在分析cDNA微陣列資料前，晶片需有嚴格的品質管制，以提高分析結果的可信度。然而文獻上缺乏客觀衡量晶片品質良窳的指標，且目前使用的影像處理軟體，如GenePix Pro，所得到的品質輸出變數繁多，在應用上不周延且頗為不便，基於此，本文利用因素分析中的主成份法來簡化這些變數，結合成少數幾個品質評估指標，並據此評估晶片的品質。透過統計模擬，所建立的三個指標可以有效地區辨晶片點品質。由實際資料的分析，發現所建議的Factor 1指標和GenePix Pro中的Flags指標性質類似，而指標Factor 2和Factor 3則可補足Flags指標在背景光部份訊息的欠缺，因而更具有偵測晶片品質的功效。

For the sake of improving the reliability of downstream analysis in microarray, monitoring array quality is necessary and essential. However, current accessible image scanner, like GenePix Pro, fails to offer objective and effective quality indexes to assess array quality. In this thesis, factor analysis was conducted to extract information contained in the feature quality measures. It turns out that three solid factors, which we called quality indexes, appear if principal component method, among others, is applied. Simulation study shows that these three quality indexes could assess the array quality efficiently. Through real data analysis, the Factor 1 index we extracted is similar to the Flags variable in GenePix Pro software. Factor 1 index, therefore, can be a substitute for the Flags variable. As to indexes Factor 2 and Factor 3, they supply information about the background, which is not available from the Flags variable.

Jones, L., Goldstein, D. R. et al. (2006). Assessment of the relationship between pre-chip and post-chip quality measures for Affymetrix GeneChip expression data. BMC Bioinformatics, 7:211.

Rocke, D. M. and Durbin, B. (2001). A model for measurement error for gene expression arrays. Journal of Computational Biology, 8, 6, 557-569.

Dumur, D. I., Nasim, S., Best, A. M., Archer, K. J. Ladd, A. C., Mas, V. R., Wilkinson, D. S., Garrett, C. T., and Ferreira-Gonzalez, A. (2004). Evaluation of quality-control criteria for Microarray gene expression analysis. Clin Chem, 50:11, 1994-2002.

Petri, A., Fleckner, J., and Matthiessen, M. W. (2004). Array-A-Lizer: A serial DNA microarray quality analyzer. BMC Bioinformatics, 5:12.

Finkelstein, D., Ewing, R., Gollub, J., Sterky, F., Cherry, M., and Somerville, S. (2002). Microarray data quality analysis: lessons from the AFGC project. Plant Molecular Biology, 48, 119-131.