scMomentum: Inference of Cell-Type-Specific Regulatory Networks and Energy Landscapes
AuthorsSoto, Larisa M.
Bernal-Tamayo, Juan P.
Kiani, Narsis A.
KAUST DepartmentBiological and Environmental Sciences and Engineering (BESE) Division
Permanent link to this recordhttp://hdl.handle.net/10754/667677
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AbstractAbstractRecent 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
CitationSoto, L. M., Bernal-Tamayo, J. P., Lehmann, R., Balsamy, S., Martinez-de-Morentin, X., Vilas-Zornoza, A., … Tegner, J. (2020). scMomentum: Inference of Cell-Type-Specific Regulatory Networks and Energy Landscapes. doi:10.1101/2020.12.30.424887
SponsorsWe acknowledge B. Li and J. Ye for initial guidance and discussion of the mathematical model. We also thank J. Ye for helping with the annotations of one of the public datasets. F.P acknowledges funding obtained Instituto de Salud Carlos II (PI17/00701, PI20/01308 and CB16/12/00489) co-funded by FEDER grant, and the AGATA grant (0011-1411-2020-000011 and 0011-1411-2020-000010) from the Government of Navarra. NAK was supported by the Karolinska Institute's funds and KA Wallenberg Foundation (KAW 2017.0077 ). L.M.S and J.P.B-T were supported by a VSRP fellowship from King Abdullah University of Science and Technology.
PublisherCold Spring Harbor Laboratory
Except where otherwise noted, this item's license is described as Archived with thanks to Cold Spring Harbor Laboratory