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Sparse linear discriminant analysis for simultaneous testing for the significance of a gene set/pathway and gene selection
Author(s) -
Michael C. Wu,
Lingsong Zhang,
ZhaoXi Wang,
David C. Christiani,
Xihong Lin
Publication year - 2009
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/btp019
Subject(s) - gene , computational biology , linear discriminant analysis , biology , gene expression profiling , selection (genetic algorithm) , gene selection , genetics , gene expression , computer science , artificial intelligence , microarray analysis techniques
Pathway and gene set-based approaches for the analysis of gene expression profiling experiments have become increasingly popular for addressing problems associated with individual gene analysis. Since most genes are not differently expressed, existing gene set tests, which consider all the genes within a gene set, are subject to considerable noise and power loss, a concern exacerbated in studies in which the degree of differential expression is moderate for truly differentially expressed genes. For a significantly differentially expressed pathway, it is also of substantial interest to select important genes that drive the differential expression of the pathway.

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