Aggregation of <i>recount3</i> RNA-seq data improves inference of consensus and tissue-specific gene co-expression networks.

ABSTRACT:

Background

Gene co-expression networks (GCNs) describe relationships among expressed genes key to maintaining cellular identity and homeostasis. However, the small sample size of typical RNA-seq experiments which is several orders of magnitude fewer than the number of genes is too low to infer GCNs reliably. recount3, a publicly available dataset comprised of 316,443 uniformly processed human RNA-seq samples, provides an opportunity to improve power for accurate network reconstruction and obtain biological insight from the resulting networks.

Results

We compared alternate aggregation strategies to identify an optimal workflow for GCN inference by data aggregation and inferred three consensus networks: a universal network, a non-cancer network, and a cancer network in

SUBMITTER: Ravichandran P 

PROVIDER: S-EPMC10849507 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature