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

• Main Authors: Guiseppi-Elie Anthony, Chaplin Michael D, Taylor G Scott, Dumur Catherine I, Archer Kellie J, Grant Geraldine, Ferreira-Gonzalez Andrea, Garrett Carleton T
• Format: Article
• Language: English
• Published: BMC 2007-11-01
• Series: BMC Bioinformatics
• Online Access: http://www.biomedcentral.com/1471-2105/8/447

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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 <it>X </it>and <it>Y </it>are measured with error. Herein we demonstrate that correcting for measurement error by estimating the "disattenuated" correlation substantially improves cross-platform correlations.</p> <p>Conclusion</p> <p>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.</p>