MOTIVATION: Molecular pathways and networks play a key role in basic and disease biology. An emerging notion is that networks encoding patterns of molecular interplay may themselves differ between contexts, such as cell type, tissue or disease (sub)type. However, while statistical testing of differences in mean expression levels has been extensively studied, testing of network differences remains challenging. Furthermore, since network differences could provide important and biologically interpretable information to identify molecular subgroups, there is a need to consider the unsupervised task of learning subgroups and networks that define them. This is a nontrivial clustering problem, with neither subgroups nor subgroup-specific networks known at the outset. RESULTS: We leverage recent ideas from high-dimensional statistics for testing and clustering in the network biology setting. The methods we describe can be applied directly to most continuous molecular measurements and networks do not need to be specified beforehand. We illustrate the ideas and methods in a case study using protein data from The Cancer Genome Atlas (TCGA). This provides evidence that patterns of interplay between signalling proteins differ significantly between cancer types. Furthermore, we show how the proposed approaches can be used to learn subtypes and the molecular networks that define them. AVAILABILITY AND IMPLEMENTATION: As the Bioconductor package nethet. CONTACT: [email protected] or [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Measurements of impedance spectra used for non-invasive glucose monitoring are affected by a variety of perturbing factors such as temperature and sweat/moisture fluctuations, changes in perfusion, and body movements. In order to quantify and compensate for these perturbing effects, a multi-sensor approach was suggested. Different sensors are used, measuring signals correlated with blood glucose and perturbing factors, respectively. Here, we investigate how the multiple sensor data can be transformed into meaningful information about changes in the concentration of blood glucose. Linear regression models and variable selection (stepwise for/back-ward and lasso) techniques are used to derive generally valid models allowing for the estimation of blood glucose concentration. We find that over-fitting is best avoided by using a special version of cross-validated prediction error as the model selection criterion. Indeed, the resulting models are reasonably small, plausible, and comprise an additive adjustment for the experimental run.