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Finak, Greg; McDavid, Andrew; Yajima, Masanao; Deng, Jingyuan; Gersuk, Vivian; Shalek, Alex K; Slichter, Chloe K; Miller, Hannah W; McElrath, M Juliana; Prlic, Martin; Linsley, Peter S; Gottardo, Raphael
Genome Biology, 12/2015, Volume: 16, Issue: 1Journal Article
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 .
