Figure 4.

Comparison of linear ICA (NMLE), nonlinear ICA with Gaussian RBF kernel (NICAgauss), and k-means clustering on the yeast cell cycle oligonucleotide array data (dataset 2). For each GO and KEGG functional category, the largest -log10(p value) within clusters from one method is plotted against the corresponding value from the other method. (a) Gene clusters based on the linear ICA components are compared with those based on k-means clustering. (b) TP (True Positives) of gene clusters based on the linear ICA components are compared with those of gene clusters based on k-means clustering. Functional categories for which clusters from NMLE have larger p values than those from k-means clustering algorithm are colored in purple. (c) SN (Sensitivity) of gene clusters based on the linear ICA components are compared with gene clusters based on k-means clustering. Functional categories corresponding to the ones in purple in Figure 4b are colored in purple. (d) Gene clusters based on the nonlinear ICA components are compared with those based on linear ICA. (e) Gene clusters based on the nonlinear ICA components are compared with those based on k-means clustering. Overall, nonlinear ICA performed better than NMLE and both methods performed better than k-means clustering.

Lee and Batzoglou Genome Biology 2003 4:R76   doi:10.1186/gb-2003-4-11-r76
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