A function generalization and attractor determination method for genetic regulatory networks

Abstract

The application provides a function generalization and attractor determination method for a gene regulation network, for each target gene in the gene regulation network, a Klimt correlation coefficient between the target gene and all regulation genes corresponding to the target gene is calculated, and a regulation gene with the highest Klimt correlation coefficient value is selected as a guide to set other unobserved values in a truth table of the gene regulation network; and based on the truth table of the gene regulation network, an attractor is obtained by traversing each state of the gene. The application uses the Klimt correlation coefficient to calculate the correlation between genes, effectively improves the problem of low accuracy of the existing algorithm, and through a large number of experimental comparisons between the algorithm using the Klimt correlation coefficient and the algorithm proposed by the prior art, it is proved that the method of the application reduces the average sensitivity error and the steady-state distribution distance, and further realizes the effect of more accurate prediction of the truth table.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of biological sequencing data processing, in particular to a function generalization method for gene regulatory network and a method for quickly determining attractor. BACKGROUND

[0002] In recent years, the rapid development of gene sequence group identification technology has provided a large amount of experimental data for the study of gene networks. Gene regulatory network is essentially a continuous and complex dynamic system, that is, a complex dynamic system network. The genetic information and genes of any cell of an organism are the same, but the performance of the same gene in different tissues and different cells is not the same. The expression of a gene not only affects other genes, but also is affected by other genes. Genes promote and inhibit each other, and in the presence of a large environment such as a specific cell and time and comprehensive environmental factors, they show an activated state and form a complex gene regulatory network.

[0003] The prediction of the truth table of the gene regulatory network is a very important step in the entire biological sequencing data research. The current research methods, such as the mode rule, the mutual information-based generalization method, and the skew-based generalization method, are all based on biological sequencing data and gene regulatory network models to predict the truth table. The mode rule is to replace all unknown values with the value of the larger proportion according to the 0 and 1 proportion of the existing truth table data. The specific implementation of the skew-based algorithm is to calculate the proportion of 0 and 1 in the observed data of the target gene, and to randomly generalize the unobserved part according to this proportion. The mutual information-based generalization algorithm is to find a gene that has the greatest impact on the regulated gene, and to supplement the remaining part according to its criteria. However, through the reproduction of the three methods, it is found that the performance in the average sensitivity error and the average steady-state distribution distance is not ideal, which indicates that the current method has low prediction accuracy and poor effect.

[0004] In addition, the determination of the attractor of a gene regulatory network is also an important step in the research. The attractor is the stable state of the gene in the evolution process. Only by determining the attractor of the gene group can it be intervened, so as to ultimately achieve the purpose of artificially interfering with the gene steady state and making it evolve in the expected direction. The current method for determining the attractor is to use machine learning algorithms to identify the attractor in the network. However, the machine learning method may be affected by data noise, and the biological understanding of the gene regulatory network may not be as deep as the dynamic system-based analysis. There are also network reconstruction-based methods to determine the attractor. Such methods reconstruct the network and identify the attractor by comparing the experimental data and the data predicted by the calculation model, such as Bayesian network reconstruction. However, the network reconstruction method requires a large amount of experimental data to ensure the accuracy of the model, and the computational complexity is extremely high when the network size is large.

[0005] Currently, there is no method that can not only predict the truth table of the gene regulatory network but also quickly determine the attractor based on the prediction. Summary of the Invention

[0006] In response to the problems existing in the prior art, the present invention proposes a function generalization and attractor determination method for gene regulatory networks, which can achieve efficient prediction of the truth table of the gene regulatory network and rapid determination of the attractor.

[0007] The technical solution of the present invention is:

[0008] A method for function generalization and attractor determination in a gene regulatory network comprises the following steps:

[0009] Step 1: For each target gene in the gene regulatory network, calculate the Cramer correlation coefficient between the target gene and all its corresponding regulatory genes, and select the regulatory gene with the highest Cramer correlation coefficient as a guide to set other unobserved values ​​in the truth table of the gene regulatory network;

[0010] Step 2: Based on the truth table of the gene regulatory network obtained in step 1, the attractor is obtained by traversing each state of the gene.

[0011] Furthermore, in step 2, the specific process of traversing each state of the gene to obtain the attractor is:

[0012] When the number of nodes in the gene regulatory network is N, the corresponding total number of states is 2 N , traverse all states;

[0013] Assume that the attractor set is S and the current state is x t , get the next state x according to the rules of the truth table t+1 , if x t =x t+1 , then judge x t Does it already exist in S? If so, end the evolution of this state and start the evolution of the next state. If not, change state x to t Put it into S and start the evolution to the next state;

[0014] If x t ≠x t+1 , then continue to obtain the next state x t+2 , and compare x t+1 and x t+2 , if x t+1 =x t+2 Then judge x t+1 Does it already exist in S? If so, end the evolution of this state and start the evolution of the next state. If not, change state x tot+1 Put in S, and start the evolution of the next state again;

[0015] By analogy, when all the states are traversed, the state set in S is the complete attractor.

[0016] Advantages

[0017] The function generalization method for gene regulatory network provided by the application has the following advantages:

[0018] 1、The application adopts the Cramér correlation coefficient to calculate the correlation between genes, effectively improving the problem of low accuracy of the existing algorithm, and through a large number of experimental comparisons between the algorithm using the Cramér correlation coefficient and the algorithm proposed by the prior art, it is proved that the method of the application reduces the average sensitivity error and the steady-state distribution distance two indicators, and further realizes the effect of more accurate prediction of the truth table.

[0019] 2、The application fully combines the function generalization algorithm and the attractor finding algorithm, effectively solves the problem of imperfect function of the prior art, and realizes more perfect processing of biological sequencing data.

[0020] Additional aspects and advantages of the application will be in part apparent and in part pointed out hereinafter in the description of the application. BRIEF DESCRIPTION OF DRAWINGS

[0021] The above and / or additional aspects and advantages of the application will become apparent and be readily understood from the following description, taken in conjunction with the accompanying drawings, in which:

[0022] Figure 1 The overall flowchart for gene regulatory network research;

[0023] Figure 2 The working interface for obtaining a complete truth table and an attractor in the second embodiment of the application;

[0024] Figure 3 The result graph in the second embodiment of the application;

[0025] Figure 4 Part of the truth table example in the content of the application.

[0026] Figure 5 The average sensitivity error index comparison graph of the algorithm corresponding to the three correlation coefficients.

[0027] Figure 6 The average steady-state distribution distance index comparison graph of the algorithm corresponding to the three correlation coefficients.

[0028] Figure 7The average sensitivity error index of the method of the present application and the prior art is compared.

[0029] Figure 8 The average steady state distribution distance index of the method of the present application and the prior art is compared. DETAILED DESCRIPTION

[0030] Embodiments of the present application are described in detail below, which are exemplary and intended to explain the present application, and cannot be understood as a limitation of the present application.

[0031] In order to be able to make a more accurate prediction on the truth table of the gene regulatory network, the applicant considers using the correlation between the regulatory genes and the regulated genes in the gene regulatory network as the basis to reflect the strength of the interaction between the two genes, and then determines the regulatory gene with the highest correlation with the regulated gene, and generalizes the unknown state in the truth table according to the most likely state of the regulated gene when the state of the regulatory gene is 0 or 1 (the gene state is 0 indicating that the gene is not expressed, and the gene state is 1 indicating that the gene is expressed).

[0032] The applicant selects Pearson correlation coefficient, Spearman rank correlation coefficient and K-L correlation coefficient for research. For each target gene in the gene regulatory network, the three correlation coefficients between the target gene and all the regulatory genes corresponding to the target gene are calculated, and according to the three correlation coefficients, three regulatory genes with the highest correlation with the target gene are obtained, respectively. The state of the target gene when the state of the corresponding gene of the three regulatory genes is 0 or 1 is counted, and the unknown state in the truth table is generalized according to the corresponding state.

[0033] Then the gene regulatory network truth table finally generalized by the three correlation coefficients is evaluated, and the performance of the predicted gene regulatory network is evaluated by two indexes of steady state distribution distance and sensitivity error. According to the data results, the prediction result of K-L correlation coefficient is the best, and finally the K-L correlation coefficient is determined to be used to calculate the correlation between the regulatory genes and the regulated genes, so the K-L correlation coefficient is used to calculate the correlation between the genes in the present application. The algorithm evaluation indexes corresponding to the three correlation coefficients are shown in Figure 5 , Figure 6 .

[0034] Based on this, the function generalization and attractor determination method for the gene regulatory network proposed in the embodiment includes the following steps:

[0035] Step 1: For each target gene in the gene regulatory network, the K-L correlation coefficient between the target gene and all the regulatory genes corresponding to the target gene is calculated, and the regulatory gene with the highest K-L correlation coefficient value is selected as the guide to set other unobserved values in the gene regulatory network truth table.

[0036] like Figure 4 The partial truth table shown here shows that the output of function f1 corresponds to the state of gene 1. When the input is 00, f1 outputs 0, indicating that the state of gene 1 is 0. Function f1 has two regulatory genes, j1=2 and j2=4. j1=2 means that the first input variable of f1 is the state of gene 2, and j2=4 means that the second input variable of f1 is the state of gene 4. The Cramer correlation coefficients between these two genes and gene 1 are calculated separately. If the Cramer correlation coefficients of the two genes with gene 1 are equal, the first regulatory gene j1=2 is selected as the guide by default (if they are not equal, the regulatory gene with the larger coefficient is selected as the guide). According to the observed part of the gene regulatory network truth table, when gene 2 is 0, gene 1 is 0; when gene 2 is 1, gene 1 is 1. Based on this, the function is generalized. When gene 2 is 0, gene 1 is 0 regardless of whether other genes are expressed; when gene 2 is 1, gene 1 is 1 regardless of whether other genes are expressed.

[0037] In order to analyze and compare the proposed algorithm with other existing algorithms, each algorithm was applied to gene regulatory networks with different connectivity, and two indicators, steady-state distribution distance and sensitivity error, were selected as evaluation indicators of the algorithm. From the two evaluation indicators, it can be seen that the Cramer generalization algorithm has the best effect. Figure 7 、 Figure 8 shown.

[0038] Step 2: Based on the truth table of the gene regulatory network obtained in step 1, the attractor is obtained by traversing each state of the gene.

[0039] The truth table can reflect the overall evolution rules of the gene, so the attractor can be found by traversing each state of the gene based on the truth table.

[0040] When the number of nodes in the gene regulatory network is N, the corresponding total number of states is 2 N , we traverse all states from 000...00 to 111...11: Assume that the attractor set is S and the current state is x t , we get its next state x according to the rules of the truth table t+1 , if x t =x t+1 , then judge x t Does it already exist in S? If so, end the evolution of this state and start the evolution of the next state. If not, change state x to t Put it into S and start the evolution of the next state; if x t ≠x t+1 , then continue to obtain the next state x t+2and compare x t+1 and x t+2 if x t+1 =x t+2 whether x t+1 has already existed in S, if yes, end the evolution of the current state and start the evolution of the next state, if not, put the state x t+1 into S and start the evolution of the next state, and so on, when all the states are traversed, the state set in S is the complete attractor.

[0041] Through the above process, the accurate prediction of the truth table of the gene regulatory network and the determination of the attractor function are realized.

[0042] When the user has the gene regulatory network model and the corresponding truth table, the method of the present application can obtain all the attractors, thereby solving the problem of imperfect function of the prior art, and further realizing the more perfect processing of the biological sequencing data.

[0043] When the user has the gene regulatory network model and the biological sequencing data, and does not have the truth table, the method of the present application is used to predict the truth table through the algorithm based on the Cramer correlation coefficient, and quickly obtain the attractor, and finally directly feed back the predicted truth table and attractor to the user.

[0044] In addition, those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can adopt a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer usable program codes.

[0045] The present application is described with reference to flowcharts and / or block diagrams according to the method, device (system), and computer program product of the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams can be realized by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a device that implements the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in a flow or multiple flows and / or blocks Figure 1 The functions specified in a flow or multiple flows and / or blocks

[0046] These computer program instructions can also be stored in a computer- readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the Figure 1 function specified in the flow or flows and / or blocks Figure 1 of the block or blocks.

[0047] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions that are executed on the computer or other programmable apparatus provide steps for implementing the Figure 1 function specified in the flow or flows and / or blocks Figure 1 of the block or blocks.

[0048] While the preferred embodiments of the application have been described, additional variations and modifications can be made to the embodiments by those of skill in the art once they have the benefit of the present disclosure. Therefore, the appended claims are intended to encompass within their scope all possible variations and modifications of the preferred embodiments. 1

[0049] It is apparent that a person skilled in the art can make various changes and modifications to the application without departing from the spirit and scope thereof. Therefore, if these modifications and changes fall within the scope of the claims and their equivalents, it is intended to include them in the application.

Claims

1. A method for function generalization and attractor determination in gene regulatory networks, characterized by: The following steps are involved: Step 1: For each target gene in the gene regulatory network, calculate the Cramer correlation coefficient between the target gene and all its corresponding regulatory genes, and select the regulatory gene with the highest Cramer correlation coefficient as a guide to set other unobserved values ​​in the truth table of the gene regulatory network; Step 2: Based on the truth table of the gene regulatory network obtained in step 1, the attractor is obtained by traversing each state of the gene; The specific process of traversing each state of the gene to obtain the attractor is: When the number of nodes in the gene regulatory network is N, the corresponding total number of states is , traverse all states; Assume that the attractor set is , the current status is , get the next state according to the rules of the truth table ,if , then judge Does it already exist in If it exists, the evolution of this state ends and the evolution of the next state begins. If it does not exist, the state Put in Then start the evolution to the next state; if , then continue to obtain the next state , and compare and ,if Then judge Does it already exist in If it exists, the evolution of this state ends and the evolution of the next state begins. If it does not exist, the state Put in Then start the evolution to the next state; And so on, after traversing all the states, The set of states in is the entire attractor.

2. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the function generalization and attractor determination method for gene regulatory networks according to claim 1 is implemented.

3. A computer device comprising a processor and a memory, characterized in that: When the processor executes the computer program stored in the memory, the function generalization and attractor determination method for gene regulatory networks according to claim 1 is implemented.

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