Genome Technology Center

Multiple-sample methodology for analyzing high-resolution
copy-number aberration data

Greg Grant, Research Assistant Professor
Computational Biology and Informatics Laboratory
Center for Bioinformatics
University of Pennsylvania

Copy-number aberrations are typical of cancer genomes, and a growing number of array-based methods have become available for measuring the aberrations at high resolution across the genome. To date the informatics community has been primarily focussed on how to process the raw data in order to make the most reliable single sample aberration calls. This typically involves utilizing multiple neighboring array elements as replicates for the local region containing them. However, when the goal is to find concordant aberrations within a class of samples, one can greatly increase the resolution of the results by taking advantage of the replication coming from the multiple samples. We present a methodology (fully implemented and freely available) to map concordant aberrations down to the native resolution of the array. The methodology has a high level component, called "Significance Testing for Aberrant Copy-number" (STAC), a permutation-based method which tests for significant concordance from aberration calls; and a lower level component, called "Multiple Sample Analizer" (MSA), which handles the pre-processing of the raw data to take advantage of the multiple sample replication. Both STAC and MSA will be described.


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