Jeffrey Regier

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.

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

The paper introduces totalVI, a probabilistic framework for joint analysis of RNA and protein data from single cells, demonstrating its utility in integration, dimensionality reduction, clustering, and differential expression testing.

This paper introduces scVI and scANVI, deep generative models for integrating single-cell RNA-seq data and annotating cell states, showing improved accuracy and scalability over existing methods.

AutoZI detects gene-specific zero-inflation in single-cell RNA sequencing data using a spike-and-slab prior and variational inference, outperforming existing methods on simulated and real data.

The paper introduces totalVI, a probabilistic framework for analyzing CITE-seq data by jointly modeling RNA and protein measurements, addressing technical noise and enabling batch correction.

scVI maps cells into a low-dimensional latent space with variational inference and neural networks, scaling past a million cells while absorbing batch effects and sensitivity differences.