MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data

TLDR

Single‑cell transcriptomics reveals gene‑expression heterogeneity but is limited by stochastic dropout and bimodal distributions where expression is either strongly non‑zero or undetectable. We propose a two‑part generalized linear model for such bimodal data that parameterizes both of these features. The model is a two‑part generalized linear framework that adjusts for cellular detection rate as nuisance variation and enables gene‑set enrichment analysis tailored to single‑cell data. It provides insights into how networks of co‑expressed genes evolve across an experimental treatment. MAST is available at https://github.com/RGLab/MAST.

Abstract

Single-cell transcriptomics reveals gene expression heterogeneity but suffers from stochastic dropout and characteristic bimodal expression distributions in which expression is either strongly non-zero or non-detectable. We propose a two-part, generalized linear model for such bimodal data that parameterizes both of these features. We argue that the cellular detection rate, the fraction of genes expressed in a cell, should be adjusted for as a source of nuisance variation. Our model provides gene set enrichment analysis tailored to single-cell data. It provides insights into how networks of co-expressed genes evolve across an experimental treatment. MAST is available at https://github.com/RGLab/MAST .