False discovery rate control

Demrrkale CY, Nettleton D, Maiti T (2010) Linear mixed model selection for false discovery rate control in microarray data analysis. Biometrics 66 621-629. doi BIOM1286 (pii)10.1111/j.l541-0420.2009.01286.x... 

Reiner, A., Yekutieli, D. Benjamini, Y. (2003). Identifying differentially expressed genes using false discovery rate controlling procedures. Bioinformatics 19(3) 368-375. 

Familywise error rate The error rate for one or more false positives among all the tests conducted. Controlling a familywise error rate leads to more stringent control than the false discovery rate control at the same nominal level. 

Statistical analysis should be appropriate to the types of outcome data collected and the number of genotypes nsed in the analysis. The handling of missing data should be clearly stated. Corrections for multiple comparisons (e.g., controlling for false discovery rates 36) should be performed if multiple statistical tests are carried out. 

Benjamini, Y., and Hochberg, Y. (1995) Controlling false discovery rate a practical and powerful approach to multiple testing. J R Statist Soc Br. 57, 289-300. 

Liu P, Hwang JT (2007) Quick calculation for sample size while controlling false discovery rate with application to microarray analysis. 

Belief that a very small p-value for a predictor (for example, a biomarker) is more likely to occur with high predictive accuracy. The multiple testing problem must not be ignored, and the false discovery rate (FDR) controlled see also Chapter 6. 

Suppose that we wish to make inferences on the parameters 0i,i = 1,g, where 9i represents the logarithm of the ratio of the expression levels of gene i under normal and disease conditions. If the ith gene has no differential expression, then the ratio is 1 and hence 0 = 0. In testing the g hypotheses Ho, 0 = 0, / = 1,..., g, suppose we set R, = 1 if H0, is rejected and Ri = 0 otherwise. Then, for any multiple testing procedure, one could in theory provide a complete description of the joint distribution of the indicator variables R, ..., Rg as a function of 0i,..., 0g in the entire parameter space. This is impractical if g > 2. Different controls of the error rate control different aspects of this joint distribution, with the most popular being weak control of the familywise error rate (FWER), strong control of the familywise error rate, and control of the false discovery rate (FDR). 

Y. Benjamini and D. Yekutieh, The control of the false discovery rate in multiple tests under dependency. Ann Stat 29 1165-1188 (2001). 

Benyamini Y, Hochberg Y. 1995. Controlling the false discovery rate A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society Series B 57 289-300. 

The raw peptide data is searched against known trypsin-digested prophetic peptides with specialized protein databases and the corresponding proteins can be identified within a confidence level and controlled false discovery rate. 

Benjamini, Y. Hochberg, Y. (1995). Controlling the False Discovery Rate A practical and Powerful Approach to Multiple Testing. Journal of The Royal Statistical Society, Vol. Ser B 57, pp. 289-300. 

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