Method for deducing gene regulation network by using single cell transcription and gene knockout data

Abstract

The invention discloses a method for deducing a gene regulation network by using single cellular transcription and gene knockout data, which comprises the following steps of: classifying genes by analyzing steady-state expression data before and after gene knockout to serve as priori knowledge so as to reduce the space-time complexity and improve the deduction accuracy; calculating an inter-gene distribution distance by utilizing single cell transcription data, establishing a multi-time-point multi-dimensional regression model for each gene according to the gene classification result, and calculating a relation factor in the model by adopting a mathematical method; performing corresponding algorithm analysis on the gene knockout data to remove part of false positive judgment, and making upfor the defect of analyzing dynamic data. According to the method, the problem of high computation complexity in analyzing the time sequence single-cell data is effectively solved, and the accuracy of deducing the gene regulation network is improved.

Description

technical field [0001] The invention relates to the field of gene regulatory network research and analysis, in particular to a method for inferring gene regulatory network using single-cell transcription and gene knockout data. Background technique [0002] As a tool for interpreting and analyzing genetic data, the gene regulatory network can reveal the regulatory relationship between genes, proteins and small molecules, understand the physiological activities and functions in biological cells, the interactions in pathways, and how to change the body. Studying gene regulatory networks from the perspective of single-cell transcription can reveal the detailed expression dynamics and functional relationships of cells, and clarify the functional role of cell-to-cell variation in different key physiological processes. However, single-cell expression data are time-course data with high time resolution, which brings high time-space complexity to the algorithm for inferring gene reg...

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Problems solved by technology

However, single-cell expression data are time-course data with high time resolution, which brings high time-space complexity to the algorithm for inferring gene regulatory networks, and at the same time loses the analysis of steady-state gene expression levels, which will reduce The precision of the final experimental results

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