rags2ridges:A One-Stop-ℓ₂-Shop for Graphical Modeling of High-Dimensional Precision Matrices

A graphical model is an undirected network representing the conditional independence properties between random variables. Graphical modeling has become part and parcel of systems or network approaches to multivariate data, in particular when the variable dimension exceeds the observation dimension. rags2ridges is an R package for graphical modeling of high-dimensional precision matrices through ridge (ℓ2) penalties. It provides a modular framework for the extraction, visualization, and analysis of Gaussian graphical models from high-dimensional data. Moreover, it can handle the incorporation of prior information as well as multiple heterogeneous data classes. As such, it provides a one-stop-ℓ2-shop for graphical modeling of high-dimensional precision matrices. The functionality of the package is illustrated with an example dataset pertaining to blood-based metabolite measurements in persons suffering from Alzheimer’s disease.</p

Interrater Reliability of Diagnostic Methods in Traditional Indian Ayurvedic Medicine

This study assesses the interrater reliability of Ayurvedic pulse (nadi), tongue (jivha), and body constitution (prakriti) assessments. Fifteen registered Ayurvedic doctors with 3–15 years of experience independently examined twenty healthy subjects. Subjects completed self-assessment questionnaires and software analyses for prakriti assessment. Weighted kappa statistics for all 105 pairs of doctors were computed for the pulse, tongue, and prakriti data sets. According to the Landis-Koch scale, the pairwise kappas ranged from poor to slight, slight to fair, and fair to moderate for pulse, tongue, and prakriti assessments, respectively. The average pairwise kappa for pulse, tongue, and prakriti was 0.07, 0.17, and 0.28, respectively. For each data set and pair of doctors, the null hypothesis of random rating was rejected for just twelve pairs of doctors for prakriti, one pair of doctors for pulse examination, and no pairs of doctors for tongue assessment. Thus, the results demonstrate a low level of reliability for all types of assessment made by doctors. There was significant evidence against random rating by software and questionnaire use and by the diagnosis preferred by the majority of doctors. Prakriti assessment appears reliable when questionnaire and software assessment are used, while other diagnostic methods have room for improvement

GMCM: Unsupervised Clustering and Meta-Analysis Using Gaussian Mixture Copula Models

Methods for clustering in unsupervised learning are an important part of the statistical toolbox in numerous scientific disciplines. Tewari, Giering, and Raghunathan (2011) proposed to use so-called Gaussian mixture copula models (GMCM) for general unsupervised learning based on clustering. Li, Brown, Huang, and Bickel (2011) independently discussed a special case of these GMCMs as a novel approach to meta-analysis in highdimensional settings. GMCMs have attractive properties which make them highly flexible and therefore interesting alternatives to other well-established methods. However, parameter estimation is hard because of intrinsic identifiability issues and intractable likelihood functions. Both aforementioned papers discuss similar expectation-maximization-like algorithms as their pseudo maximum likelihood estimation procedure. We present and discuss an improved implementation in R of both classes of GMCMs along with various alternative optimization routines to the EM algorithm. The software is freely available in the R package GMCM. The implementation is fast, general, and optimized for very large numbers of observations. We demonstrate the use of package GMCM through different applications

Targeted Fused Ridge Estimation of Inverse Covariance Matrices from Multiple High-Dimensional Data Classes

We consider the problem of jointly estimating multiple inverse covariance matrices from high-dimensional data consisting of distinct classes. An $\ell_2$-penalized maximum likelihood approach is employed. The suggested approach is flexible and generic, incorporating several other $\ell_2$-penalized estimators as special cases. In addition, the approach allows specification of target matrices through which prior knowledge may be incorporated and which can stabilize the estimation procedure in high-dimensional settings. The result is a targeted fused ridge estimator that is of use when the precision matrices of the constituent classes are believed to chiefly share the same structure while potentially differing in a number of locations of interest. It has many applications in (multi)factorial study designs. We focus on the graphical interpretation of precision matrices with the proposed estimator then serving as a basis for integrative or meta-analytic Gaussian graphical modeling. Situations are considered in which the classes are defined by data sets and subtypes of diseases. The performance of the proposed estimator in the graphical modeling setting is assessed through extensive simulation experiments. Its practical usability is illustrated by the differential network modeling of 12 large-scale gene expression data sets of diffuse large B-cell lymphoma subtypes. The estimator and its related procedures are incorporated into the R-package rags2ridges.Comment: 52 pages, 11 figure

Restricted upper airway dimensions in patients with dentofacial deformity from juvenile idiopathic arthritis

BACKGROUND: This retrospective, cross-sectional study aimed to assess the pharyngeal airway dimensions of patients with juvenile idiopathic arthritis (JIA) and moderate/severe JIA-related dentofacial deformity (mandibular retrognathia/micrognathia), and compare the results with JIA patients with a normal mandibular appearance and a group of non-JIA patients. METHODS: Seventy-eight patients were retrospectively included in a 1:1:1 manner as specified below. All patients had previously been treated at the Section of Orthodontics, Aarhus University, Denmark. All had a pretreatment cone beam computed tomography (CBCT). Group 1 (JIA+); 26 JIA patients with severe arthritis-related dentofacial deformity and mandibular retrognathia/micrognathia. Group 2 (JIA-); 26 JIA patients with normal mandibular morphology/position. Group 3 (Controls); 26 non-JIA subjects. Dentofacial morphology and upper airway dimensions, excluding the nasal cavity, were assessed in a validated three-dimensional (3D) fashion. Assessment of dentofacial deformity comprised six morphometric measures. Assessment of airway dimensions comprised nine measures. RESULTS: Five morphometric measures of dentofacial deformity were significantly deviating in the JIA+ group compared with the JIA- and control groups: Posterior mandibular height, anterior facial height, mandibular inclination, mandibular occlusal inclination, and mandibular sagittal position. Five of the airway measurements showed significant inter-group differences: JIA+ had a significantly smaller nasopharyngeal airway dimension (ad2-PNS), a smaller velopharyngeal volume, a smaller minimal cross-sectional area and a smaller minimal hydraulic diameter than JIA- and controls. No significant differences in upper airway dimensions were seen between JIA- and controls. CONCLUSION: JIA patients with severe arthritis-related dentofacial deformity and mandibular micrognathia had significantly restricted upper airway dimensions compared with JIA patients without dentofacial deformity and controls. The restrictions of upper airway dimension seen in the JIA+ group herein were previously associated with sleep-disordered breathing in the non-JIA background population. Further studies are needed to elucidate the role of dentofacial deformity and restricted airways in the development of sleep-disordered breathing in JIA