Contrastive latent variable modeling with application to case-control sequencing experiments

TL;DR

This paper introduces contrastive latent variable models tailored for count-based RNA-seq data, enabling detailed analysis of transcriptional changes and correlation shifts between conditions, with a hypothesis testing framework for differential expression.

Contribution

The paper presents novel contrastive latent variable models for sequencing data that disentangle sources of variation and enable hypothesis testing for differential expression at multiple levels.

Findings

Effective summarization of complex transcriptional changes.

Models outperform traditional methods in simulations.

Able to identify gene subsets with differential expression.

Abstract

High-throughput RNA-sequencing (RNA-seq) technologies are powerful tools for understanding cellular state. Often it is of interest to quantify and summarize changes in cell state that occur between experimental or biological conditions. Differential expression is typically assessed using univariate tests to measure gene-wise shifts in expression. However, these methods largely ignore changes in transcriptional correlation. Furthermore, there is a need to identify the low-dimensional structure of the gene expression shift to identify collections of genes that change between conditions. Here, we propose contrastive latent variable models designed for count data to create a richer portrait of differential expression in sequencing data. These models disentangle the sources of transcriptional variation in different conditions, in the context of an explicit model of variation at baseline. Moreover, we develop a model-based hypothesis testing framework that can test for global and gene subset-specific changes in expression. We test our model through extensive simulations and analyses with count-based gene expression data from perturbation and observational sequencing experiments. We find that our methods can effectively summarize and quantify complex transcriptional changes in case-control experimental sequencing data.