Handling the Cellular Complex Systems in Alzheimer's Disease Through a Graph Mining Approach.

In the last two decades, the medical sciences have changed their approach to pathogenesis as well as to the diagnosis and treatment of complex human diseases. The main reason for this change is the explosive development of biomedical technology and research, which produces a huge amount of information and data which are generated at an increasing rate. Toward this direction is the pathway analysis, a thriving research area of systems biology tools and methodologies which aim to unravel the inherent complexity of high-throughput biological data produced by the advent of omics technologies. Through this graph mining approach, we can deal with the complexity of the cellular systems of various diseases such as Alzheimer's disease. In this work, we developed a subpathway analysis method for single-cell RNA-seq experiments which isolates differentially expressed subpathways indicating potentially perturbed biological processes. The differential expression status of each gene is negotiated among well-established RNA-seq differential expression analysis tools in order to minimize false discoveries. Also, we demonstrate the efficacy of our method on a single-cell RNA-seq dataset for temporal tracking of microglia activation in neurodegeneration. Results suggest that our approach succeeds in isolating several perturbed biological processes known to be associated with neurodegeneration.