Sparse graphical models for cancer signalling
Hill, Steven M. (2012) Sparse graphical models for cancer signalling. PhD thesis, University of Warwick.
WRAP_THESIS_Hill_2012.pdf - Submitted Version
Download (5Mb) | Preview
Official URL: http://webcat.warwick.ac.uk/record=b2581671~S1
Protein signalling networks play a key role in cellular function, and their
dysregulation is central to many diseases, including cancer. Recent advances in
biochemical technology have begun to allow high-throughput, data-driven studies
of signalling. In this thesis, we investigate multivariate statistical methods, rooted
in sparse graphical models, aimed at probing questions in cancer signalling.
First, we propose a Bayesian variable selection method for identifying subsets
of proteins that jointly in
uence an output of interest, such as drug response. Ancillary
biological information is incorporated into inference using informative prior
distributions. Prior information is selected and weighted in an automated manner
using an empirical Bayes formulation. We present examples of informative pathwayand
network-based priors, and illustrate the proposed method on both synthetic and
drug response data.
Second, we use dynamic Bayesian networks to perform structure learning of
context-specific signalling network topology from proteomic time-course data. We
exploit a connection between variable selection and network structure learning to
efficiently carry out exact inference. Existing biology is incorporated using informative
network priors, weighted automatically by an empirical Bayes approach. The
overall approach is computationally efficient and essentially free of user-set parameters.
We show results from an empirical investigation, comparing the approach to
several existing methods, and from an application to breast cancer cell line data. Hypotheses
are generated regarding novel signalling links, some of which are validated
by independent experiments.
Third, we describe a network-based clustering approach for the discovery of
cancer subtypes that differ in terms of subtype-specific signalling network structure.
Model-based clustering is combined with penalised likelihood estimation of undirected
graphical models to allow simultaneous learning of cluster assignments and
cluster-specific network structure. Results are shown from an empirical investigation
comparing several penalisation regimes, and an application to breast cancer
|Item Type:||Thesis or Dissertation (PhD)|
|Subjects:||Q Science > QA Mathematics
R Medicine > RC Internal medicine > RC0254 Neoplasms. Tumors. Oncology (including Cancer)
|Library of Congress Subject Headings (LCSH):||Graphical modeling (Statistics), Cancer -- Pathophysiology -- Statistical methods, Cellular signal transduction -- Statistical methods|
|Official Date:||May 2012|
|Institution:||University of Warwick|
|Theses Department:||Department of Statistics|
|Sponsors:||Engineering and Physical Sciences Research Council (EPSRC)|
|Extent:||vi, 206 leaves : ill., charts|
Actions (login required)