Application of a correlation correction factor in a microarray cross-platform reproducibility study

Kellie J Archer¹^,4 , Catherine I Dumur² , G Scott Taylor³ , Michael D Chaplin⁴ , Anthony Guiseppi-Elie³ , Geraldine Grant⁵ , Andrea Ferreira-Gonzalez² and Carleton T Garrett²

¹Department of Biostatistics, Virginia Commonwealth University, 730 East Broad St., Richmond, VA, USA

²Department of Pathology, Virginia Commonwealth University, Richmond, VA, USA

³Center for Bioelectronics, Biosensors and Biochips, School of Engineering, Virginia Commonwealth University, Richmond, VA, USA

⁴Center for the Study of Biological Complexity, Virginia Commonwealth University, Richmond, VA, USA

⁵Molecular and Microbiological Department, George Mason University, Manassas, VA, USA

author email corresponding author email

BMC Bioinformatics 2007, 8:447doi:10.1186/1471-2105-8-447


Published:	15 November 2007

Abstract

Background

Recent research examining cross-platform correlation of gene expression intensities has yielded mixed results. In this study, we demonstrate use of a correction factor for estimating cross-platform correlations.

Results

In this paper, three technical replicate microarrays were hybridized to each of three platforms. The three platforms were then analyzed to assess both intra- and cross-platform reproducibility. We present various methods for examining intra-platform reproducibility. We also examine cross-platform reproducibility using Pearson's correlation. Additionally, we previously developed a correction factor for Pearson's correlation which is applicable when X and Y are measured with error. Herein we demonstrate that correcting for measurement error by estimating the "disattenuated" correlation substantially improves cross-platform correlations.

Conclusion

When estimating cross-platform correlation, it is essential to thoroughly evaluate intra-platform reproducibility as a first step. In addition, since measurement error is present in microarray gene expression data, methods to correct for attenuation are useful in decreasing the bias in cross-platform correlation estimates.