[轉錄]0411演講公告＠清大統研所
清華大學、交通大學
統 計 學 研 究 所
專 題 演 講
題 目: Validity and reliability of preprocessing and differential
expression combinations for Affymetrix GeneChip microarrays
主講人: 黃冠華 教授 (交通大學統計所)
時 間: 97年4月11日（星期五）10：40 - 11：30
（上午10：20-10：40茶會於統計所821室舉行）
地 點: 清大綜合三館837室
Abstract
Microarray technology has been widely used for several years and a large number
of computational analysis tools have been developed. We focus on the most
popular platform, Affymetrix GeneChip arrays. Despite the rich research on
selecting the optimal method of preprocessing and/or differential expression,
this paper is unique in the following aspects. First, we have explored the best
combination of preprocessing and differential expression methods. Second, we
have evaluated both validity (accuracy) and reliability (reproducibility) on a
variety of datasets with distinct characteristics. Third, we have compared
stochastic-model-based and physical-mode-based preprocessing algorithms and
gene-specific and empirical-Bayes’ differential expression detection. To
evaluate which combinations of preprocessing and differential expression
methods perform well, we considered 4 popular preprocessing methods (MAS 5.0,
RMA, dChip and PDNN) and 5 popular differential expression methods
(fold-change, two sample t-test, SAM, EBarrays and limma). We used three
spike-in datasets to assess the validity, and ROC curves were used for the
evaluation. To evaluate the reliability, we used another dataset from the MAQC
project, which was generated using samples hybridized to Affymetrix platform at
two different test sites. Overlap rates between two test sites were compared. I
have found that validity was more sensitive to preprocessing methods, whereas
reliability was more sensitive to differential expression methods. Considering
both validity and reliability, six combinations were recommended when a small
percentage of the genes were differentially expressed: RMA+fold-change,
RMA+SAM, RMA+limma, PDNN+fold-change, PDNN+SAM and PDNN+limma. Three
combinations were recommended when the percentage of differentially expressed
genes was large: dChip(PM-only)+fold-change, dChip(PM-only)+SAM and
dChip(PM-only)+limma.
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