Unveiling clusters of RNA transcript pairs associated with markers of Alzheimer's disease progression.

<h4>Background</h4>One primary goal of transcriptomic studies is identifying gene expression patterns correlating with disease progression. This is usually achieved by considering transcripts that independently pass an arbitrary threshold (e.g. p<0.05). In diseases involving severe pe...

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Autores principales:	Ahmed Shamsul Arefin, Luke Mathieson, Daniel Johnstone, Regina Berretta, Pablo Moscato
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/29a128d48b8b406a80eed3bbf289bd15
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spelling	oai:doaj.org-article:29a128d48b8b406a80eed3bbf289bd152021-11-18T07:04:34ZUnveiling clusters of RNA transcript pairs associated with markers of Alzheimer's disease progression.1932-620310.1371/journal.pone.0045535https://doaj.org/article/29a128d48b8b406a80eed3bbf289bd152012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23029078/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Background</h4>One primary goal of transcriptomic studies is identifying gene expression patterns correlating with disease progression. This is usually achieved by considering transcripts that independently pass an arbitrary threshold (e.g. p<0.05). In diseases involving severe perturbations of multiple molecular systems, such as Alzheimer's disease (AD), this univariate approach often results in a large list of seemingly unrelated transcripts. We utilised a powerful multivariate clustering approach to identify clusters of RNA biomarkers strongly associated with markers of AD progression. We discuss the value of considering pairs of transcripts which, in contrast to individual transcripts, helps avoid natural human transcriptome variation that can overshadow disease-related changes.<h4>Methodology/principal findings</h4>We re-analysed a dataset of hippocampal transcript levels in nine controls and 22 patients with varying degrees of AD. A large-scale clustering approach determined groups of transcript probe sets that correlate strongly with measures of AD progression, including both clinical and neuropathological measures and quantifiers of the characteristic transcriptome shift from control to severe AD. This enabled identification of restricted groups of highly correlated probe sets from an initial list of 1,372 previously published by our group. We repeated this analysis on an expanded dataset that included all pair-wise combinations of the 1,372 probe sets. As clustering of this massive dataset is unfeasible using standard computational tools, we adapted and re-implemented a clustering algorithm that uses external memory algorithmic approach. This identified various pairs that strongly correlated with markers of AD progression and highlighted important biological pathways potentially involved in AD pathogenesis.<h4>Conclusions/significance</h4>Our analyses demonstrate that, although there exists a relatively large molecular signature of AD progression, only a small number of transcripts recurrently cluster with different markers of AD progression. Furthermore, considering the relationship between two transcripts can highlight important biological relationships that are missed when considering either transcript in isolation.Ahmed Shamsul ArefinLuke MathiesonDaniel JohnstoneRegina BerrettaPablo MoscatoPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 7, Iss 9, p e45535 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ahmed Shamsul Arefin Luke Mathieson Daniel Johnstone Regina Berretta Pablo Moscato Unveiling clusters of RNA transcript pairs associated with markers of Alzheimer's disease progression.
description	<h4>Background</h4>One primary goal of transcriptomic studies is identifying gene expression patterns correlating with disease progression. This is usually achieved by considering transcripts that independently pass an arbitrary threshold (e.g. p<0.05). In diseases involving severe perturbations of multiple molecular systems, such as Alzheimer's disease (AD), this univariate approach often results in a large list of seemingly unrelated transcripts. We utilised a powerful multivariate clustering approach to identify clusters of RNA biomarkers strongly associated with markers of AD progression. We discuss the value of considering pairs of transcripts which, in contrast to individual transcripts, helps avoid natural human transcriptome variation that can overshadow disease-related changes.<h4>Methodology/principal findings</h4>We re-analysed a dataset of hippocampal transcript levels in nine controls and 22 patients with varying degrees of AD. A large-scale clustering approach determined groups of transcript probe sets that correlate strongly with measures of AD progression, including both clinical and neuropathological measures and quantifiers of the characteristic transcriptome shift from control to severe AD. This enabled identification of restricted groups of highly correlated probe sets from an initial list of 1,372 previously published by our group. We repeated this analysis on an expanded dataset that included all pair-wise combinations of the 1,372 probe sets. As clustering of this massive dataset is unfeasible using standard computational tools, we adapted and re-implemented a clustering algorithm that uses external memory algorithmic approach. This identified various pairs that strongly correlated with markers of AD progression and highlighted important biological pathways potentially involved in AD pathogenesis.<h4>Conclusions/significance</h4>Our analyses demonstrate that, although there exists a relatively large molecular signature of AD progression, only a small number of transcripts recurrently cluster with different markers of AD progression. Furthermore, considering the relationship between two transcripts can highlight important biological relationships that are missed when considering either transcript in isolation.
format	article
author	Ahmed Shamsul Arefin Luke Mathieson Daniel Johnstone Regina Berretta Pablo Moscato
author_facet	Ahmed Shamsul Arefin Luke Mathieson Daniel Johnstone Regina Berretta Pablo Moscato
author_sort	Ahmed Shamsul Arefin
title	Unveiling clusters of RNA transcript pairs associated with markers of Alzheimer's disease progression.
title_short	Unveiling clusters of RNA transcript pairs associated with markers of Alzheimer's disease progression.
title_full	Unveiling clusters of RNA transcript pairs associated with markers of Alzheimer's disease progression.
title_fullStr	Unveiling clusters of RNA transcript pairs associated with markers of Alzheimer's disease progression.
title_full_unstemmed	Unveiling clusters of RNA transcript pairs associated with markers of Alzheimer's disease progression.
title_sort	unveiling clusters of rna transcript pairs associated with markers of alzheimer's disease progression.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/29a128d48b8b406a80eed3bbf289bd15
work_keys_str_mv	AT ahmedshamsularefin unveilingclustersofrnatranscriptpairsassociatedwithmarkersofalzheimersdiseaseprogression AT lukemathieson unveilingclustersofrnatranscriptpairsassociatedwithmarkersofalzheimersdiseaseprogression AT danieljohnstone unveilingclustersofrnatranscriptpairsassociatedwithmarkersofalzheimersdiseaseprogression AT reginaberretta unveilingclustersofrnatranscriptpairsassociatedwithmarkersofalzheimersdiseaseprogression AT pablomoscato unveilingclustersofrnatranscriptpairsassociatedwithmarkersofalzheimersdiseaseprogression
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Unveiling clusters of RNA transcript pairs associated with markers of Alzheimer's disease progression.

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