Membership scoring via independent feature subspace analysis for grouping co-expressed genes

Linear decomposition models such as principal component analysis (PCA) and independent component analysis (ICA) were shown to be useful in analyzing high dimensional DNA microarray data, compared to clustering methods. Assuming that gene expression is controlled by a linear combination of uncorrelated/independent latent variables, linear modes were shown to be related to some biological functions. However, grouping co-expressed genes using these methods is not quite successful since they take some biological dependence into account. In this paper, we employ the independent feature subspace analysis (IFSA) method which finds phase- and shift-invariant features. We propose a new membership scoring method based on invariant features from IFSA and show its usefulness in grouping functionally-related genes in the presence of time-shift and expression phase variance. This is confirmed through PathCalling.