Discovering variability patterns for change detection in complex phenotype data

The phenotype is the result of a genotype expression in a given environment. Genetic and eventually protein mutations and/or environmental changes may affect the biological homeostasis leading to a pathological status of a normal phenotype. Studying the alterations of the phenotypes on a temporal basis becomes thus relevant and even determinant whether considering the biological re-assortment between the involved organisms and the cyclic nature of the pandemic outbreaks. In this paper, we present a computational solution that analyzes phenotype data in order to capture statistically evident changes emerged over time and track their repeatability. The proposed method adopts a model of analysis based on time-windows and relies on two kinds of patterns, emerging patterns and variability patterns. The first one models the changes in the phenotype detected between time-windows, while the second one models the changes in the phenotype replicated over timewindows. The application to Influenza A virus H1N1 subtype proves the usefulness of our in silico approach.