微陣列資料(microarray data)常常因許多的原因而產生缺失值(missing values)。然而，後續的分析(downstream analysis)往往都需要完整的資料才能進行分析。因此，為了提升後續的分析效果，已有許多方法被發展出來並且宣稱可以有效地解決缺失值的問題。在過去幾年，對於演算法的性能比較已經有一些初步的評估，但是這些評估卻存在著許多問題。在此研究中，我們使用大量的微陣列資料對各現存演算法進行多次的模擬，並且利用不同類型的性能指標來公平且客觀地評估各演算法的性能。研究的一開始，我們評估各演算法在不同類型資料的性能與表現。接下來探討不同的物種對演算法的性能的影響。其中，為了公平且客觀地評估演算法的性能，不僅使用統計指標，也使用兩個具有生物意義的指標來進行性能評估。我們認為以local-least-squares的方法改良與發展的演算法(例如:LLS、ILLS和SLLS)以及LS演算法在測試的微陣列資料當中的普遍表現較佳。在這研究當中，我們進行客觀且全面性的分析與比較其演算法填補缺失值的性能。有了此客觀且全面性的分析研究結果，讓相關領域之研究人員更方便地依照不同的需求來挑選最佳的演算法。除此之外，開發新式缺失值估測演算法時，能以此研究之策略來與既有演算法進行比較。

Microarray data frequently contain missing values due to various reasons. However, most downstream analyses for microarray data require complete datasets. Therefore, algorithms for missing value estimation must be developed to improve downstream analysis. Since 2001, lots of algorithms have been proposed, but the comparison performance among different algorithms is always insufficient in the numbers of benchmark datasets, the number of algorithms included and performance measure used, and the rounds of simulation performed. In this research, we used (I) nine algorithms, (II) thirteen microarray datasets, (III) 110 independent runs of the simulation procedure, and (IV) three types of measures to evaluate the performance of each imputation algorithm fairly. First, the effects of different types of datasets on the performance of each imputation algorithm were evaluated. Second, we discussed whether the datasets from different species have different impact on the performance of different algorithms. To assess the performance of each algorithm fairly, all evaluations were performed using three types of measures. In addition to the statistical measure, two other indices with more biological meanings are useful to reflect the impact of missing value imputation on downstream data analysis. Through our studies, we suggest that local-least-squares-based and least-squares methods would be better choices to handle missing values for most of datasets. In this work, we carried out a comprehensive comparison of algorithms of microarray missing value imputation. Based on such a comprehensive comparison, researchers could choose an optimal algorithm for their datasets easily. Moreover, new imputation algorithms could be compared with other existing algorithms using this comparison strategy as a standard protocol in the future.

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