6A methylation. At the same time, the reciprocal of the standard deviation of the cosine value between the normalized gene set expression levels of genes related to m6A methylation in the single cells of the sample to be tested and the angles between them and the three coordinate axes was calculated to obtain the WRE score of the single cell of the sample to be tested. The WRE score was used to further evaluate the m6A methylation level of the sample to be tested.

[0112] In conjunction with the second aspect, in some embodiments provided in the present application, genes related to m6A methylation include writing factors, reading factors, and erasing factors. The m6A methylation modification process involves three key genes: writer (writing), reader (reading), and eraser (erasing), and the relationship between them is dynamically balanced and complex. In different cell types and under different physiological and pathological conditions, writer, reader, and eraser genes may work together or mutually exclusive to finely regulate the m6A methylation level of RNA. For example, in some cells, high expression of writer genes may be accompanied by low expression of eraser genes, thereby maintaining the stability of m6A methylation in cells; while in other cells, the expression level of reader genes may be affected by the expression of writer and eraser genes. This complex interaction relationship enables m6A methylation modification to form a fine regulatory network in cells, which has a profound impact on gene expression and cell function. Therefore, in-depth exploration of the interaction mechanism between writer, reader, and eraser genes is crucial to understanding the role of m6A methylation.

[0113] In combination with the second aspect, in some embodiments provided in this application, the write factor includes at least one of METTL3, METTL14, RBM15 and WTAP; and / or,

[0114] The read factor includes at least one of YTHDF1, YTHDF2, YTHDF3, YTHDC1, YTHDC2, IGF2BP1, IGF2BP2, IGF2BP3 and EIF3A; and / or,

[0115] The erasure factor includes at least one of FTO and ALKBH5.

[0116] In conjunction with the second aspect, in some embodiments provided herein, the three coordinate axes are a write factor coordinate axis, a read factor coordinate axis, and an erase factor coordinate axis. By establishing the write factor coordinate axis, the read factor coordinate axis, and the erase factor coordinate axis, the expression levels of the write factor gene, the read factor gene, and the erase factor of the cell to be tested are mapped to the ternary phase diagram formed by the factor coordinate axis, the read factor coordinate axis, and the erase factor coordinate axis, thereby comprehensively evaluating the m6A methylation level of the cell.

[0117] In conjunction with the second aspect, in some embodiments provided herein, three coordinate axes form a triangular coordinate axis, with the three sides of the triangular coordinate axis representing the expression levels of the write factor, the read factor, and the erase factor, respectively. Each of the three vertices of the ternary phase diagram represents a molecular signature of a cellular m6A methylation gene set. These three gene types are interrelated and constrained, conforming to the coordinate system characteristics of the ternary phase diagram.

[0118] In conjunction with the second aspect, in some embodiments provided herein, the method for calculating the normalized expression levels of the gene set related to m6A methylation includes:

[0119] G1 = Geneset1 mean / (Geneset1 mean + Geneset2 mean + Geneset3 mean);

[0120] G2 = Geneset2 mean / (Geneset1 mean + Geneset2 mean + Geneset3 mean);

[0121] G3 = Geneset3 mean / (Geneset1 mean + Geneset2 mean + Geneset3 mean);

[0122]

[0123] Among them, G1 represents the expression level of the writing factor gene set;

[0124] G2 represents the expression level of the readout factor gene set;

[0125] G3 represents the expression level of the erasure factor gene set;

[0126] X i is the expression value of the i-th gene associated with the write factor;

[0127] Y i is the expression value of the i-th gene associated with the read factor;

[0128] Z i is the expression value of the i-th gene associated with the erasure factor.

[0129] In conjunction with the second aspect, in some embodiments provided herein, a method for calculating the cosine value of the angle between the m6A methylation-related gene set expression value and the three coordinate axes includes:

[0130]

[0131] in:

[0132] cosθ1 is the cosine of the angle between the expression value of the write factor gene set and the write factor coordinate axis;

[0133] cosθ2 is the cosine of the angle between the expression value of the read factor gene set and the read factor coordinate axis;

[0134] cosθ3 is the cosine of the angle between the expression value of the erasure factor gene set and the erasure factor coordinate axis;

[0135] θ1 is the angle between the expression value of the m6A methylation writing factor gene set and the writing factor coordinate axis;

[0136] θ2 is the angle between the expression value of the m6A methylation readout factor gene set and the readout factor coordinate axis;

[0137] θ3 is the angle between the expression value of the m6A methylation eraser gene set and the eraser coordinate axis.

[0138] For each single cell, calculate the angles θ1, θ2, and θ3 relative to the three vertices in the ternary graph. These angles reflect the differences in expression between m6A-related gene sets within the single cell. A cosine value closer to 1 indicates a higher expression level of the m6A methylated gene set type represented by that vertex in the single cell.

[0139] In conjunction with the second aspect, in some embodiments provided herein, a method for calculating the WRE score of a sample to be tested includes:

[0140]

[0141] In conjunction with the second aspect, in some embodiments provided herein, if WRE ≤ the first threshold, the m6A methylation level of the cell to be tested is in an inactive state;

[0142] If WRE>the first threshold, the m6A methylation level of the tested cell is in an active state.

[0143] In general, the higher the WRE score, the more balanced the expression of the three m6A methylation-related gene sets, and the more active the m6A methylation activity. The first threshold is usually the mean or median of the WRE score.

[0144] If WRE ≤ WRE mean or WRE median, the m6A methylation level is closer to a state where one or two m6A-related gene sets (writing factors, reading factors, or erasing factors) are dominant, that is, the m6A methylation level of the tested cell is inactive.

[0145] If WRE>WRE mean or WRE median, the expression levels of the three types of m6A methylation-related gene sets are more balanced, the m6A methylation activity is more active, and the m6A methylation level of the tested cell is active.

[0146] The mean or median WRE score is usually the mean or median of the WRE scores calculated for all cells in a single sequencing run.

[0147] The technical solution provided in this application is described in detail below with reference to embodiments.

[0148] Example 1

[0149] Sample acquisition: obtain the sample to be tested and separate the single cell suspension;

[0150] Sequencing platform: Single-cell RNA sequencing (scRNA-seq) was performed using the 10x Genomics platform to obtain single-cell transcriptome data;

[0151] Data processing: Cell Ranger software was used for data preprocessing, including quality control, gene expression matrix generation, and cell cluster analysis.

[0152] Gene selection: Screening for writers (such as METTL3 and METTL14), readers (such as YTHDF1 and YTHDF2), and erasers (such as FTO and ALKBH5) related to m6A methylation;

[0153] Expression profiling analysis: Calculate the expression levels of Writer, Reader, and Eraser genes in each single cell and construct the expression profile of m6A methylation-related genes;

[0154] Visualization tool development: Based on gene expression profiles, the WREm6A prism visualization tool was developed. Single-cell data were mapped to a two-dimensional space through dimensionality reduction analysis (such as t-SNE or UMAP) to form a color prism image, with different colors representing different m6A methylation states.

[0155] For each cell, the expression values of the Writer, Reader, and Eraser feature genes are calculated (the normalized expression values are converted into gene set scores), and the expression values of each cell are mapped into the space of a ternary phase diagram, as shown in Figure 4 As shown in (A), Writer, Reader, and Eraser correspond to three coordinate axes respectively, and finally the density value is calculated according to the cell distribution density ( Figure 4 (B) to (D)). Single-cell transcriptome sequencing data from 6 cases of esophageal squamous cell carcinoma tissues were analyzed, and different sample phenotypes showed different WREm6A prism images ( Figure 4 (E) to (G)), it can be seen intuitively that Figure 4 (E) and (F) are driven by reader genes, while Figure 4 (G) Driven by Eraser genes and Writer genes. In the analysis of different cell subpopulations, Figure 4 ((H) to (K)) Reflect the more distinct WREm6A prism patterns exhibited in different cell subsets.

[0156] The cells can be divided into 4 regions according to the three gene coordinates, such as Figure 5As shown, region 1 indicates that the methylation modification process is active, but the high expression of writer and eraser genes may offset each other, resulting in dynamic changes in the m6A modification level. Region 2 indicates that the methylation modification level is low, but due to the high expression of reader genes, the cell's response to a small amount of m6A modification may be enhanced. Region 3 indicates that the methylation level is high, but due to the low expression of reader genes, the cell's response to m6A modification is weakened. Region 4 indicates a low methylation state, and the cell may be in a dedifferentiation or dysfunctional state.

[0157] m6A methylation is determined by the dynamic balance of writer, reader, and eraser genes, which are visualized as the sides of a triangle in a ternary phase diagram, creating the WREm6A prism. Individual cells are distributed within the prism using a reduced dimensionality distribution based on writer, reader, and eraser genes. This is then supplemented with multidimensional data on m6A methylation and clinical characteristics, enabling visualization of multimodal data related to single-cell m6A methylation.

[0158] Based on a ternary phase diagram, multidimensional data, including the expression profiles of writers, readers, and erasers, is visualized as the WREm6A prism. This innovative visualization technology intuitively presents complex m6A methylation data, much like light passing through a prism disperses into a rainbow, providing insights into and constructing single-cell m6A methylation maps. It not only intuitively reflects the overall m6A methylation status of cells but also rapidly identifies differences and characteristics in m6A methylation across different cell types or states, providing a new and efficient analytical tool for m6A methylation research.

[0159] The terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned drawings are intended to cover non-exclusive inclusions. For example, a process, method, apparatus, product or equipment comprising a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units inherent to these processes, methods, products or equipment. The terms "first", "second" and "third" are used to distinguish different objects, etc., and do not represent a sequence, nor do they limit the "first", "second" and "third" to different types.

[0160] In the description of the embodiments of this application, the words "exemplary," "for example," or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary," "for example," or "for example" in the embodiments of this application should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary," "for example," or "for example" is intended to present the relevant concepts in a concrete manner.

[0161] In the description of the embodiments of the present application, unless otherwise specified, “ / ” means or, for example, A / B can mean A or B; “and / or” in the text is merely a description of the association relationship of associated objects, indicating that three relationships may exist, for example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, in the description of the embodiments of the present application, “multiple” refers to two or more than two.

[0162] In some processes described in the embodiments of the present application, multiple operations or steps are included that appear in a specific order. However, it should be understood that these operations or steps may not be performed in the order in which they appear in the embodiments of the present application or may be performed in parallel. The sequence numbers of the operations are only used to distinguish between different operations, and the sequence numbers themselves do not represent any order of execution. In addition, these processes may include more or fewer operations, and these operations or steps may be performed in sequence or in parallel, and these operations or steps may be combined.

[0163] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform, of course, it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes a number of instructions for enabling a terminal device to execute the methods described in each embodiment of the present application.

[0164] The above are only preferred embodiments of the present application and do not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A method for modeling a visualization model of m6A methylation levels, characterized in that: The following steps are involved: Obtain the normalized expression values of genes related to m6A methylation in single cells of the sample to be tested; According to the normalized expression values of genes related to m6A methylation in the single cells of the sample to be tested, the normalized gene set expression values of genes related to m6A methylation in the single cells of the sample to be tested are calculated respectively; The normalized gene set expression levels of genes related to m6A methylation in the single cells of the sample to be tested are mapped to the three coordinate axes of the ternary phase diagram respectively, and the distribution of the normalized gene set expression levels of genes related to m6A methylation in the single cells of the sample to be tested in the ternary phase diagram is obtained; Based on the normalized gene set expression levels of genes related to m6A methylation in the single cells of the sample to be tested and the angles between them and the three coordinate axes, the reciprocal of the standard deviation of the cosine values between the normalized gene set expression levels of genes related to m6A methylation in the single cells of the sample to be tested and the three coordinate axes is calculated to obtain the WRE score of the single cells of the sample to be tested; The m6A methylation level of the sample to be tested is evaluated based on the distribution of the normalized gene set expression levels of the single cell of the sample to be tested in the ternary phase diagram and / or the WRE score of the single cell of the sample to be tested.

2. The m6A methylation level visualization modeling method according to claim 1, wherein Genes related to m6A methylation include writers, readers, and erasers.

3. The m6A methylation level visualization modeling method according to claim 2, wherein: The write factor includes at least one of METTL3, METTL14, RBM15 and WTAP; and / or, The read factor includes at least one of YTHDF1, YTHDF2, YTHDF3, YTHDC1, YTHDC2, IGF2BP1, IGF2BP2, IGF2BP3 and EIF3A; and / or, The erasure factor includes at least one of FTO and ALKBH5.

4. The m6A methylation level visualization modeling method according to claim 2, wherein The three coordinate axes are respectively a write factor coordinate axis, a read factor coordinate axis, and an erase factor coordinate axis.

5. The m6A methylation level visualization modeling method according to claim 2, wherein The three coordinate axes form a triangular coordinate axis, and the three sides of the triangular coordinate axis represent the expression levels of the write factor, the read factor, and the erase factor, respectively.

6. The m6A methylation level visualization modeling method according to claim 2, wherein The calculation method of the normalized gene set expression level of genes related to m6A methylation includes: G1 = Geneset1 mean / (Geneset1 mean + Geneset2 mean + Geneset3 mean); G2 = Geneset2 mean / (Geneset1 mean + Geneset2 mean + Geneset3 mean); G3 = Geneset3 mean / (Geneset1 mean + Geneset2 mean + Geneset3 mean); Among them, G1 represents the expression level of the writing factor gene set; G2 represents the expression level of the readout factor gene set; G3 represents the expression level of the erasure factor gene set; X i is the expression value of the i-th gene associated with the write factor; Y i is the expression value of the i-th gene associated with the read factor; Z i is the expression value of the i-th gene associated with the erasure factor.

7. The m6A methylation level visualization modeling method according to claim 6, wherein The calculation method of the cosine value of the angle between the expression value of the m6A methylation-related gene set and the three coordinate axes includes: in: cosθ1 is the cosine of the angle between the expression value of the write factor gene set and the write factor coordinate axis; cosθ2 is the cosine of the angle between the expression value of the read factor gene set and the read factor coordinate axis; cosθ3 is the cosine of the angle between the expression value of the erasure factor gene set and the erasure factor coordinate axis; θ1 is the angle between the expression value of the m6A methylation writing factor gene set and the writing factor coordinate axis; θ2 is the angle between the expression value of the m6A methylation readout factor gene set and the readout factor coordinate axis; θ3 is the angle between the expression value of the m6A methylation eraser gene set and the eraser coordinate axis.

8. The m6A methylation level visualization modeling method according to claim 7, wherein The calculation method of the WRE score of the sample to be tested includes:

9. The m6A methylation level visualization modeling method according to claim 8, wherein: If WRE ≤ the first threshold, the m6A methylation level of the tested cell is in an inactive state; If WRE>the first threshold, the m6A methylation level of the tested cell is in an active state.

10. A device for modeling a visualization model of m6A methylation levels, characterized in that: include: The first acquisition unit is used to obtain the normalized expression value of genes related to m6A methylation in a single cell of the sample to be tested; The first calculation unit is used to calculate the normalized gene set expression amount of the genes related to m6A methylation in the single cell of the sample to be tested according to the normalized expression value of the genes related to m6A methylation in the single cell of the sample to be tested; The first execution unit is used to map the normalized gene set expression levels of genes related to m6A methylation in the single cell of the sample to be tested to the three coordinate axes of the ternary phase diagram, respectively, to obtain the distribution of the normalized gene set expression levels of genes related to m6A methylation in the single cell of the sample to be tested in the ternary phase diagram; The second calculation unit is used to calculate the reciprocal of the standard deviation of the cosine value between the normalized gene set expression amount of the gene related to m6A methylation in the single cell of the sample to be tested and the angle between the gene set expression amount and the three coordinate axes according to the normalized gene set expression amount of the gene related to m6A methylation in the single cell of the sample to be tested and the angle between the gene set expression amount and the three coordinate axes to obtain the WRE score of the single cell of the sample to be tested; and a second execution unit, configured to evaluate the m6A methylation level of the sample to be tested based on the distribution of the normalized gene set expression levels of the single cell of the sample to be tested in the ternary phase diagram and / or the WRE score of the single cell of the sample to be tested.

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