Advanced SearchSearch Tips
Effect of Genetic Correlations on the P Values from Randomization Test and Detection of Significant Gene Groups
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Effect of Genetic Correlations on the P Values from Randomization Test and Detection of Significant Gene Groups
Yi, Mi-Sung; Song, Hae-Hiang;
  PDF(new window)
At an early stage of genomic investigations, a small sample of microarrays is used in gene expression experiments to identify small subsets of candidate genes for a further accurate investigation. Unlike the statistical analysis methods for a large sample of microarrays, an appropriate statistical method for identifying small subsets is a randomization test that provides exact P values. These exact P values from a randomization test for a small sample of microarrays are discrete. The possible existence of differentially expressed genes in the sample of a full set of genes can be tested for the null hypothesis of a uniform distribution. Subsets of smaller P values are of prime interest for a further accurate investigation and identifying these outlier cells from a multinomial distribution of P values is possible by M test of Fuchs et al. (1980). Above all, the genome-wide gene expressions in microarrays are correlated, but the majority of statistical analysis methods in the microarray analysis are based on an independence assumption of genes and ignore the possibly correlated expression levels. We investigated with simulation studies the effect that correlated gene expression levels could have on the randomization test results and M test results, and found that the effects are often not ignorable.
Randomization test;exact P value;significant gene groups;outlier cells;
 Cited by
유전자군 분석의 방법론과 응용,이태원;;

응용통계연구, 2012. vol.25. 2, pp.269-277 crossref(new window)
A Method for Gene Group Analysis and Its Application, Korean Journal of Applied Statistics, 2012, 25, 2, 269  crossref(new windwow)
Bohrer, R., Chow, W., Faith, R., Joshi, V. and Wu, C. F. (1981). Multiple three-decision rules for factorial simple effects: Bonferroni wins again!, Journal of the American Statistical Association, 76, 119-124 crossref(new window)

Dondrup, M., Huser, A. T., Mertens, D. and Goesmann, A. (2009). An evaluation framework for statistical tests on microarray data, Journal of Biotechnology, 140, 18-26 crossref(new window)

Fierro, A. C., Vandenbussche, F., Engelen, K., Van de Peer, Y. and Marchal, K. (2008). Meta analysis of gene expression data within and across species, Current Genomics, 9, 525-534 crossref(new window)

Fisher, R. A. (1935). The Design of Experiments, Oliver and Boyd, Edinburgh

Fuchs, C. and Kenett, R. (1980). A test for detecting outlying cells in the multinomial distribution and two-way contingency tables, Journal of the American Statistical Association, 75, 395-398 crossref(new window)

Gadbury, G. L., Page, G. P., Heo, M., Mountz, J. D. and Allison, D. B. (2003). Randomization tests for small samples: An application for genetic expression data, Journal of the Royal Statistical Society. Series C (Applied Statistics), 52, 365-376 crossref(new window)

Gibbons, J. D. and Pratt, J. W. (1975). P-values: Interpretation and methodology, The American Statistician, 29, 20-25 crossref(new window)

Hu, J. and Wright, F. A. (2007). Assessing differential gene expression with small sample sizes in oligonucleotide arrays using a mean-variance model, Biometrics, 63, 41-49 crossref(new window)

Lambert, D. (1985). Robust two-sample permutation tests, The Annals of Statistics, 13, 606-625 crossref(new window)

Murie, C. and Nadon, R. (2008). A correction for estimating error when using the Local Pooled Error Statistical Test, Bioinformatics, 24, 1735-1736 crossref(new window)

Parmigiani, G., Garrett, E. S., Anbazhagan, R. and Gabrielson, E. (2002). A statistical framework for expression-based molecular classification in cancer, Journal of The Royal Statistical Society. Series B, 64, 717-736 crossref(new window)

Sidak, Z. (1968). On multivariate normal probabilities on rectangles: Their dependence on correlations, The Annals of Mathematical statistics, 39, 1425-1434

Welch, W. J. (1990). Construction of permutation tests, Journal of the American Statistical Association, 85, 693-698 crossref(new window)