DNetPRO: A network approach for low-dimensional signatures from high-throughput data

Abstract

One of the main objectives of high-throughput genomics studies is to obtain a low-dimensional set of observables - a signature - for sample classification purposes (diagnosis, prognosis, stratification). We propose DNetPRO, Discriminant Analysis with Network PROcessing, a supervised network-based signature identification method. The algorithm is easily scalable, allowing efficient computing for high number of observables (103 –105). We show applications on real high-throughput genomic datasets in which our method outperforms existing results or is compatible with them, but with a smaller number of selected features. Moreover, the geometrical simplicity of the resulting class-separation surfaces allows a clearer interpretation of the obtained signatures in comparison to nonlinear classification models.