Spatial clustering of array CGH features in combination with hierarchical multiple testing.
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.
National Cancer Institute, USA. kimki2@mail.nih.gov
Kyung In
KI
Kim
Etienne
E
Roquain
Mark A
MA
van de Wiel
D000465:Algorithms; D016000:Cluster Analysis; D055028:Comparative Genomic Hybridization / Q000379:methods*; D056915:DNA Copy Number Variations; D004273:DNA, Neoplasm / Q000032:analysis*; D056726:Genetic Association Studies; D006801:Humans; D008962:Models, Theoretical; D009369:Neoplasms / Q000235:genetics*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods; D016415:Sequence Alignment / Q000379:methods*
D004273:DNA, Neoplasm