Research article
A multivariate prediction model for microarray cross-hybridization
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* Corresponding author: Yian A Chen chenya@musc.edu
1 Department of Biostatistics, Bioinformatics, and Epidemiology, Medical University of South Carolina, Charleston, SC, USA
2 Center for Genomic Medicine, National Taiwan University, Taipei, Taiwan
3 Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
4 Key Laboratory of Molecular and Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, P. R China
5 Department of Biomedical Engineering, Georgia Tech, Atlanta, GA, USA
6 Department of Biostatistics and Applied Mathematics, University of Texas MD Anderson Cancer Center, Houston, TX, USA
BMC Bioinformatics 2006, 7:101 doi:10.1186/1471-2105-7-101
Published: 1 March 2006Abstract
Background
Expression microarray analysis is one of the most popular molecular diagnostic techniques in the post-genomic era. However, this technique faces the fundamental problem of potential cross-hybridization. This is a pervasive problem for both oligonucleotide and cDNA microarrays; it is considered particularly problematic for the latter. No comprehensive multivariate predictive modeling has been performed to understand how multiple variables contribute to (cross-) hybridization.
Results
We propose a systematic search strategy using multiple multivariate models [multiple linear regressions, regression trees, and artificial neural network analyses (ANNs)] to select an effective set of predictors for hybridization. We validate this approach on a set of DNA microarrays with cytochrome p450 family genes. The performance of our multiple multivariate models is compared with that of a recently proposed third-order polynomial regression method that uses percent identity as the sole predictor. All multivariate models agree that the 'most contiguous base pairs between probe and target sequences,' rather than percent identity, is the best univariate predictor. The predictive power is improved by inclusion of additional nonlinear effects, in particular target GC content, when regression trees or ANNs are used.
Conclusion
A systematic multivariate approach is provided to assess the importance of multiple sequence features for hybridization and of relationships among these features. This approach can easily be applied to larger datasets. This will allow future developments of generalized hybridization models that will be able to correct for false-positive cross-hybridization signals in expression experiments.