iterClust: a statistical framework for iterative clustering analysis

MOTIVATION: In a scenario where populations A, B1 and B2 (subpopulations of B) exist, pronounced differences between A and B may mask subtle differences between B1 and B2. RESULTS: Here we present iterClust, an iterative clustering framework, which can separate more pronounced differences (e.g. A an...

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Detalles Bibliográficos
Autores principales: Ding, Hongxu, Wang, Wanxin, Califano, Andrea
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2018
Materias:
Applications Notes
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6084607/
https://www.ncbi.nlm.nih.gov/pubmed/29579153
http://dx.doi.org/10.1093/bioinformatics/bty176
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author Ding, Hongxu
Wang, Wanxin
Califano, Andrea
author_facet Ding, Hongxu
Wang, Wanxin
Califano, Andrea
author_sort Ding, Hongxu
collection PubMed
description MOTIVATION: In a scenario where populations A, B1 and B2 (subpopulations of B) exist, pronounced differences between A and B may mask subtle differences between B1 and B2. RESULTS: Here we present iterClust, an iterative clustering framework, which can separate more pronounced differences (e.g. A and B) in starting iterations, followed by relatively subtle differences (e.g. B1 and B2), providing a comprehensive clustering trajectory. AVAILABILITY AND IMPLEMENTATION: iterClust is implemented as a Bioconductor R package. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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spelling pubmed-60846072018-08-14 iterClust: a statistical framework for iterative clustering analysis Ding, Hongxu Wang, Wanxin Califano, Andrea Bioinformatics Applications Notes MOTIVATION: In a scenario where populations A, B1 and B2 (subpopulations of B) exist, pronounced differences between A and B may mask subtle differences between B1 and B2. RESULTS: Here we present iterClust, an iterative clustering framework, which can separate more pronounced differences (e.g. A and B) in starting iterations, followed by relatively subtle differences (e.g. B1 and B2), providing a comprehensive clustering trajectory. AVAILABILITY AND IMPLEMENTATION: iterClust is implemented as a Bioconductor R package. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2018-08-15 2018-03-22 /pmc/articles/PMC6084607/ /pubmed/29579153 http://dx.doi.org/10.1093/bioinformatics/bty176 Text en © The Author(s) 2018. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Applications Notes
Ding, Hongxu
Wang, Wanxin
Califano, Andrea
iterClust: a statistical framework for iterative clustering analysis
title iterClust: a statistical framework for iterative clustering analysis
title_full iterClust: a statistical framework for iterative clustering analysis
title_fullStr iterClust: a statistical framework for iterative clustering analysis
title_full_unstemmed iterClust: a statistical framework for iterative clustering analysis
title_short iterClust: a statistical framework for iterative clustering analysis
title_sort iterclust: a statistical framework for iterative clustering analysis
topic Applications Notes
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6084607/
https://www.ncbi.nlm.nih.gov/pubmed/29579153
http://dx.doi.org/10.1093/bioinformatics/bty176
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AT wangwanxin iterclustastatisticalframeworkforiterativeclusteringanalysis
AT califanoandrea iterclustastatisticalframeworkforiterativeclusteringanalysis

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