Comparative study of gene set enrichment methods |
| |
Authors: | Luca Abatangelo Rosalia Maglietta Angela Distaso Annarita D'Addabbo Teresa Maria Creanza Sayan Mukherjee and Nicola Ancona |
| |
Institution: | (1) Istituto di Studi sui Sistemi Intelligenti per l'Automazione, CNR, Via Amendola 122/D-I, Bari, Italy;(2) Institute for Genome Science and Policy, Duke University, Durham, NC, USA |
| |
Abstract: | Background The analysis of high-throughput gene expression data with respect to sets of genes rather than individual genes has many advantages.
A variety of methods have been developed for assessing the enrichment of sets of genes with respect to differential expression.
In this paper we provide a comparative study of four of these methods: Fisher's exact test, Gene Set Enrichment Analysis (GSEA),
Random-Sets (RS), and Gene List Analysis with Prediction Accuracy (GLAPA). The first three methods use associative statistics,
while the fourth uses predictive statistics. We first compare all four methods on simulated data sets to verify that Fisher's
exact test is markedly worse than the other three approaches. We then validate the other three methods on seven real data
sets with known genetic perturbations and then compare the methods on two cancer data sets where our a priori knowledge is
limited. |
| |
Keywords: | |
本文献已被 SpringerLink 等数据库收录! |
|