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MAST (Model-based Analysis of Single-cell Transcriptomics)

repository·devel·Indexed 42 days ago

https://github.com/rglab/mast

A Bioconductor statistical framework for analyzing single-cell RNA sequencing data. MAST uses two-part generalized linear models to handle bimodal and zero-inflated gene expression, providing tools for quality control, differential expression tests with covariate adjustment, gene set enrichment analysis (GSEA), and correlation analysis.

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What's inside MAST

  1. Overview of MAST capabilities

    devel

    MAST (Model-based Analysis of Single-cell Transcriptomics) fits two-part, generalized linear models designed for bimodal and/or zero-inflated single-cell gene expression data.

    Key features include:

    • Data Manipulation: Easy importing, subsetting, and manipulation of expression matrices.
    • Quality Control: Filtering of low-quality cells.
    • Noise Handling: Adaptive thresholding of background noise.
    • Differential Expression: Univariate differential expression tests with adjustment for covariates.
    • Enrichment Analysis: Gene set enrichment analysis (GSEA) corrected for covariates and gene-gene correlations.
    • Correlation Analysis: Exploration of gene-gene correlations and co-expression.

  2. Install MAST via Bioconductor

    devel

    MAST is a Bioconductor package. To install or update it, use BiocManager. Avoid installing directly from the GitHub repository unless you are running Bioconductor Devel, as it may not function correctly for standard analyses.

    install.packages("BiocManager") # Needed to install all Bioconductor packages
BiocManager::install("MAST")

  3. Access MAST vignettes

    devel

    MAST provides several instructional vignettes to guide users through analysis workflows. You can access them directly within your R session using the vignette() function.

    Available vignettes:

    • MAST-Intro: General introduction to the package.
    • MAITAnalysis: Analysis workflows.
    • MAST-interoperability: Guidance on working with other tools.
    vignette('MAST-Intro')
vignette('MAITAnalysis')
vignette('MAST-interoperability')

  4. Convert MASTClassic SingleCellAssay objects

    devel

    If you are working with legacy data analyzed using the MASTClassic format, you can convert those SingleCellAssay objects into the modern SingleCellExperiment format used by the current version of MAST using the convertMastClassicToSingleCellAssay() function.

    convertMastClassicToSingleCellAssay(your_old_object)