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


CONCLUSIONS
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.
Archer
Kellie J
KJ
Department of Biostatistics, Virginia Commonwealth University, 730 East Broad St,, Richmond, VA, USA. kjarcher@vcu.edu
Dumur
Catherine I
CI
Taylor
G Scott
GS
Chaplin
Michael D
MD
Guiseppi-Elie
Anthony
A
Grant
Geraldine
G
Ferreira-Gonzalez
Andrea
A
Garrett
Carleton T
CT
D002138:Calibration; D020869:Gene Expression Profiling / Q000706:statistics & numerical data*; D006801:Humans; D046228:Microarray Analysis / Q000295:instrumentation* / Q000379:methods*; D013223:Statistics as Topic / Q000379:methods*