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We also investigated genome-wide CNV calling of each method, quantifying performance in terms of precision (specificity) and recall (sensitivity). For the C0902 cell line we derived absolute integer copy numbers from the segmentation obtained by array CGH, CNVkit, CopywriteR, and CONTRA. Treating the array CGH calls as the truth set, we then compared the deletions and duplications from each caller at each copy number state between each caller and array CGH using BEDtools [44], using calls with at least 50% overlap as matches, and calculated the precision and recall for gains and losses of at least one copy and at least two copies (Fig 7). To evaluate performance on larger and smaller CNVs separately, we split the array CGH calls into subsets with CNV sizes above and below 5 megabases, the median size of CNV calls by array CGH, and recalculated precision and recall within each subset. As a check on these results, we also calculated precision and recall across each basepair in all CNVs in lieu of the 50% overlap criterion. As with the gene-level analysis presented above, under most of these metrics CNVkit appears to be competitive with or superior to the other methods.
OCR stands for Optical character reader/recognition. 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