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Making sense out of massive data by going beyond differential expression

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dc.contributor.author Schmid, Patrick Raphael
dc.contributor.author Palmer, Nathan Patrick
dc.contributor.author Kohane, Isaac
dc.contributor.author Berger, Bonnie
dc.date.accessioned 2012-11-15T21:05:43Z
dc.date.available 2012-11-15T21:05:43Z
dc.date.issued 2012-03
dc.date.submitted 2011-11
dc.identifier.issn 0027-8424
dc.identifier.issn 1091-6490
dc.identifier.uri http://hdl.handle.net/1721.1/74658
dc.description.abstract With the rapid growth of publicly available high-throughput transcriptomic data, there is increasing recognition that large sets of such data can be mined to better understand disease states and mechanisms. Prior gene expression analyses, both large and small, have been dichotomous in nature, in which phenotypes are compared using clearly defined controls. Such approaches may require arbitrary decisions about what are considered “normal” phenotypes, and what each phenotype should be compared to. Instead, we adopt a holistic approach in which we characterize phenotypes in the context of a myriad of tissues and diseases. We introduce scalable methods that associate expression patterns to phenotypes in order both to assign phenotype labels to new expression samples and to select phenotypically meaningful gene signatures. By using a nonparametric statistical approach, we identify signatures that are more precise than those from existing approaches and accurately reveal biological processes that are hidden in case vs. control studies. Employing a comprehensive perspective on expression, we show how metastasized tumor samples localize in the vicinity of the primary site counterparts and are overenriched for those phenotype labels. We find that our approach provides insights into the biological processes that underlie differences between tissues and diseases beyond those identified by traditional differential expression analyses. Finally, we provide an online resource (http://concordia.csail.mit.edu) for mapping users’ gene expression samples onto the expression landscape of tissue and disease. en_US
dc.language.iso en_US
dc.publisher National Academy of Sciences en_US
dc.relation.isversionof http://dx.doi.org/10.1073/pnas.1118792109 en_US
dc.rights Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. en_US
dc.source PNAS en_US
dc.title Making sense out of massive data by going beyond differential expression en_US
dc.type Article en_US
dc.identifier.citation Schmid, P. R. et al. “Making Sense Out of Massive Data by Going Beyond Differential Expression.” Proceedings of the National Academy of Sciences 109.15 (2012): 5594–5599. ©2012 by the National Academy of Sciences en_US
dc.contributor.department Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science en_US
dc.contributor.department Massachusetts Institute of Technology. Department of Mathematics en_US
dc.contributor.mitauthor Schmid, Patrick Raphael
dc.contributor.mitauthor Palmer, Nathan Patrick
dc.contributor.mitauthor Berger, Bonnie
dc.contributor.mitauthor Kohane, Isaac
dc.relation.journal Proceedings of the National Academy of Sciences en_US
dc.identifier.mitlicense PUBLISHER_POLICY en_US
dc.eprint.version Final published version en_US
dc.type.uri http://purl.org/eprint/type/JournalArticle en_US
eprint.status http://purl.org/eprint/status/PeerReviewed en_US
dspace.orderedauthors Schmid, P. R.; Palmer, N. P.; Kohane, I. S.; Berger, B. en


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