Methodology article

A new multitest correction (SGoF) that increases its statistical power when increasing the number of tests

Antonio Carvajal-Rodríguez^1*, Jacobo de Uña-Alvarez² and Emilio Rolán-Alvarez¹

• * Corresponding author: Antonio Carvajal-Rodríguez acraaj@uvigo.es

Author Affiliations

¹ Departamento de Bioquímica, Genética e Inmunología, Facultad de Biología, Universidad de Vigo, 36310, Vigo, Spain

² Departamento de Estadística e Investigación Operativa, Facultad de Ciencias Económicas y Empresariales, Universidad de Vigo, 36310, Vigo, Spain

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BMC Bioinformatics 2009, 10:209 doi:10.1186/1471-2105-10-209

Published: 8 July 2009

Abstract

Background

The detection of true significant cases under multiple testing is becoming a fundamental issue when analyzing high-dimensional biological data. Unfortunately, known multitest adjustments reduce their statistical power as the number of tests increase. We propose a new multitest adjustment, based on a sequential goodness of fit metatest (SGoF), which increases its statistical power with the number of tests. The method is compared with Bonferroni and FDR-based alternatives by simulating a multitest context via two different kinds of tests: 1) one-sample t-test, and 2) homogeneity G-test.

Results

It is shown that SGoF behaves especially well with small sample sizes when 1) the alternative hypothesis is weakly to moderately deviated from the null model, 2) there are widespread effects through the family of tests, and 3) the number of tests is large.

Conclusion

Therefore, SGoF should become an important tool for multitest adjustment when working with high-dimensional biological data.