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• W2136477743 abstract "An important data analysis task in statistical genomics involves the integration of genome-wide gene-level measurements with preexisting data on the same genes. A wide variety of statistical methodologies and computational tools have been developed for this general task. We emphasize one particular distinction among methodologies, namely whether they process gene sets one at a time (uniset) or simultaneously via some multiset technique. Owing to the complexity of collections of gene sets, the multiset approach offers some advantages, as it naturally accommodates set-size variations and among-set overlaps. However, this approach presents both computational and inferential challenges. After reviewing some statistical issues that arise in uniset analysis, we examine two model-based multiset methods for gene list data." @default.
• W2136477743 created "2016-06-24" @default.
• W2136477743 creator A5048840755 @default.
• W2136477743 creator A5072759658 @default.
• W2136477743 date "2015-04-10" @default.
• W2136477743 modified "2023-10-17" @default.
• W2136477743 title "Multiset Statistics for Gene Set Analysis" @default.
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• W2136477743 doi "https://doi.org/10.1146/annurev-statistics-010814-020335" @default.
• W2136477743 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/4405258" @default.
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