bioRxivSystems BiologyPreprint
scMomentum: Inference of Cell-Type-Specific Regulatory Networks and Energy Landscapes
Larisa M. SotoiD, Juan P. Bernal-TamayoiD, Robert LehmanniD, Subash Balsamy, Xabier Martinez‐de‐MorentiniD, Amaia Vilas‐ZornozaiD, Patxi San-Martin, Felipe PrósperiD, David Gómez-CabreroiD, Narsis A. KianiiD, Jesper N. TegneriD
Abstract
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. AvailabilityscMomentum is available as a Python package at https://github.com/larisa-msoto/scMomentum.git
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