Stability Analysis of Oncogenetic Trees

for an improved diagnosis and therapy of diseases. Mixture models of oncogenetic trees can be used to estimate the ordered
accumulation of genetic events in disease progression. From these evolutionary models, a genetic progression score can be
derived that estimates the genetic status of human tumors. These mixture models have been previously implemented in the
programming language C. Building up on this code, we implement methods for estimation, prediction and simulation of oncogenetic
trees and for estimating the genetic progression score for each sample as a package in the statistical programming language R. This
provides the biomedical community with an easy to use, flexible, open access tool. Furthermore, we improve the model estimation by
introducing new cluster assignements and we correct the formula for calculating the genetic progression score. In the methodological
part of the thesis, we analyze the stability of estimated oncogenetic tree mixture models. First, measures for quantifying the variability in
the estimated genetic progression scores for single tumors, the tree topologies and the probability distributions induced by the model,
will be compared and evaluated. Second, classical bootstrap method will be used to calculate confidence intervals for the genetic
progression score derived from the estimated tree models.