[轉錄]0509演講公告＠交大統研所
※ [本文轉錄自 NCTU-STAT96G 看板]
作者: cyz ( ) 看板: NCTU-STAT96G
標題: 0509演講公告＠交大統研所
時間: Tue May 6 13:07:00 2008
國立交通大學、清華大學
統計學研究所
專題演講
題 目：Global Testing of Differential Gene Expression in Functional Groups
主講人：蔡政安教授(中國醫藥大學生物統計中心)
時 間：97年5月9日(星期五)上午10:40-11:30
(上午10:20-10:40茶會於交大統計所428室舉行)
地 點：交大綜合一館427室
Abstract
Recent advances in cDNA microarray technology provide exciting tools for
studying the expression levels of thousands of distinct genes simultaneously.
An important aim in such experiments is to identify a subset of genes that
are differentially expressed genes between different conditions. Recently, in
addition to attempting to understand biological functions of each individual
gene, gene-class testing (GCT) or gene set enrichment analysis (GSEA) has
been proposed for gene expression analysis. A gene class may refer to a group
of genes with related functions or a set of genes grouped together based on
biologically relevant information.
Tian et al. (2005) presented two hypotheses for the purpose of GSEA, in which
the Q1 hypothesis tests the relative strength of association with the
phenotypes among the gene classes, and the Q2 hypothesis assesses the
statistical significance. These two hypotheses are related but not
equivalent. In this talk, I will investigate the null distributions of gene
classes under Q1 and Q2 for three one-sided and two two-sided test
statistics. We applied the five statistics to a diabetes dataset with 143
gene classes. In each statistic, the null distributions among the 143 gene
classes under Q1 are different. In each statistic, the null distributions of
the gene classes under Q1 and under Q2 are different, and their rankings of
significance can be different too. We clarify the one-sided and two-sided
hypotheses, and discuss some issues regarding the Q1 and Q2 hypotheses for
gene class ranking in the GCT.
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