MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data

By Greg Finak, Andrew McDavid, Masanao Yajima, Jingyuan Deng, V. Gersuk, A. Shalek, Chloe K. Slichter, Hannah W. Miller, Juliana McElrath, Martin Prlic and 3 more. Genome Biology, 2015

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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.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/200f728eee7b2cff84fcd8d2d9b47da3a5a2ab82

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In Molecular Biology

Citations in the corpus

Bayesian Inference for a Generative Model of Transcriptome Profiles from Single-cell RNA Sequencing (2018, methodology): “Vertical bars denotes significance threshold for the Bayes factor of scVI (c) or 0.05 for corrected p-values for DESeq2 (d), edgeR (e), and MAST (f).”
Design and computational analysis of single-cell RNA-sequencing experiments (2016, methodology): “Recent methods, including MAST [76] and SCDE [77], have been developed to accommodate bimodality in expression levels resulting from an abundance of zero (or low) values.”
SCANPY: large-scale single-cell gene expression data analysis (2018, methodology): “Background Simple integrated analysis work flows for single-cell transcriptomic data [1] have been enabled by frameworks such as SEURAT [2], MONOCLE [3], SCDE/PAGODA [4], MAST [5], CELL RANGER [6], SCATER [7], and SCRAN [8].”
bigSCale: an analytical framework for big-scale single-cell data (2017, methodology): “For the WB1 sample harboring a deleted allele, bigSCale presented the highest sensitivity by detecting 12 down-regulated genes, followed by Monocle2 (Qiu et al. 2017), BPSC (Vu et al. 2016), SCDE (Kharchenko et al. 2014), MAST (Finak et al. 2015), Seurat (Satija et al. 2015), and scDD (Fig.”
Single-cell mRNA quantification and differential analysis with Census (2017, background): “Potential contaminating fibroblast cells with transcript counts of Mef2c less than 5 and Myf5 less than 1 were removed which yields 142 cells for down-stream analysis.”
A test metric for assessing single-cell RNA-seq batch correction (2018, background): “Also, dropout and cellular detection rates 4,5 are closely correlated to library size (Supplementary Fig.”
Best practices for single-cell analysis across modalities (2023, neutral): “3).”