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

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.

Researchers at the Fred Hutchinson Cancer Research Center propose a two-part, generalized linear model for such bimodal data that parameterizes both of these features. They argue that the cellular detection rate, the fraction of genes expressed in a cell, should be adjusted for as a source of nuisance variation. Their 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.

Single-cell expression (log ₂ -transcripts per million) of the top 100 genes identified as differentially expressed between cytokine (IL18, IL15, IL12)-stimulated (purple) and non-stimulated (pink) MAIT cells using MAST (a). Partial residuals for up-regulated and down- regulated genes are accumulated to yield an activation score (b), and this score suggests that the stimulated cells have a more heterogeneous response to stimulation than do the non-stimulated cells

Availability – MAST is available at https://github.com/RGLab/MAST