  The variation in DNA copy number carries information on the modalities of
genome evolution and misregulation of DNA replication in cancer cells; its
study can be helpful to localize tumor suppressor genes, distinguish different
populations of cancerous cell, as well identify genomic variations responsible
for disease phenotypes. A number of different high throughput technologies can
be used to identify copy number variable sites, and the literature documents
multiple effective algorithms. We focus here on the specific problem of
detecting regions where variation in copy number is relatively common in the
sample at hand: this encompasses the cases of copy number polymorphisms,
related samples, technical replicates, and cancerous sub-populations from the
same individual. We present an algorithm based on regularization approaches
with significant computational advantages and competitive accuracy. We
illustrate its applicability with simulated and real data sets.
