{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,4,18]],"date-time":"2024-04-18T15:40:16Z","timestamp":1713454816274},"reference-count":31,"publisher":"Oxford University Press (OUP)","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2012,3,15]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Motivation: There is now a large literature on statistical methods for the meta-analysis of genomic data from multiple studies. However, a crucial assumption for performing many of these analyses is that the data exhibit small between-study variation or that this heterogeneity can be sufficiently modelled probabilistically.<\/jats:p><jats:p>Results: In this article, we propose \u2018assumption weighting\u2019, which exploits a weighted hypothesis testing framework proposed by Genovese et al. to incorporate tests of between-study variation into the meta-analysis context. This methodology is fast and computationally simple to implement. Several weighting schemes are considered and compared using simulation studies. In addition, we illustrate application of the proposed methodology using data from several high-profile stem cell gene expression datasets.<\/jats:p><jats:p>Availability: \u00a0http:\/\/works.bepress.com\/debashis_ghosh\/50\/<\/jats:p><jats:p>Contact: \u00a0ghoshd@psu.edu<\/jats:p>","DOI":"10.1093\/bioinformatics\/bts037","type":"journal-article","created":{"date-parts":[[2012,1,28]],"date-time":"2012-01-28T05:35:03Z","timestamp":1327728903000},"page":"807-814","source":"Crossref","is-referenced-by-count":6,"title":["Assumption weighting for incorporating heterogeneity into meta-analysis of genomic data"],"prefix":"10.1093","volume":"28","author":[{"given":"Yihan","family":"Li","sequence":"first","affiliation":[{"name":"1 Department of Statistics and 2Department of Public Health Sciences, Penn State University, University Park, PA 16802, USA"}]},{"given":"Debashis","family":"Ghosh","sequence":"additional","affiliation":[{"name":"1 Department of Statistics and 2Department of Public Health Sciences, Penn State University, University Park, PA 16802, USA"},{"name":"1 Department of Statistics and 2Department of Public Health Sciences, Penn State University, University Park, PA 16802, USA"}]}],"member":"286","published-online":{"date-parts":[[2012,1,27]]},"reference":[{"key":"2023012512202454400_B1","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1111\/j.2517-6161.1995.tb02031.x","article-title":"Controlling the false discovery rate: a practical and powerful approach to multiple testing","volume":"57","author":"Benjamini","year":"1995","journal-title":"J. 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