Comparison of statistical methods for gene set enrichment tests
| Autor: | Hsin-Ying Tsai, 蔡欣穎 |
|---|---|
| Rok vydání: | 2014 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 102 Microarray aims to simultaneously monitor the expression of thousands of genes. It is usually the objective to mine important information from the data, such as the representative genes that differentially expressed (DE) under different conditions. In recent years, several gene set enrichment tests have been proposed to search for a DE gene set under different conditions. The gene set enrichment tests can be divided into two categories, univariate and multivariate methods. The former summarizes univariate statistics from each gene in the set to infer whether the gene set is significantly DE or not, while the latter considers the correlation among genes by assuming multinormal distribution and using multivariate analysis. In this study, we compared seven gene set enrichment tests by simulations. The tests were also practiced on a real microarray dataset. The results showed that Hotellin''s T2 and gene set enrichment analysis were the most powerful. Wilcoxon rank sum test and Kolmogorov-Smirnov test had the best sensitivity and specificity. In conclusion, gene set enrichment analysis was the most robust method to detect DE gene sets. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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