Assessment of gene set analysis methods based on microarray data |
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Authors: | Hamid Alavi-Majd Soheila Khodakarim Farid Zayeri Mostafa Rezaei-Tavirani Seyyed Mohammad Tabatabaei Maryam Heydarpour-Meymeh |
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Affiliation: | 1. Department of Biostatistics, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran;2. Department of Epidemiology, Faculty of Public Health, Shahid Beheshti University of Medical Sciences, Tehran, Iran;3. Proteomics Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran;4. Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran |
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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 T2 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. |
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Keywords: | GSA, gene set analysis KEGG, Kyoto Encyclopedia of Genes and Genomes GO, gene ontology GSEA, gene set enrichment analysis VTE, venous thromboembolism NEG, no observed cytogenetic abnormalities RMA, robust multichip analysis |
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