资 源 简 介
Copy number change is an important form of structural variation in the human genome. Somatic copy number alterations can cause overexpression of oncogenes and loss of tumor suppressor genes in tumorigenesis. The recent development of SNP array technology has facilitated studies on copy number changes at a genome-wide scale, with high resolution. However, tumor samples often consist of mixed cancer and normal cells. Such tissue heterogeneity will cause inaccurate analysis of copy number changes in clinical samples and could significantly confound subsequent marker identification and diagnostic classification rooted in specific cells.
We report here a statistically-principled in silico approach, Bayesian Analysis of COpy number Mixtures (BACOM), to accurately estimate genomic deletions and normal tissue contamination, and accordingly recover the true copy number profile in cancer cells. We have developed a cross-platform and open source Java application that implements the who
