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Open Access Open Badges Research article

Integrative prescreening in analysis of multiple cancer genomic studies

Rui Song1*, Jian Huang2 and Shuangge Ma3

Author Affiliations

1 Department of Statistics, Colorado State University, Fort Collins, USA

2 Department of Statistics and Actuarial Science, University of Iowa, Iowa City, USA

3 School of Public Health, Yale University, New Haven, USA

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BMC Bioinformatics 2012, 13:168  doi:10.1186/1471-2105-13-168

Published: 16 July 2012



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.


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.


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.