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BMC Bioinformatics. 2012; 13: 168.
Published online Jul 16, 2012. doi:  10.1186/1471-2105-13-168
PMCID: PMC3436748
Integrative prescreening in analysis of multiple cancer genomic studies
Rui Song,corresponding author1 Jian Huang,2 and Shuangge Ma3
1Department of Statistics, Colorado State University, Fort Collins, USA
2Department of Statistics and Actuarial Science, University of Iowa, Iowa City, USA
3School of Public Health, Yale University, New Haven, USA
corresponding authorCorresponding author.
Rui Song: song/at/stat.colostate.edu; Jian Huang: jian-huang/at/uiowa.edu; Shuangge Ma: shuangge.ma/at/yale.edu
Received August 16, 2011; Accepted May 18, 2012.
Abstract
Background
In high throughput cancer genomic studies, results from the analysis of single datasets often suffer from a lack of reproducibility because of small sample sizes. Integrative analysis can effectively pool and analyze multiple datasets and provides a cost effective way to improve reproducibility. In integrative analysis, simultaneously analyzing all genes profiled may incur high computational cost. A computationally affordable remedy is prescreening, which fits marginal models, can be conducted in a parallel manner, and has low computational cost.
Results
An integrative prescreening approach is developed for the analysis of multiple cancer genomic datasets. Simulation shows that the proposed integrative prescreening has better performance than alternatives, particularly including prescreening with individual datasets, an intensity approach and meta-analysis. We also analyze multiple microarray gene profiling studies on liver and pancreatic cancers using the proposed approach.
Conclusions
The proposed integrative prescreening provides an effective way to reduce the dimensionality in cancer genomic studies. It can be coupled with existing analysis methods to identify cancer markers.
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