Identification of Differential Gene Groups From Single-Cell Transcriptomes Using Network Entropy.

ABSTRACT: A complex tissue contains a variety of cells with distinct molecular signatures. Single-cell RNA sequencing has characterized the transcriptomes of different cell types and enables researchers to discover the underlying mechanisms of cellular heterogeneity. A critical task in single-cell transcriptome studies is to uncover transcriptional differences among specific cell types. However, the intercellular transcriptional variation is usually confounded with high level of technical noise, which masks the important biological signals. Here, we propose a new computational method DiffGE for differential analysis, adopting network entropy to measure the expression dynamics of gene groups among different cell types and to identify the highly differential gene groups. To evaluate the effectiveness

SUBMITTER: Gan Y 

PROVIDER: S-EPMC7649823 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature