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. Mehlman, M. Langevin, Y. Liu, J. Samaran, G. Misrachi, A. Nazaret, O. Clivio, C. Xu, T. Ashuach, M. Lotfollahi, V. Svensson, E. Beltrame, V. Kleshchevnikov, C. Talavera-Lopez, L. Pachter, F.J. Theis, A. Streets, M.I. Jordan, J. Regier, N. Yosef.
Nature Biotechnology, 2022
ResolVI: addressing noise and bias in spatial transcriptomics.
C. Ergen, N. Yosef.
bioRxiv, 2025
scVIVA: a probabilistic framework for representation of cells and their environments in spatial transcriptomics.
N. Levy, F. Ingelfinger, A. Bakulin, G. Cinnirella, P. Boyeau, B. Nadler, C. Ergen, N. Yosef.
bioRxiv, 2025
CytoVI: Deep generative modeling of antibody-based single cell technologies.
F. Ingelfinger, N. Levy, C. Ergen, A. Bakulin, A. Becker, P. Boyeau, M. Kim, D. Ditz, J. Dirks, J. Maaskola, T. Wertheimer, R. Zeiser, C.C. Widmer, I. Amit, N. Yosef.
bioRxiv, 2025
scvi-hub: an actionable repository for model-driven single-cell analysis.
C. Ergen, V. Valiollah Pour Amiri, M. Kim, O. Kronfeld, A. Streets, A. Gayoso, N. Yosef.
Nature Methods, 2025
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, I. Yofe, E. David, A. Wagner, Y. Addadi, O. Golani, F. Ronchese, M.I. Jordan, I. Amit†, N. Yosef†
Nature Biotechnology, 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
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, 2021
Decision-making with auto-encoding variational Bayes.
R. Lopez, P. Boyeau, N. Yosef, M. Jordan, J. Regier.
Advances in Neural Information Processing Systems, 2020
Interpretable factor models of single-cell RNA-seq via variational autoencoders.
V. Svensson, A. Gayoso, N. Yosef, L. Pachter.
Bioinformatics, 2020
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 Workshop on Computational Biology, 2019
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
Detecting zero-inflated denes in single-cell transcriptomics data.
O. Clivio, R. Lopez, J. Regier, A. Gayoso, MI. Jordan, N Yosef.
Machine Learning in Computational Biology meeting, 2019
Information constraints on auto-encoding variational Bayes.
R. Lopez, J. Regier, M. Jordan, N. Yosef.
Advances in Neural Information Processing Systems, 2018
A deep generative model for semi-supervised classification with noisy labels.
M. Langevin, E. Mehlman, J. Regier, R. Lopez, M.I. Jordan, N. Yosef.
Bay Area Machine Learning Symposium, 2018
Deep generative modeling for single-cell transcriptomics.
R. Lopez, J. Regier, MB. Cole, M. Jordan, N. Yosef.
Nature Methods, 2018