RT Journal Article
SR Electronic
T1 PySCNet: A tool for reconstructing and analyzing gene regulatory network from single-cell RNA-Seq data
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.12.18.423482
DO 10.1101/2020.12.18.423482
A1 Ming Wu
A1 Tim Kacprowski
A1 Dietmar Zehn
YR 2020
UL http://biorxiv.org/content/early/2020/12/21/2020.12.18.423482.abstract
AB Summary The Advanced capacities of high throughput single cell technologies have facilitated a great understanding of complex biological systems, ranging from cell heterogeneity to molecular expression kinetics. Several pipelines have been introduced to standardize the scRNA-seq analysis workflow. These include cell population identification, cell marker detection and cell trajectory reconstruction. Yet, establishing a systematized pipeline to capture regulatory relationships among transcription factors (TFs) and genes at the cellular level still remains challenging. Here we present PySCNet, a python toolkit that enables reconstructing and analyzing gene regulatory networks (GRNs) from single cell transcriptomic data. PySCNet integrates competitive gene regulatory construction methodologies for cell specific or trajectory specific GRNs and allows for gene co-expression module detection and gene importance evaluation. Moreover, PySCNet offers a user-friendly dashboard website, where GRNs can be customized in an intuitive way.Availability Source code and documentation are available: https://github.com/MingBit/PySCNetContact ming.wu{at}tum.deCompeting Interest StatementThe authors have declared no competing interest.