Cargando…

CGHregions: Dimension Reduction for Array CGH Data with Minimal Information Loss

An algorithm to reduce multi-sample array CGH data from thousands of clones to tens or hundreds of clone regions is introduced. This reduction of the data is performed such that little information is lost, which is possible due to the high dependencies between neighboring clones. The algorithm is ex...

Descripción completa

Detalles Bibliográficos
Autores principales: van de Wiel, Mark A., van Wieringen, Wessel N.
Formato: Texto
Lenguaje:English
Publicado: Libertas Academica 2007
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2675846/
https://www.ncbi.nlm.nih.gov/pubmed/19455235
_version_ 1782166719875252224
author van de Wiel, Mark A.
van Wieringen, Wessel N.
author_facet van de Wiel, Mark A.
van Wieringen, Wessel N.
author_sort van de Wiel, Mark A.
collection PubMed
description An algorithm to reduce multi-sample array CGH data from thousands of clones to tens or hundreds of clone regions is introduced. This reduction of the data is performed such that little information is lost, which is possible due to the high dependencies between neighboring clones. The algorithm is explained using a small example. The potential beneficial effects of the algorithm for downstream analysis are illustrated by re-analysis of previously published colorectal cancer data. Using multiple testing corrections suitable for these data, we provide statistical evidence for genomic differences on several clone regions between MSI+ and CIN+ tumors. The algorithm, named CGHregions, is available as an easy-to-use script in R.
format Text
id pubmed-2675846
institution National Center for Biotechnology Information
language English
publishDate 2007
publisher Libertas Academica
record_format MEDLINE/PubMed
spelling pubmed-26758462009-05-19 CGHregions: Dimension Reduction for Array CGH Data with Minimal Information Loss van de Wiel, Mark A. van Wieringen, Wessel N. Cancer Inform Original Research An algorithm to reduce multi-sample array CGH data from thousands of clones to tens or hundreds of clone regions is introduced. This reduction of the data is performed such that little information is lost, which is possible due to the high dependencies between neighboring clones. The algorithm is explained using a small example. The potential beneficial effects of the algorithm for downstream analysis are illustrated by re-analysis of previously published colorectal cancer data. Using multiple testing corrections suitable for these data, we provide statistical evidence for genomic differences on several clone regions between MSI+ and CIN+ tumors. The algorithm, named CGHregions, is available as an easy-to-use script in R. Libertas Academica 2007-02-08 /pmc/articles/PMC2675846/ /pubmed/19455235 Text en © 2007 The authors. http://creativecommons.org/licenses/by/3.0 This article is an open-access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).
spellingShingle Original Research
van de Wiel, Mark A.
van Wieringen, Wessel N.
CGHregions: Dimension Reduction for Array CGH Data with Minimal Information Loss
title CGHregions: Dimension Reduction for Array CGH Data with Minimal Information Loss
title_full CGHregions: Dimension Reduction for Array CGH Data with Minimal Information Loss
title_fullStr CGHregions: Dimension Reduction for Array CGH Data with Minimal Information Loss
title_full_unstemmed CGHregions: Dimension Reduction for Array CGH Data with Minimal Information Loss
title_short CGHregions: Dimension Reduction for Array CGH Data with Minimal Information Loss
title_sort cghregions: dimension reduction for array cgh data with minimal information loss
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2675846/
https://www.ncbi.nlm.nih.gov/pubmed/19455235
work_keys_str_mv AT vandewielmarka cghregionsdimensionreductionforarraycghdatawithminimalinformationloss
AT vanwieringenwesseln cghregionsdimensionreductionforarraycghdatawithminimalinformationloss