A statistical approach for detecting genomic aberrations in heterogeneous tumor samples from single nucleotide polymorphism genotyping data

We describe a statistical method for the characterization of genomic aberrations in single nucleotide polymorphism microarray data acquired from cancer genomes. Our approach allows us to model the joint effect of polyploidy, normal DNA contamination and intra-tumour heterogeneity within a single uni...

Descripción completa

Detalles Bibliográficos
Autores principales: Yau, Christopher, Mouradov, Dmitri, Jorissen, Robert N, Colella, Stefano, Mirza, Ghazala, Steers, Graham, Harris, Adrian, Ragoussis, Jiannis, Sieber, Oliver, Holmes, Christopher C
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2010
Materias:
Method
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2965384/
https://www.ncbi.nlm.nih.gov/pubmed/20858232
http://dx.doi.org/10.1186/gb-2010-11-9-r92
_version_ 1782189510366330880
author Yau, Christopher
Mouradov, Dmitri
Jorissen, Robert N
Colella, Stefano
Mirza, Ghazala
Steers, Graham
Harris, Adrian
Ragoussis, Jiannis
Sieber, Oliver
Holmes, Christopher C
author_facet Yau, Christopher
Mouradov, Dmitri
Jorissen, Robert N
Colella, Stefano
Mirza, Ghazala
Steers, Graham
Harris, Adrian
Ragoussis, Jiannis
Sieber, Oliver
Holmes, Christopher C
author_sort Yau, Christopher
collection PubMed
description We describe a statistical method for the characterization of genomic aberrations in single nucleotide polymorphism microarray data acquired from cancer genomes. Our approach allows us to model the joint effect of polyploidy, normal DNA contamination and intra-tumour heterogeneity within a single unified Bayesian framework. We demonstrate the efficacy of our method on numerous datasets including laboratory generated mixtures of normal-cancer cell lines and real primary tumours.
format Text
id pubmed-2965384
institution National Center for Biotechnology Information
language English
publishDate 2010
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-29653842010-10-28 A statistical approach for detecting genomic aberrations in heterogeneous tumor samples from single nucleotide polymorphism genotyping data Yau, Christopher Mouradov, Dmitri Jorissen, Robert N Colella, Stefano Mirza, Ghazala Steers, Graham Harris, Adrian Ragoussis, Jiannis Sieber, Oliver Holmes, Christopher C Genome Biol Method We describe a statistical method for the characterization of genomic aberrations in single nucleotide polymorphism microarray data acquired from cancer genomes. Our approach allows us to model the joint effect of polyploidy, normal DNA contamination and intra-tumour heterogeneity within a single unified Bayesian framework. We demonstrate the efficacy of our method on numerous datasets including laboratory generated mixtures of normal-cancer cell lines and real primary tumours. BioMed Central 2010 2010-09-21 /pmc/articles/PMC2965384/ /pubmed/20858232 http://dx.doi.org/10.1186/gb-2010-11-9-r92 Text en Copyright ©2010 Yau et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Method
Yau, Christopher
Mouradov, Dmitri
Jorissen, Robert N
Colella, Stefano
Mirza, Ghazala
Steers, Graham
Harris, Adrian
Ragoussis, Jiannis
Sieber, Oliver
Holmes, Christopher C
A statistical approach for detecting genomic aberrations in heterogeneous tumor samples from single nucleotide polymorphism genotyping data
title A statistical approach for detecting genomic aberrations in heterogeneous tumor samples from single nucleotide polymorphism genotyping data
title_full A statistical approach for detecting genomic aberrations in heterogeneous tumor samples from single nucleotide polymorphism genotyping data
title_fullStr A statistical approach for detecting genomic aberrations in heterogeneous tumor samples from single nucleotide polymorphism genotyping data
title_full_unstemmed A statistical approach for detecting genomic aberrations in heterogeneous tumor samples from single nucleotide polymorphism genotyping data
title_short A statistical approach for detecting genomic aberrations in heterogeneous tumor samples from single nucleotide polymorphism genotyping data
title_sort statistical approach for detecting genomic aberrations in heterogeneous tumor samples from single nucleotide polymorphism genotyping data
topic Method
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2965384/
https://www.ncbi.nlm.nih.gov/pubmed/20858232
http://dx.doi.org/10.1186/gb-2010-11-9-r92
work_keys_str_mv AT yauchristopher astatisticalapproachfordetectinggenomicaberrationsinheterogeneoustumorsamplesfromsinglenucleotidepolymorphismgenotypingdata
AT mouradovdmitri astatisticalapproachfordetectinggenomicaberrationsinheterogeneoustumorsamplesfromsinglenucleotidepolymorphismgenotypingdata
AT jorissenrobertn astatisticalapproachfordetectinggenomicaberrationsinheterogeneoustumorsamplesfromsinglenucleotidepolymorphismgenotypingdata
AT colellastefano astatisticalapproachfordetectinggenomicaberrationsinheterogeneoustumorsamplesfromsinglenucleotidepolymorphismgenotypingdata
AT mirzaghazala astatisticalapproachfordetectinggenomicaberrationsinheterogeneoustumorsamplesfromsinglenucleotidepolymorphismgenotypingdata
AT steersgraham astatisticalapproachfordetectinggenomicaberrationsinheterogeneoustumorsamplesfromsinglenucleotidepolymorphismgenotypingdata
AT harrisadrian astatisticalapproachfordetectinggenomicaberrationsinheterogeneoustumorsamplesfromsinglenucleotidepolymorphismgenotypingdata
AT ragoussisjiannis astatisticalapproachfordetectinggenomicaberrationsinheterogeneoustumorsamplesfromsinglenucleotidepolymorphismgenotypingdata
AT sieberoliver astatisticalapproachfordetectinggenomicaberrationsinheterogeneoustumorsamplesfromsinglenucleotidepolymorphismgenotypingdata
AT holmeschristopherc astatisticalapproachfordetectinggenomicaberrationsinheterogeneoustumorsamplesfromsinglenucleotidepolymorphismgenotypingdata
AT yauchristopher statisticalapproachfordetectinggenomicaberrationsinheterogeneoustumorsamplesfromsinglenucleotidepolymorphismgenotypingdata
AT mouradovdmitri statisticalapproachfordetectinggenomicaberrationsinheterogeneoustumorsamplesfromsinglenucleotidepolymorphismgenotypingdata
AT jorissenrobertn statisticalapproachfordetectinggenomicaberrationsinheterogeneoustumorsamplesfromsinglenucleotidepolymorphismgenotypingdata
AT colellastefano statisticalapproachfordetectinggenomicaberrationsinheterogeneoustumorsamplesfromsinglenucleotidepolymorphismgenotypingdata
AT mirzaghazala statisticalapproachfordetectinggenomicaberrationsinheterogeneoustumorsamplesfromsinglenucleotidepolymorphismgenotypingdata
AT steersgraham statisticalapproachfordetectinggenomicaberrationsinheterogeneoustumorsamplesfromsinglenucleotidepolymorphismgenotypingdata
AT harrisadrian statisticalapproachfordetectinggenomicaberrationsinheterogeneoustumorsamplesfromsinglenucleotidepolymorphismgenotypingdata
AT ragoussisjiannis statisticalapproachfordetectinggenomicaberrationsinheterogeneoustumorsamplesfromsinglenucleotidepolymorphismgenotypingdata
AT sieberoliver statisticalapproachfordetectinggenomicaberrationsinheterogeneoustumorsamplesfromsinglenucleotidepolymorphismgenotypingdata
AT holmeschristopherc statisticalapproachfordetectinggenomicaberrationsinheterogeneoustumorsamplesfromsinglenucleotidepolymorphismgenotypingdata

Ejemplares similares