Deep Generative Models for Single-cell Transcriptomics

Last updated on Oct 1, 2020
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An artistic representation of single-cell RNA sequencing.

Single-cell Variational Inference (scVI) is a set of tools for probabilistic analysis of single-cell RNA-sequencing data. We propose to solve quantitative biological questions with tailored and scalable Bayesian machinery. Examples of biological questions include cell-type discovery, data integration, transfer of annotations and differential expression analysis. Examples of statistical tools include deep generative models, stochastic variational inference, Bayesian decision theory and domain adaptation.

All the models in our codebase may fit millions of transcriptomic measurements in a few hours. In addition to the original publication, we recommend the following ressources to get more familiar with scVI:

Deep Generative Models Variational Inference scRNA-seq
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Romain Lopez
Ph.D. candidate

My research interests lie at the intersection of statistics, computation and modeling. A significant part of my research is driven by building more statistically accurate and faster machine learning software for analyzing biological data, with a focus on single-cell transcriptomics.

Publications

Joint Probabilistic Modeling of Paired Transcriptome and Proteome Measurements in Single Cells

The paired measurement of RNA and surface protein abundance in single cells with CITE-seq is a promising approach to connect …
Adam Gayoso*, Zoë Steier*, Romain Lopez, Jeffrey Regier, Kristopher L Nazor, Aaron Streets, Nir Yosef.
PDF Code Project

Probabilistic Harmonization and Annotation of Single-cell Transcriptomics Data with Deep Generative Models

As single-cell transcriptomics becomes a mainstream technology, the natural next step is to integrate the accumulating data in order to …
Chenling Xu*, Romain Lopez*, Edouard Mehlman*, Jeffrey Regier, Michael I. Jordan, Nir Yosef.
PDF Code Project

A Joint Model of RNA Expression and Surface Protein Abundance in Single Cells

Cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) combines unbiased single-cell transcriptome measurements with …
Adam Gayoso, Romain Lopez, Zoë Steier, Jeffrey Regier, Aaron Streets, Nir Yosef.
PDF Code Project

Deep Generative Models for Detecting Differential Expression in Single Cells

Detecting differentially expressed genes is important for characterizing subpopulations of cells. However, in scRNA-seq data, nuisance …
Pierre Boyeau, Romain Lopez, Jeffrey Regier, Adam Gayoso, Michael I. Jordan, Nir Yosef.
PDF Code Project

Detecting Zero-Inflated Genes in Single-Cell Transcriptomics Data

In single-cell RNA sequencing data, biological processes or technical factors may induce an overabundance of zero measurements. …
Oscar Clivio, Romain Lopez, Jeffrey Regier, Adam Gayoso, Michael I. Jordan, Nir Yosef.
PDF Code Project

A Joint Model of Unpaired Data from scRNA-seq and Spatial Transcriptomics for Imputing Missing Gene Expression Measurements

Spatial studies of transcriptome provide biologists with gene expression maps of heterogeneous and complex tissues. However, most …
Romain Lopez*, Achille Nazaret*, Maxime Langevin*, Jules Samaran*, Jeffrey Regier*, Michael I Jordan, Nir Yosef.
PDF Code Project

Deep Generative Modeling for Single-cell Transcriptomics

Single-cell transcriptome measurements can reveal unexplored biological diversity, but they suffer from technical noise and bias that …
Romain Lopez, Jeffrey Regier, Michael B. Cole, Michael I. Jordan, Nir Yosef.
PDF Code Project

A Deep Generative Model for Gene Expression profiles from Single-cell RNA Sequencing with Application to Differential Expression

We propose a probabilistic model for interpreting gene expression levels that are observed through single-cell RNA sequencing. In the …
Romain Lopez, Jeffrey Regier, Michael I. Jordan, Nir Yosef.
PDF Code Project