MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data
Abstract
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 .
References
The Mouse Genome Database (MGD): premier model organism resource for mammalian genomics and genetics
Citations
Overcoming confounding plate effects in differential expression analyses of single-cell RNA-seq data
A statistical approach for identifying differential distributions in single-cell RNA-seq experiments
JOINT for large-scale single-cell RNA-sequencing analysis via soft-clustering and parallel computing
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