Mutually-exclusively constrained graph Laplacian approach to heterogeneous cancer driver gene identification

Abstract

The present invention provides a method for identifying heterogeneous cancer driver genes based on a mutually exclusive constrained graph Laplacian. First, obtain cancer genome variation data and gene interaction network; then, use a matrix model to describe the heterogeneity of cancer, and use mutually exclusive constraint matrix decomposition to estimate the sample parameters of heterogeneity cancer; Then, construct the mutual exclusion constraint matrix decomposition optimization function regularized by the joint association and interaction network, and correct the parameters of the driving genes affected by the interaction in the local samples through iterative solution; finally, use the outlier test method to identify the driving genes . The invention can solve the problem of differential estimation of parameters of cancer samples and effective identification of driver genes in local samples affected by interaction, and realizes the identification of driver genes mutated in local samples from gene variation data of heterogeneous cancer samples.

Description

technical field [0001] The invention belongs to the technical fields of bioinformatics and genome data mining, and in particular relates to a method for identifying heterogeneous cancer driver genes based on mutually exclusive constraint graph Laplacian. Background technique [0002] Cancer is a high-incidence malignant disease, mainly caused by mutations in driver genes. However, in the cancer genome, there are a large number of concomitant mutations that have nothing to do with carcinogenesis, causing serious confusion in the identification of driver genes. Because driver gene mutations are more likely to occur in multiple samples at the same time than concomitant mutations, existing studies mainly use the gene variation data of cancer samples, regard driver genes as high-frequency mutation genes in multiple samples, and examine gene Statistical Significance of Mutation Rates, Finding Driver Genes with Significant High-Frequency Variations in Multiple Samples. For exampl...

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Problems solved by technology

The invention can solve the problem of differential estimation of parameters of cancer samples and effective identification of driver genes in local samples affected by the interaction, and realizes the identification of driver genes that mutate in local samples from the gene variation data of heterogeneous cancer samples

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