Figure 2.

Comparison of linear ICA (NMLE), nonlinear ICA with Gaussian RBF kernel (NICAgauss), and PCA, on the yeast cell cycle spotted array data (dataset 1). For each functional category within GO and KEGG, the value of -log₁₀ (p value) with the smallest p value 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 PCA when C for PCA is fixed to its optimal value 37.5. (b) Gene clusters based on the linear ICA components are compared with those based on PCA with different values of C. (c) Gene clusters based on the nonlinear ICA components are compared with those based on linear ICA. (d) Gene clusters based on the nonlinear ICA components are compared with those based on PCA. Overall, nonlinear ICA performed slightly better than NMLE, and both methods performed significantly better than PCA.