Abstract
When working with high-dimensional biological data the so-called multiple hypothesis testing problem emerges. That is, when many separate tests are performed, several will be significant by chance provoking false positive results. Many statistical methods have been developed to deal with this problem. An important topic concerning multiple hypothesis testing efforts applied to high-throughput experiments is the intrinsic inter-dependency in gene effects. Here we simulate data resembling the testing scenario used in a well-known data set from breast cancer microarray studies. The objective of the study is to see the impact of high correlation within gene blocks onto the multiple-testing correction methods as Sequential Bonferroni (SB), Benjamini and Hochberg FDR (BH) and Sequential Goodness of Fit (SGoF).
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References
Efron, B.: Correlation and Large-Scale Simultaneous Significance Testing. Journal of the American Statistical Association 102, 93–103 (2007)
Farcomeni, A.: A review of modern multiple hypothesis testing, with particular attention to the false discovery proportion. Stat. Methods Med. Res. 17, 347–388 (2008)
Shi, J., Levinson, D.F., Whittemore, A.S.: Significance levels for studies with correlated test statistics. Biostatistics 9, 458–466 (2008)
Storey, J.D., Taylor, J.E., Siegmund, D.: Strong control, conservative point estimation and simultaneous conservative consistency of false discovery rates: a unified approach. Journal of the Royal Statistical Society Series B-Statistical Methodology 66, 187–205 (2004)
Storey, J.D., Tibshirani, R.: Statistical significance for genomewide studies. Proc. Natl. Acad. Sci. U. S. A. 100, 9440–9445 (2003)
Hedenfalk, I., Duggan, D., Chen, Y., Radmacher, M., Bittner, M., Simon, R., Meltzer, P., Gusterson, B., Esteller, M., Kallioniemi, O.P., Wilfond, B., Borg, A., Trent, J., Raffeld, M., Yakhini, Z., Ben-Dor, A., Dougherty, E., Kononen, J., Bubendorf, L., Fehrle, W., Pittaluga, S., Gruvberger, S., Loman, N., Johannsson, O., Olsson, H., Sauter, G.: Gene-expression profiles in hereditary breast cancer. N. Engl. J. Med. 344, 539–548 (2001)
Storey, J.D., Day, J., Leek, J.: The optimal discovery procedure II: applications to comparative microarray experiments (2005), http://www.bepress.com/uwbiostat/paper260
Holm, S.: A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979)
Benjamini, Y., Hochberg, Y.: Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society. Series B (Methodological) 57, 289–300 (1995)
Carvajal-Rodriguez, A., de Uña, A., Rolan-Alvarez, E.: A new multitest correction (SGoF) that increases its statistical power when increasing the number of tests. BMC Bioinformatics 10, 209 (2009)
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Carvajal-Rodríguez, A., de Uña-Álvarez, J. (2011). A Simulation Study on the Impact of Strong Dependence in High-Dimensional Multiple-Testing I: The Case without Effects. In: Rocha, M.P., Rodríguez, J.M.C., Fdez-Riverola, F., Valencia, A. (eds) 5th International Conference on Practical Applications of Computational Biology & Bioinformatics (PACBB 2011). Advances in Intelligent and Soft Computing, vol 93. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19914-1_32
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DOI: https://doi.org/10.1007/978-3-642-19914-1_32
Publisher Name: Springer, Berlin, Heidelberg
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