1. Application of a correlation correction factor in a microarray cross-platform reproducibility study
- Author
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G. Scott Taylor, Andrea Ferreira-Gonzalez, Michael D. Chaplin, Catherine I. Dumur, Kellie J. Archer, Anthony Guiseppi-Elie, Carleton T. Garrett, and Geraldine Grant
- Subjects
Computer science ,Statistics as Topic ,lcsh:Computer applications to medicine. Medical informatics ,computer.software_genre ,Biochemistry ,Correlation ,Structural Biology ,Calibration ,Humans ,Microarray databases ,lcsh:QH301-705.5 ,Molecular Biology ,Reproducibility ,Observational error ,business.industry ,Gene Expression Profiling ,Applied Mathematics ,Pattern recognition ,Microarray Analysis ,Computer Science Applications ,Gene expression profiling ,lcsh:Biology (General) ,Gene chip analysis ,lcsh:R858-859.7 ,Data mining ,Artificial intelligence ,business ,computer ,Research Article - 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.
- Published
- 2007