Probabilistic Models for Contrastive Dimension Reduction with Applications to Sequencing Data

• Activity Number: 52 - Contrastive Dimension Reduction: Exploring Differential Patterns in High-Dimensional Data
• Type: Topic Contributed
• Date/Time: Sunday, August 7, 2022 : 4:00 PM to 5:50 PM
• Sponsor: Section on Statistical Learning and Data Science
• Author(s): Andrew Jones* and Barbara E. Engelhardt
• Companies: Princeton University and Princeton University
• Keywords: contrastive; dimension reduction; sequencing data; hypothesis testing; exploratory data analysis

Abstract

High-throughput sequencing 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 probabilistic models designed for creating 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 models through extensive simulations and analyses with gene and protein expression data from perturbation and observational sequencing experiments.