Gene analysis method and device, computer equipment and storage medium

Abstract

The invention relates to a gene analysis method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring a gene expression quantity sequence corresponding to each gene; the gene expression quantity sequence comprises a plurality of gene expression quantities sequenced according to time points; for each current gene in the genes, under the condition that the change trend of the gene expression quantity in the gene expression quantity sequence is assumed to meet a preset change trend, respectively checking the deviation degree between the change trend of the gene expression quantity in each subsequence corresponding to the current gene and the preset change trend, obtaining a sub-test probability corresponding to each sub-sequence; each subsequence is selected from a gene expression quantity sequence; combining the sub-test probabilities corresponding to the subsequences to obtain a test probability corresponding to the current gene; and according to the inspection probability corresponding to each gene, determining the gene according with a preset change trend. By adopting the method, the limitation of gene analysis can be reduced.

Description

Technical Field

[0001] The present application relates to the field of data analysis technology, and in particular to a gene analysis method, device, computer equipment and storage medium. Background Art

[0002] In recent years, studying the expression of genes in biological processes has been of great significance for exploring the functions of genes. The current gene analysis methods mainly analyze the differences between genes in two gene grouping pairs, or analyze the differences between a gene grouping and other gene groups. This gene analysis method can only be applied to the analysis of multiple independent genes. However, in many cases, biological processes, such as embryonic development, are continuous processes. The above-mentioned gene analysis methods cannot be used to analyze genes with continuous biological processes, which has great limitations. Summary of the invention

[0003] Based on this, it is necessary to provide a genetic analysis method, apparatus, computer device, computer readable storage medium and computer program product to address the above technical problems, which can reduce the limitations of genetic analysis.

[0004] In a first aspect, the present application provides a gene analysis method, comprising:

[0005] Obtaining a gene expression sequence corresponding to each gene; the gene expression sequence includes multiple gene expression levels sorted by time points;

[0006] For each current gene in each gene, assuming that the change trend of the gene expression amount in the gene expression amount sequence meets the preset change trend, the deviation between the change trend of the gene expression amount in each subsequence corresponding to the current gene and the preset change trend is tested respectively, and the sub-test probability corresponding to each subsequence is obtained; each subsequence is selected from the gene expression amount sequence;

[0007] Combine the sub-test probabilities corresponding to each subsequence to obtain the test probability corresponding to the current gene;

[0008] According to the test probability corresponding to each gene, the genes that meet the preset change trend are determined.

[0009] In a second aspect, the present application provides a gene analysis device, comprising:

[0010] A sequence acquisition module is used to obtain a gene expression sequence corresponding to each gene; the gene expression sequence includes multiple gene expression levels sorted by time points;

[0011] A probability test module is used to test the deviation between the change trend of the gene expression amount in each subsequence and the preset change trend for each current gene in each gene, assuming that the change trend of the gene expression amount in the gene expression amount sequence meets the preset change trend, and obtain the sub-test probability corresponding to each subsequence; each subsequence is selected from the gene expression amount sequence;

[0012] The probability merging module is used to merge the sub-test probabilities corresponding to each subsequence to obtain the test probability corresponding to the current gene;

[0013] The gene analysis module is used to determine the genes that meet the preset change trend according to the test probability corresponding to each gene.

[0014] In some embodiments, each subsequence includes a corresponding gene expression group. The probability test module is also used to group the gene expression amounts in the gene expression amount sequence corresponding to the current gene to obtain each gene expression amount group; wherein the time points corresponding to the gene expression amounts belonging to the same group are adjacent; for each current gene expression amount group in each gene expression amount group, assuming that the change trend of the gene expression amount in the gene expression amount sequence meets the preset change trend, according to the difference between the gene expression amounts in the current gene expression amount group, the deviation degree between the change trend of the gene expression amount in the current gene expression amount group and the preset change trend is tested to obtain the sub-test probability corresponding to the current gene expression amount group.

[0015] In some embodiments, the probability merging module is also used to integrate the sub-test probabilities corresponding to each sub-sequence to obtain an integrated test probability when the number of gene expression sequences meets the first preset requirement; on the basis of assuming that the integrated test probability obeys a preset chi-square distribution, the test probability corresponding to the current gene is obtained by testing the significance level of the integrated test probability in the chi-square distribution.

[0016] In some embodiments, the probability merging module is also used to determine the reference test probabilities corresponding to each sub-test probability when the number of gene expression sequences meets the second preset requirement; each reference test probability is the probability that there is a significant difference in the gene expression levels in each subsequence; based on each reference test probability, the probability that the current gene discontinuously meets the preset change trend is determined to obtain the test probability corresponding to the current gene.

[0017] In some embodiments, the preset change trend includes an up-regulation change trend and a down-regulation change trend, and the sub-test probabilities corresponding to each subsequence include each up-regulation sub-probability and each down-regulation sub-probability. The gene analysis device of the present application also includes a gene classification module, which is used to integrate the up-regulation sub-probability and the down-regulation sub-probability of the current subsequence for each current subsequence corresponding to the current gene, and obtain the integration value corresponding to the current subsequence; the integration value is used to characterize the degree to which the gene expression in the current subsequence meets the up-regulation change trend and the down-regulation change trend; for each current gene in each gene, according to the integration value corresponding to each subsequence, the integration sequence corresponding to the current gene is determined; the integration sequence is used to characterize the overall change trend of the gene expression in the current gene; according to the integration sequence corresponding to each gene, each gene is clustered to obtain a gene clustering result; wherein, the overall change trend of genes belonging to the same category meets the preset proximity condition.

[0018] In some embodiments, the gene classification module is also used to determine the target up-regulated sub-probability corresponding to the current sub-sequence based on the up-regulated sub-probability of the current sub-sequence; the target up-regulated sub-probability is the probability that the current sub-sequence satisfies the up-regulated change trend; determine the target down-regulated sub-probability corresponding to the current sub-sequence based on the down-regulated sub-probability of the current sub-sequence; the target down-regulated sub-probability is the probability that the current sub-sequence satisfies the down-regulated change trend; integrate the target up-regulated sub-probability and the target down-regulated sub-probability corresponding to the current sub-sequence to obtain the integrated value corresponding to the current sub-sequence.

[0019] In some embodiments, the gene classification module is also used to determine the cluster centers corresponding to each preset change trend category; for the current gene in each gene, according to the integrated sequence corresponding to the current gene, determine the membership of the current gene to each cluster center, and the distance from the current gene to each cluster center; the membership is used to measure the degree of association between the current gene and the corresponding change trend category; according to the membership and distance of each gene corresponding to each cluster center, determine the probability that each gene belongs to each change trend category, and obtain the gene clustering result.

[0020] In a third aspect, the present application provides a computer device, the computer device comprising a memory and a processor, the memory storing a computer program, and the processor implementing the steps in the above method when executing the computer program.

[0021] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which implements the steps in the above method when executed by a processor.

[0022] In a fifth aspect, the present application provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, it implements the steps in the above method.

[0023] The above-mentioned gene analysis method, device, computer equipment, computer-readable storage medium and computer program product obtain a gene expression sequence corresponding to each gene, including multiple gene expression amounts sorted by time points. For each current gene in each gene, under the assumption that the change trend of the gene expression amount in the gene expression sequence meets the preset change trend, the deviation between the change trend of the gene expression amount in each subsequence corresponding to the current gene and the preset change trend is respectively tested, and the sub-test probability corresponding to each subsequence is obtained. Through each sub-test probability, it is possible to know the multiple local change trends corresponding to the multiple gene expression amounts obtained by sorting according to the time point in the current gene. Each subsequence is selected from the gene expression sequence; the sub-test probabilities corresponding to each subsequence are merged to obtain the test probability corresponding to the current gene, and the test probability can reflect the overall change trend of the multiple gene expression amounts corresponding to the current gene. According to the test probabilities corresponding to each gene, the genes that meet the preset change trend are determined. The present application can effectively find genes that continuously meet the preset change trend in time sequence by designing a hypothesis test based on the preset change trend, and can realize the analysis of genes with continuous biological processes, reducing the limitations of gene analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 A schematic diagram of a gene analysis method provided in an embodiment of the present application;

[0025] Figure 2 A schematic diagram of another gene analysis method provided in an embodiment of the present application;

[0026] Figure 3 A variation trend of gene expression level corresponding to a gene provided in an embodiment of the present application;

[0027] Figure 4 A variation trend of gene expression level corresponding to another gene provided in an embodiment of the present application;

[0028] Figure 5 A variation trend of gene expression level corresponding to another gene provided in an embodiment of the present application;

[0029] Figure 6 A variation trend of gene expression level corresponding to another gene provided in an embodiment of the present application;

[0030] Figure 7 A variation trend of gene expression level corresponding to another gene provided in an embodiment of the present application;

[0031] Figure 8 A variation trend of gene expression level corresponding to another gene provided in an embodiment of the present application;

[0032] Fig. 9 A variation trend of gene expression level corresponding to another gene provided in an embodiment of the present application;

[0033] Fig.10 A variation trend of gene expression level corresponding to another gene provided in an embodiment of the present application;

[0034] Fig.11 A structural block diagram of a gene analysis device provided in an embodiment of the present application;

[0035] Fig.12 An internal structure diagram of a computer device provided in an embodiment of the present application;

[0036] Fig.13 An internal structure diagram of another computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0037] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0038] In some embodiments, Figure 1 As shown, a gene analysis method is provided. This embodiment uses the method applied to a computer device as an example. It can be understood that the computer device can be a server or a terminal. The method can be implemented by the server or the terminal alone, or through the interaction between the server and the terminal. In this embodiment, the method includes the following steps:

[0039] S102, obtaining the gene expression level sequence corresponding to each gene.

[0040] Here, each gene refers to one or more genes to be analyzed.

[0041] Gene expression refers to the level of transcription and translation of a gene into protein in cells or tissues under specific conditions. The level of gene expression can reflect the activity of a gene. By comparing the gene expression under different conditions, we can understand the changes of genes in different biological processes. The gene expression sequence corresponding to each gene includes multiple gene expression levels sorted by time point.

[0042] In some embodiments, the gene expression level corresponding to each gene can be at least one of single-cell data from multiple slices in time series, spatiotemporal transcriptome data, or data from multiple cell clusters on a single slice.

[0043] It should be noted that the gene expression sequence can be a continuous gene expression at a time node, such as the gene expression corresponding to embryonic development from 9.5 days to 16.5 days. The gene expression sequence can also represent the gene expression obtained by continuous gradient experimental treatment. For example, by gradient treatment of the drug concentration from 2 mg / L to 200 mg / L, the gene expression corresponding to each drug concentration can be obtained. In addition, continuous cell clustering in a single piece usually results from trajectory inference. Among them, trajectory inference refers to the inference of the evolutionary trajectory or developmental process between cell types or states based on single-cell transcriptome data.

[0044] Specifically, the computer device can directly obtain the gene expression amount sequence corresponding to each gene. The computer device can also first obtain the gene expression amount corresponding to each gene, and then sort the obtained gene expression amounts according to the time point sequence to obtain the gene expression amount sequence corresponding to each gene.

[0045] S104, for each current gene in each gene, assuming that the change trend of the gene expression amount in the gene expression amount sequence meets the preset change trend, respectively check the degree of deviation between the change trend of the gene expression amount in each subsequence corresponding to the current gene and the preset change trend, and obtain the sub-test probability corresponding to each subsequence.

[0046] Among them, each subsequence is selected from adjacent nodes of the gene expression sequence. For example, if the gene expression sequence includes gene expression 1, gene expression 2 and gene expression 3, the corresponding subsequence may include gene expression 1 and gene expression 2, or may include gene expression 2 and gene expression 3.

[0047] The preset change trend refers to the trend of the gene expression amount changing in a preset direction. In some embodiments, the preset change trend can be an upward change trend, indicating that the change in the gene expression amount is upwardly regulated, that is, the expression level increases. In other embodiments, the preset change trend can be a downward change trend, indicating that the change in the gene expression amount is downwardly regulated, that is, the expression level decreases.

[0048] In some embodiments, when the preset change trend is an upward change trend, the sub-test probability corresponding to each subsequence is an upward sub-probability, and the upward sub-probability is used to characterize the upward regulation of the gene expression amount in the corresponding subsequence. When the preset change trend is a downward change trend, the sub-test probability corresponding to each subsequence is a downward sub-probability, and the downward sub-probability is used to characterize the downward regulation of the gene expression amount in the corresponding subsequence.

[0049] Specifically, for each current gene in each gene, under the assumption that the change trend of the gene expression amount in the gene expression amount sequence satisfies the up-regulation change trend, the computer device respectively checks the deviation between the change trend of the gene expression amount in each subsequence corresponding to the current gene and the up-regulation change trend, and obtains the up-regulation sub-probability corresponding to each subsequence. For each current gene in each gene, under the assumption that the change trend of the gene expression amount in the gene expression amount sequence satisfies the down-regulation change trend, the computer device respectively checks the deviation between the change trend of the gene expression amount in each subsequence corresponding to the current gene and the down-regulation change trend, and obtains the down-regulation sub-probability corresponding to each subsequence.

[0050] S106, combining the sub-test probabilities corresponding to the sub-sequences to obtain the test probability corresponding to the current gene.

[0051] Among them, the test probability corresponding to the current gene is used to characterize the overall change trend of the gene expression level in the current gene, such as whether the gene expression level in the current gene is continuously upregulated as a whole, or whether the gene expression level in the current gene is continuously downregulated as a whole.

[0052] In some embodiments, the test probability corresponding to the current gene includes at least one of an up-regulation test probability or a down-regulation test probability. The up-regulation test probability corresponding to the current gene is used to characterize whether the gene expression amount in the current gene is continuously up-regulated as a whole. The down-regulation test probability corresponding to the current gene is used to characterize whether the expression amount of each gene in the current gene is continuously down-regulated as a whole.

[0053] Specifically, the computer device combines the up-regulated sub-probabilities corresponding to each sub-sequence to obtain the up-regulated test probability corresponding to the current gene. The computer device combines the down-regulated sub-probabilities corresponding to each sub-sequence to obtain the down-regulated test probability corresponding to the current gene.

[0054] S108, determining genes that meet the preset change trend according to the test probabilities corresponding to the genes.

[0055] Specifically, the computer device can directly compare the up-regulation test probability corresponding to each gene with the preset up-regulation probability range, so as to determine the gene corresponding to the test probability that satisfies the up-regulation probability range as a gene that conforms to the up-regulation trend, that is, a continuously up-regulated gene. The computer device can directly compare the down-regulation test probability corresponding to each gene with the preset down-regulation probability range, so as to determine the gene corresponding to the test probability that satisfies the down-regulation probability range as a gene that conforms to the down-regulation trend, that is, a continuously down-regulated gene.

[0056] In some embodiments, the computer device may further sort the up-regulation test probabilities corresponding to each gene from small to large or from large to small, so as to select several genes according to the sorting conditions as genes that meet the up-regulation trend, that is, genes that are continuously up-regulated. The computer device may further sort the down-regulation test probabilities corresponding to each gene from small to large or from large to small, so as to select several genes according to the sorting conditions as genes that meet the down-regulation trend, that is, genes that are continuously down-regulated.

[0057] The above gene analysis method obtains a gene expression sequence corresponding to each gene, including multiple gene expression amounts sorted by time points. For each current gene in each gene, under the assumption that the change trend of the gene expression amount in the gene expression sequence meets the preset change trend, the deviation between the change trend of the gene expression amount in each subsequence corresponding to the current gene and the preset change trend is tested respectively, and the sub-test probabilities corresponding to each subsequence are obtained. Through each sub-test probability, it is possible to know the multiple local change trends corresponding to the multiple gene expression amounts obtained by sorting according to the time point in the current gene. Each subsequence is selected from the gene expression sequence; the sub-test probabilities corresponding to each subsequence are merged to obtain the test probability corresponding to the current gene, which can reflect the overall change trend of the multiple gene expression amounts corresponding to the current gene. According to the test probabilities corresponding to each gene, the genes that meet the preset change trend are determined. The present application can effectively find genes that continuously meet the preset change trend in time sequence by designing a hypothesis test based on the preset change trend, and can realize the analysis of genes with continuous biological processes, reducing the limitations of gene analysis.

[0058] In some embodiments, each subsequence includes a corresponding gene expression amount group. S104 specifically includes but is not limited to: grouping the gene expression amounts in the gene expression amount sequence corresponding to the current gene to obtain each gene expression amount group; for each current gene expression amount group in each gene expression amount group, assuming that the change trend of the gene expression amount in the gene expression amount sequence meets the preset change trend, based on the difference between the gene expression amounts in the current gene expression amount group, checking the deviation between the change trend of the gene expression amount in the current gene expression amount group and the preset change trend, and obtaining the sub-test probability corresponding to the current gene expression amount group.

[0059] Among them, the time points corresponding to the expression levels of genes belonging to the same group are adjacent.

[0060] Specifically, the computer device divides the gene expression amounts of adjacent time points in the gene expression amount sequence corresponding to the current gene into the same group to obtain each gene expression amount group. For each current gene expression amount group in each gene expression amount group, under the assumption that the change trend of the gene expression amount in the gene expression amount sequence meets the upward change trend, the computer device checks the deviation between the change trend of the gene expression amount in the current gene expression amount group and the upward change trend according to the difference between the gene expression amounts in the current gene expression amount group, and obtains the upward sub-probability corresponding to the current gene expression amount group. For each current gene expression amount group in each gene expression amount group, under the assumption that the change trend of the gene expression amount in the gene expression amount sequence meets the downward change trend, according to the difference between the gene expression amounts in the current gene expression amount group, the deviation between the change trend of the gene expression amount in the current gene expression amount group and the downward change trend is checked, and the downward sub-probability corresponding to the current gene expression amount group is obtained.

[0061] It can be seen that in the present embodiment, by assuming that the variation trend of the gene expression amount in the gene expression sequence meets the preset variation trend, and according to the difference between the gene expression amounts in each gene expression amount group, the deviation of each gene expression amount group from the preset variation trend is determined, thereby generating independent results for the preset variation trend in each gene expression amount group, which is convenient for subsequent gene analysis. For example, for the situation where the preset variation trend is an upward variation trend, an independent result for the upward variation trend in each gene expression group can be generated. For the situation where the preset variation trend is a downward variation trend, an independent result for the downward variation trend in each gene expression group can be generated. Thus, it is beneficial to detect whether the gene expression amount of each gene is continuously up-regulated according to the independent result for the upward variation trend in each gene expression group of the gene, and it is beneficial to detect whether the expression of each gene is continuously down-regulated according to the independent result for the downward variation trend in each gene expression group of the gene, thereby effectively screening out genes whose gene expression amount is continuously up-regulated or continuously down-regulated.

[0062] In some embodiments, the subtest probability corresponding to the current gene expression group can be determined by some hypothesis testing methods, such as the Student's t-test method. The Student's t-test can also be called a t-test. The t-test is a statistical method for comparing whether the means of two groups of samples are significantly different.

[0063] In some embodiments, the computer device can continuously perform a unilateral t-test on the gene expression levels in each gene expression level group, such as performing a unilateral left test (One-tailed left test, LT) and a unilateral right test (One-tailed right test, RT) respectively, to obtain a unilateral t-test result. The unilateral left test is used to test whether a hypothesis is less than or lower than a certain specific value, and in the embodiment of the present application, it indicates whether one of the gene expression levels in the same gene expression level group is less than or lower than another gene expression level. The unilateral right test is used to test whether a hypothesis is greater than or higher than a certain specific value, and in the embodiment of the present application, it indicates whether one of the gene expression levels in the same gene expression level group is greater than or higher than another gene expression level.

[0064] In practical applications, the computer device can divide the two gene expression levels at adjacent time points in the gene expression level sequence corresponding to the current gene into the same group to obtain multiple gene expression level groups. The computer device can continuously perform a unilateral left test on the gene expression levels in each gene expression level group to obtain a down-regulation sub-probability sequence composed of multiple down-regulation sub-probabilities. The computer device can continuously perform a unilateral right test on the gene expression levels in each gene expression level group to obtain an up-regulation sub-probability sequence composed of multiple up-regulation sub-probabilities. Among them, the down-regulation sub-sequence and up-regulation sub-sequence of the corresponding gene are the number of multiple time points corresponding to the gene minus 1, and the down-regulation sub-probability sequence and the up-regulation sub-probability sequence are respectively recorded as Pg i and Pl i . Where i∈{Z+|i≤T-1}, which means i is a positive integer and i≤T-1, is one of the subsequences. It should be noted that the one-sided t-test can effectively produce independent results for the expression of continuously up-regulated or down-regulated genes, and the obtained up-regulated sub-probability sequence and down-regulated sub-probability sequence are used for subsequent gene analysis.

[0065] In some embodiments, S106 specifically includes but is not limited to: when the number of gene expression sequences meets the first preset requirement, the sub-test probabilities corresponding to each sub-sequence are integrated to obtain the integrated test probability; on the basis of assuming that the integrated test probability obeys the preset chi-square distribution, the test probability corresponding to the current gene is obtained by testing the significance level of the integrated test probability in the chi-square distribution.

[0066] The first preset requirement may be that the number of gene expression sequence is less than or equal to a preset number.

[0067] Chi-square distribution is one of the commonly used probability distributions in statistics. It is used to describe the distribution of a random variable X. The definition of chi-square distribution is based on the square of the standard normal distribution.

[0068] In some embodiments, integrating the test probability includes at least one of adjusting the combined test probability upward or adjusting the combined test probability downward.

[0069] Specifically, when the number of gene expression sequences meets the first preset requirement, the computer device integrates the logarithm of the up-regulation sub-probability corresponding to each subsequence by negative two to obtain the up-regulation combined test probability. On the basis of assuming that the up-regulation combined test probability obeys the preset chi-square distribution, by testing the significance level of the up-regulation combined test probability in the chi-square distribution, the up-regulation test probability corresponding to the current gene is obtained. The computer device also integrates the down-regulation sub-probability corresponding to each subsequence to obtain the down-regulation combined test probability. On the basis of assuming that the down-regulation combined test probability obeys the preset chi-square distribution, by testing the significance level of the down-regulation combined test probability in the chi-square distribution, the down-regulation test probability corresponding to the current gene is obtained. It can be seen that in the present embodiment, by merging the sub-test probabilities corresponding to each sequence to obtain the test probability corresponding to the gene, the overall change trend of the gene expression in the gene can be determined, such as whether it is continuously up-regulated or continuously down-regulated.

[0070] In some embodiments, when the number of gene expression sequences meets the first preset requirement, the sub-test probabilities corresponding to each sub-sequence can be combined by Fisher's method of combining p-values ​​to obtain a comprehensive test probability. The Fisher's method of combining p-values ​​is implemented based on a basic assumption, which can be a statistic of each sub-test probability, such as the sum of the negative square logarithm of each sub-test probability obeys a chi-square distribution with a degree of freedom of 2k, that is, Where k is the number of subsequences, i is one of the k subsequences, and P i is the subtest probability corresponding to one of the subsequences, and 2k refers to the degree of freedom, which is used to determine the shape of the chi-square distribution. After determining the preset chi-square distribution, the significance level of each subtest probability on the chi-square distribution with a degree of freedom of 2k is tested to obtain the integrated probability, that is, the test probability of the gene. It should be noted that Fisher's P value integration method performs better when the number of gene expression levels is small or there are fewer time points.

[0071] In some embodiments, the sub-test probabilities corresponding to each subsequence are the probabilities that there is no significant difference in the gene expression in each subsequence. S106 specifically also includes but is not limited to: when the number of gene expression sequences meets the second preset requirement, determining the reference test probabilities corresponding to each sub-test probability; based on each reference test probability, determining the probability that the current gene does not continuously meet the preset change trend, and obtaining the test probability corresponding to the current gene.

[0072] The second preset requirement may be that the number of gene expression sequence is greater than a preset number.

[0073] Each reference test probability is the probability that there is a significant difference in the gene expression in each subsequence. In some embodiments, the reference test probability includes at least one of an up-regulation test probability or a down-regulation test probability.

[0074] Specifically, when the number of gene expression sequences meets the second preset requirement, the computer device determines the corresponding reference test probabilities of each up-regulation sub-probability according to the difference between 1 and each up-regulation sub-probability. The computer device determines the probability of the current gene discontinuously meeting the upward trend of change according to each up-regulation reference test probability, and obtains the corresponding upward test probability of the current gene. When the number of gene expression sequences meets the second preset requirement, the computer device determines the corresponding reference test probabilities of each down-regulation sub-probability according to the difference between 1 and each down-regulation sub-probability. The computer device determines the probability of the current gene discontinuously meeting the downward trend of change according to each down-regulation reference test probability, and obtains the corresponding downward test probability of the current gene. It can be seen that in the present embodiment, by merging the sub-test probabilities corresponding to each sequence to obtain the test probability corresponding to the gene, it is possible to determine the overall trend of change of the gene expression in the gene, such as whether it is continuously up-regulated or continuously down-regulated.

[0075] In some embodiments, when the number of gene expression sequences meets the second preset requirement, the sub-test probabilities corresponding to each subsequence can also be merged by the False Discovery Rate (FDR) method to obtain a comprehensive test probability. Among them, the False Discovery Rate is one of the probability methods for controlling false discoveries in multiple hypothesis testing, and its main idea is to control the number of false positives found. Among them, the process of merging sub-test efficiencies by the False Discovery Rate method can be: for one of the sub-test probabilities P, the sub-test efficiency can be regarded as the probability that the gene expression at this time point is not significantly greater than or less than the gene expression at the next time point, and 1-P can be regarded as the probability of its alternative hypothesis, that is, the probability that the gene expression at this time point is significantly greater than or less than the next time point. Then, the change process of each time point is regarded as an independent realization, and its joint probability of occurring simultaneously can be Then P i The probability of discontinuous increase or decrease is Thus, the integrated test probability is obtained. Where k is the number of subsequences, i is one of the k subsequences, P i represents the probability of the ith sub-test, P combination is the integrated test probability. It should be noted that the false discovery rate method performs better when there are more gene expression values ​​or more time points.

[0076] In some embodiments, the preset change trend includes an up-regulation change trend and a down-regulation change trend, and the sub-test probabilities corresponding to each subsequence include each up-regulation sub-probability and each down-regulation sub-probability. After S104, the gene analysis method of the present application specifically includes but is not limited to: for each current subsequence corresponding to the current gene, the up-regulation sub-probability and the down-regulation sub-probability of the current subsequence are integrated to obtain the integration value corresponding to the current subsequence; for each current gene in each gene, according to the integration value corresponding to each subsequence, the integration sequence corresponding to the current gene is determined; according to the integration sequence corresponding to each gene, each gene is clustered to obtain a gene clustering result.

[0077] Among them, the up-regulation test probability is used to characterize the probability that the subsequences corresponding to the current gene deviate from the up-regulation trend, and the down-regulation test probability is used to characterize the probability that the subsequences corresponding to the current gene deviate from the down-regulation trend.

[0078] The integration value is used to characterize the extent to which the gene expression in the current subsequence satisfies the up-regulation trend and the down-regulation trend. The integration sequence is used to characterize the overall trend of the gene expression in the current gene.

[0079] In addition, the overall change trends of genes belonging to the same category meet the preset proximity conditions, such as the overall change trends of genes of the same category are the same, or are close enough.

[0080] Specifically, for each current subsequence corresponding to the current gene, the computer device integrates the up-regulation sub-probability and down-regulation sub-probability of the current subsequence to obtain an integration value corresponding to the current subsequence. For each current gene in each gene, the computer device directly divides the integration values ​​corresponding to each subsequence into a sequence to obtain the integration sequence corresponding to the current gene. The computer device performs clustering processing on each gene according to the integration sequence corresponding to each gene, and obtains the gene clustering result. It can be seen that in the gene clustering of the present application, not only the similarity of each gene expression amount is considered, but also the variation law of gene expression amount at the time point time is considered, so as to perform gene clustering. In this way, genes with similar variation laws in the time sequence can be found as the same class of genes. Compared with the traditional method of clustering based on the similarity between gene expression amounts, flexibility is better.

[0081] It should be noted that clustering genes in the present application refers to clustering the changing trends of the expression levels of each gene generated during the continuous changes of the genes.

[0082] In some embodiments, Figure 2 As shown, the general process of the gene analysis method provided in the embodiment of the present application is: obtaining multiple gene expression levels (i.e., gene expression level sequences) corresponding to the gene in the time series, and grouping these gene expression levels in pairs according to the time sequence to obtain multiple gene expression level groups, such as 4 gene expression level groups. Then, continuous t tests are performed on these gene expression level groups respectively, and after the continuous t tests are performed, the up-regulation test probability and the down-regulation test probability of the gene are obtained by merging the Fisher's P value, so as to determine whether the gene is continuously up-regulated or continuously down-regulated according to the up-regulation test probability and the down-regulation test probability. Figure 2 The first group of graphs 202 on the far right shows the trend of continuous up-regulation or continuous down-regulation. After continuous t-tests, the genes are judged similarly by the similarity of the trend patterns to find specific genes, that is, specific genes are found based on the similarity comparison of the gene change trends. For example, Figure 2 In the second group of graphs 204 on the far right, the similarity of genes is determined by comparing the change trends of two genes, thereby finding specific genes similar to the corresponding genes. After performing continuous t-tests, the fuzzy clustering algorithm is used to group genes based on the change trends. For example, for a gene to be grouped, the change trend of the gene is determined to be similar to Figure 2 If the change trend is similar to that in the third group of graphs 206 on the far right, the gene and the genes with similar change trends in 206 can be classified into the same group.

[0083] In some embodiments, the step of "integrating the up-modulation sub-probability and the down-modulation sub-probability of the current subsequence to obtain an integrated value corresponding to the current subsequence" specifically includes but is not limited to: determining a target up-modulation sub-probability corresponding to the current subsequence based on the up-modulation sub-probability of the current subsequence; determining a target down-modulation sub-probability corresponding to the current subsequence based on the down-modulation sub-probability of the current subsequence; and integrating the target up-modulation sub-probability and the target down-modulation sub-probability corresponding to the current subsequence to obtain an integrated value corresponding to the current subsequence.

[0084] The target up-adjustment sub-probability is the probability that the current sub-sequence meets the up-adjustment change trend, and the target down-adjustment sub-probability is the probability that the current sub-sequence meets the down-adjustment change trend.

[0085] Specifically, the computer device determines the target up-adjustment sub-probability corresponding to the current subsequence based on the difference between 1 and the up-adjustment sub-probability of the current subsequence. The computer device determines the target down-adjustment sub-probability corresponding to the current subsequence based on the difference between 1 and the down-adjustment sub-probability of the current subsequence. The computer device selects a maximum value from the target up-adjustment sub-probability and the target down-adjustment sub-probability corresponding to the current subsequence to obtain a preliminary integration value. In the case where the target up-adjustment sub-probability is greater than the target down-adjustment sub-probability, the preliminary integration value is determined as the final integration value. In the case where the target up-adjustment sub-probability is less than the target down-adjustment sub-probability, the opposite of the preliminary integration value is determined as the final integration value. In the case where the target up-adjustment sub-probability is equal to the target down-adjustment sub-probability, the final integration value is 0. It can be seen that in this embodiment, through the above-mentioned integration method, the value range of the integration value is determined to be between -1 and 1, wherein 1 represents that the gene expression level of the subsequence is upregulated, -1 represents that the gene expression level of the subsequence is downregulated, and 0 represents that the gene expression level of the subsequence remains unchanged. The constructed integration value can reflect the degree of upregulation and downregulation of the corresponding subsequence, thereby facilitating the subsequent determination of the overall upregulation or downregulation of the gene expression level in the gene.

[0086] In some embodiments, the up-modulation sub-probability and the down-modulation sub-probability corresponding to each sub-sequence can be integrated by formula (1):

[0087]

[0088] Among them, i is one of the subsequences, f i is the integration value corresponding to the i-th subsequence, Pl i represents the probability of down-regulation corresponding to the ith subsequence, Pg i represents the probability of the up-regulation corresponding to the ith subsequence, 1-Pl i represents the probability of down-regulating the target corresponding to the ith subsequence, 1-Pg i represents the probability of the target up-regulation corresponding to the ith subsequence, maximun(1-Pli ,1-Pg i ) means taking 1-Pl i and 1-Pg i The maximum value among them is the preliminary integration value.

[0089] In some embodiments, the step of "clustering each gene according to the integration sequence corresponding to each gene to obtain a gene clustering result" specifically includes but is not limited to: determining the cluster centers corresponding to each preset change trend category; for the current gene in each gene, determining the membership of the current gene to each cluster center and the distance from the current gene to each cluster center according to the integration sequence corresponding to the current gene; determining the probability that each gene belongs to each change trend category according to the membership and distance of each gene corresponding to each cluster center, to obtain a gene clustering result.

[0090] Among them, the membership degree is used to measure the degree of association between the current gene and the corresponding change trend category.

[0091] Specifically, computer equipment determines the cluster center that each preset variation trend category corresponds to respectively.For the current gene in each gene, computer equipment determines the membership of the current gene and each cluster center respectively, and the distance of the current gene to each cluster center respectively according to the integrated sequence corresponding to the current gene.According to the product of the membership of each cluster center and the distance corresponding to each gene respectively, determine that each gene belongs to the probability of each variation trend category respectively, obtain gene clustering result.Achieve more flexible gene clustering result, its gene clustering result comprises that the variation trend of each gene meets the scoring (i.e. membership) of each preset variation trend category.Due to continuous score value, the application can more flexibly adjust the gene quantity included in each preset variation trend category.

[0092] In some embodiments, the gene clustering result may also include genes belonging to the corresponding change trend category. After determining the membership of each gene to the corresponding change trend category, the genes with membership greater than the custom threshold are determined as genes belonging to the change trend category. Alternatively, these memberships are sorted, and the genes ranked in the top N are determined as genes belonging to the change trend category.

[0093] In some embodiments, the present application can perform soft clustering on each gene by a fuzzy C-means clustering method, which obtains a more flexible gene clustering result by multiplying the convergence membership with each sample, that is, the distance from the integrated sequence to the cluster center.

[0094] In some embodiments, the embodiments of the present application can also provide a method for filtering genes according to a certain temporal change trend. The basic principle is: with preset values, such as 0.95 and -0.95 as the significance level, when the integration value in the integration sequence is greater than 0.95, it is regarded as up-regulated, and when the integration value in the integration sequence is less than -0.95, it is regarded as negative regulation. In this way, genes that meet the change trend can be screened according to the set temporal change trend, such as screening out genes that are continuously up-regulated, continuously down-regulated, and first continuously up-regulated and then continuously down-regulated.

[0095] In general, the embodiments of the present application provide effective solutions for the possible changing trends and corresponding gene screening and gene clustering in continuous biological processes or continuously changing clusters, thereby helping to analyze genes related to specific biological processes and reducing the limitations of gene analysis.

[0096] In some embodiments, Figures 3 to 8 As shown in FIG. 1 , the expression trends of each gene, namely, gene A to gene F, at different time points are shown. Figures 3 to 8 The horizontal axis shown in the figure represents each time point, such as days, and the vertical axis represents the expression level of each gene. It can be seen from the gene expression levels at each time point that these genes all show a significant trend of continuous upregulation. Since the function of the gene is also related to the brain or nerves, for example, the Trim2 gene has the function of protecting neurons, and gene ontology enrichment found that its continuously upregulated genes are related to the development of neuronal projections. These findings are highly related to the development process of the mouse brain and provide a solution for the mining of temporal biological information.

[0097] In some embodiments, Fig. 9 and Fig.10 As shown in the figure, the changing trends of gene expression levels of each gene at different time points are shown. Fig. 9 and Fig.10 The horizontal axis shown in represents each time point, such as the number of days, and the vertical axis represents the expression level of each gene. Fig. 9 The temporal variation trend of each gene shown in the example of the present application is compared with Fig.10 The variation trend of each gene expression obtained by the traditional method shown is more advantageous in biological information mining. For example, in the development process of mouse brain embryos from 9.5 days to 16.5 days, more similar variation trends can be found. In terms of gene ontology function, the gene module of this application is related to processes such as neuronal death and temperature perception, and is more related to processes such as neuronal brain development.

[0098] In some embodiments, the gene analysis method of the present application further includes but is not limited to the following steps:

[0099] (1) Obtain the gene expression sequence corresponding to each gene.

[0100] (2) For each current gene in each gene, the gene expression levels in the gene expression level sequence corresponding to the current gene are grouped to obtain each gene expression level group.

[0101] (3) For each current gene expression group in each gene expression group, assuming that the change trend of the gene expression in the gene expression sequence satisfies the up-regulation change trend and the down-regulation change trend, according to the difference between the gene expression in the current gene expression group, the deviation degree between the change trend of the gene expression in the current gene expression group and the up-regulation change trend and the down-regulation change trend is tested, and the up-regulation sub-probability and down-regulation sub-probability corresponding to the current gene expression group are obtained. Jump to step (4) to execute steps (5) to (7), and execute step (10), or execute steps (8) to (10), or jump to step (11) to execute steps (11) to (17).

[0102] (4) When the number of gene expression sequences meets the first preset requirement, execute steps (5) to (7); when the number of gene expression sequences meets the second preset requirement, execute steps (8) to (9).

[0103] (5) Integrate the up-adjustment sub-probabilities corresponding to each sub-sequence to obtain the up-adjustment combined test probability.

[0104] (6) Integrate the down-regulation sub-probabilities corresponding to each subsequence to obtain the down-regulation combined test probability;

[0105] (7) On the basis of assuming that the up-adjusted combined test probability and the down-adjusted combined test probability obey the preset chi-square distribution, the up-regulation test probability and the down-regulation test probability corresponding to the current gene are obtained by testing the significance level of the up-adjusted combined test probability and the down-adjusted combined test probability in the chi-square distribution.

[0106] (8) Determine the up-adjustment reference test probabilities corresponding to the up-adjustment sub-probabilities and the down-adjustment reference test probabilities corresponding to the down-adjustment sub-probabilities respectively.

[0107] (9) According to each up-regulation reference test probability and each down-regulation reference test probability, the probability that the current gene discontinuously satisfies the up-regulation change trend and the down-regulation change trend is determined respectively, and the up-regulation test probability and the down-regulation test probability corresponding to the current gene are obtained.

[0108] (10) According to the up-regulation test probability and down-regulation test probability corresponding to each gene, genes that meet the continuous up-regulation trend or the continuous down-regulation trend are determined.

[0109] (11) For each current subsequence corresponding to the current gene, determine the target up-regulated subsequence probability corresponding to the current subsequence according to the up-regulated subsequence probability of the current subsequence.

[0110] (12) According to the down-tuning sub-probability of the current sub-sequence, determine the target down-tuning sub-probability corresponding to the current sub-sequence.

[0111] (13) Integrate the target up-adjustment sub-probability and the target down-adjustment sub-probability corresponding to the current sub-sequence to obtain an integrated value corresponding to the current sub-sequence.

[0112] (14) For each current gene in each gene, determine the integration sequence corresponding to the current gene according to the integration values ​​corresponding to each subsequence.

[0113] (15) determining the cluster centers corresponding to each preset change trend category;

[0114] (16) For the current gene in each gene, determine the membership degree of the current gene to each cluster center and the distance from the current gene to each cluster center according to the integrated sequence corresponding to the current gene.

[0115] (17) According to the membership degree and distance of each gene corresponding to each cluster center, the probability that each gene belongs to each change trend category is determined to obtain the gene clustering result.

[0116] It should be understood that, although the steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indications of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear description in this article, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0117] Based on the same inventive concept, the present application also provides a gene analysis device. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme described in the above method, so the specific limitations in one or more gene analysis device embodiments provided below can refer to the limitations on the gene analysis method above, and will not be repeated here.

[0118] like Fig.11 As shown, the embodiment of the present application provides a gene analysis device, comprising:

[0119] The sequence acquisition module 1102 is used to acquire the gene expression sequence corresponding to each gene; the gene expression sequence includes multiple gene expression levels sorted by time points;

[0120] The probability test module 1104 is used to test the deviation between the variation trend of the gene expression amount in each subsequence and the preset variation trend for each current gene in each gene, assuming that the variation trend of the gene expression amount in the gene expression amount sequence satisfies the preset variation trend, and obtain the sub-test probability corresponding to each subsequence; each subsequence is selected from the gene expression amount sequence;

[0121] The probability merging module 1106 is used to merge the sub-test probabilities corresponding to each subsequence to obtain the test probability corresponding to the current gene;

[0122] The gene analysis module 1108 is used to determine the genes that meet the preset change trend according to the test probabilities corresponding to each gene.

[0123] The above-mentioned gene analysis device obtains a gene expression sequence corresponding to each gene, including multiple gene expression amounts sorted by time points. For each current gene in each gene, under the assumption that the change trend of the gene expression amount in the gene expression sequence meets the preset change trend, the deviation between the change trend of the gene expression amount in each subsequence corresponding to the current gene and the preset change trend is respectively tested, and the sub-test probabilities corresponding to each subsequence are obtained. Through each sub-test probability, it is possible to know the multiple local change trends corresponding to the multiple gene expression amounts obtained by sorting according to the time point in the current gene. Each subsequence is selected from the gene expression sequence; the sub-test probabilities corresponding to each subsequence are merged to obtain the test probability corresponding to the current gene, which can reflect the overall change trend of the multiple gene expression amounts corresponding to the current gene. According to the test probabilities corresponding to each gene, the genes that meet the preset change trend are determined. The present application can effectively find genes that continuously meet the preset change trend in time sequence by designing a hypothesis test based on the preset change trend, and can realize the analysis of genes with continuous biological processes, reducing the limitations of gene analysis.

[0124] In some embodiments, each subsequence includes a corresponding gene expression amount group. The probability test module 1104 is also used to group the gene expression amounts in the gene expression amount sequence corresponding to the current gene to obtain each gene expression amount group; wherein the time points corresponding to the gene expression amounts belonging to the same group are adjacent; for each current gene expression amount group in each gene expression amount group, assuming that the change trend of the gene expression amount in the gene expression amount sequence meets the preset change trend, according to the difference between the gene expression amounts in the current gene expression amount group, the deviation degree between the change trend of the gene expression amount in the current gene expression amount group and the preset change trend is tested to obtain the sub-test probability corresponding to the current gene expression amount group.

[0125] In some embodiments, the probability merging module 1106 is also used to integrate the sub-test probabilities corresponding to each sub-sequence to obtain an integrated test probability when the number of gene expression sequences meets the first preset requirement; on the basis of assuming that the integrated test probability obeys a preset chi-square distribution, the test probability corresponding to the current gene is obtained by testing the significance level of the integrated test probability in the chi-square distribution.

[0126] In some embodiments, the probability merging module 1106 is also used to determine the reference test probabilities corresponding to each sub-test probability when the number of gene expression sequences meets the second preset requirement; each reference test probability is the probability that there is a significant difference in the gene expression levels in each subsequence; based on each reference test probability, the probability that the current gene discontinuously meets the preset change trend is determined to obtain the test probability corresponding to the current gene.

[0127] In some embodiments, the preset change trend includes an up-regulation change trend and a down-regulation change trend, and the sub-test probabilities corresponding to each subsequence include each up-regulation sub-probability and each down-regulation sub-probability. The gene analysis device of the present application also includes a gene classification module, which is used to integrate the up-regulation sub-probability and the down-regulation sub-probability of the current subsequence for each current subsequence corresponding to the current gene, and obtain the integration value corresponding to the current subsequence; the integration value is used to characterize the degree to which the gene expression in the current subsequence meets the up-regulation change trend and the down-regulation change trend; for each current gene in each gene, according to the integration value corresponding to each subsequence, the integration sequence corresponding to the current gene is determined; the integration sequence is used to characterize the overall change trend of the gene expression in the current gene; according to the integration sequence corresponding to each gene, each gene is clustered to obtain a gene clustering result; wherein, the overall change trend of genes belonging to the same category meets the preset proximity condition.

[0128] In some embodiments, the gene classification module is also used to determine the target up-regulated sub-probability corresponding to the current sub-sequence based on the up-regulated sub-probability of the current sub-sequence; the target up-regulated sub-probability is the probability that the current sub-sequence satisfies the up-regulated change trend; determine the target down-regulated sub-probability corresponding to the current sub-sequence based on the down-regulated sub-probability of the current sub-sequence; the target down-regulated sub-probability is the probability that the current sub-sequence satisfies the down-regulated change trend; integrate the target up-regulated sub-probability and the target down-regulated sub-probability corresponding to the current sub-sequence to obtain the integrated value corresponding to the current sub-sequence.

[0129] In some embodiments, the gene classification module is also used to determine the cluster centers corresponding to each preset change trend category; for the current gene in each gene, according to the integrated sequence corresponding to the current gene, determine the membership of the current gene to each cluster center, and the distance from the current gene to each cluster center; the membership is used to measure the degree of association between the current gene and the corresponding change trend category; according to the membership and distance of each gene corresponding to each cluster center, determine the probability that each gene belongs to each change trend category, and obtain the gene clustering result.

[0130] Each module in the above gene analysis device can be implemented in whole or in part by software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each module.

[0131] In some embodiments, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Fig.12 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data related to gene analysis. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the steps in the above-mentioned gene analysis method are implemented.

[0132] In some embodiments, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Fig.13As shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. Wherein, the processor, the memory and the input / output interface are connected via a system bus, and the communication interface, the display unit and the input device are connected to the system bus via the input / output interface. Wherein, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented by WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, the steps in the above-mentioned gene analysis method are implemented. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen; the input device of the computer device can be a touch layer covering the display screen, or a key, trackball or touchpad provided on the computer device housing, or an external keyboard, touchpad or mouse, etc.

[0133] Those skilled in the art will understand that Fig.12 or Fig.13 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0134] In some embodiments, a computer device is provided. The computer device includes a memory and a processor. The memory stores a computer program. When the processor executes the computer program, the steps in the above method embodiments are implemented.

[0135] In some embodiments, a computer-readable storage medium is provided, which stores a computer program. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0136] In some embodiments, a computer program product is provided. The computer program product includes a computer program. When the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0137] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant laws, regulations and standards of relevant countries and regions.

[0138] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0139] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0140] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A gene analysis method, characterized in that: include: Obtaining a gene expression sequence corresponding to each gene; the gene expression sequence includes a plurality of gene expression levels sorted by time points; For each current gene in the genes, assuming that the change trend of the gene expression amount in the gene expression amount sequence meets the preset change trend, respectively test the degree of deviation between the change trend of the gene expression amount in each subsequence corresponding to the current gene and the preset change trend, and obtain the sub-test probability corresponding to each subsequence; Each subsequence is selected from the gene expression sequence; Merging the sub-test probabilities corresponding to the sub-sequences to obtain the test probability corresponding to the current gene; Genes that meet the preset change trend are determined according to the test probabilities corresponding to the genes.

2. The method according to claim 1, characterized in that Each subsequence includes a corresponding gene expression amount group; under the assumption that the change trend of the gene expression amount in the gene expression amount sequence meets the preset change trend, respectively checking the deviation between the change trend of the gene expression amount in each subsequence corresponding to the current gene and the preset change trend, and obtaining the sub-test probability corresponding to each subsequence, including: Grouping the gene expression levels in the gene expression level sequence corresponding to the current gene to obtain gene expression level groups; wherein the time points corresponding to the gene expression levels belonging to the same group are adjacent; For each current gene expression group in the gene expression groups, assuming that the changing trend of the gene expression amounts in the gene expression amount sequence meets the preset changing trend, the degree of deviation between the changing trend of the gene expression amounts in the current gene expression amount group and the preset changing trend is tested according to the differences between the gene expression amounts in the current gene expression amount group to obtain the sub-test probability corresponding to the current gene expression amount group.

3. The method according to claim 1, characterized in that The combining the sub-test probabilities corresponding to the sub-sequences to obtain the test probability corresponding to the current gene includes: In the case where the number of the gene expression sequence meets the first preset requirement, integrating the sub-test probabilities corresponding to the sub-sequences to obtain an integrated test probability; On the basis of assuming that the integrated test probability obeys a preset chi-square distribution, the test probability corresponding to the current gene is obtained by testing the significance level of the integrated test probability in the chi-square distribution.

4. The method according to claim 1, characterized in that: The sub-test probabilities corresponding to the sub-sequences are probabilities that there is no significant difference in the gene expression levels in the sub-sequences; and the sub-test probabilities corresponding to the sub-sequences are combined to obtain the test probability corresponding to the current gene, including: When the number of gene expression sequences meets the second preset requirement, determining reference test probabilities corresponding to each sub-test probability; each reference test probability is a probability that there is a significant difference in gene expression in each sub-sequence; According to the reference test probabilities, the probability that the current gene does not continuously meet the preset change trend is determined, and the test probability corresponding to the current gene is obtained.

5. The method according to any one of claims 1 to 4, characterized in that: The preset change trend includes an upward change trend and a downward change trend; the sub-test probabilities corresponding to each subsequence include each upward sub-probability and each downward sub-probability; the method further includes: For each current subsequence corresponding to the current gene, the up-regulated sub-probability and the down-regulated sub-probability of the current subsequence are integrated to obtain an integrated value corresponding to the current subsequence; the integrated value is used to characterize the degree to which the gene expression amount in the current subsequence satisfies the up-regulated change trend and the down-regulated change trend; For each current gene in the genes, determining the integration sequence corresponding to the current gene according to the integration values ​​corresponding to each subsequence; the integration sequence is used to characterize the overall change trend of the gene expression amount in the current gene; According to the integration sequences corresponding to the genes, the genes are clustered to obtain gene clustering results; wherein the overall change trend of genes belonging to the same category meets the preset proximity condition.

6. The method according to claim 5, characterized in that The integrating the up-modulation sub-probability and the down-modulation sub-probability of the current sub-sequence to obtain an integrated value corresponding to the current sub-sequence includes: Determine a target up-adjustment sub-probability corresponding to the current sub-sequence according to the up-adjustment sub-probability of the current sub-sequence; the target up-adjustment sub-probability is the probability that the current sub-sequence satisfies the up-adjustment change trend; Determine, according to the down-adjustment sub-probability of the current sub-sequence, a target down-adjustment sub-probability corresponding to the current sub-sequence; the target down-adjustment sub-probability is the probability that the current sub-sequence satisfies the down-adjustment change trend; The target up-adjustment sub-probability and the target down-adjustment sub-probability corresponding to the current sub-sequence are integrated to obtain an integrated value corresponding to the current sub-sequence.

7. The method according to claim 5, characterized in that The step of clustering the genes according to the integration sequences corresponding to the genes to obtain gene clustering results includes: Determine the cluster centers corresponding to each preset change trend category; For a current gene among the genes, determine the membership degree of the current gene to each cluster center and the distance from the current gene to each cluster center according to the integration sequence corresponding to the current gene; the membership degree is used to measure the degree of association between the current gene and the corresponding change trend category; According to the membership degree and distance of each gene corresponding to each cluster center, the probability that each gene belongs to each change trend category is determined to obtain a gene clustering result.

8. A gene analysis device, characterized in that: include: A sequence acquisition module is used to acquire a gene expression sequence corresponding to each gene; the gene expression sequence includes a plurality of gene expression sequences sorted by time points; A probability test module is used to test the deviation between the variation trend of the gene expression amount in each subsequence and the preset variation trend for each current gene in the genes, assuming that the variation trend of the gene expression amount in the gene expression amount sequence meets the preset variation trend, and obtain the sub-test probability corresponding to each subsequence; Each subsequence is selected from the gene expression sequence; A probability merging module, used to merge the sub-test probabilities corresponding to the sub-sequences to obtain the test probability corresponding to the current gene; The gene analysis module is used to determine the genes that meet the preset change trend according to the test probabilities corresponding to the genes.

9. A computer device, comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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