Analysing Tumour Heterogeneity with Advanced Statistical Models

Tumour heterogeneity is a phenomena, which describes the characteristics of cancer cells. Drug responsiveness and therapeutics of cancer depend on the accurate detection of tumour heterogeneity. Recent research has considered inferring tumour heterogeneity using computational models. In this thesis, we develop computational methods to infer tumour heterogeneity. The thesis makes several major research contributions: it infers the allelic composition of mutations and clonal frequencies, and discovers the known and unknown interactions between genes of single and different disease subtypes. Hence, these major contributions to computational tumour heterogeneity research give the more precise prediction of tumour heterogeneity.