MAST fits two-part, generalized linear models that are specially adapted for bimodal and/or zero-inflated single cell gene expression data.

MAST supports:

• Easy importing, subsetting and manipulation of expression matrices
• Filtering of low-quality cells
• Adaptive thresholding of background noise
• Tests for univariate differential expression, with adjustment for covariates
• Gene set enrichment analysis, corrected for covariates and gene-gene correlations
• Exploration of gene-gene correlations and co-expression

A range of examples are available from the paper published in Genome Biology. A vignette (in need of updating) is also available in the package via vignette('MAST-intro').

• MAST will soon be ported to use SummarizedExperiment under the hood, and submitted to Bioconductor. See the summarizedExpt branch.

If you have previously installed the package SingleCellAssay you will want to remove it as MAST supercedes SingleCellAssay. (If both MAST and SingleCellAssay are attached, opaque S4 dispatch errors will result.) Remove it with:

 remove.packages('SingleCellAssay')

Then you may install or update MAST with:

 install.packages('devtools')
 library(devtools)
 install_github('RGLab/MAST')
 # *or* if you don't have a working latex setup
 install_github(RGLab/'MAST', build_vignettes=FALSE)
 vignette('MAST-intro')