Format

Send to

Choose Destination
See comment in PubMed Commons below
Bioinformatics. 2009 Aug 15;25(16):2035-41. doi: 10.1093/bioinformatics/btp363. Epub 2009 Jun 15.

Comments on the analysis of unbalanced microarray data.

Author information

1
Department of Biostatistics, Box 357232, University of Washington, Seattle, WA 98195, USA. katiek@u.washington.edu

Abstract

MOTIVATION:

Permutation testing is very popular for analyzing microarray data to identify differentially expressed (DE) genes; estimating false discovery rates (FDRs) is a very popular way to address the inherent multiple testing problem. However, combining these approaches may be problematic when sample sizes are unequal.

RESULTS:

With unbalanced data, permutation tests may not be suitable because they do not test the hypothesis of interest. In addition, permutation tests can be biased. Using biased P-values to estimate the FDR can produce unacceptable bias in those estimates. Results also show that the approach of pooling permutation null distributions across genes can produce invalid P-values, since even non-DE genes can have different permutation null distributions. We encourage researchers to use statistics that have been shown to reliably discriminate DE genes, but caution that associated P-values may be either invalid, or a less-effective metric for discriminating DE genes.

PMID:
19528084
PMCID:
PMC2732368
DOI:
10.1093/bioinformatics/btp363
[Indexed for MEDLINE]
Free PMC Article
PubMed Commons home

PubMed Commons

0 comments
How to join PubMed Commons

    Supplemental Content

    Full text links

    Icon for Silverchair Information Systems Icon for PubMed Central
    Loading ...
    Support Center