Integrative prescreening in analysis of multiple cancer genomic studies.

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, particu

SUBMITTER: Song R 

PROVIDER: S-EPMC3436748 | biostudies-literature | 2012 Jul

REPOSITORIES: biostudies-literature