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

BMC Bioinformatics

Methodology Article

Zeny Feng¹  Xiaojian Yang¹  Yawen Xu²  Xiaogang Wang²  Xin Gao²  Shicheng Wu² 

[1] Department of Mathematics and Statistics, 50 Stone Road East, N1G 2W1, Guelph, Ontario, Canada;Department of Mathematics and Statistics, York University, 4700 Keele, Street, M3J 1P3, Toronto, Ontario, Canada;

关键词: False Discovery Rate;    Rank Aggregation;    Data Platform;    Decision Line;    Lower False Discovery Rate;   

DOI  :  10.1186/1471-2105-13-320

 received in 2012-02-21, accepted in 2012-11-02,  发布年份 2012

来源: Springer

【 摘 要 】

BackgroundIt 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.ResultsIn 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.ConclusionIn 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.

【 授权许可】

Unknown   
© Wu et al.; licensee BioMed Central Ltd. 2012. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.