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Gayoso, A.* ; Lopez, R.* ; Xing, G.* ; Boyeau, P.* ; Valiollah Pour Amiri, V.* ; Hong, J.* ; Wu, K.* ; Jayasuriya, M.* ; Mehlman, E.* ; Langevin, M.* ; Liu, Y.* ; Samaran, J.* ; Misrachi, G.* ; Nazaret, A.* ; Clivio, O.* ; Xu, C.* ; Ashuach, T.* ; Gabitto, M.* ; Lotfollahi, M. ; Svensson, V.* ; da Veiga Beltrame, E.* ; Kleshchevnikov, V.* ; Talavera-López, C.* ; Pachter, L.* ; Theis, F.J. ; Streets, A.* ; Jordan, M.I.* ; Regier, J.* ; Yosef, N.*

A Python library for probabilistic analysis of single-cell omics data.

Nat. Biotechnol. 40, 163-166 (2022)
Research data DOI PMC
Open Access Green as soon as Postprint is submitted to ZB.
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Publication type Article: Journal article
Document type Scientific Article
Language english
Publication Year 2022
HGF-reported in Year 2022
ISSN (print) / ISBN 1087-0156
e-ISSN 1546-1696
Journal Nature Biotechnology
Quellenangaben Volume: 40, Issue: 2, Pages: 163-166 Article Number: , Supplement: ,
Publisher Nature Publishing Group
Publishing Place New York, NY
Reviewing status Peer reviewed
Institute(s) Institute of Computational Biology (ICB)
POF-Topic(s) 30205 - Bioengineering and Digital Health
Research field(s) Enabling and Novel Technologies
PSP Element(s) G-503800-001
Grants NIH Training Grant
EPSRC Centre for Doctoral Training in Modern Statistics and Statistical Machine Learning
Chan-Zuckerberg Foundation Network
NIGMS of the National Institutes of Health
DOI 10.1038/s41587-021-01206-w
WOS ID WOS:000752215600004
WOS ID WOS:000756661700003
Scopus ID 85124370073
PubMed ID 35132262
Erfassungsdatum 2022-04-21
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