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

Kellie J Archer1, Catherine I Dumur, G Scott Taylor

  • 1Department of Biostatistics, Virginia Commonwealth University, 730 East Broad St,, Richmond, VA, USA. kjarcher@vcu.edu

BMC Bioinformatics
|November 17, 2007
PubMed
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This study introduces a correction factor to improve cross-platform correlation estimates for gene expression data. Correcting for measurement error significantly enhances the accuracy of these correlations.

Area of Science:

  • Genomics
  • Bioinformatics
  • Microarray Analysis

Background:

  • Cross-platform correlation of gene expression intensities has shown inconsistent results in recent studies.
  • Assessing reproducibility across different microarray platforms is crucial for reliable data interpretation.

Purpose of the Study:

  • To demonstrate the utility of a correction factor for estimating cross-platform correlations.
  • To improve the accuracy of gene expression correlation estimates between different microarray platforms.

Main Methods:

  • Technical replicate microarrays were hybridized across three distinct platforms.
  • Intra- and cross-platform reproducibility were assessed using various methods, including Pearson's correlation.
  • A previously developed correction factor for Pearson's correlation, accounting for measurement error, was applied.

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Main Results:

  • Intra-platform reproducibility was thoroughly examined for each platform.
  • Cross-platform reproducibility was assessed using Pearson's correlation.
  • Applying the "disattenuated" correlation correction factor substantially improved cross-platform correlation estimates by accounting for measurement error.

Conclusions:

  • Thorough evaluation of intra-platform reproducibility is essential before estimating cross-platform correlations.
  • Methods correcting for measurement error attenuation are valuable for reducing bias in microarray gene expression cross-platform correlation estimates.