MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data
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13059_2015_Article_844.pdf
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Author(s)
Finak, Greg
McDavid, Andrew
Yajima, Masanao
Deng, Jingyuan
Gersuk, Vivian
Prlic, Martin
Gottardo, Raphael
Slichter, Chloe K.
Miller, Hannah W.
McElrath, M. Juliana
Date Issued
December 2015
Journal
Genome Biology
Publisher
BioMed Central
Citation
Finak, Greg, et al. "MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data." Genome Biology. 2015 Dec 10;16(1):278.
Version
Final published version
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.
MIT Department
Institute for Medical Engineering and Science
Massachusetts Institute of Technology. Department of Chemistry
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1186/s13059-015-0844-5
