RT Journal Article
SR Electronic
T1 scMomentum: Inference of Cell-Type-Specific Regulatory Networks and Energy Landscapes
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.12.30.424887
DO 10.1101/2020.12.30.424887
A1 Larisa M. Soto
A1 Juan P. Bernal-Tamayo
A1 Robert Lehmann
A1 Subash Balsamy
A1 Xabier Martinez-de-Morentin
A1 Amaia Vilas-Zornoza
A1 Patxi San-Martin
A1 Felipe Prosper
A1 David Gomez-Cabrero
A1 Narsis A. Kiani
A1 Jesper Tegner
YR 2020
UL http://biorxiv.org/content/early/2020/12/30/2020.12.30.424887.abstract
AB Recent progress in single-cell genomics has generated multiple tools for cell clustering, annotation, and trajectory inference; yet, inferring their associated regulatory mechanisms is unresolved. Here we present scMomentum, a model-based data-driven formulation to predict gene regulatory networks and energy landscapes from single-cell transcriptomic data without requiring temporal or perturbation experiments. scMomentum provides significant advantages over existing methods with respect to computational efficiency, scalability, network structure, and biological application.Availability scMomentum is available as a Python package at https://github.com/larisa-msoto/scMomentum.gitCompeting Interest StatementThe authors have declared no competing interest.