https://scholars.lib.ntu.edu.tw/handle/123456789/521805
標題: | Floating prioritized subset analysis: A powerful method to detect differentially expressed genes | 作者: | WAN-YU LIN WEN-CHUNG LEE |
公開日期: | 2011 | 出版社: | Elsevier B.V. | 卷: | 55 | 期: | 1 | 起(迄)頁: | 903-913 | 來源出版物: | Computational Statistics and Data Analysis | 摘要: | 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. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-77958041525&doi=10.1016%2fj.csda.2010.07.023&partnerID=40&md5=69bc82d960f5ddc27228ae7d9d37cb11 https://scholars.lib.ntu.edu.tw/handle/123456789/521805 |
ISSN: | 0167-9473 | DOI: | 10.1016/j.csda.2010.07.023 | SDG/關鍵字: | 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 |
顯示於: | 流行病學與預防醫學研究所 |
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