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


CONCLUSIONS
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.
Klebanov
Lev
L
Department of Biostatistics and Computational Biology, University of Rochester, 601 Elmwood Avenue, Rochester, Box 630, New York 14642, USA. levkleb@yahoo.com
Chen
Linlin
L
Yakovlev
Andrei
A
D015870:Gene Expression; D006824:Hybridization, Genetic; D015233:Models, Statistical; D009693:Nucleic Acid Hybridization / Q000235:genetics*; D020411:Oligonucleotide Array Sequence Analysis; D012313:RNA / Q000235:genetics; D013223:Statistics as Topic
D012313:RNA