Variance Component Selection With Applications to Microbiome Taxonomic Data.
AffiliationUniv Arizona, Dept Epidemiol & Biostat
Univ Arizona, Dept Med, Div Pulm Allergy Crit Care & Sleep Med
KeywordsHuman Immunodeficiency Virus (HIV)
variance component models
MetadataShow full item record
PublisherFRONTIERS MEDIA SA
CitationZhai J, Kim J, Knox KS, Twigg HL III, Zhou H and Zhou JJ (2018) Variance Component Selection With Applications to Microbiome Taxonomic Data. Front. Microbiol. 9:509. doi: 10.3389/fmicb.2018.00509
JournalFRONTIERS IN MICROBIOLOGY
Rights© 2018 Zhai, Kim, Knox, Twigg, Zhou and Zhou. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY).
Collection InformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at email@example.com.
AbstractHigh-throughput sequencing technology has enabled population-based studies of the role of the human microbiome in disease etiology and exposure response. Microbiome data are summarized as counts or composition of the bacterial taxa at different taxonomic levels. An important problem is to identify the bacterial taxa that are associated with a response. One method is to test the association of specific taxon with phenotypes in a linear mixed effect model, which incorporates phylogenetic information among bacterial communities. Another type of approaches consider all taxa in a joint model and achieves selection via penalization method, which ignores phylogenetic information. In this paper, we consider regression analysis by treating bacterial taxa at different level as multiple random effects. For each taxon, a kernel matrix is calculated based on distance measures in the phylogenetic tree and acts as one variance component in the joint model. Then taxonomic selection is achieved by the lasso (least absolute shrinkage and selection operator) penalty on variance components. Our method integrates biological information into the variable selection problem and greatly improves selection accuracies. Simulation studies demonstrate the superiority of our methods versus existing methods, for example, group-lasso. Finally, we apply our method to a longitudinal microbiome study of Human Immunodeficiency Virus (HIV) infected patients. We implement our method using the high performance computing language Julia. Software and detailed documentation are freely available at https://github.com/JingZhai63/VCselection.
NoteOpen access journal.
UA Open Access Publishing Fund.
VersionFinal published version
SponsorsNIH [K01DK106116, HG006139, GM105785, GM53275, UO1 HL121831, UO1 HL098960]; Arizona Biomedical Research Commission (ABRC) grant; NSF [DMS-1645093]
Except where otherwise noted, this item's license is described as © 2018 Zhai, Kim, Knox, Twigg, Zhou and Zhou. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY).
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