Repeated measures ASCA+ for analysis of longitudinal intervention studies with multivariate outcome data

Longitudinal intervention studies with repeated measurements over time are an important type of experimental design in biomedical research. Due to the advent of “omics”-sciences (genomics, transcriptomics, proteomics, metabolomics), longitudinal studies generate increasingly multivariate outcome dat...

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Autores principales:	Torfinn S. Madssen, Guro F. Giskeødegård, Age K. Smilde, Johan A. Westerhuis
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/7bfbe33a1485452cb4d892e8cf8684d5
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spelling	oai:doaj.org-article:7bfbe33a1485452cb4d892e8cf8684d52021-11-25T05:42:04ZRepeated measures ASCA+ for analysis of longitudinal intervention studies with multivariate outcome data1553-734X1553-7358https://doaj.org/article/7bfbe33a1485452cb4d892e8cf8684d52021-11-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8604364/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Longitudinal intervention studies with repeated measurements over time are an important type of experimental design in biomedical research. Due to the advent of “omics”-sciences (genomics, transcriptomics, proteomics, metabolomics), longitudinal studies generate increasingly multivariate outcome data. Analysis of such data must take both the longitudinal intervention structure and multivariate nature of the data into account. The ASCA+-framework combines general linear models with principal component analysis and can be used to separate and visualize the multivariate effect of different experimental factors. However, this methodology has not yet been developed for the more complex designs often found in longitudinal intervention studies, which may be unbalanced, involve randomized interventions, and have substantial missing data. Here we describe a new methodology, repeated measures ASCA+ (RM-ASCA+), and show how it can be used to model metabolic changes over time, and compare metabolic changes between groups, in both randomized and non-randomized intervention studies. Tools for both visualization and model validation are discussed. This approach can facilitate easier interpretation of data from longitudinal clinical trials with multivariate outcomes. Author summary Clinical trials are increasingly generating large amounts of complex biological data. Examples can include measuring metabolism or gene expression in tissue or blood sampled repeatedly over the course of a treatment. In such cases, one might wish to compare changes in not one, but hundreds, or thousands of variables simultaneously. In order to effectively analyze such data, both the study design and the multivariate nature of the data should be considered during data analysis. ANOVA simultaneous component analysis+ (ASCA+) is a statistical method which combines general linear models with principal component analysis, and provides a way to separate and visualize the effects of different factors on complex biological data. In this work, we describe how repeated measures linear mixed models, a class of models commonly used when analyzing changes over time and treatment effects in longitudinal studies, can be used together with ASCA+ for analyzing clinical trials in a novel method called repeated measures-ASCA+ (RM-ASCA+).Torfinn S. MadssenGuro F. GiskeødegårdAge K. SmildeJohan A. WesterhuisPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 17, Iss 11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Torfinn S. Madssen Guro F. Giskeødegård Age K. Smilde Johan A. Westerhuis Repeated measures ASCA+ for analysis of longitudinal intervention studies with multivariate outcome data
description	Longitudinal intervention studies with repeated measurements over time are an important type of experimental design in biomedical research. Due to the advent of “omics”-sciences (genomics, transcriptomics, proteomics, metabolomics), longitudinal studies generate increasingly multivariate outcome data. Analysis of such data must take both the longitudinal intervention structure and multivariate nature of the data into account. The ASCA+-framework combines general linear models with principal component analysis and can be used to separate and visualize the multivariate effect of different experimental factors. However, this methodology has not yet been developed for the more complex designs often found in longitudinal intervention studies, which may be unbalanced, involve randomized interventions, and have substantial missing data. Here we describe a new methodology, repeated measures ASCA+ (RM-ASCA+), and show how it can be used to model metabolic changes over time, and compare metabolic changes between groups, in both randomized and non-randomized intervention studies. Tools for both visualization and model validation are discussed. This approach can facilitate easier interpretation of data from longitudinal clinical trials with multivariate outcomes. Author summary Clinical trials are increasingly generating large amounts of complex biological data. Examples can include measuring metabolism or gene expression in tissue or blood sampled repeatedly over the course of a treatment. In such cases, one might wish to compare changes in not one, but hundreds, or thousands of variables simultaneously. In order to effectively analyze such data, both the study design and the multivariate nature of the data should be considered during data analysis. ANOVA simultaneous component analysis+ (ASCA+) is a statistical method which combines general linear models with principal component analysis, and provides a way to separate and visualize the effects of different factors on complex biological data. In this work, we describe how repeated measures linear mixed models, a class of models commonly used when analyzing changes over time and treatment effects in longitudinal studies, can be used together with ASCA+ for analyzing clinical trials in a novel method called repeated measures-ASCA+ (RM-ASCA+).
format	article
author	Torfinn S. Madssen Guro F. Giskeødegård Age K. Smilde Johan A. Westerhuis
author_facet	Torfinn S. Madssen Guro F. Giskeødegård Age K. Smilde Johan A. Westerhuis
author_sort	Torfinn S. Madssen
title	Repeated measures ASCA+ for analysis of longitudinal intervention studies with multivariate outcome data
title_short	Repeated measures ASCA+ for analysis of longitudinal intervention studies with multivariate outcome data
title_full	Repeated measures ASCA+ for analysis of longitudinal intervention studies with multivariate outcome data
title_fullStr	Repeated measures ASCA+ for analysis of longitudinal intervention studies with multivariate outcome data
title_full_unstemmed	Repeated measures ASCA+ for analysis of longitudinal intervention studies with multivariate outcome data
title_sort	repeated measures asca+ for analysis of longitudinal intervention studies with multivariate outcome data
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/7bfbe33a1485452cb4d892e8cf8684d5
work_keys_str_mv	AT torfinnsmadssen repeatedmeasuresascaforanalysisoflongitudinalinterventionstudieswithmultivariateoutcomedata AT gurofgiskeødegard repeatedmeasuresascaforanalysisoflongitudinalinterventionstudieswithmultivariateoutcomedata AT ageksmilde repeatedmeasuresascaforanalysisoflongitudinalinterventionstudieswithmultivariateoutcomedata AT johanawesterhuis repeatedmeasuresascaforanalysisoflongitudinalinterventionstudieswithmultivariateoutcomedata
_version_	1718414525756080128

Repeated measures ASCA+ for analysis of longitudinal intervention studies with multivariate outcome data

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