A power law global error model for the identification of differentially expressed genes in microarray data

doi:10.1186/1471-2105-5-203

BMC Bioinformatics

Volume 5

Viewing options:

Abstract
Full text
PDF (1.6MB)
Additional files

Associated material:

Related literature:

Articles citing this article
on BioMed Central
on Google Scholar
on ISI Web of Science
on PubMed Central
Other articles by authors
on Google Scholar

Pavelka N
Pelizzola M
Vizzardelli C
Capozzoli M
Splendiani A
Granucci F
Ricciardi-Castagnoli P

on PubMed

Pavelka N
Pelizzola M
Vizzardelli C
Capozzoli M
Splendiani A
Granucci F
Ricciardi-Castagnoli P
Related articles/pages
on Google

on Google Scholar

on PubMed

Tools:

Post to:

Methodology article

A power law global error model for the identification of differentially expressed genes in microarray data

Norman Pavelka^* , Mattia Pelizzola^* , Caterina Vizzardelli^* , Monica Capozzoli , Andrea Splendiani , Francesca Granucci and Paola Ricciardi-Castagnoli

Department of Biotechnology and Bioscience, University of Milano-Bicocca, Piazza della Scienza 2, 20126 Milan, Italy

author email corresponding author email* Contributed equally

BMC Bioinformatics 2004, 5:203doi:10.1186/1471-2105-5-203


Published:	17 December 2004

Abstract

Background

High-density oligonucleotide microarray technology enables the discovery of genes that are transcriptionally modulated in different biological samples due to physiology, disease or intervention. Methods for the identification of these so-called "differentially expressed genes" (DEG) would largely benefit from a deeper knowledge of the intrinsic measurement variability. Though it is clear that variance of repeated measures is highly dependent on the average expression level of a given gene, there is still a lack of consensus on how signal reproducibility is linked to signal intensity. The aim of this study was to empirically model the variance versus mean dependence in microarray data to improve the performance of existing methods for identifying DEG.

Results

In the present work we used data generated by our lab as well as publicly available data sets to show that dispersion of repeated measures depends on location of the measures themselves following a power law. This enables us to construct a power law global error model (PLGEM) that is applicable to various Affymetrix GeneChip data sets. A new DEG identification method is therefore proposed, consisting of a statistic designed to make explicit use of model-derived measurement spread estimates and a resampling-based hypothesis testing algorithm.

Conclusions

The new method provides a control of the false positive rate, a good sensitivity vs. specificity trade-off and consistent results with varying number of replicates and even using single samples.