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Genome Biol. 2015 Dec 10;16:278. doi: 10.1186/s13059-015-0844-5.

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

Author information

1
Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA. gfinak@fredhutch.org.
2
Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA. amcdavid@fredhutch.org.
3
Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA. myajima@fredhutch.org.
4
Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA. jdeng@fredhutch.org.
5
Benaroya Research Institute at Virginia Mason, Seattle, WA, 98101, USA. vgersuk@benaroyaresearch.org.
6
Institute for Medical Engineering & Science, MIT, Boston, MA, 01239-4307, USA. shalek@mit.edu.
7
Department of Chemistry, MIT, Boston, MA, 01239-4307, USA. shalek@mit.edu.
8
Ragon Institute of MGH, MIT, & Harvard, Boston, MA, 02139-3583, USA. shalek@mit.edu.
9
Broad Institute of MIT & Harvard, Boston, MA, 01242, USA. shalek@mit.edu.
10
Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA. cslichte@fredhutch.org.
11
Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA. hwmiller@fredhutch.org.
12
Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA. jmcelrat@fredhutch.org.
13
Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA. mprlic@fredhutch.org.
14
Benaroya Research Institute at Virginia Mason, Seattle, WA, 98101, USA. plinsley@benaroyaresearch.org.
15
Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA. rgottard@fredhutch.org.
16
Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA. rgottard@fredhutch.org.

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 .

PMID:
26653891
PMCID:
PMC4676162
DOI:
10.1186/s13059-015-0844-5
[Indexed for MEDLINE]
Free PMC Article

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