Novel Consensus Gene Selection Criteria for Distributed GPU Partial Least Squares-Based Gene Microarray Analysis in Diffused Large B Cell Lymphoma (DLBCL) and Related Findings.

This paper proposes a novel consensus gene selection criteria for partial least squares-based gene microarray analysis. By quantifying the extent of consistency and distinctiveness of the differential gene expressions across different double cross validations (CV) or randomizations in terms of occurrence and randomization p-values, the proposed criteria are able to identify a more comprehensive genes associated with the underlying disease. A Distributed GPU implementation has been proposed to accelerate the gene selection problem and about 8-11 times speed up has been achieved based on the microarray datasets considered. Simulation results using various cancer gene microarray datasets show that the proposed approach is able to achieve highly comparable classification accuracy in comparing with many conventional approaches. Furthermore, enrichment analysis on the selected genes for Diffused Large B Cell Lymphoma (DLBCL) and Prostate Cancer datasets and show that only the proposed approach is able to identify gene lists enriched in different pathways with significant p-values. In contrast, sufficient statistical significance cannot be found for conventional SVM-RFE and the t-test. The reliability in identifying and establishing statistical significance of the gene findings makes the proposed approach an attractive alternative for cancer related researches based on gene expression profiling or other similar data.