PT  - JOURNAL ARTICLE
AU  - Tarun Kumar
AU  - Ramanathan Sethuraman
AU  - Sanga Mitra
AU  - Balaraman Ravindran
AU  - Manikandan Narayanan
TI  - MultiCens: Multilayer network centrality measures to uncover molecular mediators of tissue-tissue communication
AID  - 10.1101/2022.05.15.492007
DP  - 2022 Jan 01
TA  - bioRxiv
PG  - 2022.05.15.492007
4099  - http://biorxiv.org/content/early/2022/05/16/2022.05.15.492007.short
4100  - http://biorxiv.org/content/early/2022/05/16/2022.05.15.492007.full
AB  - With the evolution of multicellularity, communication among cells in different organs/tissues became pivotal to life. Molecular basis of such communication has long been studied, but genome-wide screens for biomolecules/genes mediating tissue-tissue signaling are lacking. To systematically identify inter-tissue mediators, we present a novel computational approach MultiCens (Multilayer/Multi-tissue network Centrality measures). Unlike single-layer network methods, MultiCens can distinguish within- vs. across-layer connectivity to quantify the “influence” of any gene in a tissue on a query set of genes of interest in another tissue. MultiCens enjoys theoretical guarantees on convergence and decomposability, and excels on synthetic benchmarks. On human multi-tissue datasets, MultiCens predicts known and novel genes linked to hormones. MultiCens further reveals shifts in gene network architecture among four brain regions in Alzheimer’s disease. MultiCens-prioritized hypotheses from these two diverse applications, and potential future ones like “Multi-tissue-expanded Gene Ontology” analysis, can enable whole-body yet molecular-level investigations in humans.Competing Interest StatementThe authors have declared no competing interest.