Figure 3

Unsupervised hierarchical clustering of the normalized raw data. (A) Data reconstructed by PCA; (B) and the data reconstructed by FastICA; (C) C1–8: control samples, AD1–5: severe AD samples. Red and green blocks represent signal increase and decrease from the mean respectively. For the PCA reconstructed data, the first 10 principle components were applied and their cumulative contribution of the corresponding eigenvalues was 95.5%. For ICA-derived data, the genes with loadings that exceed the threshold (= 2) were considered significant, and the remaining genes with lower values were considered as noises and set to zero. Here, by comparing them to the original data, both PCA and ICA-derived data greatly improved the clustering results of AD microarray data.