Robust discovery of gene regulatory networks from single-cell gene expression data by Causal Inference Using Composition of Transactions.

ABSTRACT: Gene regulatory networks (GRNs) drive organism structure and functions, so the discovery and characterization of GRNs is a major goal in biological research. However, accurate identification of causal regulatory connections and inference of GRNs using gene expression datasets, more recently from single-cell RNA-seq (scRNA-seq), has been challenging. Here we employ the innovative method of Causal Inference Using Composition of Transactions (CICT) to uncover GRNs from scRNA-seq data. The basis of CICT is that if all gene expressions were random, a non-random regulatory gene should induce its targets at levels different from the background random process, resulting in distinct patterns in the whole relevance network of gene-gene associations. CICT proposes novel network features derived from

SUBMITTER: Shojaee A 

PROVIDER: S-EPMC10612495 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature