A new method for analysis of microarray gene expression experiments referred to as Sum-based Meta-analytical Enrichment?(SME) is proposed in this manuscript. SME is a combined enrichment and meta-analytical approach t...A new method for analysis of microarray gene expression experiments referred to as Sum-based Meta-analytical Enrichment?(SME) is proposed in this manuscript. SME is a combined enrichment and meta-analytical approach to infer?on the association of gene sets with particular phenotypes. SME allows enrichment to be performed across datasets,?which to our knowledge was not earlier possible. As a proof of concept study, this technique is applied to datasets from?Oncomine, a publicly available cancer microarray database. The genes that are significantly up-/down-regulated?(p-value ≤ 10-4) in various cancer types in Oncomine were listed. These genes were assigned to biological processes?using GO annotations. The SME algorithm was applied to identify a list of GO processes most deregulated in 4 major?cancer types. For validation we examined whether the processes predicted by SME were already documented in literature.SME method identified several known processes for the 4 cancer types and identified several novel processes?which are biologically plausible. Nearly all the pathways identified by SME as common to the 4 cancers were found to?contribute to processes which are widely regarded as cancer hallmarks. SME provides an intuitive yet objective ‘process-centric’ interpretation of the ‘gene-centric’ output of individual microarray comparison studies. The methods described?here should be applicable in the next-generation sequencing based gene expression analysis as well.展开更多
文摘A new method for analysis of microarray gene expression experiments referred to as Sum-based Meta-analytical Enrichment?(SME) is proposed in this manuscript. SME is a combined enrichment and meta-analytical approach to infer?on the association of gene sets with particular phenotypes. SME allows enrichment to be performed across datasets,?which to our knowledge was not earlier possible. As a proof of concept study, this technique is applied to datasets from?Oncomine, a publicly available cancer microarray database. The genes that are significantly up-/down-regulated?(p-value ≤ 10-4) in various cancer types in Oncomine were listed. These genes were assigned to biological processes?using GO annotations. The SME algorithm was applied to identify a list of GO processes most deregulated in 4 major?cancer types. For validation we examined whether the processes predicted by SME were already documented in literature.SME method identified several known processes for the 4 cancer types and identified several novel processes?which are biologically plausible. Nearly all the pathways identified by SME as common to the 4 cancers were found to?contribute to processes which are widely regarded as cancer hallmarks. SME provides an intuitive yet objective ‘process-centric’ interpretation of the ‘gene-centric’ output of individual microarray comparison studies. The methods described?here should be applicable in the next-generation sequencing based gene expression analysis as well.
