Functional clustering of periodic transcriptional profiles through ARMA(p,q).

<h4>Background</h4>Gene clustering of periodic transcriptional profiles provides an opportunity to shed light on a variety of biological processes, but this technique relies critically upon the robust modeling of longitudinal covariance structure over time.<h4>Methodology</h4>...

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Autores principales:	Ning Li, Timothy McMurry, Arthur Berg, Zhong Wang, Scott A Berceli, Rongling Wu
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2010
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/a0b5f56ebfc544768d9ac37b92d162e8
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spelling	oai:doaj.org-article:a0b5f56ebfc544768d9ac37b92d162e82021-11-25T06:24:31ZFunctional clustering of periodic transcriptional profiles through ARMA(p,q).1932-620310.1371/journal.pone.0009894https://doaj.org/article/a0b5f56ebfc544768d9ac37b92d162e82010-04-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/20419127/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Background</h4>Gene clustering of periodic transcriptional profiles provides an opportunity to shed light on a variety of biological processes, but this technique relies critically upon the robust modeling of longitudinal covariance structure over time.<h4>Methodology</h4>We propose a statistical method for functional clustering of periodic gene expression by modeling the covariance matrix of serial measurements through a general autoregressive moving-average process of order (p,q), the so-called ARMA(p,q). We derive a sophisticated EM algorithm to estimate the proportions of each gene cluster, the Fourier series parameters that define gene-specific differences in periodic expression trajectories, and the ARMA parameters that model the covariance structure within a mixture model framework. The orders and of the ARMA process that provide the best fit are identified by model selection criteria.<h4>Conclusions</h4>Through simulated data we show that whenever it is necessary, employment of sophisticated covariance structures such as ARMA is crucial in order to obtain unbiased estimates of the mean structure parameters and increased precision of estimation. The methods were implemented on recently published time-course gene expression data in yeast and the procedure was shown to effectively identify interesting periodic clusters in the dataset. The new approach will provide a powerful tool for understanding biological functions on a genomic scale.Ning LiTimothy McMurryArthur BergZhong WangScott A BerceliRongling WuPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 5, Iss 4, p e9894 (2010)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ning Li Timothy McMurry Arthur Berg Zhong Wang Scott A Berceli Rongling Wu Functional clustering of periodic transcriptional profiles through ARMA(p,q).
description	<h4>Background</h4>Gene clustering of periodic transcriptional profiles provides an opportunity to shed light on a variety of biological processes, but this technique relies critically upon the robust modeling of longitudinal covariance structure over time.<h4>Methodology</h4>We propose a statistical method for functional clustering of periodic gene expression by modeling the covariance matrix of serial measurements through a general autoregressive moving-average process of order (p,q), the so-called ARMA(p,q). We derive a sophisticated EM algorithm to estimate the proportions of each gene cluster, the Fourier series parameters that define gene-specific differences in periodic expression trajectories, and the ARMA parameters that model the covariance structure within a mixture model framework. The orders and of the ARMA process that provide the best fit are identified by model selection criteria.<h4>Conclusions</h4>Through simulated data we show that whenever it is necessary, employment of sophisticated covariance structures such as ARMA is crucial in order to obtain unbiased estimates of the mean structure parameters and increased precision of estimation. The methods were implemented on recently published time-course gene expression data in yeast and the procedure was shown to effectively identify interesting periodic clusters in the dataset. The new approach will provide a powerful tool for understanding biological functions on a genomic scale.
format	article
author	Ning Li Timothy McMurry Arthur Berg Zhong Wang Scott A Berceli Rongling Wu
author_facet	Ning Li Timothy McMurry Arthur Berg Zhong Wang Scott A Berceli Rongling Wu
author_sort	Ning Li
title	Functional clustering of periodic transcriptional profiles through ARMA(p,q).
title_short	Functional clustering of periodic transcriptional profiles through ARMA(p,q).
title_full	Functional clustering of periodic transcriptional profiles through ARMA(p,q).
title_fullStr	Functional clustering of periodic transcriptional profiles through ARMA(p,q).
title_full_unstemmed	Functional clustering of periodic transcriptional profiles through ARMA(p,q).
title_sort	functional clustering of periodic transcriptional profiles through arma(p,q).
publisher	Public Library of Science (PLoS)
publishDate	2010
url	https://doaj.org/article/a0b5f56ebfc544768d9ac37b92d162e8
work_keys_str_mv	AT ningli functionalclusteringofperiodictranscriptionalprofilesthrougharmapq AT timothymcmurry functionalclusteringofperiodictranscriptionalprofilesthrougharmapq AT arthurberg functionalclusteringofperiodictranscriptionalprofilesthrougharmapq AT zhongwang functionalclusteringofperiodictranscriptionalprofilesthrougharmapq AT scottaberceli functionalclusteringofperiodictranscriptionalprofilesthrougharmapq AT ronglingwu functionalclusteringofperiodictranscriptionalprofilesthrougharmapq
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Functional clustering of periodic transcriptional profiles through ARMA(p,q).

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