Spatial Clustering of Array CGH Features in Combination with Hierarchical Multiple Testing

Kim Kyung In, Roquain Etienne and A van de Wiel Mark
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Kim Kyung In: National Cancer Institute
Roquain Etienne: Université Pierre et Marie Curie
A van de Wiel Mark: VU University Medical Center

Statistical Applications in Genetics and Molecular Biology, 2010, vol. 9, issue 1, 25

Abstract: 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 a 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.

Keywords: quadratic exponential model; spatial dependency; array comparative genomic hybridization; FWER; hierarchical testing (search for similar items in EconPapers)
Date: 2010
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Citations: View citations in EconPapers (3)

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DOI: 10.2202/1544-6115.1532

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