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Copy number variations (CNV) and allelic imbalance in tumor tissue can show strong segmentation. Their graphical presentation can be enhanced by appropriate smoothing. Existing signal and scatterplot smoothers do not respect segmentation well. We present novel algorithms that use a penalty on thenorm of differences of neighboring values. Visualization is our main goal, but we compare classification performance to that of VEGA.

Visualization of Genomic Changes by Segmented Smoothing Using an L0 Penalty