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

Archer KJ, Dumur CI, Taylor GS, Chaplin MD, Guiseppi-Elie A, Grant G, Ferreira-Gonzalez A, Garrett CT - BMC Bioinformatics (2007)

Bottom Line: Recent research examining cross-platform correlation of gene expression intensities has yielded mixed results.Herein we demonstrate that correcting for measurement error by estimating the "disattenuated" correlation substantially improves cross-platform correlations.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.

View Article: PubMed Central - HTML - PubMed

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

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.

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GMU. Pairwise scatterplots and Pearson's correlation for GMU arrays restricted to the 1,288 genes in common among the three platforms.
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Figure 3: GMU. Pairwise scatterplots and Pearson's correlation for GMU arrays restricted to the 1,288 genes in common among the three platforms.

Mentions: Prior to estimating cross-platform correlations, we performed a thorough examination of intra-platform reproducibility, as recommended [29]. Since the Stratagene Total Human RNA was used as both the experimental and reference sample, the expected log2 ratio for all genes is 1, so that no correlation is expected when comparing two arrays in terms of the log2 ratio. Therefore for two channel arrays, we restricted attention to intensities from one channel as well as to the post-normalized intensities from that same channel. For the Affymetrix GeneChip, intensities were highly correlated across the set of three technical replicates for all expression summary methods (Table 1 and Figure 1). The GMU arrays were strongly correlated, though the C3B arrays were not highly correlated (Figures 2 and 3).


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

Archer KJ, Dumur CI, Taylor GS, Chaplin MD, Guiseppi-Elie A, Grant G, Ferreira-Gonzalez A, Garrett CT - BMC Bioinformatics (2007)

GMU. Pairwise scatterplots and Pearson's correlation for GMU arrays restricted to the 1,288 genes in common among the three platforms.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2211756&req=5

Figure 3: GMU. Pairwise scatterplots and Pearson's correlation for GMU arrays restricted to the 1,288 genes in common among the three platforms.
Mentions: Prior to estimating cross-platform correlations, we performed a thorough examination of intra-platform reproducibility, as recommended [29]. Since the Stratagene Total Human RNA was used as both the experimental and reference sample, the expected log2 ratio for all genes is 1, so that no correlation is expected when comparing two arrays in terms of the log2 ratio. Therefore for two channel arrays, we restricted attention to intensities from one channel as well as to the post-normalized intensities from that same channel. For the Affymetrix GeneChip, intensities were highly correlated across the set of three technical replicates for all expression summary methods (Table 1 and Figure 1). The GMU arrays were strongly correlated, though the C3B arrays were not highly correlated (Figures 2 and 3).

Bottom Line: Recent research examining cross-platform correlation of gene expression intensities has yielded mixed results.Herein we demonstrate that correcting for measurement error by estimating the "disattenuated" correlation substantially improves cross-platform correlations.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.

View Article: PubMed Central - HTML - PubMed

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

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.

Show MeSH