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100458 |
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|a Finak, Greg
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|a Institute for Medical Engineering and Science
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|a Massachusetts Institute of Technology. Department of Chemistry
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|a Shalek, Alex
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|a McDavid, Andrew
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|a Yajima, Masanao
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|a Deng, Jingyuan
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|a Gersuk, Vivian
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|a Prlic, Martin
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|a Gottardo, Raphael
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|a Slichter, Chloe K.
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|a Miller, Hannah W.
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|a McElrath, M. Juliana
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|a Linsley, Peter S.
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|a Shalek, Alex
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|a MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data
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|b BioMed Central,
|c 2015-12-21T17:40:51Z.
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|z Get fulltext
|u http://hdl.handle.net/1721.1/100458
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|a 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.
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|a en
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|a Article
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|t Genome Biology
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