cn.FARMS: a latent variable model to detect copy number variations in microarray data with a low false discovery rate.
Nucleic Acids Res
; 39(12): e79, 2011 Jul.
Article
en En
| MEDLINE
| ID: mdl-21486749
ABSTRACT
Cost-effective oligonucleotide genotyping arrays like the Affymetrix SNP 6.0 are still the predominant technique to measure DNA copy number variations (CNVs). However, CNV detection methods for microarrays overestimate both the number and the size of CNV regions and, consequently, suffer from a high false discovery rate (FDR). A high FDR means that many CNVs are wrongly detected and therefore not associated with a disease in a clinical study, though correction for multiple testing takes them into account and thereby decreases the study's discovery power. For controlling the FDR, we propose a probabilistic latent variable model, 'cn.FARMS', which is optimized by a Bayesian maximum a posteriori approach. cn.FARMS controls the FDR through the information gain of the posterior over the prior. The prior represents the null hypothesis of copy number 2 for all samples from which the posterior can only deviate by strong and consistent signals in the data. On HapMap data, cn.FARMS clearly outperformed the two most prevalent methods with respect to sensitivity and FDR. The software cn.FARMS is publicly available as a R package at http//www.bioinf.jku.at/software/cnfarms/cnfarms.html.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Programas Informáticos
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Modelos Estadísticos
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Análisis de Secuencia por Matrices de Oligonucleótidos
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Variaciones en el Número de Copia de ADN
Tipo de estudio:
Evaluation_studies
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Prognostic_studies
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Risk_factors_studies
Idioma:
En
Revista:
Nucleic Acids Res
Año:
2011
Tipo del documento:
Article
País de afiliación:
Austria
Pais de publicación:
ENGLAND
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ESCOCIA
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GB
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GREAT BRITAIN
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INGLATERRA
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REINO UNIDO
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SCOTLAND
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UK
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UNITED KINGDOM