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 .

Keywords

BiologyTranscriptomeComputational biologyGeneGene expressionGene expression profilingRNA-SeqGeneticsFalse discovery rate

MeSH Terms

AnimalsData InterpretationStatisticalDendritic CellsGene Expression ProfilingGenetic VariationHumansLinear ModelsMiceSequence AnalysisRNASingle-Cell AnalysisTranscriptome

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Publication Info

Year
2015
Type
article
Volume
16
Issue
1
Pages
278-278
Citations
3262
Access
Closed

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Cite This

Greg Finak, Andrew McDavid, Masanao Yajima et al. (2015). MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data. Genome biology , 16 (1) , 278-278. https://doi.org/10.1186/s13059-015-0844-5

Identifiers

DOI
10.1186/s13059-015-0844-5
PMID
26653891
PMCID
PMC4676162

Data Quality

Data completeness: 90%