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Open Access Highly Accessed Methodology article

Multiple-platform data integration method with application to combined analysis of microarray and proteomic data

Shicheng Wu1, Yawen Xu1, Zeny Feng2, Xiaojian Yang2, Xiaogang Wang1 and Xin Gao1*

Author Affiliations

1 Department of Mathematics and Statistics, York University, 4700 Keele, Street, Toronto, Ontario, M3J 1P3 Canada

2 Department of Mathematics and Statistics, 50 Stone Road East, Guelph, Ontario, N1G 2W1 Canada

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BMC Bioinformatics 2012, 13:320  doi:10.1186/1471-2105-13-320

Published: 2 December 2012

Abstract

Background

It is desirable in genomic studies to select biomarkers that differentiate between normal and diseased populations based on related data sets from different platforms, including microarray expression and proteomic data. Most recently developed integration methods focus on correlation analyses between gene and protein expression profiles. The correlation methods select biomarkers with concordant behavior across two platforms but do not directly select differentially expressed biomarkers. Other integration methods have been proposed to combine statistical evidence in terms of ranks and p-values, but they do not account for the dependency relationships among the data across platforms.

Results

In this paper, we propose an integration method to perform hypothesis testing and biomarkers selection based on multi-platform data sets observed from normal and diseased populations. The types of test statistics can vary across the platforms and their marginal distributions can be different. The observed test statistics are aggregated across different data platforms in a weighted scheme, where the weights take into account different variabilities possessed by test statistics. The overall decision is based on the empirical distribution of the aggregated statistic obtained through random permutations.

Conclusion

In both simulation studies and real biological data analyses, our proposed method of multi-platform integration has better control over false discovery rates and higher positive selection rates than the uncombined method. The proposed method is also shown to be more powerful than rank aggregation method.