title: Assessment of gene set analysis methods based on microarray data.
abstract: Gene set analysis (GSA) incorporates biological information into statistical knowledge to identify gene sets differently expressed between two or more phenotypes. It allows us to gain an insight into the functional working mechanism of cells beyond the detection of differently expressed gene sets. In order to evaluate the competence of GSA approaches, three self-contained GSA approaches with different statistical methods were chosen; Category, Globaltest and Hotelling's T(2) together with their assayed power to identify the differences expressed via simulation and real microarray data. The Category does not take care of the correlation structure, while the other two deal with correlations. In order to perform these methods, R and Bioconductor were used. Furthermore, venous thromboembolism and acute lymphoblastic leukemia microarray data were applied. The results of three GSAs showed that the competence of these methods depends on the distribution of gene expression in a dataset. It is very important to assay the distribution of gene expression data before choosing the GSA method to identify gene sets differently expressed between phenotypes. On the other hand, assessment of common genes among significant gene sets indicated that there was a significant agreement between the result of GSA and the findings of biologists.
authors.affiliation: Department of Biostatistics, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
authors.forename: Hamid
authors.initials: H
authors.lastname: Alavi-Majd
authors.affiliation: Department of Epidemiology, Faculty of Public Health, Shahid Beheshti University of Medical Sciences, Tehran, Iran. Electronic address: lkhodakarim@gmail.com.
authors.forename: Soheila
authors.initials: S
authors.lastname: Khodakarim
authors.affiliation: Proteomics Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
authors.forename: Farid
authors.initials: F
authors.lastname: Zayeri
authors.affiliation: Proteomics Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
authors.forename: Mostafa
authors.initials: M
authors.lastname: Rezaei-Tavirani
authors.affiliation: Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
authors.forename: Seyyed Mohammad
authors.initials: SM
authors.lastname: Tabatabaei
authors.affiliation: Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
authors.forename: Maryam
authors.initials: M
authors.lastname: Heydarpour-Meymeh
mesh_terms: D030541:Databases, Genetic; D015870:Gene Expression; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010641:Phenotype; D054198:Precursor Cell Lymphoblastic Leukemia-Lymphoma / Q000235:genetics; D059467:Transcriptome / Q000235:genetics*; D054556:Venous Thromboembolism / Q000235:genetics
keywords: Category; GO; GSA; GSEA; Gene set; Globaltest; Hotelling's T(2); KEGG; Kyoto Encyclopedia of Genes and Genomes; NEG; RMA; VTE; gene ontology; gene set analysis; gene set enrichment analysis; no observed cytogenetic abnormalities; robust multichip analysis; venous thromboembolism