Analyzing gene expression data in terms of gene sets: methodological issues
Author(s) -
Jelle J. Goeman,
Peter Bühlmann
Publication year - 2007
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btm051
Subject(s) - set (abstract data type) , gene , computer science , sampling (signal processing) , independence (probability theory) , expression (computer science) , computational biology , statistical hypothesis testing , data mining , biology , genetics , statistics , mathematics , filter (signal processing) , computer vision , programming language
Many statistical tests have been proposed in recent years for analyzing gene expression data in terms of gene sets, usually from Gene Ontology. These methods are based on widely different methodological assumptions. Some approaches test differential expression of each gene set against differential expression of the rest of the genes, whereas others test each gene set on its own. Also, some methods are based on a model in which the genes are the sampling units, whereas others treat the subjects as the sampling units. This article aims to clarify the assumptions behind different approaches and to indicate a preferential methodology of gene set testing.
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