Related Experiment Video
Updated: Jul 10, 2026
An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations
Published on: November 3, 2010
Revisiting adverse effects of cross-hybridization in Affymetrix gene expression data: do they matter for correlation
Lev Klebanov1, Linlin Chen, Andrei Yakovlev
1Department of Biostatistics and Computational Biology, University of Rochester, 601 Elmwood Avenue, Rochester, Box 630, New York 14642, USA. levkleb@yahoo.com
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.
Insights
Multiple targeting in microarrays does not cause spurious correlations. Our analysis of gene expression data suggests long-range correlations are biological, not technological flaws, challenging previous conclusions.
Area of Science:
- Bioinformatics
- Genomics
- Computational Biology
Background:
- Revisiting a study by Okoniewski and Miller on microarray data analysis.
- Addressing conclusions that multiple targeting in short oligonucleotide microarrays induces spurious correlations.
Purpose of the Study:
- To experimentally and computationally re-evaluate the impact of multiple targeting on correlation inference in microarray data.
- To investigate the role of cross-hybridization in gene expression data correlation.
Main Methods:
- Analysis of biological microarray datasets.
- Probabilistic modeling of cross-hybridization effects.
- Critique of simulation models used in prior studies.
Main Results:
- Identified flaws in the original study's data suitability and simulation model.
- Demonstrated that removing multiply targeted probe sets does not alter correlation coefficient histograms.
- Showed cross-hybridization is complex and its effects are not directly observable.
Conclusions:
- Multiple targeting does not appear to significantly affect the correlation structure of Affymetrix gene expression data.
- Observed long-range correlations are likely biological in origin, not a technological artifact.
- A stochastic model aids in understanding probe set interactions in microarray data.
Related Concept Videos
02:34DNA Microarrays
01:30Crossing Over
01:27Correlation and Causation
00:42Cross-reactivity
01:23Correlation of Experimental Data
For example, a spherical particle moving through a viscous fluid experiences drag. Dimensional analysis shows that the drag force depends on the particle's diameter, velocity, and...