高密度寡核甘酸基因陣列實驗是新的生物技術，可在一個晶片上蒐集到數千至上萬個基因資料，資料處理的過程相當繁複，包括背景訊號的修正、正規化、探針背景的修正及探針組資料的整合，本研究首先將介紹各資料處理步驟。其中正規化的目的是要修正資料中由實驗產生的系統化變異，去除實驗誤差，使資料更為純淨，則後續所做的統計分析才會更為精確。之後再詳細介紹三種正規化方法，包括：尺度調整法、循環平滑調整法及百分位調整法。並將以一組實際資料來說明正規化後的結果。最終採取電腦模擬的方式，以平均四分位距、平均標準差、Diff統計量及離群值的個數這四個量化準則，來研究各正規化方法的效果，以及比較這三種正規化方法的優劣，同時也將探討此四種準則的適當性。

High-density oligonucleotide array gene experiment is a new biological technology. More than thousands of gene data can be obtained in an array. The data processing includes background correction, normalization, probe specific background correction and summarizing the probe set value into one expression measure. The goal of normalization is to remove the systematic variation induced in the experiment while keeping the biological variation of interest. Using the purified data, one will obtain more accurate conclusions in subsequent statistical analysis. Firstly, we introduce the data processing procedures. Three normalization methods, which include Scaling, Cyclic Loess and Quantile, are explained in detail and illustrated by a real data set. Moreover, a simulation study is conducted to compare the three methods. Four quantities, Mean of IQR, Mean of Standard Deviation, Diff Statistics and Outlier, are proposed for assessment. Not only the performances of the three normalization methods but also the properties of the four proposed criteria are given and studied in this research.

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