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

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    MAST: a flexib...pdf (3.566Mb)  Published version
    13059_2015_844...pdf (3.847Mb)  Supporting documentation
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    This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
    Date Issued
    2015-12-10
    Related DOI
    10.1186/s13059-015-0844-5
    Author
    Finak, Greg
    McDavid, Andrew
    Yajima, Masanao
    Deng, Jingyuan
    Gersuk, Vivian
    Shalek, Alex K.
    Slichter, Chloe K.
    Miller, Hannah W.
    McElrath, M. Juliana
    Prlic, Martin
    Linsley, Peter S.
    Gottardo, Raphael
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    Permanent Link
    https://hdl.handle.net/2144/27896
    Citation
    Greg Finak, Andrew McDavid, Masanao Yajima, Jingyuan Deng, Vivian Gersuk, Alex K Shalek, Chloe K Slichter, Hannah W Miller, M Juliana McElrath, Martin Prlic, Peter S Linsley, Raphael Gottardo. 2015. "MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data.." Genome Biol, Volume 16: 278.
    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 .
    Rights
    This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
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    • CAS: Mathematics & Statistics: Scholarly Papers [71]
    • BU Open Access Articles [813]

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