CONTRASTIVE LATENT VARIABLE MODELING WITH APPLICATION TO CASE-CONTROL SEQUENCING EXPERIMENTS

Andrew Jones, F. William Townes, Didong Li, Barbara E. Engelhardt

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

High-throughput RNA-sequencing (RNA-seq) technologies are powerful tools for understanding cellular state. Often, it is of interest to quantify and to summarize changes in cell state that occur between experimental or biological conditions. Differential expression is typically assessed using uni-variate tests to measure genewise 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. More-over, we develop a model-based hypothesis testing framework that can test for global and gene subset-specific changes in expression. We evaluate our model through extensive simulations and analyses with count-based gene expression data from perturbation and observational sequencing experiments. We find that our methods effectively summarize and quantify complex transcriptional changes in case-control experimental sequencing data.

Original languageEnglish (US)
Pages (from-to)1268-1291
Number of pages24
JournalAnnals of Applied Statistics
Volume16
Issue number3
DOIs
StatePublished - Sep 2022
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Modeling and Simulation
  • Statistics, Probability and Uncertainty

Keywords

  • case-control data
  • contrastive models
  • differential expression
  • Latent variable models
  • RNA sequencing

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