Decoupling Linear and Nonlinear Associations of Gene Expression

Handle URI:
http://hdl.handle.net/10754/292462
Title:
Decoupling Linear and Nonlinear Associations of Gene Expression
Authors:
Itakura, Alan
Abstract:
The FANTOM consortium has generated a large gene expression dataset of different cell lines and tissue cultures using the single-molecule sequencing technology of HeliscopeCAGE. This provides a unique opportunity to investigate novel associations between gene expression over time and different cell types. Here, we create a MatLab wrapper for a powerful and computationally intensive set of statistics known as Maximal Information Coefficient, and then calculate this statistic for a large, comprehensive dataset containing gene expression of a variety of differentiating tissues. We then distinguish between linear and nonlinear associations, and then create gene association networks. Following this analysis, we are then able to identify clusters of linear gene associations that then associate nonlinearly with other clusters of linearity, providing insight to much more complex connections between gene expression patterns than previously anticipated.
Advisors:
Ravasi, Timothy ( 0000-0002-9950-465X )
Committee Member:
Gao, Xin ( 0000-0002-7108-3574 ) ; Ryu, Taewoo
KAUST Department:
Biological and Environmental Sciences and Engineering (BESE) Division
Program:
Bioscience
Issue Date:
May-2013
Type:
Thesis
Appears in Collections:
Bioscience Program; Theses; Biological and Environmental Sciences and Engineering (BESE) Division

Full metadata record

DC FieldValue Language
dc.contributor.advisorRavasi, Timothyen
dc.contributor.authorItakura, Alanen
dc.date.accessioned2013-05-21T08:30:31Z-
dc.date.available2013-05-21T08:30:31Z-
dc.date.issued2013-05en
dc.identifier.urihttp://hdl.handle.net/10754/292462en
dc.description.abstractThe FANTOM consortium has generated a large gene expression dataset of different cell lines and tissue cultures using the single-molecule sequencing technology of HeliscopeCAGE. This provides a unique opportunity to investigate novel associations between gene expression over time and different cell types. Here, we create a MatLab wrapper for a powerful and computationally intensive set of statistics known as Maximal Information Coefficient, and then calculate this statistic for a large, comprehensive dataset containing gene expression of a variety of differentiating tissues. We then distinguish between linear and nonlinear associations, and then create gene association networks. Following this analysis, we are then able to identify clusters of linear gene associations that then associate nonlinearly with other clusters of linearity, providing insight to much more complex connections between gene expression patterns than previously anticipated.en
dc.language.isoenen
dc.subjectNonlinear associationsen
dc.subjectLinear associationsen
dc.subjectFANTOMen
dc.subjectModularityen
dc.titleDecoupling Linear and Nonlinear Associations of Gene Expressionen
dc.typeThesisen
dc.contributor.departmentBiological and Environmental Sciences and Engineering (BESE) Divisionen
thesis.degree.grantorKing Abdullah University of Science and Technologyen_GB
dc.contributor.committeememberGao, Xinen
dc.contributor.committeememberRyu, Taewooen
thesis.degree.disciplineBioscienceen
thesis.degree.nameMaster of Scienceen
dc.person.id118445en
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