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computes the distances between all pairs of gene expression pro les from two clusters and the average of these distances becomes the distance between the clusters. Single linkage de nes the distance between two clusters as the distance between the two closest representatives of these clusters. Complete linkage de nes the distance between two clusters as the distance between the two farthest representatives. The difference between these three de nitions is illustrated in Figure 4.15. 4.3.5.4 Cluster Validation Regardless of the type of clustering, all obtained clusters need to be evaluated for biological validity before proceeding to further analysis. Visual validation is aimed at determining whether there are outliers in clusters or whether the gene expression pro les within each cluster are correlated to each other. If a problem is detected by validation, clusters are often re ned by adjusting the number of clusters (parameter k), the distance measuring method, or even by repeating the clustering with a different clustering method. Microarray data sets are highly dimensional. It is often dif cult to provide a clear view of gene expression pro le types within each cluster. By reducing the dimension of the microarray data set to two or three dimensions, analysis can be simpli ed and a visual overview of the data can be generated, which may provide useful information on gene expression pro le clustering. Such a dimensionality reduction is typically achieved with principal component analysis (PCA). This technique nds the orthogonal components (also called principal components) of the input vectors and retains two or three orthogonal components with the highest variance. A visual examination of the projected clusters can help determine an appropriate number of distinct clusters for clustering as illustrated in Figure 4.16. java pdf generation itext Api2Pdf releases java client library to convert HTML to PDF and more!
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