MOTIVATION: Gaussian graphical models (GGMs) are network representations of random variables (as nodes) and their partial correlations (as edges). GGMs overcome the challenges of high-dimensional data analysis by using shrinkage methodologies. Therefore, they have become useful to reconstruct gene regulatory networks from gene-expression profiles. However, it is often ignored that the partial correlations are 'shrunk' and that they cannot be compared/assessed directly. Therefore, accurate (differential) network analyses need to account for the number of variables, the sample size, and also the shrinkage value, otherwise, the analysis and its biological interpretation would turn biased. To date, there are no appropriate methods to account for these factors and address these issues. RESULTS: We derive the statistical properties of the partial correlation obtained with the Ledoit-Wolf shrinkage. Our result provides a toolbox for (differential) network analyses as (i) confidence intervals, (ii) a test for zero partial correlation (null-effects) and (iii) a test to compare partial correlations. Our novel (parametric) methods account for the number of variables, the sample size and the shrinkage values. Additionally, they are computationally fast, simple to implement and require only basic statistical knowledge. Our simulations show that the novel tests perform better than DiffNetFDR-a recently published alternative-in terms of the trade-off between true and false positives. The methods are demonstrated on synthetic data and two gene-expression datasets from Escherichia coli and Mus musculus. AVAILABILITY AND IMPLEMENTATION: The R package with the methods and the R script with the analysis are available in https://github.com/V-Bernal/GeneNetTools. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The assessment of the quality of body movements in real-time is of utmost importance in exergames, that is digital games controlled by body movements, that are designed for the elderly population. In consideration of the fact that among elderly people the number of injuries and fatalities (caused by fall incidences) is increasing, the ultimate goal of exergames is not only to provide fun, entertainment and exercise but also to improve postural control and balance. It is known that improving balance can reduce the number of falls among the elderly population. Real-time assessment of body movements during exergaming could be used to adapt the difficulty of the game as a function of the quality of the movements of the player, as well as to provide immediate feedback. This in turn could increase motivation to play and therefore increase the effectiveness of exergames as tools to improve balance. In a previous study we identified curvature and speed of motion trajectories as promising metrics for balance quantification using bi-dimensional force plate data [Soancatl et al. 2016]. The main aims of this study are (1) to investigate whether curvature and speed could be used to quantify balance using three-dimensional trajectories derived from whole body movements as recorded by Kinect, and (2) to identify which body parts provide the most insight into balance. We consider measures to be suitable for balance quantification if they can differentiate between two groups (older and younger participants). This categorization can provide insight into balance control, as in general it is known that younger adults (here: younger than 60 years) have better postural control than older adults.