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Revisiting adverse effects of cross-hybridization in Affymetrix gene expression data: do they matter for correlation analysis?

Klebanov L, Chen L, Yakovlev A - Biol. Direct (2007)

Bottom Line: The authors of that paper came to the conclusion that the process of multiple targeting in short oligonucleotide microarrays induces spurious correlations and this effect may deteriorate the inference on correlation coefficients.The design of their study and supporting simulations cast serious doubt upon the validity of this conclusion.As the problem stands now, there is no compelling reason to believe that multiple targeting causes a large-scale effect on the correlation structure of Affymetrix gene expression data.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Biostatistics and Computational Biology, University of Rochester, 601 Elmwood Avenue, Rochester, Box 630, New York 14642, USA. levkleb@yahoo.com

ABSTRACT

Background: This work was undertaken in response to a recently published paper by Okoniewski and Miller (BMC Bioinformatics 2006, 7: Article 276). The authors of that paper came to the conclusion that the process of multiple targeting in short oligonucleotide microarrays induces spurious correlations and this effect may deteriorate the inference on correlation coefficients. The design of their study and supporting simulations cast serious doubt upon the validity of this conclusion. The work by Okoniewski and Miller drove us to revisit the issue by means of experimentation with biological data and probabilistic modeling of cross-hybridization effects.

Results: We have identified two serious flaws in the study by Okoniewski and Miller: (1) The data used in their paper are not amenable to correlation analysis; (2) The proposed simulation model is inadequate for studying the effects of cross-hybridization. Using two other data sets, we have shown that removing multiply targeted probe sets does not lead to a shift in the histogram of sample correlation coefficients towards smaller values. A more realistic approach to mathematical modeling of cross-hybridization demonstrates that this process is by far more complex than the simplistic model considered by the authors. A diversity of correlation effects (such as the induction of positive or negative correlations) caused by cross-hybridization can be expected in theory but there are natural limitations on the ability to provide quantitative insights into such effects due to the fact that they are not directly observable.

Conclusion: The proposed stochastic model is instrumental in studying general regularities in hybridization interaction between probe sets in microarray data. As the problem stands now, there is no compelling reason to believe that multiple targeting causes a large-scale effect on the correlation structure of Affymetrix gene expression data. Our analysis suggests that the observed long-range correlations in microarray data are of a biological nature rather than a technological flaw.

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Variation coefficients for gene expression levels in the TELL data set.
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Figure 4: Variation coefficients for gene expression levels in the TELL data set.

Mentions: where v = σν/μ is the variation coefficient of the r.v. ν. Figure 4 displays the histogram of variation coefficients in the TELL data. The histogram is extremely narrow, indicating that the variation coefficient is effectively constant across genes. The same fact was documented by Wu and Irizarry [20]. In the TELL data set, the mean variation coefficient is equal to 0.235. Using this value as an estimate of v and solving the quadratic inequality (11) with respect to μ, we have μ > 3.98 × 106. This lower bound for μ is quite close to the mean total expression, i.e., the sum of the mean values of all gene expressions, the latter being equal to 4.22 × 106.


Revisiting adverse effects of cross-hybridization in Affymetrix gene expression data: do they matter for correlation analysis?

Klebanov L, Chen L, Yakovlev A - Biol. Direct (2007)

Variation coefficients for gene expression levels in the TELL data set.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2211459&req=5

Figure 4: Variation coefficients for gene expression levels in the TELL data set.
Mentions: where v = σν/μ is the variation coefficient of the r.v. ν. Figure 4 displays the histogram of variation coefficients in the TELL data. The histogram is extremely narrow, indicating that the variation coefficient is effectively constant across genes. The same fact was documented by Wu and Irizarry [20]. In the TELL data set, the mean variation coefficient is equal to 0.235. Using this value as an estimate of v and solving the quadratic inequality (11) with respect to μ, we have μ > 3.98 × 106. This lower bound for μ is quite close to the mean total expression, i.e., the sum of the mean values of all gene expressions, the latter being equal to 4.22 × 106.

Bottom Line: The authors of that paper came to the conclusion that the process of multiple targeting in short oligonucleotide microarrays induces spurious correlations and this effect may deteriorate the inference on correlation coefficients.The design of their study and supporting simulations cast serious doubt upon the validity of this conclusion.As the problem stands now, there is no compelling reason to believe that multiple targeting causes a large-scale effect on the correlation structure of Affymetrix gene expression data.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Biostatistics and Computational Biology, University of Rochester, 601 Elmwood Avenue, Rochester, Box 630, New York 14642, USA. levkleb@yahoo.com

ABSTRACT

Background: This work was undertaken in response to a recently published paper by Okoniewski and Miller (BMC Bioinformatics 2006, 7: Article 276). The authors of that paper came to the conclusion that the process of multiple targeting in short oligonucleotide microarrays induces spurious correlations and this effect may deteriorate the inference on correlation coefficients. The design of their study and supporting simulations cast serious doubt upon the validity of this conclusion. The work by Okoniewski and Miller drove us to revisit the issue by means of experimentation with biological data and probabilistic modeling of cross-hybridization effects.

Results: We have identified two serious flaws in the study by Okoniewski and Miller: (1) The data used in their paper are not amenable to correlation analysis; (2) The proposed simulation model is inadequate for studying the effects of cross-hybridization. Using two other data sets, we have shown that removing multiply targeted probe sets does not lead to a shift in the histogram of sample correlation coefficients towards smaller values. A more realistic approach to mathematical modeling of cross-hybridization demonstrates that this process is by far more complex than the simplistic model considered by the authors. A diversity of correlation effects (such as the induction of positive or negative correlations) caused by cross-hybridization can be expected in theory but there are natural limitations on the ability to provide quantitative insights into such effects due to the fact that they are not directly observable.

Conclusion: The proposed stochastic model is instrumental in studying general regularities in hybridization interaction between probe sets in microarray data. As the problem stands now, there is no compelling reason to believe that multiple targeting causes a large-scale effect on the correlation structure of Affymetrix gene expression data. Our analysis suggests that the observed long-range correlations in microarray data are of a biological nature rather than a technological flaw.

Show MeSH