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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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Histogram of log2 average Affymetrix MAS5 signal. Histogram of log2 average Affymetrix MAS5 signal for the Stratagene Total Human RNA using the 1,288 genes in common among the three platforms.
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Figure 5: Histogram of log2 average Affymetrix MAS5 signal. Histogram of log2 average Affymetrix MAS5 signal for the Stratagene Total Human RNA using the 1,288 genes in common among the three platforms.

Mentions: In calculating cross-platform correlation, we assumed that the correlation estimated using the using the 1288 matching probes across the three platforms are representative of expected correlation of genes in the human genome that could be represented on the plaforms. Examination of absolute tag counts for the Stratagene Total Human RNA obtained using Serial Analysis of Gene Expression data (available from GEO #GSM1734) revealed that the intensity distribution of the 1,288 genes in common among the three platforms is not representative of the range of expected values (Figures 4, 5, 6, 7). Thus the commonly invoked procedure of estimating cross-platform consistency using only probes in common to all platforms is demonstrated to suffer from bias related to genomic coverage and probe annotation. Future studies comparing commercially available and custom designed arrays need to take this into consideration.


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)

Histogram of log2 average Affymetrix MAS5 signal. Histogram of log2 average Affymetrix MAS5 signal for the Stratagene Total Human RNA using 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 5: Histogram of log2 average Affymetrix MAS5 signal. Histogram of log2 average Affymetrix MAS5 signal for the Stratagene Total Human RNA using the 1,288 genes in common among the three platforms.
Mentions: In calculating cross-platform correlation, we assumed that the correlation estimated using the using the 1288 matching probes across the three platforms are representative of expected correlation of genes in the human genome that could be represented on the plaforms. Examination of absolute tag counts for the Stratagene Total Human RNA obtained using Serial Analysis of Gene Expression data (available from GEO #GSM1734) revealed that the intensity distribution of the 1,288 genes in common among the three platforms is not representative of the range of expected values (Figures 4, 5, 6, 7). Thus the commonly invoked procedure of estimating cross-platform consistency using only probes in common to all platforms is demonstrated to suffer from bias related to genomic coverage and probe annotation. Future studies comparing commercially available and custom designed arrays need to take this into consideration.

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