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dc.contributor.authorHeilbut, Adrian Marken_US
dc.date.accessioned2016-07-14T15:40:23Z
dc.date.available2016-07-14T15:40:23Z
dc.date.issued2016
dc.identifier.urihttps://hdl.handle.net/2144/17054
dc.description.abstractRather than designing focused experiments to test individual hypotheses, scientists now commonly acquire measurements using massively parallel techniques, for post hoc interrogation. The resulting data is both high-dimensional and structured, in that observed variables are grouped and ordered into related subspaces, reflecting both natural physical organization and factorial experimental designs. Such structure encodes critical constraints and clues to interpretation, but typical unsupervised learning methods assume exchangeability and fail to account adequately for the structure of data in a flexible and interpretable way. In this thesis, I develop computational methods for exploratory analysis of structured high-dimensional data, and apply them to study gene expression regulation in Parkinson’s (PD) and Huntington’s diseases (HD). BOMBASTIC (Block-Organized, Model-Based, Tree-Indexed Clustering) is a methodology to cluster and visualize data organized in pre-specified subspaces, by combining independent clusterings of blocks into hierarchies. BOMBASTIC provides a formal specification of the block-clustering problem and a modular implementation that facilitates integration, visualization, and comparison of diverse datasets and rapid exploration of alternative analyses. These tools, along with standard methods, were applied to study gene expression in mouse models of neurodegenerative diseases, in collaboration with Dr. Myriam Heiman and Dr. Robert Fenster. In PD, I analyzed cell-type-specific expression following levodopa treatment to study mechanisms underlying levodopa-induced dyskinesia (LID). I identified likely regulators of the transcriptional changes leading to LID and implicated signaling pathways amenable to pharmacological modulation (Heiman, Heilbut et al, 2014). In HD, I analyzed multiple mouse models (Kuhn, 2007), cell-type specific profiles of medium spiny neurons (Fenster, 2011), and an RNA-Seq dataset profiling multiple tissue types over time and across an mHTT allelic series (CHDI, 2015). I found evidence suggesting that altered activity of the PRC2 complex significantly contributes to the transcriptional dysregulation observed in striatal neurons in HD.en_US
dc.language.isoen_US
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internationalen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectBioinformaticsen_US
dc.subjectTimeseriesen_US
dc.subjectClusteringen_US
dc.subjectGene expressionen_US
dc.subjectStructured clustering representationsen_US
dc.subjectHuntington's diseaseen_US
dc.subjectParkinson's diseaseen_US
dc.titleStructured clustering representations and methodsen_US
dc.typeThesis/Dissertationen_US
dc.date.updated2016-06-21T19:35:28Z
etd.degree.nameDoctor of Philosophyen_US
etd.degree.leveldoctoralen_US
etd.degree.disciplineBioinformatics GRSen_US
etd.degree.grantorBoston Universityen_US


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Attribution-NonCommercial-NoDerivatives 4.0 International
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 International