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

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

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 para...

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Bibliographic Details
Main Authors: Finak, Greg (Author), McDavid, Andrew (Author), Yajima, Masanao (Author), Deng, Jingyuan (Author), Gersuk, Vivian (Author), Prlic, Martin (Author), Gottardo, Raphael (Author), Slichter, Chloe K. (Author), Miller, Hannah W. (Author), McElrath, M. Juliana (Author), Linsley, Peter S. (Author), Shalek, Alex (Contributor)
Other Authors: Institute for Medical Engineering and Science (Contributor), Massachusetts Institute of Technology. Department of Chemistry (Contributor)
Format: Article
Language:English
Published: BioMed Central, 2015-12-21T17:40:51Z.
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