Skip to main content
SpringerLink
Log in

Power estimation of the t test for detecting differential gene expression

  • Original Paper
  • Published:
Functional & Integrative Genomics Aims and scope Submit manuscript

Abstract

There exist now a number of statistical methods for detecting differential gene expression in experiments with microarray data. In trials under two conditions, a version of the two-sample t statistic is usually used. However, the problem of estimating the power for these tests has so far been insufficiently studied. In this paper, we propose a method to calculate the power of the robust t test for detecting differential gene expression in experiments with twins. We discuss also the results of the implementation of this method to simulated data.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

References

  • Begun A (2006) Robust method for detecting differential gene expression in twin studies. Bioinformatics 22(23):2905–2909

    Article  PubMed  CAS  Google Scholar 

  • Efron B, Tibshirani R, Goss V, Chu G (2000) Microarrays and their use in a comparative experiment. http://www-sta.stanford.edu/~tibs/research.html

  • Efron B, Tibshirani R, Storey JD, Tusher V (2001) Empirical Bayes analysis of a microarray experiment. J Am Stat Assoc 96:1151–1160

    Article  Google Scholar 

  • McLachlan GL, Basford KE (1988) Mixture models: inference and application to clustering. Dekker, New York

    Google Scholar 

  • Pan W (2003) On the use of permutation in and the performance of a class of nonparametric methods to detect differential gene expression. Bioinformatics 19(11):1333–1339

    Article  PubMed  CAS  Google Scholar 

  • Pan W, Lin J, Le C (2002) How many replicates of arrays are required to detect gene expression changes in microarray experiments? A mixture model approach. Genome Biol 3:1–11

    Google Scholar 

  • Smyth GK (2004) Linear models and empirical Bayes methods foe assessing differential expression in microarray experiments. Statistical Applications in Genetics and Molecular Biology 3(1) Article 3:1–26

  • Smyth GK, Michaud J, Scott HS (2004) Use of within-array replicate spots for assessing differential expression in microarray experiments. Bioinformatics 21(9):2067–2075

    Article  Google Scholar 

  • Tibshirani R (2005) A simple method for assessing sample sizes in microarray experiments. Department of Health Research and Policy and Department of Statistics, Stanford University, Stanford, CA 94305:1–8

  • Tusher VG, Tibshirani R, Chu G (2001) Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci 98:5116–5121

    Article  PubMed  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Medical Informatics and Statistics, Kiel University, Brunswiker Str. 10, 24105, Kiel, Germany

    Alexander Begun

Authors
  1. Alexander Begun
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alexander Begun.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Begun, A. Power estimation of the t test for detecting differential gene expression. Funct Integr Genomics 8, 109–113 (2008). https://doi.org/10.1007/s10142-007-0061-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10142-007-0061-8

Keywords

Search

Navigation