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MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data
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MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data
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Journal Article
MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data
2015
Overview
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
.
Publisher
BioMed Central,Springer Nature B.V
Subject
/ Animals
/ Biology
/ Biomedical and Life Sciences
/ Data Interpretation, Statistical
/ Dendritic Cells - metabolism
/ Gene Expression Profiling - methods
/ Gene set enrichment analysis
/ genes
/ Genomes
/ Genomics
/ Humans
/ Method
/ Mice
/ Microbial Genetics and Genomics
/ RNA
