Single-cell tumor microenvironment data analysis method based on gene regulatory networks

By constructing a single-sample gene regulatory network and identifying differentially expressed genes, the problem of not considering internal gene regulatory relationships in single-cell identification methods is solved, improving the accuracy of cell subpopulation identification and understanding of biological functions, especially in assessing cell stemness in the tumor microenvironment.

CN116246713BActive Publication Date: 2026-04-21JILIN UNIV FIRST HOSPITAL
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Patent Information

Application Number
CN202310296417.6
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2023-03-24
Publication Date
2026-04-21
Estimated Expiration
2043-03-24

AI Technical Summary

Technical Problem

Existing single-cell identification methods mainly determine the commonalities and specificities between cells by gene expression distribution, without considering the actual internal gene regulatory relationships within the cell, leading to clustering bias and insufficient identification of differentially expressed genes.

Method used

By constructing a single-sample gene regulatory network, using the Degree gene characterization matrix and SNN algorithm to identify cell subpopulations, and identifying differentially expressed genes through the Wilcox test, combined with GO enrichment analysis, we can reveal the driving biological functions.

Benefits of technology

It effectively avoids the clustering bias caused by uneven expression distribution, captures genes with large differences in regulatory patterns, and improves the accuracy of cell subpopulation identification and understanding of biological functions, especially in assessing the degree of cell stemness in the tumor microenvironment.

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Abstract

This invention discloses a method for analyzing single-cell tumor microenvironment data based on gene regulatory networks, comprising the following steps: acquiring raw data values ​​of several cells, performing preprocessing and identifying characteristic genes; constructing a degree gene characterization matrix based on the obtained regulatory relationships between characteristic genes, and identifying cell subpopulations; and performing cell entropy analysis, differential degree gene identification, and gene function enrichment analysis on the cell subpopulations. This invention uses the SCILE algorithm to assess the overall stemness entropy of the cell based on the importance of each gene in the gene regulatory network of each cell. Compared to traditional expression-based assessment methods, this method avoids the impact of highly expressed, low-regulated genes on cell stemness.
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