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The best solution is to use only the broad alterations, using the file broad_values_by_arm.txt. In particular, for each sample, the highest threshold is computed as the sum of the noise (low-level) threshold (e.g. 0.1) plus the maximum value of copy number variation for the sample over all the arms. Similarly, the lowest threshold is the minimum value for ...


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There are many methods for the analysis of CNV. If you are an R user I would recommend you to take a look at the Bioconductor package list, in particular the section for copy number variation. Currently it contains 50 packages!


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Your suggested approach of comparing to the baseline distribution on a point-by-point basis isn't bad, although it's going to be susceptible to small false positives from noise. You'd probably want to only use events that span a certain minimum number of consecutive observations. You might also want to look into circular binary segmentation, as described ...


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Yes, one sample can contain different alterations. For each patient there is typically one tumour specimen that is removed. That specimen may be divided up into several samples (e.g. one for DNA sequencing, one for RNA sequencing, one for methylation microarray, and one for copy-number variation microarray), however each sample contains thousands of ...



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