Subgroup-specific co-expression network identification method

Abstract

The invention discloses a subgroup-specific co-expression network identification method. The invention provides a method for identifying the specific co-expression network of a subgroup of samples to be tested, comprising the following steps: 1) processing the original UMI number values ​​of each gene in each sample in the subgroup of samples to be tested to be normalized; 2) converting the original UMI Filter the homogenization results, 3) calculate the pairwise weighted median correlation bicor of the filtered genes; 4) filter the weighted median correlation results to obtain strong co-expression gene relationship pairs; 5) filter the cell subgroups to be tested Strongly co-expressed gene relationship pairs, subtracting the shared strong co-expressed gene relationship pairs in all cells in the subgroup, that is, to obtain the specific co-expressed gene pairs of the cell subgroup; and then using the cell subgroup Group-specific co-expressed gene pairs were used to construct a subgroup-specific gene co-expression network of the sample to be tested. The method for subgroups of the present invention is more targeted and more sensitive.

Description

technical field [0001] The invention belongs to the field of biotechnology, and in particular relates to a subgroup-specific co-expression network identification method. Background technique [0002] With the development of sequencing technology and the decline of sequencing costs, the sample size for gene expression research is increasing, especially the introduction of 10x single-cell RNA-Seq technology, which makes the number of samples for gene expression research jump to a thousand. For expression The study of differential changes has changed from simple sample-to-sample and group-to-group comparisons to clustering of subgroups of samples based on gene expression changes, and then looking for specifically expressed genes based on the results of the subgroups , or differentially expressed genes, to explain the functions performed by different subgroups of cells, or the reasons why individuals of different subgroups have differences. [0003] The current method used to f...

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Problems solved by technology

[0005] 1. It is impossible to guarantee that all cells / samples are in the same state at the same time in one sampling, and the gene expression level is not necessarily consistent. Moreover, due to the low initial amount of single-cell data, there will often be some loss of expressed genes. It is simple, crude and inaccurate to take the average of gene expression in subgroups for analysis;

[0006] 2. By selecting genes with significant differences, only target genes with particularly obvious differences can be found, but regulatory genes with less drastic changes cannot be identified, which has limited effect on understanding the function of subgroups or explaining the reasons for phenotypic differences

[0007] 3. The regulation of gene expression in organisms is real-time and dynamic, and the realization of most functions is not achieved by drastic changes in one or several independent genes, but is regulated by a group of genes synergistically. The degree of change of the gene alone may not be significant, and it cannot pass the screening of the multiple of difference and p-value

[0009] 1. This method is only effective when the number of samples is small (a dozen to one hundred). When the number of samples increases, the consumption of memory and time increases sharply; in addition, the number of samples (cells) of 10x single-cell data reaches thousands level, sometimes even exceeding the number of expressed genes, at this time, the WGCNA algorithm is completely inapplicable

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