Floating prioritized subset analysis: A powerful method to detect differentially expressed genes
Journal
Computational Statistics and Data Analysis
Journal Volume
55
Journal Issue
1
Pages
903-913
Date Issued
2011
Author(s)
Abstract
Controlling the false discovery rate (FDR) is a powerful approach to deal with a large num- ber of hypothesis tests, such as in gene expression data analyses and genome-wide associa- tion studies. To further boost power, here we propose a floating prioritized subset analysis (floating PSA) that can more effectively use prior knowledge and detect more genes that are differentially expressed. Genes are first allocated into two subsets: a prioritized subset and a non-prioritized subset, according to investigators' prior biological knowledge. We allow the FDRs of the two subsets to vary freely (to float) but aim to control the overall FDR at a desired level. An algorithm for the floating PSA is developed to detect the largest number of true positives. Theoretical justifications of the algorithm are given, and computer simu- lation studies show that the method has good statistical properties. We apply this method to detect genes that are differentially expressed between acute lymphoblastic leukemia and acute myeloid leukemia patients. The result shows that our floating PSA identifies 32 more genes (permutation-based FDR = 0:0427) than the conventional (fixed) FDR control. Another example is a colon cancer study, and our floating PSA identifies 43 more genes (permutation-based FDR = 0:0502). The floating PSA method is to be recommended for the detection of differentially expressed genes, in light of its power, robustness, and ease of implementation. ? 2010 Elsevier B.V. All rights reserved.
SDGs
Other Subjects
Bioassay; Diseases; Genes; Microarrays; Set theory; Statistical tests; Acute lymphoblastic leukemia; Acute myeloid leukemia; Differentially expressed gene; False discovery rate; Gene expression data analysis; Multiple comparison; Multiple hypothesis testing; Simultaneous inference; Gene expression
Publisher
Elsevier B.V.
Type
journal article