Statistical quantification of genomic tumoral alterations with a mixture model

Christine Keribin 1, 2 Yi Liu 2 Tatiana Popova 3 Yves Rozenholc 2
2 SELECT - Model selection in statistical learning
Inria Saclay - Ile de France, LMO - Laboratoire de Mathématiques d'Orsay, CNRS - Centre National de la Recherche Scientifique : UMR
Abstract : The study of genomic DNA alterations (recurrent regions of alteration, patterns of instability) contributes to tumor classification, and becomes of great importance for the personalization of cancer treatments. The use of Single-Nucleotide Polymorphism (SNP) arrays or of New Generation Sequences (NGS) techniques allows the simultaneous estimation of segmented copy number (CN) and B-allele frequency (BAF) profiles along the whole genome. In this context, Popova (2009) proposed the GAP method, based on pattern recognition with (BAF, CN) maps to detect genotype status of each segment in complex tumoral genome profiles. It takes into account the fact that the observations on these maps are necessarily placed on centers that depend --up to a proper scaling of the CN-- only on the unknown proportion of non tumoral tissue in the sample. Being deterministic and manually tuned, this method appears sensitive to noise. To overcome this drawback, we set a mixture model, allowing the automatic estimation of the proportion of non tumoral tissue and the test of genotype for each segment along the whole genome. We present the model, its estimation with an adapted EM algorithm and results on simulated data.
Type de document :
Communication dans un congrès
ERCIM 2014, Dec 2014, Pisa, Italy. 2014
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https://hal.inria.fr/hal-01095984
Contributeur : Christine Keribin <>
Soumis le : mardi 16 décembre 2014 - 15:48:17
Dernière modification le : jeudi 11 janvier 2018 - 06:22:14

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  • HAL Id : hal-01095984, version 1

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Christine Keribin, Yi Liu, Tatiana Popova, Yves Rozenholc. Statistical quantification of genomic tumoral alterations with a mixture model. ERCIM 2014, Dec 2014, Pisa, Italy. 2014. 〈hal-01095984〉

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