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

Examples and vignettes

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

Vignettes are available in the package via vignette('MAITAnalysis') or vignette('MAST-intro').

New Features and announcements

  • MAST has been ported to use SummarizedExperiment under the hood. The main difference is that the data container is now transposed to follow bioconductor standards.
  • The older version will remain accessible on github under branch MASTClassic

Installation Instructions

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:

source("https://bioconductor.org/biocLite.R")
biocLite("MAST")

Converting old MASTClassic SingleCellAssay objects

If you have data analyzed using MASTClassic, starting with MAST package version 1.0.4 you can convert objects from MASTClassic format to the new format based on SummarizedExperiment using convertMastClassicToSingleCellAssay().