Generalized estimating equation modeling on correlated microbiome sequencing data with longitudinal measures.
Existing models for assessing microbiome sequencing such as operational taxonomic units (OTUs) can only test predictors' effects on OTUs. There is limited work on how to estimate the correlations between multiple OTUs and incorporate such relationship into models to evaluate longitudinal OTU me...
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Online Access: | https://doi.org/10.1371/journal.pcbi.1008108 |
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doaj-b3e124a6b1ee492a8260c277bdd10e0b2021-04-21T15:17:34ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582020-09-01169e100810810.1371/journal.pcbi.1008108Generalized estimating equation modeling on correlated microbiome sequencing data with longitudinal measures.Bo ChenWei XuExisting models for assessing microbiome sequencing such as operational taxonomic units (OTUs) can only test predictors' effects on OTUs. There is limited work on how to estimate the correlations between multiple OTUs and incorporate such relationship into models to evaluate longitudinal OTU measures. We propose a novel approach to estimate OTU correlations based on their taxonomic structure, and apply such correlation structure in Generalized Estimating Equations (GEE) models to estimate both predictors' effects and OTU correlations. We develop a two-part Microbiome Taxonomic Longitudinal Correlation (MTLC) model for multivariate zero-inflated OTU outcomes based on the GEE framework. In addition, longitudinal and other types of repeated OTU measures are integrated in the MTLC model. Extensive simulations have been conducted to evaluate the performance of the MTLC method. Compared with the existing methods, the MTLC method shows robust and consistent estimation, and improved statistical power for testing predictors' effects. Lastly we demonstrate our proposed method by implementing it into a real human microbiome study to evaluate the obesity on twins.https://doi.org/10.1371/journal.pcbi.1008108 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Bo Chen Wei Xu |
spellingShingle |
Bo Chen Wei Xu Generalized estimating equation modeling on correlated microbiome sequencing data with longitudinal measures. PLoS Computational Biology |
author_facet |
Bo Chen Wei Xu |
author_sort |
Bo Chen |
title |
Generalized estimating equation modeling on correlated microbiome sequencing data with longitudinal measures. |
title_short |
Generalized estimating equation modeling on correlated microbiome sequencing data with longitudinal measures. |
title_full |
Generalized estimating equation modeling on correlated microbiome sequencing data with longitudinal measures. |
title_fullStr |
Generalized estimating equation modeling on correlated microbiome sequencing data with longitudinal measures. |
title_full_unstemmed |
Generalized estimating equation modeling on correlated microbiome sequencing data with longitudinal measures. |
title_sort |
generalized estimating equation modeling on correlated microbiome sequencing data with longitudinal measures. |
publisher |
Public Library of Science (PLoS) |
series |
PLoS Computational Biology |
issn |
1553-734X 1553-7358 |
publishDate |
2020-09-01 |
description |
Existing models for assessing microbiome sequencing such as operational taxonomic units (OTUs) can only test predictors' effects on OTUs. There is limited work on how to estimate the correlations between multiple OTUs and incorporate such relationship into models to evaluate longitudinal OTU measures. We propose a novel approach to estimate OTU correlations based on their taxonomic structure, and apply such correlation structure in Generalized Estimating Equations (GEE) models to estimate both predictors' effects and OTU correlations. We develop a two-part Microbiome Taxonomic Longitudinal Correlation (MTLC) model for multivariate zero-inflated OTU outcomes based on the GEE framework. In addition, longitudinal and other types of repeated OTU measures are integrated in the MTLC model. Extensive simulations have been conducted to evaluate the performance of the MTLC method. Compared with the existing methods, the MTLC method shows robust and consistent estimation, and improved statistical power for testing predictors' effects. Lastly we demonstrate our proposed method by implementing it into a real human microbiome study to evaluate the obesity on twins. |
url |
https://doi.org/10.1371/journal.pcbi.1008108 |
work_keys_str_mv |
AT bochen generalizedestimatingequationmodelingoncorrelatedmicrobiomesequencingdatawithlongitudinalmeasures AT weixu generalizedestimatingequationmodelingoncorrelatedmicrobiomesequencingdatawithlongitudinalmeasures |
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1714667557500223488 |