A global structural EM algorithm for a model of cancer progression
2011 (English)Manuscript (preprint) (Other academic)
Cancer has complex patterns of progression that include converging as well as diverging progressional pathways. Vogelstein’s path model of colon cancer was clearly a pioneering contribution to cancer research. Since then, several attempts have been made at obtaining mathematical models of cancer progression, devising training algorithms,and applying these to cross-sectional data.Beerenwinkel et al. provided, what they coined, EM-like algorithms for OncogeneticTrees (OTs) and mixtures of such. Given the small size of current and future datasets, it is important to minimize the number of parameters of a model. For this reason,also we focus on tree-based models and introduce Hidden-variable Oncogenetic Trees(HOTs). In contrast to OTs, HOTs allow for errors in the data and thereby provide more realistic modeling. We also design global structural EM algorithms for learning HOTs and mixtures of HOTs (HOT-mixtures). The algorithms are global in the sense that, during the M-step, they find a structure that yields a global maximum of the expected complete log-likelihood rather than merely one that improves it.The algorithm for single HOTs performs very well on reasonable-sized data sets,while that for HOT-mixtures requires data sets of sizes obtainable only with tomorrows more cost efficient technologies. To facilitate analysis of complex cytogenetic data sets requiring more than one HOT, we devise a decomposition strategy based on PrincipalComponent Analysis and train parameters on a colon cancer data set. The method so obtained is then successfully applied to kidney cancer.
Place, publisher, year, edition, pages
Bioinformatics and Systems Biology Computer Science
IdentifiersURN: urn:nbn:se:kth:diva-10973OAI: oai:DiVA.org:kth-10973DiVA: diva2:233575
FunderScience for Life Laboratory - a national resource center for high-throughput molecular bioscience
QC 201008122009-09-012009-09-012016-02-02Bibliographically approved