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|Title:||Bayesian hierarchical modelling for inferring genetic interactions in yeast|
|Abstract:||Identifying genetic interactions for a given microorganism, such as yeast, is difﬁcult. Quantitative Fitness Analysis (QFA) is a high-throughput experimental and computa tional methodology for quantifying the ﬁtness of microbial cultures. QFA can be used to compare between ﬁtness observations for different genotypes and thereby infer genetic interaction strengths. Current “naive” frequentist statistical approaches used in QFA do not model between-genotype variation or difference in genotype variation under differ ent conditions. In this thesis, a Bayesian approach is introduced to evaluate hierarchical models that better reﬂect the structure or design of QFA experiments. First, a two-stage approach is presented: a hierarchical logistic model is ﬁtted to microbial culture growth curves and then a hierarchical interaction model is ﬁtted to ﬁtness summaries inferred for each genotype. Next, a one-stage Bayesian approach is presented: a joint hierarchi cal model which simultaneously models ﬁtness and genetic interaction, thereby avoiding passing information between models via a univariate ﬁtness summary. The new hierarchical approaches are then compared using a dataset examining the effect of telomere defects on yeast. By better describing the experimental structure, new evidence is found for genes and complexes which interact with the telomere cap. Various extensions of these models, including models for data transformation, batch effects and intrinsically stochastic growth models are also considered.|
|Appears in Collections:||Institute for Cell and Molecular Biosciences|
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|Heydari, J. 14.pdf||Thesis||23.71 MB||Adobe PDF||View/Open|
|dspacelicence.pdf||Licence||43.82 kB||Adobe PDF||View/Open|
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