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dc.contributor.authorSoto, Larisa M.
dc.contributor.authorBernal-Tamayo, Juan P.
dc.contributor.authorLehmann, Robert
dc.contributor.authorBalsamy, Subash
dc.contributor.authorMartinez-de-Morentin, Xabier
dc.contributor.authorVilas-Zornoza, Amaia
dc.contributor.authorSan-Martin, Patxi
dc.contributor.authorProsper, Felipe
dc.contributor.authorGomez-Cabrero, David
dc.contributor.authorKiani, Narsis A.
dc.contributor.authorTegner, Jesper
dc.date.accessioned2021-02-25T11:44:39Z
dc.date.available2021-02-25T11:44:39Z
dc.date.issued2020-12-31
dc.identifier.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
dc.identifier.doi10.1101/2020.12.30.424887
dc.identifier.urihttp://hdl.handle.net/10754/667677
dc.description.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
dc.description.sponsorshipWe 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.
dc.publisherCold Spring Harbor Laboratory
dc.relation.urlhttp://biorxiv.org/lookup/doi/10.1101/2020.12.30.424887
dc.rightsArchived with thanks to Cold Spring Harbor Laboratory
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.titlescMomentum: Inference of Cell-Type-Specific Regulatory Networks and Energy Landscapes
dc.typePreprint
dc.contributor.departmentBiological and Environmental Sciences and Engineering (BESE) Division
dc.contributor.departmentBioscience Program
dc.eprint.versionPre-print
dc.contributor.institutionMucosal and Salivary Biology Division, King's College London Dental Institute, London, United Kingdom.
dc.contributor.institutionCentro de Investigación Medica Aplicada, Navarra, Spain.
dc.contributor.institutionCentro de Investigación Biomédica en Red (CIBERONC).
dc.contributor.institutionClínica Universidad de Navarra, Navarra, Spain.
dc.contributor.institutionNavarrabiomed, Complejo Hospitalario de Navarra, Universidad Pública de Navarra, Navarra, Spain.
dc.contributor.institutionAlgorithmic Dynamics Lab, Department of Oncology-Pathology & Center of Molecular Medicine, Karolinska institutet, Stockholm, Sweden.
dc.contributor.institutionUnit of Computational Medicine, Department of Medicine, Center for Molecular Medicine, Karolinska Institutet, Karolinska University Hospital, L8:05, SE-171 76, Stockholm, Sweden.
dc.contributor.institutionScience for Life Laboratory, Tomtebodavagen 23A, SE-17165, Solna, Sweden.
kaust.personSoto, Larisa M.
kaust.personBernal-Tamayo, Juan P.
kaust.personLehmann, Robert
kaust.personBalsamy, Subash
kaust.personGomez-Cabrero, David
kaust.personTegner, Jesper
refterms.dateFOA2021-02-25T11:47:04Z
kaust.acknowledged.supportUnitVSRP fellowship


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Archived with thanks to Cold Spring Harbor Laboratory
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