Conditional random pattern model for copy number aberration detection

DNA copy number aberration (CNA) is very important in the pathogenesis of tumors and other diseases. For example, CNAs may result in suppression of anti-oncogenes and activation of oncogenes, which would cause certain types of cancers. High density single nucleotide polymorphism (SNP) array data is widely used for the CNA detection. However, it is nontrivial to detect the CNA automatically because the signals obtained from high density SNP arrays often have low signal-to-noise ratio (SNR), which might be caused by whole genome amplification, mixtures of normal and tumor cells, experimental noise or other technical limitations. With the reduction in SNR, many false CNA regions are often detected and the true CNA regions are missed. Thus, more sophisticated statistical models are needed to make the CNAs detection, using the low SNR signals, more robust and reliable.

This paper presents a conditional random pattern (CRP) model for CNA detection where much contextual cues are explored to suppress the noise and improve CNA detection accuracy. Both simulated and the real data are used to evaluate the proposed model, and the validation results show that the CRP model is more robust and reliable in the presence of noise for CNA detection using high density SNP array data, compared to a number of widely used software packages.

The proposed conditional random pattern (CRP) model could effectively detect the CNA regions in the presence of noise.