Aggregation of recount3 RNA-seq data improves inference of consensus and tissue-specific gene coexpression networks.

ABSTRACT: Gene coexpression networks (GCNs) describe relationships among genes that maintain cellular identity and homeostasis. However, typical RNA-seq experiments often lack sufficient sample sizes for reliable GCN inference. recount3, a data set with 316,443 processed human RNA-seq samples, provides an opportunity to improve network reconstruction. However, GCN inference from public data is challenged by confounders and inconsistent labeling. To address this, we develop a pipeline to annotate samples based on cell-type composition. By comparing aggregation strategies, we find that regressing confounders within studies and prioritizing larger studies optimizes network reconstruction. We apply these findings to infer three consensus networks (universal, cancer, noncancer) and 27 context-specific ne

SUBMITTER: Ravichandran P 

PROVIDER: S-EPMC12401056 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature