Abstract
The remarkable advancement of single-cell RNA sequencing (scRNA-seq) technology has empowered researchers to probe gene expression at the single-cell level with unprecedented precision. To gain a profound understanding of the heterogeneity inherent in cell fate determination, a central challenge lies in the comprehensive analysis of the dynamic regulatory alterations that underlie transcriptional differences and the accurate inference of the differentiation trajectory. Here, we propose the method scRDEN, a robust framework that infers important cell sub-populations and differential expression networks of multiple genes along the differentiation directions of each branch by converting the unstable gene expression values in cells into relatively stable gene-gene interactions (global features) and extracting the order of differential expression (network features), and further integrating the expression features of different dimension reduction methods. When applied to five published scRNA-seq datasets from human and mouse cell differentiation, scRDEN not only successfully captures the stable cell subpopulations with potential marker genes, measures the transcriptional differences of gene pairs to identify the rank differential expression network along the differentiation direction of each branch. In addition, in multiple gene rank differential expression networks, the rank expression directly related to transcription factors/marker genes shows a significant strengthening and weakening trend along with their expression changes, and the distribution of diversity and cluster coefficient show a non-monotonic change trend, including the cases of increasing first and then decreasing or decreasing first and then increasing. This may correspond to the mechanism of cells gradually differentiating into stable functions. It is particularly noteworthy that scRDEN method yielded exceptional results when applied to the large-scale, multi-branched, double-batch mouse dentate gyrus data. This outstanding performance provides novel and valuable insights into large-scale, multi-batch trajectory inference and the study of transcriptional mechanism regulation during the processes of differentiation and development.