![]() ![]() Ideally, a comprehensive pathway analysis method would be able to take into consideration all aspects of the phenomena described by a pathway. Since biochemical reactions are usually carried out by enzymes which are coded for by genes, in a metabolic pathway genes are associated with edges rather than nodes. Metabolic pathways are graphs that use nodes to represent biochemical compounds, and edges to describe biochemical reactions that involve such compounds. Gene signaling pathways are graphs that use nodes to represent genes, or gene products, and edges to represent signals that go from one gene to another. In general, biological pathways can be divided into gene signaling pathways, and metabolic pathways. As biological annotations started to include descriptions of gene interactions in the form of pathways (found in resources such as KEGG, BioCarta, and Reactome ), the identification of the pathways involved in various conditions has emerged as a ubiquitous bioinformatics task. ![]() For instance the identification of Gene Ontology (GO) terms enriched in differentially expressed genes was used as early as 1999, but became widespread only after the first on-line GO analysis tools were made available. Since the beginning of the microarray-based expression profiling experiments, researchers were interested in finding common “themes” among the genes identified as differentially expressed between two conditions. Unfortunately, the steady increase in the amount of data generated in the past decade from such experiments was not paralleled by the evolution of analytical methods used to extract knowledge from such datasets and, therefore, there is a gap between our ability to measure gene expression data and to extract workable knowledge from it. Microarray-based gene expression profiling experiments, which are routine today, allow researchers to identify, for instance, genes differentially expressed (DE) between diseased and normal patient samples or genes that change in expression over time during a treatment. PADOG was implemented as an R package available at. The advantages of PADOG over other existing approaches are shown to be stable to changes in the database of gene sets to be analyzed. PADOG significantly improves gene set ranking and boosts sensitivity of analysis using information already available in the gene expression profiles and the collection of gene sets to be analyzed. Unlike most gene set analysis methods which are validated through the analysis of 2-3 data sets followed by a human interpretation of the results, the validation employed here uses 24 different data sets and a completely objective assessment scheme that makes minimal assumptions and eliminates the need for possibly biased human assessments of the analysis results. We demonstrate the usefulness of the method when analyzing gene sets that correspond to the KEGG pathways, and hence we called our method P athway A nalysis with D own-weighting of O verlapping G enes ( PADOG). The gene weights are designed to emphasize the genes appearing in few gene sets, versus genes that appear in many gene sets. In this work we propose a new gene set analysis method that computes a gene set score as the mean of absolute values of weighted moderated gene t-scores. Most gene set analysis methods treat genes equally, regardless how specific they are to a given gene set. The identification of gene sets that are significantly impacted in a given condition based on microarray data is a crucial step in current life science research.
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