Fellenberg K, Hauser N C, Brors B, Neutzner A, Hoheisel J D, Vingron M
Department of Theoretical Bioinformatics, German Cancer Research Center, PO 101949, D-69009 Heidelberg, Germany.
Proc Natl Acad Sci U S A. 2001 Sep 11;98(19):10781-6. doi: 10.1073/pnas.181597298. Epub 2001 Sep 4.
Correspondence analysis is an explorative computational method for the study of associations between variables. Much like principal component analysis, it displays a low-dimensional projection of the data, e.g., into a plane. It does this, though, for two variables simultaneously, thus revealing associations between them. Here, we demonstrate the applicability of correspondence analysis to and high value for the analysis of microarray data, displaying associations between genes and experiments. To introduce the method, we show its application to the well-known Saccharomyces cerevisiae cell-cycle synchronization data by Spellman et al. [Spellman, P. T., Sherlock, G., Zhang, M. Q., Iyer, V. R., Anders, K., Eisen, M. B., Brown, P. O., Botstein, D. & Futcher, B. (1998) Mol. Biol. Cell 9, 3273-3297], allowing for comparison with their visualization of this data set. Furthermore, we apply correspondence analysis to a non-time-series data set of our own, thus supporting its general applicability to microarray data of different complexity, underlying structure, and experimental strategy (both two-channel fluorescence-tag and radioactive labeling).
对应分析是一种用于研究变量之间关联的探索性计算方法。与主成分分析非常相似，它展示了数据的低维投影，例如投影到一个平面上。然而，它是同时对两个变量进行这样的操作，从而揭示它们之间的关联。在这里，我们证明了对应分析在微阵列数据分析中的适用性和高价值，展示了基因与实验之间的关联。为了介绍该方法，我们展示了它在Spellman等人 [Spellman, P. T., Sherlock, G., Zhang, M. Q., Iyer, V. R., Anders, K., Eisen, M. B., Brown, P. O., Botstein, D. & Futcher, B. (1998) Mol. Biol. Cell 9, 3273 - 3297] 著名的酿酒酵母细胞周期同步化数据中的应用，以便与他们对该数据集的可视化结果进行比较。此外，我们将对应分析应用于我们自己的一个非时间序列数据集，从而支持其对不同复杂性、底层结构和实验策略（双通道荧光标记和放射性标记）的微阵列数据的普遍适用性。
