A mixture model for estimating the local false discovery rate in DNA microarray analysis
Author(s) -
Jiangang Liao,
Yong Lin,
Zachariah Selvanayagam,
Weichung Joe Shih
Publication year - 2004
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/bth310
Subject(s) - false discovery rate , multiple comparisons problem , inference , computational biology , dna microarray , biology , mixture model , smoothing , statistical inference , false positive rate , gene , covariate , microarray , data mining , statistics , computer science , mathematics , genetics , gene expression , artificial intelligence
Statistical methods based on controlling the false discovery rate (FDR) or positive false discovery rate (pFDR) are now well established in identifying differentially expressed genes in DNA microarray. Several authors have recently raised the important issue that FDR or pFDR may give misleading inference when specific genes are of interest because they average the genes under consideration with genes that show stronger evidence for differential expression. The paper proposes a flexible and robust mixture model for estimating the local FDR which quantifies how plausible each specific gene expresses differentially.
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