  A number of bioinformatic or biostatistical methods are available for
analyzing DNA copy number profiles measured from microarray or sequencing
technologies. In the absence of rich enough gold standard data sets, the
performance of these methods is generally assessed using unrealistic simulation
studies, or based on small real data analyses. We have designed and implemented
a framework to generate realistic DNA copy number profiles of cancer samples
with known truth. These profiles are generated by resampling real SNP
microarray data from genomic regions with known copy-number state. The original
real data have been extracted from dilutions series of tumor cell lines with
matched blood samples at several concentrations. Therefore, the signal-to-noise
ratio of the generated profiles can be controlled through the (known)
percentage of tumor cells in the sample. In this paper, we describe this
framework and illustrate some of the benefits of the proposed data generation
approach on a practical use case: a comparison study between methods for
segmenting DNA copy number profiles from SNP microarrays. This study indicates
that no single method is uniformly better than all others. It also helps
identifying pros and cons for the compared methods as a function of
biologically informative parameters, such as the fraction of tumor cells in the
sample and the proportion of heterozygous markers. Availability: R package
jointSeg: http://r-forge.r-project.org/R/?group\_id=1562
