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


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 (In press) , 2021
GitHub
bioRxiv preprint
Reproducibility code
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 (in press) , 2020
GitHub
bioRxiv preprint
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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