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A Python library for probabilistic analysis of single-cell omics data.
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 FJ, Streets A, Jordan MI, Regier J, Yosef N. Gayoso A, et al. Nat Biotechnol. 2022 Feb;40(2):163-166. doi: 10.1038/s41587-021-01206-w. Nat Biotechnol. 2022. PMID: 35132262 No abstract available.
Deep generative modeling for single-cell transcriptomics.
Lopez R, Regier J, Cole MB, Jordan MI, Yosef N. Lopez R, et al. Nat Methods. 2018 Dec;15(12):1053-1058. doi: 10.1038/s41592-018-0229-2. Epub 2018 Nov 30. Nat Methods. 2018. PMID: 30504886 Free PMC article.
The scverse project provides a computational ecosystem for single-cell omics data analysis.
Virshup I, Bredikhin D, Heumos L, Palla G, Sturm G, Gayoso A, Kats I, Koutrouli M; Scverse Community; Berger B, Pe'er D, Regev A, Teichmann SA, Finotello F, Wolf FA, Yosef N, Stegle O, Theis FJ. Virshup I, et al. Nat Biotechnol. 2023 May;41(5):604-606. doi: 10.1038/s41587-023-01733-8. Nat Biotechnol. 2023. PMID: 37037904 No abstract available.