MAST: a Flexible Statistical Framework for Assessing Transcriptional Changes and Characterizing Heterogeneity in Single-cell RNA Sequencing Data

Overview

Journal Genome Biol

Specialties Biology
Genetics

Date 2015 Dec 15

PMID 26653891

Citations 1388

Authors

Greg Finak

Andrew McDavid

Masanao Yajima

Jingyuan Deng

Vivian Gersuk

Alex K Shalek

Chloe K Slichter

Hannah W Miller

M Juliana McElrath

Martin Prlic

Peter S Linsley

Raphael Gottardo

Affiliations

Soon will be listed here.

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 .

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