dc.contributor |
Universitat Politècnica de Catalunya. Departament d'Estadística i Investigació Operativa |
dc.contributor |
Rosell, David |
dc.contributor |
Ginebra Molins, Josep |
dc.contributor.author |
Peña Pizarro, Víctor |
dc.date |
2013-07 |
dc.identifier.uri |
http://hdl.handle.net/2099.1/19442 |
dc.language.iso |
eng |
dc.publisher |
Universitat Politècnica de Catalunya |
dc.rights |
info:eu-repo/semantics/openAccess |
dc.subject |
Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica |
dc.subject |
Experimental design |
dc.subject |
Alternative splicing |
dc.subject |
Design of experiments |
dc.subject |
RNA sequencing |
dc.subject |
Disseny d'experiments |
dc.subject |
Classificació AMS::62 Statistics::62K Design of experiments |
dc.title |
Design of RNA-sequencing alternative splicing experiments |
dc.type |
info:eu-repo/semantics/masterThesis |
dc.description.abstract |
Institut de Recerca Biomèdica (IRB)/ Institute for Reserarch in Biomedicine (IRB) |
dc.description.abstract |
Alternative Splicing (AS) is a biological process that allows a single gene to code for different proteins. The aim of this thesis is to design RNA-sequencing AS experiments. Traditional experiment design approaches require a prior guess regarding the future observed data, which is unfeasible in a high-dimensional setup like ours. To address this issue, our approach uses posterior predictive simulations (based on pilot data) to evaluate the expected utility of multiple experimental settings. This methodology is applied to one sample and multiple sample problems, and case-studies with real data are discussed. An implementation has been coded for the Bioconductor library casper.. Alternative Splicing (AS) is a biological process that allows a single gene to code for different proteins. The aim is to design RNA-sequencing AS experiments. Traditional approaches require a prior guess regarding the future observed data, which is unfeasible in a high-dimensional setup like ours. Our approach uses posterior predictive simulations (based on pilot data) to evaluate the expected utility of multiple experimental settings. This methodology is applied to one sample and multiple sample problems, and case-studies with real data are discussed. |