single-cell variational inference tools

End-to-end analysis of single cell omics data with deep generative models.

Please check out the scvi-tools website, or help contribute on GitHub.

Relevant publications

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 ,
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
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
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
MultiVI: deep generative model for the integration of multi-modal data.
T. Ashuach*, M. Gabitto*, M. Jordan, N. Yosef
Nature Methods , 2023
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
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
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
PeakVI: A Deep Generative Model for Single Cell Chromatin Accessibility Analysis.
T. Ashuach, DA. Reidenbach, A. Gayoso, N. Yosef
Cell Reports Methods , 2022
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
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
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
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
News and Views