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dc.contributor.authorSchmid, Patrick Raphael
dc.contributor.authorPalmer, Nathan Patrick
dc.contributor.authorKohane, Isaac
dc.contributor.authorBerger, Bonnie
dc.date.accessioned2012-10-16T14:11:56Z
dc.date.available2012-10-16T14:11:56Z
dc.date.issued2012-03
dc.date.submitted2011-11
dc.identifier.issn0027-8424
dc.identifier.issn1091-6490
dc.identifier.urihttp://hdl.handle.net/1721.1/74011
dc.description.abstractWith 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.isoen_US
dc.publisherNational Academy of Sciencesen_US
dc.relation.isversionofhttp://dx.doi.org/10.1073/pnas.1118792109en_US
dc.rightsCreative Commons Attributionen_US
dc.rights.urihttp://creativecommons.org/licenses/by/2.5/en_US
dc.sourcePLoSen_US
dc.titleMaking sense out of massive data by going beyond differential expressionen_US
dc.typeArticleen_US
dc.identifier.citationSchmid, 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 Sciencesen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.contributor.mitauthorSchmid, Patrick Raphael
dc.contributor.mitauthorPalmer, Nathan Patrick
dc.contributor.mitauthorBerger, Bonnie
dc.contributor.mitauthorKohane, Isaac
dc.relation.journalProceedings of the National Academy of Sciencesen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dspace.orderedauthorsSchmid, P. R.; Palmer, N. P.; Kohane, I. S.; Berger, B.en
dc.identifier.orcidhttps://orcid.org/0000-0002-2724-7228
mit.licensePUBLISHER_CCen_US
mit.metadata.statusComplete


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