scvi-tools

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


scvi-tools: a library for deep probabilistic analysis of single-cell omics data.
A. Gayoso*, R. Lopez*, G. Xing*, P. Boyeau, 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, C. Talavera-Lopez, L. Pachter, F.J. Theis, A. Streets, M.I. Jordan, J. Regier, N. Yosef
bioRxiv , 2021
GitHub
bioRxiv preprint
Multi-resolution deconvolution of spatial transcriptomics data reveals continuous patterns of inflammation
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†
bioRxiv , 2021
GitHub
bioRxiv preprint
PeakVI: A Deep Generative Model for Single Cell Chromatin Accessibility Analysis.
T. Ashuach, DA. Reidenbach, A. Gayoso, N. Yosef
bioRxiv , 2021
GitHub
bioRxiv preprint
MultiVI: deep generative model for the integration of multi-modal data.
T. Ashuach, M. Gabitto, M. Jordan, N. Yosef
bioRxiv , 2021
GitHub
bioRxiv preprint
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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