Pierre Boyeau

The paper introduces CSDE, a method that improves differential expression analysis in spatial transcriptomics by incorporating expert feedback to mitigate preprocessing biases.

MrVI is a deep generative model that identifies sample-level stratifications and cellular differences without prior cell-type grouping, applied to COVID-19 and IBD data.

CytoVI is a deep generative model for antibody-based single cell technologies that improves integration, imputation, and analysis across different platforms and antibody panels.

scVIVA is a deep generative model that integrates cell-intrinsic and neighboring gene expression to produce embeddings and gene profiles, revealing spatially restricted tumor-promoting endothelial cells and niche-associated T cell states in breast cancer.

This paper introduces VI-VS, a framework for identifying conditionally dependent features in multiomic data while controlling false discovery rates.

The paper introduces lvm-DE, a Bayesian method for differential expression analysis in scRNA-seq using deep generative models, improving log fold change estimation and differential gene detection.

The paper introduces DestVI, a method for multi-resolution spatial transcriptomics analysis that captures continuous cell state variation, and applies it to study inflammation in lymph nodes and tumors.

scvi-tools provides a Python package for probabilistic analysis of single-cell omics data, offering standardized interfaces and extensible tools for various analysis tasks.