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Cluster Aanlysis of Gene Expression Profiles via Flexible Count Models for RNA-seq Data

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ucalgary_2015_ruan_ji.pdf (1.041Mb)
Advisor
de Leon, Alexander
Author
Ruan, Ji
Accessioned
2015-06-10T15:21:17Z
Available
2015-11-20T08:00:30Z
Issued
2015-06-10
Submitted
2015
Other
RNA-seq Data
Clustering
EM algorithm
Subject
Statistics
Type
Thesis
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Abstract
Clustering RNA-seq data is used to characterize environment-induced (e.g., treatment) differences in gene expression profiles by separating genes into clusters based on their expression patterns. Wang et al. [2013] recently adopted the bi-Poisson distribution, obtained via the trivariate reduction method, as a model for clustering bivariate RNA-seq data. We discuss the inadequacy of the bi-Poisson distribution in modelling the correlation between dependent Poisson counts, and its impact on clustering such data. We introduce an alternative Gaussian copula model that incorporates a flexible dependence structure for the counts, report simulation results to compare the performance of the Gaussian copula and bi-Poisson models, and investigate the impact on clustering of Poisson counts of misspecified dependence structures. We illustrate our methodology on a lung cancer RNA-seq data.
Corporate
University of Calgary
Faculty
Graduate Studies
Doi
http://dx.doi.org/10.11575/PRISM/25338
Uri
http://hdl.handle.net/11023/2293
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