Pairwise Variable Selection for High-Dimensional Model-Based Clustering

Variable selection for clustering is an important and challenging problem in high-dimensional data analysis. Existing variable selection methods for model-based clustering select informative variables in a “one-in-all-out” manner; that is, a variable is selected if at least one pair of clusters is separable by this variable and removed if it cannot separate any of the clusters. In many applications, however, it is of interest to further establish exactly which clusters are separable by each informative variable. To address this question, we propose a pairwise variable selection method for high-dimensional model-based clustering. The method is based on a new pairwise penalty. Results on simulated and real data show that the new method performs better than alternative approaches that use ℓ 1 and ℓ ∞ penalties and offers better interpretation.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/78587/1/j.1541-0420.2009.01341.x.pd

Non-technical skills assessments in undergraduate medical education: A focused BEME systematic review: BEME Guide no. 54

Consensus on how to assess non-technical skills is lacking. This systematic review aimed to evaluate the evidence regarding non-technical skills assessments in undergraduate medical education, to describe the tools used, learning outcomes and the validity, reliability and psychometrics of the instruments. A standardized search of online databases was conducted and consensus reached on included studies. Data extraction, quality assessment, and content analysis were conducted per Best Evidence in Medical Education guidelines. Nine papers met the inclusion criteria. Assessment methods broadly fell into three categories: simulated clinical scenarios, objective structured clinical examinations, and questionnaires or written assessments. Tools to assess non-technical skills were often developed locally, without reference to conceptual frameworks. Consequently, the tools were rarely validated, limiting dissemination and replication. There were clear themes in content and broad categories in methods of assessments employed. The quality of this evidence was poor due to lack of theoretical underpinning, with most assessments not part of normal process, but rather produced as a specific outcome measure for a teaching-based study. While the current literature forms a good starting position for educators developing materials, there is a need for future work to address these weaknesses as such tools are required across health education

Interaction of Nonlinear Schr\"odinger Solitons with an External Potential

Employing a particularly suitable higher order symplectic integration algorithm, we integrate the 1-$d$ nonlinear Schr\"odinger equation numerically for solitons moving in external potentials. In particular, we study the scattering off an interface separating two regions of constant potential. We find that the soliton can break up into two solitons, eventually accompanied by radiation of non-solitary waves. Reflection coefficients and inelasticities are computed as functions of the height of the potential step and of its steepness.Comment: 14 pages, uuencoded PS-file including 10 figure

A Case-control study in an Orcadian population investigating the rRelationship between human plasma N-glycans and metabolic syndrome

Background: Alterations in glycosylation patterns have long been known to reflect changes in cell metabolism. In this study, we investigated the relationship between human N-glycan profiles and metabolic syndrome. Method: Between 2005 and 2011, 2,155 individuals from the Orkney Islands (UK) were recruited and biological material, alongside phenotypic measures were collected. Individual N-glycan profiles were measured in plasma using weak anion exchange high performance liquid chromatography and calibrated hydrophilic interaction liquid chromatography. Pre-specified criteria were used to identify 564 cases with metabolic syndrome and 1475 controls. We applied logistic regression to test for association between this binary outcome against measured plasma N-glycans. We also assessed the correlation between N-glycan traits and individual components of metabolic syndrome and compared this to results found in similar analyses based in Chinese and Croatian populations. Results: 21 N-glycan traits were found to be associated with either an increased or a decreased likelihood of participants having metabolic syndrome, including monosialylated plasma N-glycans (OR of 1.49 (95%CI 1.33, 1.67), q=1.26E-12) and core fucosylated plasma N-glycans (OR of 0.81(95% CI 0.72-0.90), q=7.75E-4). Notably, consistent results in both sections of this analysis demonstrated the protective association of higher levels of core fucosylated N-glycans. Conclusion: Our results demonstrate that metabolic syndrome is associated with an alteration in plasma N-glycosylation patterns. The metabolic role of core fucosylated N-glycans is of particular interest for future study

The Clustering of Regression Models Method with Applications in Gene Expression Data

Identification of differentially expressed genes and clustering of genes are two important and complementary objectives addressed with gene expression data. For the differential expression question, many per-gene analytic methods have been proposed. These methods can generally be characterized as using a regression function to independently model the observations for each gene; various adjustments for multiplicity are then used to interpret the statistical significance of these per-gene regression models over the collection of genes analyzed. Motivated by this common structure of per-gene models, we propose a new model-based clustering method -- the clustering of regression models method, which groups genes that share a similar relationship to the covariate(s). This method provides a unified approach for a family of clustering procedures and can be applied for data collected with various experimental designs. In addition, when combined with per-gene methods for assessing differential expression that employ the same regression modeling structure, an integrated framework for the analysis of microarray data is obtained. The proposed methodology was applied to two real microarray datasets, one from a breast cancer study and the other from a yeast cell cycle study

Computing Power and Sample Size for Case-Control Association Studies with Copy Number Polymorphism: Application of Mixture-Based Likelihood Ratio Test

Recent studies suggest that copy number polymorphisms (CNPs) may play an important role in disease susceptibility and onset. Currently, the detection of CNPs mainly depends on microarray technology. For case-control studies, conventionally, subjects are assigned to a specific CNP category based on the continuous quantitative measure produced by microarray experiments, and cases and controls are then compared using a chi-square test of independence. The purpose of this work is to specify the likelihood ratio test statistic (LRTS) for case-control sampling design based on the underlying continuous quantitative measurement, and to assess its power and relative efficiency (as compared to the chi-square test of independence on CNP counts). The sample size and power formulas of both methods are given. For the latter, the CNPs are classified using the Bayesian classification rule. The LRTS is more powerful than this chi-square test for the alternatives considered, especially alternatives in which the at-risk CNP categories have low frequencies. An example of the application of the LRTS is given for a comparison of CNP distributions in individuals of Caucasian or Taiwanese ethnicity, where the LRTS appears to be more powerful than the chi-square test, possibly due to misclassification of the most common CNP category into a less common category

Computational cluster validation for microarray data analysis: experimental assessment of Clest, Consensus Clustering, Figure of Merit, Gap Statistics and Model Explorer

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