HAL : hal-00477606, version 2

arXiv : 1004.5300

DOI : 10.2202/1544-6115.1532

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Statistical Applications in Genetics and Molecular Biology (2010) Vol. 9 : Iss. 1, Article 40.

Versions disponibles :

v1 (29-04-2010)

v2 (19-12-2010)

Spatial clustering of array CGH features in combination with hierarchical multiple testing

Kyung In Kim 1, Etienne Roquain 2, Mark Van De Wiel 3, 4

(16/11/2010)

We propose a new approach for clustering DNA features using array CGH data from multiple tumor samples. We distinguish data-collapsing: joining contiguous DNA clones or probes with extremely similar data into regions, from clustering: joining contiguous, correlated regions based on a maximum likelihood principle. The model-based clustering algorithm accounts for the apparent spatial patterns in the data. We evaluate the randomness of the clustering result by a cluster stability score in combination with cross-validation. Moreover, we argue that the clustering really captures spatial genomic dependency by showing that coincidental clustering of independent regions is very unlikely. Using the region and cluster information, we combine testing of these for association with a clinical variable in an hierarchical multiple testing approach. This allows for interpreting the significance of both regions and clusters while controlling the Family-Wise Error Rate simultaneously. We prove that in the context of permutation tests and permutation-invariant clusters it is allowed to perform clustering and testing on the same data set. Our procedures are illustrated on two cancer data sets.

1 :	National Cancer Institute, Division of Cancer Treatment and Diagnosis
	National Institutes of Health (NIH)
2 :	Laboratoire de Probabilités et Modèles Aléatoires (LPMA)
	CNRS : UMR7599 – Université Pierre et Marie Curie [UPMC] - Paris VI – Université Paris VII - Paris Diderot
3 :	Department of Mathematics [Amsterdam]
	Universiteit van Amsterdam
4 :	Dep. of Epidemiology and Biostatistics
	Medical Center, Vrije Universiteit

Domaine

:

Statistiques/Applications Statistiques/Méthodologie

Mots Clés : quadratic exponential model – spatial dependency – array Comparative Genomic Hybridization – Family-Wise Error Rate – permutation tests

hal-00477606, version 2

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Contributeur : Etienne Roquain <>

Soumis le : Dimanche 19 Décembre 2010, 01:18:52

Dernière modification le : Dimanche 19 Décembre 2010, 17:44:09