The techniques outlined in (Jones et al., 2010) were followed to analyze copy number changes. Sequence quality filtering was used to remove all reads of low mapping quality (Q < 10). Due to the varying amounts of sequence reads from each sample, aligned reference reads were first used to define genomic bins of equal reference coverage to which depths of alignments of sequence from each of the tumor samples were compared. This resulted in a measurement of the relative number of aligned reads from the tumors and reference in bins of variable length along the genome, where bin width is inversely proportional to the number of mapped reference reads. A hidden Markov model (HMM) was used to classify and segment continuous regions of copy number loss, neutrality, or gain using methodology outlined previously (Shah et al., 2006). The five states reported by the HMM were: loss (1), neutral (2), gain (3), amplification (4), and high-level amplification (5). 

[ref 1] Jones, S. J., Laskin, J., Li, Y. Y., Griffith, O. L., An, J., Bilenky, M., Butterfield, Y. S., Cezard, T., Chuah, E., Corbett, R., et al. (2010). Evolution of an adenocarcinoma in response to selection by targeted kinase inhibitors. Genome Biol 11, R82.
[ref 2] Shah, S. P., Xuan, X., DeLeeuw, R. J., Khojasteh, M., Lam, W. L., Ng, R., and Murphy, K. P. (2006). Integrating copy number polymorphisms into array CGH analysis using a robust HMM. Bioinformatics 22, e431-439.