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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...
Autores principales: | , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
Libertas Academica
2007
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2675846/ https://www.ncbi.nlm.nih.gov/pubmed/19455235 |
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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 |