RT Journal Article
SR Electronic
T1 Identifying progressive gene network perturbation from single-cell RNA-seq data
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 297275
DO 10.1101/297275
A1 Mukherjee, Sumit
A1 Carignano, Alberto
A1 Seelig, Georg
A1 Lee, Su-In
YR 2018
UL http://biorxiv.org/content/early/2018/04/07/297275.abstract
AB Identifying the gene regulatory networks that control development or disease is one of the most important problems in biology. Here, we introduce a computational approach, called PIPER (ProgressIve network PERturbation), to identify the perturbed genes that drive differences in the gene regulatory network across different points in a biological progression. PIPER employs algorithms tailor-made for single cell RNA sequencing (scRNA-seq) data to jointly identify gene networks for multiple progressive conditions. It then performs differential network analysis along the identified gene networks to identify master regulators. We demonstrate that PIPER outperforms state-of-the-art alternative methods on simulated data and is able to predict known key regulators of differentiation on real scRNA-Seq datasets.