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Multiple curve comparisons with an application to the formation of the dorsal funiculus of mutant mice


Herberich, Esther; Hassler, Christine; Hothorn, Torsten (2014). Multiple curve comparisons with an application to the formation of the dorsal funiculus of mutant mice. International Journal of Biostatistics, 10(2):289-302.

Abstract

Much biological experimental data are represented as curves, including measurements of growth, hormone, or enzyme levels, and physical structures. Here we consider the multiple testing problem of comparing two or more nonlinear curves. We model smooth curves of unknown form nonparametrically using penalized splines. We use random effects to model subject-specific deviations from the group-level curve. We present an approach that allows examination of overall differences between the curves of multiple groups and detection of sections in which the curves differ. Adjusted p-values for each single comparison can be obtained by exploiting the connection between semiparametric mixed models and linear mixed models and employing an approach for multiple testing in general parametric models. In simulations, we show that the probability of false-positive findings of differences between any two curves in at least one position can be controlled by a pre-specified error level. We apply our method to compare curves describing the form of the mouse dorsal funiculus - a morphological curved structure in the spinal cord - in mice wild-type for the gene encoding EphA4 or heterozygous with one of two mutations in the gene.

Abstract

Much biological experimental data are represented as curves, including measurements of growth, hormone, or enzyme levels, and physical structures. Here we consider the multiple testing problem of comparing two or more nonlinear curves. We model smooth curves of unknown form nonparametrically using penalized splines. We use random effects to model subject-specific deviations from the group-level curve. We present an approach that allows examination of overall differences between the curves of multiple groups and detection of sections in which the curves differ. Adjusted p-values for each single comparison can be obtained by exploiting the connection between semiparametric mixed models and linear mixed models and employing an approach for multiple testing in general parametric models. In simulations, we show that the probability of false-positive findings of differences between any two curves in at least one position can be controlled by a pre-specified error level. We apply our method to compare curves describing the form of the mouse dorsal funiculus - a morphological curved structure in the spinal cord - in mice wild-type for the gene encoding EphA4 or heterozygous with one of two mutations in the gene.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > Epidemiology, Biostatistics and Prevention Institute (EBPI)
Dewey Decimal Classification:610 Medicine & health
Language:English
Date:5 June 2014
Deposited On:26 Aug 2014 14:52
Last Modified:21 Nov 2017 17:26
Publisher:De Gruyter
ISSN:1557-4679
Publisher DOI:https://doi.org/10.1515/ijb-2013-0003
PubMed ID:24902010

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