Evaluation of Prediction-Oriented Model Selection Metrics for Extended Redundancy Analysis.

Extended redundancy analysis (ERA) is a statistical method that relates multiple sets of predictors to response variables. In ERA, the conventional approach of model evaluation tends to overestimate the performance of a model since the performance is assessed using the same sample used for model development. To avoid the overly optimistic assessment, we introduce a new model evaluation approach for ERA, which utilizes computer-intensive resampling methods to assess how well a model performs on unseen data. Specifically, we suggest several new model evaluation metrics for ERA that compute a model's performance on out-of-sample data, i.e., data not used for model development. Although considerable work has been done in machine learning and statistics to examine the utility of cross-validation and bootstrap variants for assessing such out-of-sample predictive performance, to date, no research has been carried out in the context of ERA. We use simulated and real data examples to compare the proposed model evaluation approach with the conventional one. Results show the conventional approach always favor more complex ERA models, thereby failing to prevent the problem of overfitting in model selection. Conversely, the proposed approach can select the true ERA model among many mis-specified (i.e., underfitted and overfitted) models.

Model-based recursive partitioning of extended redundancy analysis with an application to nicotine dependence among US adults.

Extended redundancy analysis (ERA) is used to reduce multiple sets of predictors to a smaller number of components and examine the effects of these components on a response variable. In various social and behavioural studies, auxiliary covariates (e.g., gender, ethnicity) can often lead to heterogeneous subgroups of observations, each of which involves distinctive relationships between predictor and response variables. ERA is currently unable to consider such covariate-dependent heterogeneity to examine whether the model parameters vary across subgroups differentiated by covariates. To address this issue, we combine ERA with model-based recursive partitioning in a single framework. This combined method, MOB-ERA, aims to partition observations into heterogeneous subgroups recursively based on a set of covariates while fitting a specified ERA model to data. Upon the completion of the partitioning procedure, one can easily examine the difference in the estimated ERA parameters across covariate-dependent subgroups. Moreover, it produces a tree diagram that aids in visualizing a hierarchy of partitioning covariates, as well as interpreting their interactions. In the analysis of public data concerning nicotine dependence among US adults, the method uncovered heterogeneous subgroups characterized by several sociodemographic covariates, each of which yielded different directional relationships between three predictor sets and nicotine dependence.

Pathway analysis of rare variants for the clustered phenotypes by using hierarchical structured components analysis.

BACKGROUNDS: Recent large-scale genetic studies often involve clustered phenotypes such as repeated measurements. Compared to a series of univariate analyses of single phenotypes, an analysis of clustered phenotypes can be useful for substantially increasing statistical power to detect more genetic associations. Moreover, for the analysis of rare variants, incorporation of biological information can boost weak effects of the rare variants. RESULTS: Through simulation studies, we showed that the proposed method outperforms other method currently available for pathway-level analysis of clustered phenotypes. Moreover, a real data analysis using a large-scale whole exome sequencing dataset of 995 samples with metabolic syndrome-related phenotypes successfully identified the glyoxylate and dicarboxylate metabolism pathway that could not be identified by the univariate analyses of single phenotypes and other existing method. CONCLUSION: In this paper, we introduced a novel pathway-level association test by combining hierarchical structured components analysis and penalized generalized estimating equations. The proposed method analyzes all pathways in a single unified model while considering their correlations. C/C++ implementation of PHARAOH-GEE is publicly available at http://statgen.snu.ac.kr/software/pharaoh-gee/ .

Two-Way Regularized Fuzzy Clustering of Multiple Correspondence Analysis.

Multiple correspondence analysis (MCA) is a useful tool for investigating the interrelationships among dummy-coded categorical variables. MCA has been combined with clustering methods to examine whether there exist heterogeneous subclusters of a population, which exhibit cluster-level heterogeneity. These combined approaches aim to classify either observations only (one-way clustering of MCA) or both observations and variable categories (two-way clustering of MCA). The latter approach is favored because its solutions are easier to interpret by providing explicitly which subgroup of observations is associated with which subset of variable categories. Nonetheless, the two-way approach has been built on hard classification that assumes observations and/or variable categories to belong to only one cluster. To relax this assumption, we propose two-way fuzzy clustering of MCA. Specifically, we combine MCA with fuzzy k-means simultaneously to classify a subgroup of observations and a subset of variable categories into a common cluster, while allowing both observations and variable categories to belong partially to multiple clusters. Importantly, we adopt regularized fuzzy k-means, thereby enabling us to decide the degree of fuzziness in cluster memberships automatically. We evaluate the performance of the proposed approach through the analysis of simulated and real data, in comparison with existing two-way clustering approaches.

Discovery of a novel protein tyrosine phosphatase-1B inhibitor, KR61639: potential development as an antihyperglycemic agent.

Protein tyrosine phosphatase-1B (PTP-1B), a negative regulator of insulin signaling, may be an attractive therapeutic target for type 2 diabetes mellitus. High throughput screening (HTS) for PTP-1B inhibitors using compounds from the Korea Chemical Bank identified several hits (active compounds). Among them, a hit with 1,2-naphthoquinone scaffold was chosen for lead development. KR61639, [4-[1-(1H-indol-3-yl)-3,4-dioxo-3,4-dihydro-naphthalen-2-ylmethyl]-phenoxy]-acetic acid tert-butyl ester, inhibited human recombinant PTP-1B with an IC(50) value of 0.65 microM in a noncompetitive manner. KR61639 showed modest selectivity over several phosphatases and increased insulin-stimulated glycogen synthesis in HepG2 cells and stimulated 2-deoxyglucose uptake in 3T3/L1 adipocytes. In addition, in vivo study using ob/ob mouse demonstrated that KR61639 exerted a hypoglycemic action when given orally. Thus, KR61639 may be a good starting point for lead optimization in developing a novel antidiabetic agent.

Effect of nordihydroguaiaretic acid on the secretion of lipoprotein lipase.

Nordihydroguaiaretic acid (NDGA), an inhibitor of lipoxygenase, inhibits the secretion of proteins and causes the redistribution of resident Golgi proteins into the endoplasmic reticulum (ER). In this study, the effect of NDGA on lipoprotein lipase (LPL) secretion was investigated in 3T3-L1 adipocytes, and compared with those of brefeldin A (BFA), a well-known fungal metabolite that exhibits similar ER-Golgi redistribution. Both BFA and NDGA blocked secretions of LPL. In the presence of BFA, the active and dimeric LPL was accumulated in adipocytes. After endoglycosidase H (endo H) digestion, the proportion of LPL subunits with partially endo H-sensitive oligosaccharide was significantly increased with BFA. However, in the presence of NDGA, the cellular LPL became inactive, and only the endo H-sensitive fraction of the LPL subunit was observed. An increase of the aggregated forms was observed in the fractions of the sucrose-density gradient ultracentrifugation. These properties of LPL in the NDGA-treated cells were similar to those of LPL that is retained in ER, and the effects of NDGA could not be reversed by BFA. These results indicate that the inhibitory mechanism of NDGA on the LPL secretion is functionally different from the ER-Golgi redistribution that is induced by BFA.