Detection of dna copy number changes using statistical change point analysis

Cancer development is usually associated with DNA copy number changes in the genome. DNA copy number changes correspond to chromosomal aberrations and signify abnormality of a cell. Therefore, identifying statistically significant DNA copy number changes is evidently crucial in cancer research, clinical diagnostic applications, and other related genomic research. The problem can be formulated with a statistical change point theory. We propose to use the mean and variance change point model to study the DNA copy number changes from the microarray comparative genomic hybridization (aCGH) profile. The approximate p-value of identifying a change point is derived from the use of Schwarz information criterion (SIC). The proposed method has been validated by Monte-Carlo simulation and applications to aCGH profiles from several cell lines (fibroblast cancer cell line, breast tumor cell line, and breast cancer cell line). The results indicate that the proposed method is effective in identifying DNA copy number changes.