scvi-tools

Consensus prediction of cell type labels with popV
C. Ergen, G. Xing, C. Xu, M. Jayasuriya, E. McGeever, A.O. Pisco, A. Streets, N. Yosef
bioRxiv ,
10.1101/2023.08.18.553912
GitHub
bioRxiv preprint

Deep generative modeling for quantifying sample-level heterogeneity in single-cell omics
P. Boyeau*, J. Hong*, A. Gayoso, M. Jordan, E. Azizi, N. Yosef
bioRxiv , 2022
10.1101/2022.10.04.510898
bioRxiv preprint

Deep generative modeling of transcriptional dynamics for RNA velocity analysis in single cells
A. Gayoso*, P. Weiler*, M. Lotfollahi, D. Klein, J. Hong, A. Streets, FJ. Theis†, N. Yosef†
Nature Methods (to appear) , 2023
10.1101/2022.08.12.503709
bioRxiv preprint

An Empirical Bayes Method for Differential Expression Analysis of Single Cells with Deep Generative Models
P. Boyeau, J. Regier, A. Gayoso, M.I. Jordan, R. Lopez†, N. Yosef†
Proc Natl Acad Sci , 2023
10.1073/pnas.2209124120

MultiVI: deep generative model for the integration of multi-modal data.
T. Ashuach*, M. Gabitto*, M. Jordan, N. Yosef
Nature Methods , 2023
10.1038/s41592-023-01909-9
GitHub

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†
Nature Biotechnology , 2023
10.1038/s41587-023-01733-8

A Python library for probabilistic analysis of single-cell omics data.
A. Gayoso*, R. Lopez*, G. Xing*, P. Boyeau, V. Valiollah Pour Amiri, J. Hong, K. Wu, M. Jayasuriya, E. Melhman, M. Langevin, Y. Liu, J. Samaran, G. Misrachi, A. Nazaret, O. Clivio, C. Xu, T. Ashuach, M. Lotfollahi, V.Svensson, E. da Veiga Beltrame, V. Kleshchevnikov, C. Talavera-Lopez, L. Pachter, F.J. Theis, A. Streets, M.I. Jordan, J. Regier, N. Yosef
Nature Biotechnology , 2022
10.1038/s41587-021-01206-w
GitHub
bioRxiv preprint

DestVI identifies continuums of cell types in spatial transcriptomics data
R. Lopez*, B. Li*, H. Keren-Shaul*, P. Boyeau, M. Kedmi, D. Pilzer, A.Jelinski, E. David, A. Wagner, Y. Addadi, M.I. Jordan, I. Amit†, N. Yosef†
Nature Biotechnology , 2022
10.1038/s41587-022-01272-8
GitHub

PeakVI: A Deep Generative Model for Single Cell Chromatin Accessibility Analysis.
T. Ashuach, DA. Reidenbach, A. Gayoso, N. Yosef
Cell Reports Methods , 2022
10.1016/j.crmeth.2022.100182
GitHub

Joint probabilistic modeling of single-cell multi-omic data with totalVI.
A. Gayoso*, Z. Steier*, R. Lopez, J. Regier, KL. Nazor, A. Streets†, N Yosef†
Nature Methods , 2021
10.1038/s41592-020-01050-x
GitHub
bioRxiv preprint
Reproducibility code

Reconstructing unobserved cellular states from paired single-cell lineage tracing and transcriptomics data.
K. Ouardini, R. Lopez, MG. Jones, S. Prillo, R. Zhang, MI. Jordan, N. Yosef
ICML 2021 Workshop on Computational Biology , 2021
10.1101/2021.05.28.446021
GitHub
bioRxiv preprint

Probabilistic harmonization and annotation of single-cell transcriptomics data with deep generative models.
C. Xu*, R. Lopez*, E. Mehlman*, J. Regier, M.I. Jordan, N. Yosef
Molecular Systems Biology , 2020
10.15252/msb.20209620
GitHub
Reproducibility code

A joint model of unpaired data from scRNA-seq and spatial transcriptomics for imputing missing gene expression measurements.
R. Lopez*, A. Nazaret*, M. Langevin*, J. Samaran*, J. Regier*, M.I. Jordan, N. Yosef
ICML 2019 Workshop on Computational Biology (spotlight presentation; best student’s poster) , 2019
arXiv preprint
Reproducibility code

Deep generative models for detecting differential expression in single cells.
P. Boyeau, R. Lopez, J. Regier, A. Gayoso, MI. Jordan, N. Yosef
Machine Learning in Computational Biology meeting , 2019
bioRxiv preprint

Detecting zero-inflated genes in single-cell transcriptomics data.
O. Clivio, R. Lopez, J. Regier, A. Gayoso, MI. Jordan, N Yosef
Machine Learning in Computational Biology meeting (spotlight presentation) , 2019
bioRxiv preprint
Reproducibility code

Deep generative modeling for single-cell transcriptomics.
R. Lopez, J. Regier, MB. Cole, M. Jordan, N. Yosef.
Nature Methods , 2018
10.1038/s41592-018-0229-2
GitHub
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