Mhaned Oubounyt1*, Jan Baumbach1, and Maria L. Elkjaer1
1 Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany
mhaned.oubounyt [at] uni-hamburg.de
Abstract
Differences in co-expression networks between two or multiple cell (sub)types across conditions is a pressing problem in single-cell RNA sequencing (scRNA-seq). A key challenge is to define those co-variations that differ between or among cell types and/or conditions and phenotypes to examine small regulatory networks that can explain mechanistic differences. To this end, we developed SCANet, an all-in-one Python package that uses state-of-the-art algorithms to facilitate the workflow of a combined single-cell GCN and GRN pipeline including inference of gene co-expression modules from scRNA-seq, followed by trait and cell type associations, hub gene detection, co-regulatory networks, and drug-gene interactions. To illustrate the power of SCANet, we examined data from two studies. First, we identify the drivers of the mechanotype of a cytokine storm associated with increased mortality in patients with acute respiratory illness. Secondly, we find 20 drugs for 8 potential pharmacological targets in cellular driver mechanisms in the intestinal stem cells of obese mice. SCANet is available as a free, open source, and user-friendly Python package that can be easily integrated in systems biology pipelines.
Keywords: small single cell networks, GRN, GCN, mechanotyping, drug repurposing
