Figure 11

Theoretical framework of ICA algorithms on microarray gene expression data. In the ICA model for microarray gene expression data, the matrix Xis the only one we know. According to the hypothesis (left frame) that gene profiles are linear combinations of statistically independent components Sand the latent variables A, the demixing process of ICA (right frame) can be applied to extract the latent variables (once we get the demixing matrix W, we can obtain latent variables Aeasily by A= W^-1) and gene signatures that have specific biological interpretations (Yis the estimator of S).