Dataset Information

Variable Selection for Skewed Model-Based Clustering: Application to the Identification of Novel Sleep Phenotypes.

ABSTRACT: In sleep research, applying finite mixture models to sleep characteristics captured 8 through multiple data types, including self-reported sleep diary, a wrist monitor capturing movement (actigraphy), and brain waves (polysomnography), may suggest new phenotypes that reflect underlying disease mechanisms. However, a direct mixture model application is challenging because there are many sleep variables from which to choose, and sleep variables are often highly skewed even in homogenous samples. Moreover, previous sleep research findings indicate that some of the most clinically interesting solutions will be those that incorporate all three data types. Thus, we present two novel skewed variable selection algorithms based on the multivariate skew normal (MSN) distribution: one that selects the best set of variables ignoring data type and another that embraces the exploratory nature of clustering and suggests multiple statistically plausible sets of variables that each incorporate all data types. Through a simulation study we empirically compare our approach with other asymmetric and normal dimension reduction strategies for clustering. Finally, we demonstrate our methods using a sample of older adults with and without insomnia. The proposed MSN-based variable selection algorithm appears to be suitable for both MSN and multivariate normal cluster distributions, especially with moderate to large sample sizes.

SUBMITTER: Wallace ML

PROVIDER: S-EPMC6510512 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Publications

Variable Selection for Skewed Model-Based Clustering: Application to the Identification of Novel Sleep Phenotypes.

Wallace Meredith L ML Buysse Daniel J DJ Germain Anne A Hall Martica H MH Iyengar Satish S

Journal of the American Statistical Association 20180516 521

In sleep research, applying finite mixture models to sleep characteristics captured 8 through multiple data types, including self-reported sleep diary, a wrist monitor capturing movement (actigraphy), and brain waves (polysomnography), may suggest new phenotypes that reflect underlying disease mechanisms. However, a direct mixture model application is challenging because there are many sleep variables from which to choose, and sleep variables are often highly skewed even in homogenous samples. M ...[more]

PMID: 31086426

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Project description:MotivationThe recently proposed knockoff filter is a general framework for controlling the false discovery rate (FDR) when performing variable selection. This powerful new approach generates a 'knockoff' of each variable tested for exact FDR control. Imitation variables that mimic the correlation structure found within the original variables serve as negative controls for statistical inference. Current applications of knockoff methods use linear regression models and conduct variable selection only for variables existing in model functions. Here, we extend the use of knockoffs for machine learning with boosted trees, which are successful and widely used in problems where no prior knowledge of model function is required. However, currently available importance scores in tree models are insufficient for variable selection with FDR control.ResultsWe propose a novel strategy for conducting variable selection without prior model topology knowledge using the knockoff method with boosted tree models. We extend the current knockoff method to model-free variable selection through the use of tree-based models. Additionally, we propose and evaluate two new sampling methods for generating knockoffs, namely the sparse covariance and principal component knockoff methods. We test and compare these methods with the original knockoff method regarding their ability to control type I errors and power. In simulation tests, we compare the properties and performance of importance test statistics of tree models. The results include different combinations of knockoffs and importance test statistics. We consider scenarios that include main-effect, interaction, exponential and second-order models while assuming the true model structures are unknown. We apply our algorithm for tumor purity estimation and tumor classification using Cancer Genome Atlas (TCGA) gene expression data. Our results show improved discrimination between difficult-to-discriminate cancer types.Availability and implementationThe proposed algorithm is included in the KOBT package, which is available at https://cran.r-project.org/web/packages/KOBT/index.html.Supplementary informationSupplementary data are available at Bioinformatics online.

Dataset Information

Variable Selection for Skewed Model-Based Clustering: Application to the Identification of Novel Sleep Phenotypes.

Publications

Variable Selection for Skewed Model-Based Clustering: Application to the Identification of Novel Sleep Phenotypes.

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