Open Access Open Badges Research article

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

Kellie J Archer14*, Catherine I Dumur2, G Scott Taylor3, Michael D Chaplin4, Anthony Guiseppi-Elie3, Geraldine Grant5, Andrea Ferreira-Gonzalez2 and Carleton T Garrett2

Author Affiliations

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

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

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

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

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

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

Published: 15 November 2007



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.


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.


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.