A multi-Poisson dynamic mixture model to cluster developmental patterns of gene expression by RNA-seq

Dynamic changes of gene expression reflect an intrinsic mechanism of how an organism responds to developmental and environmental signals. With the increasing availability of expression data across a time-space scale by RNA-seq, the classification of genes as per their biological function using RNA-s...
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Autor*in:	Ye, Meixia [verfasserIn] Wang, Zhong Wang, Yaqun Wu, Rongling

Format:	Artikel
Sprache:	Englisch

Erschienen:	2015

Rechteinformationen:	Nutzungsrecht: © The Author 2014. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Schlagwörter:	Gene expression Ribonucleic acid--RNA Bioinformatics Computer simulation Poisson distribution

Übergeordnetes Werk:	Enthalten in: Briefings in bioinformatics - Oxford : Oxford Univ. Press, 2000, 16(2015), 2, Seite 205-215
Übergeordnetes Werk:	volume:16 ; year:2015 ; number:2 ; pages:205-215

Links:	Volltext Link aufrufen Link aufrufen

DOI / URN:	10.1093/bib/bbu013

Katalog-ID:	OLC1960986201

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doi_str_mv	10.1093/bib/bbu013
dewey-full	570 004
title_sort	multi-poisson dynamic mixture model to cluster developmental patterns of gene expression by rna-seq
title_auth	A multi-Poisson dynamic mixture model to cluster developmental patterns of gene expression by RNA-seq
abstract	Dynamic changes of gene expression reflect an intrinsic mechanism of how an organism responds to developmental and environmental signals. With the increasing availability of expression data across a time-space scale by RNA-seq, the classification of genes as per their biological function using RNA-seq data has become one of the most significant challenges in contemporary biology. Here we develop a clustering mixture model to discover distinct groups of genes expressed during a period of organ development. By integrating the density function of multivariate Poisson distribution, the model accommodates the discrete property of read counts characteristic of RNA-seq data. The temporal dependence of gene expression is modeled by the first-order autoregressive process. The model is implemented with the Expectation-Maximization algorithm and model selection to determine the optimal number of gene clusters and obtain the estimates of Poisson parameters that describe the pattern of time-dependent expression of genes from each cluster. The model has been demonstrated by analyzing a real data from an experiment aimed to link the pattern of gene expression to catkin development in white poplar. The usefulness of the model has been validated through computer simulation. The model provides a valuable tool for clustering RNA-seq data, facilitating our global view of expression dynamics and understanding of gene regulation mechanisms.
abstractGer	Dynamic changes of gene expression reflect an intrinsic mechanism of how an organism responds to developmental and environmental signals. With the increasing availability of expression data across a time-space scale by RNA-seq, the classification of genes as per their biological function using RNA-seq data has become one of the most significant challenges in contemporary biology. Here we develop a clustering mixture model to discover distinct groups of genes expressed during a period of organ development. By integrating the density function of multivariate Poisson distribution, the model accommodates the discrete property of read counts characteristic of RNA-seq data. The temporal dependence of gene expression is modeled by the first-order autoregressive process. The model is implemented with the Expectation-Maximization algorithm and model selection to determine the optimal number of gene clusters and obtain the estimates of Poisson parameters that describe the pattern of time-dependent expression of genes from each cluster. The model has been demonstrated by analyzing a real data from an experiment aimed to link the pattern of gene expression to catkin development in white poplar. The usefulness of the model has been validated through computer simulation. The model provides a valuable tool for clustering RNA-seq data, facilitating our global view of expression dynamics and understanding of gene regulation mechanisms.
abstract_unstemmed	Dynamic changes of gene expression reflect an intrinsic mechanism of how an organism responds to developmental and environmental signals. With the increasing availability of expression data across a time-space scale by RNA-seq, the classification of genes as per their biological function using RNA-seq data has become one of the most significant challenges in contemporary biology. Here we develop a clustering mixture model to discover distinct groups of genes expressed during a period of organ development. By integrating the density function of multivariate Poisson distribution, the model accommodates the discrete property of read counts characteristic of RNA-seq data. The temporal dependence of gene expression is modeled by the first-order autoregressive process. The model is implemented with the Expectation-Maximization algorithm and model selection to determine the optimal number of gene clusters and obtain the estimates of Poisson parameters that describe the pattern of time-dependent expression of genes from each cluster. The model has been demonstrated by analyzing a real data from an experiment aimed to link the pattern of gene expression to catkin development in white poplar. The usefulness of the model has been validated through computer simulation. The model provides a valuable tool for clustering RNA-seq data, facilitating our global view of expression dynamics and understanding of gene regulation mechanisms.
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container_issue	2
title_short	A multi-Poisson dynamic mixture model to cluster developmental patterns of gene expression by RNA-seq
url	http://dx.doi.org/10.1093/bib/bbu013 http://www.ncbi.nlm.nih.gov/pubmed/24817567 http://search.proquest.com/docview/1666838763
remote_bool	false
author2	Wang, Zhong Wang, Yaqun Wu, Rongling
author2Str	Wang, Zhong Wang, Yaqun Wu, Rongling
ppnlink	341354120
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author2_role	oth oth oth
doi_str	10.1093/bib/bbu013
up_date	2024-07-03T23:26:55.249Z
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