Biology – Quantitative Biology – Quantitative Methods
Scientific paper
2004-05-19
Bioinformatics, vol 20, no 16, pp 2821-2828, November 2004
Biology
Quantitative Biology
Quantitative Methods
9 pages, 1 table; to appear in Bioinformatics
Scientific paper
10.1093/bioinformatics/bth336
Four reasons why you might wish to read this paper: 1. We have devised a new statistical T test to determine differentially expressed genes (DEG) in the context of microarray experiments. This statistical test adds a new member to the traditional T-test family. 2. An exact formula for calculating the detection power of this T test is presented, which can also be fairly easily modified to cover the traditional T tests. 3. We have presented an accurate yet computationally very simple method to estimate the fraction of non-DEGs in a set of genes being tested. This method is superior to an existing one which is computationally much involved. 4. We approach the multiple testing problem from a fresh angle, and discuss its relation to the classical Bonferroni procedure and to the FDR (false discovery rate) approach. This is most useful in the analysis of microarray data, where typically several thousands of genes are being tested simultaneously.
Gant Timothy W.
Zhang Shu-Dong
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