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 sunflower horse halter 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 70 qt storage bin with existing analysis methods to identify cancer markers.