DBGSA: a novel method of distance-based gene set analysis

Abstract

When compared with single gene functional analysis, gene set analysis (GSA) can extract more information from gene expression profiles. Currently, several gene set methods have been proposed, but most of the methods cannot detect gene sets with a large number of minor-effect genes. Here, we propose a novel distance-based gene set analysis method. The distance between two groups of genes with different phenotypes based on gene expression should be larger if a certain gene set is significantly associated with the given phenotype. We calculated the distance between two groups with different phenotypes, estimated the significant P-values using two permutation methods and performed multiple hypothesis testing adjustments. This method was performed on one simulated data set and three real data sets. After a comparison and literature verification, we determined that the gene resampling-based permutation method is more suitable for GSA, and the centroid statistical and average linkage statistical distance methods are efficient, especially in detecting gene sets containing more minor-effect genes. We believe that this distance-based method will assist us in finding functional gene sets that are significantly related to a complex trait. Additionally, we have prepared a simple and publically available Perl and R package

Genome-Scale Metabolic Models: A Link between Bioinformatics and Systems Biology

“…Hereby, high-scoring metabolites can be identified and these are called reporter metabolites meaning metabolites that have strong transcriptional/translational changes around their neighbors. The GSA has been popular for analysis of omics data aiding biological interpretations, so many algorithms have been proposed in the past decade (Kim and Volsky, 2005;Li et al, 2012;Subramanian et al, 2005;Taylor and Tibshirani, 2006). Subsequently, the reporter algorithm was further generalized to identify reporter features (Oliveira et al, 2008) that basically uses the concept of gene set analysis (GSA; Huang da et al, 2009;Hung et al, 2012;Maciejewski, 2013;Subramanian et al, 2005).…”

Enriching the gene set analysis of genome-wide data by incorporating directionality of gene expression and combining statistical hypotheses and methods

“…Here as well, the literature is unclear on the best procedure. For instance, Li et al (24) prefer randomizing the genes, Goeman and Bühlmann (4) suggest randomizing the sample labels, whereas Nam and Kim (19) recommend using both approaches if possible.…”

Identification of Alternatively-Activated Pathways between Primary Breast Cancer and Liver Metastatic Cancer Using Microarray Data

“…Common approaches to identify pathways or gene expression signatures or any other list of differentially-expressed genes include individual gene analysis (IGA) and gene set analysis (GSA) [30,31]. Both methods are used to investigate whether the identified gene signatures are statistically enriched with genes from predefined pathways.…”