開源人年會 2022

Gene regulatory network governs the complex gene expression programs in various biological phenomena,
including cell development, cell fate decision and oncogenesis. Single-cell techniques provide higher
resolution than traditional bulk RNA sequencing in gene expression, but also bring larger noises and
sparse expression measurements. It is difficult to infer gene regulatory network with noisy and sparse
gene expression profiles. Furthermore, inference of a complete gene regulatory network across different
cell types is also challenging. Here, we propose to address the problem by constructing context-dependent
gene regulatory networks (CDGRN) with single-cell RNA sequencing data. A gene regulatory network is
decomposed into subgraphs, which correspond to distinct transcriptomic contexts. Each subgraph of gene
regulatory network is composed of the consensus active regulation pairs of transcription factors and
their target genes shared by a group of cells. The activities of each regulation pair in different cell
groups were inferred by a Gaussian mixture model using both the spliced and unspliced transcript
expression levels. In addition, we found that the union of gene regulation pairs in all contexts poses
sufficient information for reconstruction of differentiation trajectory. The connection between gene
regulation in molecular level and cell differentiation in macroscopic view can be established by CDGRN.
The cell cycle, cell differentiation or tissue-specific functions are enriched along developmental
progression in each context. Surprisingly, we observed that network entropy for CDGRN decreases along
differentiation progression, which implies the differentiation direction. In conclusion, we leverage the
advantage of single-cell RNA sequencing and establish the connection in both molecular regulation and
differentiation trajectory. The context-dependent network entropy may indicate maturity of cells in
certain context.