README.md

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MAST: Model-based Analysis of Single-cell Transcriptomics

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:

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

New Features and announcements

Getting Help

For bug reports (something seems broken): open a bug report here. For general questions, please submit a question to the bioconductor support site so that others can benefit from the discussion.

Citation

If you find MAST useful in your work, please consider citing the paper: MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data G Finak, A McDavid, M Yajima, J Deng, V Gersuk, AK Shalek, CK Slichter et al Genome biology 16 (1), 278

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().



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MAST documentation built on Nov. 17, 2017, 1:28 p.m.