<u>Romain Lopez</u>

Latest

• Learning causal representations of single cells via sparse mechanism shift modeling
• NODAGS-Flow: Nonlinear cyclic causal structure learning
• An empirical Bayes method for differential expression analysis of single cells with deep generative models
• Disentangling shared and group-specific variations in single-cell transcriptomics data with multiGroupVI
• Large-scale differentiable causal discovery of factor graphs
• Learning causal representations of single cells via sparse mechanism shift modeling
• DestVI identifies continuums of cell types in spatial transcriptomics data
• A Python library for probabilistic analysis of single-cell omics data
• Reconstructing unobserved cellular states from paired single-cell lineage tracing and transcriptomics data
• Charting Cellular States, One Cell at a Time: Computational, Inferential and Modeling Perspectives
• Joint probabilistic modeling of single-cell multi-omic data with totalVI
• Learning from eXtreme bandit feedback
• Probabilistic harmonization and annotation of single-cell transcriptomics data with deep generative models
• Decision-making with auto-encoding variational Bayes
• Enhancing scientific discoveries in molecular biology with deep generative models
• Cost-effective incentive allocation via structured counterfactual inference
• A joint model of RNA expression and surface protein abundance in single cells
• A joint model of unpaired data from scRNA-seq and spatial transcriptomics for imputing missing gene expression measurements
• Deep generative models for detecting differential expression in single cells
• Detecting zero-inflated genes in single-cell transcriptomics data
• Scrublet: computational identification of cell doublets in single-cell transcriptomic data
• Deep generative modeling for single-cell transcriptomics
• Information constraints on auto-encoding variational Bayes
• A deep generative model for semi-supervised classification with noisy labels
• A deep generative model for gene expression profiles from single-cell RNA sequencing with application to differential expression
• A deep generative model for gene expression profiles from single-cell RNA sequencing