將許多DNA片段固定於玻片上的微陣列(Microarray)技術，可用於檢驗核酸樣本中所含特殊DNA序列的定量分析研究。對於探討基因圖譜以及突變原因等有關基因表現的問題上，微陣列系統無論在遺傳學研究或是醫學臨床應用方面，皆是有力的工具之一。
整個微陣列的製作與實驗過程中，需要兩部份的硬體系統，分別為點印系統(arrayer)與掃瞄系統(scanner)。將欲進行研究之mRNA在反轉錄成cDNA的過程中以螢光物質標誌，與先前點印於玻片上之已知位置且可代表個別基因的DNA片段進行配對接合(hybridization)反應。清除沒有配對成功的生物樣本後，可用掃瞄系統分析得知配對接合之情形。對我們自行架設的掃瞄系統而言，經測試並改良後其偵測螢光分子之濃度極限可達 M，相當於將螢光染劑稀釋萬倍。由實驗結果得知，此系統已接近微陣列實驗所需之掃瞄要求。

同時可觀察上萬個基因表現的強弱，是微陣列系統的優點之一。在不同的時間點，或給予不同的實驗條件時，同時觀察大量的基因表現情形，便可得到不同時間或實驗條件時各基因表現強弱的關係圖。將表現曲線變化相近的基因排列在相鄰位置，可初步將這些基因分類，或進一步由已知作用的基因推測未知基因之功用，稱之為群集分析(cluster analysis)。本文以樹狀群集、K個平均群集進行電腦模擬，再兼取二者之優點設計出K個空間樹狀群集法。另外提出目前文獻中常用之樹狀群集可能造成的分類誤差，並以二度或三度空間之主分量分析法修正之。經由比較各種不同群集結果的方法，期盼在基因分類的判斷上能提供更準確的佐證。

DNA microarrays, microscopic arrays of large sets of DNA sequences immobilized on slides, are powerful tools in identifying or quantifying many specific DNA sequences in complex nucleic acid samples. DNA microarrays have been used in genetic mapping studies, mutational analysis and in genome wide monitoring of gene expression, and will become standard tools in research and clinical applications.
Making and using printed DNA microarrays requires two pieces of hardware, named arrayer and scanner. The mRNA samples turn into cDNA targets, which were labeled fluorescence dyes simultaneously by reverse transcription. The different DNA probes printed previously on slides were hybridized with the cDNA. After cleaning the non-hybridized cDNA, the scanning results were analyzed by computer. In our scanning system, the limited concentration of fluorescent detection can achieve M, and it will suffice the microarray experiments.

One of the advantages of microarray is quantifying the thousands of gene expressions simultaneously. The relation between different moments or conditions with the strength of gene expressions will be obtained by observing these experiments. Grouping the similar variation genes together will presume the function of the unknown genes by some genes we have known, named cluster analysis. The way of calculating distance between any two genes will be decided first. The computer simulations show the results of the hierarchical clustering and the K-means partitional clustering. We then design K-hierarchical clustering by acquiring their strong points. Besides, the principal component analysis is used in these simulations to reduce the errors of the hierarchical clustering and compare with the different clustering results.

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