Starting with copy number estimates for each marker (either taken directly from the vendor’s input file or calculated previously), the goal is to derive a list of regions where adjacent markers share the same copy number.

There are two algorithms available for copy number region detection: Genomic segmentation and Hidden Markov Model (HMM). Both algorithms look for trends across multiple adjacent markers. The genomic segmentation algorithm identifies breakpoints in the data, i.e., changes in copy number between two neighboring regions. The HMM algorithm looks for discrete changes of whole number copy number states (e.g., 0, 1, 2 … with no upper limit) and will find regions with those numbers of copies. Therefore, the HMM model performs better in cases of homogeneous samples where copy numbers can be anticipated such as clinical syndromes with underlying copy number aberrations. Genomic segmentation is preferable for heterogeneous samples with unpredictable copy numbers such as cancer because tumor biopsies often contain “contaminating” healthy tissue and cancer cells can have heterogeneous copy number aberrations.

The number of copies of each marker created in the previous step will be used to detect the genomic regions with copy number variation, i.e., to identify amplifications and deletions across the genome.

• Select the IC_IntensitiesSNP6pairedcopynumber spreadsheet in the Analysis tab
• Select Detect Amplifications and Deletions from the Copy Number Analysis section of the workflow

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