Heuristic principal component analysis-based unsupervised feature extraction and its application to gene expression analysis of amyotrophic lateral sclerosis data sets

We applied principal component analysis (PCA)-based unsupervised feature extraction (FE) to amyotrophic lateral sclerosis (ALS) gene expression profiles. ALS is a debilitating neurodegenerative disorder with no effective therapy. The relevant gene expression profiles contained a small number of samples (from a few to tens) with a large number of features (several tens of thousands). Although it is important to recognize critical genes from gene expression profiles, a small-sample-large-feature situation makes FE difficult. In PCA-based unsupervised FE, features rather than samples are embedded into a low dimensional space, and critical genes are identified as outliers that are supposed to obey group-oriented behavior. The 29 candidate genes identified as critical for ALS by this methodology turned out to be biologically feasible based on comparisons with numerous previous studies. Together, they formed a collected gene regulation/protein binding network within which the known, but not explicitly identified in this study, three ALS-causing genes, SOD1, TDP-43, and SETX, could be naturally placed/embedded. Among the 29 genes, the translated chemokine receptor CCR6 protein was considered to be a potential therapy target and its antagonists/agonists were identified using the in silico drug discovery software ChooseLD. The ten top-ranked antagonists/agonists shared structures with many compounds that were previously known to bind to various proteins.