Publications

2023

Romain Lopez*, Natasa Tagasovska*, Stephen Ra, Kyunghyun Cho, Jonathan K. Pritchard, Aviv Regev. Learning causal representations of single cells via sparse mechanism shift modeling. Conference on Causal Learning and Reasoning, 2023.

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Muralikrishnna G. Sethuraman, Romain Lopez, Rahul Mohan, Faramarz Fekri, Tommaso Biancalani, Jan-Christian Hütter. NODAGS-Flow: Nonlinear cyclic causal structure learning. International Conference on Artificial Intelligence and Statistics, 2023.

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Pierre Boyeau, Jeffrey Regier, Adam Gayoso, Michael I. Jordan, Romain Lopez*, Nir Yosef*. An empirical Bayes method for differential expression analysis of single cells with deep generative models. Proceedings of the National Academy of Sciences (in press), 2023.

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2022

Ethan Weinberger, Romain Lopez, Jan-Christian Hütter, Aviv Regev. Disentangling shared and group-specific variations in single-cell transcriptomics data with multiGroupVI. Machine Learning in Computational Biology (MLCB), 2022.

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Romain Lopez, Jan-Christian Huetter, Jonathan K. Pritchard, Aviv Regev. Large-scale differentiable causal discovery of factor graphs. Advances in Neural Information Processing Systems, 2022.

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Romain Lopez*, Natasa Tagasovska*, Stephen Ra, Kyunghyun Cho, Jonathan Pritchard, Aviv Regev. Learning causal representations of single cells via sparse mechanism shift modeling. NeurIPS Workshop on Causality for Real-world Impact, 2022.

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Romain Lopez*, Baoguo Li*, Hadas Keren-Shaul*, Pierre Boyeau, Merav Kedmi, David Pilzer, Adam Jelinski, Ido Yofe, Eyal David, Allon Wagner, Can Ergen, Yoseph Addadi, Ofra Golani, Franca Ronchese, Michael I Jordan, Ido Amit, Nir Yosef. DestVI identifies continuums of cell types in spatial transcriptomics data. Nature Biotechnology, 2022.

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Adam Gayoso*, Romain Lopez*, Galen Xing*, Pierre Boyeau, Katherine Wu, Michael Jayasuriya, Edouard Mehlman, Maxime Langevin, Yining Liu, Jules Samaran, Gabriel Misrachi, Achille Nazaret, Oscar Clivio, Chenling Xu, Tal Ashuach, Mariano Gabitto, Mohammad Lotfollahi, Valentine Svensson, Eduardo da Veiga Beltrame, Vitalii Kleshchevnikov, Carlos Talavera-Lopez, Lior Pachter, Fabian J Theis, Aaron Streets, Michael I. Jordan, Jeffrey Regier, Nir Yosef. A Python library for probabilistic analysis of single-cell omics data. Nature Biotechnology, 2022.

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2021

Khalil Ouardini, Romain Lopez, Matthew G. Jones, Sebastian Prillo, Richard Zhang, Michael I. Jordan, Nir Yosef. Reconstructing unobserved cellular states from paired single-cell lineage tracing and transcriptomics data. ICML Workshop in Computational Biology, 2021.

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Romain Lopez. Charting Cellular States, One Cell at a Time: Computational, Inferential and Modeling Perspectives. eScholarship, 2021.

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Adam Gayoso*, Zoë Steier*, Romain Lopez, Jeffrey Regier, Kristopher L. Nazor, Aaron Streets, Nir Yosef. Joint probabilistic modeling of single-cell multi-omic data with totalVI. Nature Methods, 2021.

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Romain Lopez, Inderjit Dhillon, Michael I. Jordan. Learning from eXtreme bandit feedback. AAAI Conference on Artificial Intelligence, 2021.

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Chenling Xu*, Romain Lopez*, Edouard Mehlman*, Jeffrey Regier, Michael I. Jordan, Nir Yosef. Probabilistic harmonization and annotation of single-cell transcriptomics data with deep generative models. Molecular Systems Biology, 2021.

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2020

Romain Lopez, Pierre Boyeau, Nir Yosef, Michael I. Jordan, Jeffrey Regier. Decision-making with auto-encoding variational Bayes. Advances in Neural Information Processing Systems, 2020.

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Romain Lopez, Adam Gayoso, Nir Yosef. Enhancing scientific discoveries in molecular biology with deep generative models. Molecular Systems Biology, 2020.

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Romain Lopez, Chenchen Li, Xiang Yan, Junwu Xiong, Michael I. Jordan, Yuan Qi, Le Song. Cost-effective incentive allocation via structured counterfactual inference. AAAI Conference on Artificial Intelligence, 2020.

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2019

Adam Gayoso, Romain Lopez, Zoë Steier, Jeffrey Regier, Aaron Streets, Nir Yosef. A joint model of RNA expression and surface protein abundance in single cells. Machine Learning in Computational Biology (MLCB), 2019.

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Romain Lopez*, Achille Nazaret*, Maxime Langevin*, Jules Samaran*, Jeffrey Regier*, Michael I. Jordan, Nir Yosef. A joint model of unpaired data from scRNA-seq and spatial transcriptomics for imputing missing gene expression measurements. ICML Workshop in Computational Biology, 2019.

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Pierre Boyeau, Romain Lopez, Jeffrey Regier, Adam Gayoso, Michael I. Jordan, Nir Yosef. Deep generative models for detecting differential expression in single cells. Machine Learning in Computational Biology (MLCB), 2019.

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Oscar Clivio, Romain Lopez, Jeffrey Regier, Adam Gayoso, Michael I. Jordan, Nir Yosef. Detecting zero-inflated genes in single-cell transcriptomics data. Machine Learning in Computational Biology (MLCB), 2019.

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Samuel L. Wolock, Romain Lopez, Allon M. Klein. Scrublet: computational identification of cell doublets in single-cell transcriptomic data. Cell Systems, 2019.

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2018

Romain Lopez, Jeffrey Regier, Michael B. Cole, Michael I. Jordan, Nir Yosef. Deep generative modeling for single-cell transcriptomics. Nature Methods, 2018.

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Romain Lopez, Jeffrey Regier, Michael I. Jordan, Nir Yosef. Information constraints on auto-encoding variational Bayes. Advances in Neural Information Processing Systems, 2018.

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Maxime Langevin, Edouard Mehlman, Jeffrey Regier, Romain Lopez, Michael I. Jordan, Nir Yosef. A deep generative model for semi-supervised classification with noisy labels. Bay Area Machine Learning Symposium, 2018.

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2017

Romain Lopez, Jeffrey Regier, Michael I. Jordan, Nir Yosef. A deep generative model for gene expression profiles from single-cell RNA sequencing with application to differential expression. NeurIPS Machine Learning workshop in Computational Biology, 2017.

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Romain Lopez, Jeffrey Regier, Michael I. Jordan, Nir Yosef. A deep generative model for gene expression profiles from single-cell RNA sequencing. Bay Area Machine Learning Symposium, 2017.

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