Controlling the False Discovery Rate for the Sam Method

10.29973/JCSA.200612.0002

Huey-Miin Hsueh；Chen-An Tsai

Significance Analysis of Microarray ； False Discovery Rate ； Per-Comparisonwise Error Rate

中國統計學報

44卷4期（2006 / 12 / 01）

364 - 381

英文

The Significant Analysis of Microarray (SAM) proposed by Tusher, Tibshirani and Chu (2001) is nowadays a standard statistical procedure for detecting differentially expressed genes in microarray studies. Given a threshold △ of the deviation between a t-like statistic and its empirical expectation, an estimated false discovery rate (FDR) is reported in additional to the conclusion of significance. However, the deviation between the statistic and its expectation is not easy to interpret as a conventional error measure. In practice, researchers often found the determination of the △ is quite difficult. SAM suggests to try several different △'s in the analysis and use the result which is correspondent to an adequate FDR level. In this paper, we propose a SAM-based approach, in which, instead of △, the level of per-comparisonwise error rate (PCER) is specified. The new approach involves the kernel quantile estimation method in resampling data to improve the efficiency of the sample quantiles. To control the FDR of a conclusion, the BH step-up multiple testing procedure is utilized. Simulation studies are conducted to show that the proposed approach achieves adaptive control of FDR in various settings. The proposed approach is demonstrated with a real microarray dataset.

1. Benjamini, Y.,Hochberg, Y.(1995).Controlling the false discovery rate: a practical and powerful approach to multiple testing.Journal of the Royal Statistical Society, Series,57,289-300.
2. Benjamini, Y.,Yekutieli, D.(2001).The control of the false discovery rate in multiple testing under dependency.Annal of Statistics,29,1165-1188.
3. Brown, P. O.,Botstein, D.(1999).Exploring the new world of the genome with DNA microarrays.Nature Genetics,21,33-37.
4. Golub, T. R.,Slonim, D. K.,Tamayo, P.,Huard, C.,Gaasenbeek, M.,Mesirov, J. P.,Coller, H.,Loh, M. L.,Downing, J. R.,Caligiuri, M. A.,Bloomfield, C. D.,Lander, E. S.(1999).Molecular classification of cancer: class discovery and class prediction by gene expression monitoring.Science,286,531-537.
5. Jung, S.-H.,Jang, W.(2006).How accurately can we control the FDR in analyzing microarray data?.Bioinformatics,22,1730-1736.
6. Lin, D. Y.(2005).An efficient Monte Carlo approach to assessing statistical significance in genomic studies.Bioinformatics,21,781-787.
7. Sheather, S.J.,Marron, J.S.(1990).Kernel quantile estimators.Journal of the American Statistical Association,85,410-416.
8. Storey, J. D.,Tibshirani R.(2003).Statistical significance for genome-wide studies.Proceedings of the National Academy of Sciences,100,9440-9445.
9. Tusher, V. G.,Tibshirani, R,Chu, G.(2001).Significance analysis of microarrays applied to the ionizing radiation response.Proceedings of the National Academy of Sciences,98,5116-5121.
10. Wiel, M. A.(2004).Significance analysis of microarrays using rank scores.Kwantitative Methoden,71,25-37.
11. Xie, Y.,Pan, W.,Khodursky, A. B.(2005).A note on using permutation-based false discovery rate estimates to compare different analysis methods for microarray data.Bioinformatics,21,4280-4288.