Significance analysis of groups of genes in expression profiling studies
Author(s) -
James J. Chen,
Taewon Lee,
Robert R. Delongchamp,
Tao Chen,
ChenAn Tsai
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/btm310
Subject(s) - gene expression profiling , profiling (computer programming) , computational biology , gene , gene expression , expression (computer science) , biology , genetics , computer science , programming language
Gene class testing (GCT) is a statistical approach to determine whether some functionally predefined classes of genes express differently under two experimental conditions. GCT computes the P-value of each gene class based on the null distribution and the gene classes are ranked for importance in accordance with their P-values. Currently, two null hypotheses have been considered: the Q1 hypothesis tests the relative strength of association with the phenotypes among the gene classes, and the Q2 hypothesis assesses the statistical significance. These two hypotheses are related but not equivalent.
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