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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 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
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