Algorithms for hierarchical clustering of gene expression data

dc.contributor.author	Komarina, Srikanth
dc.contributor.department	Electrical and Computer Engineering
dc.date	2020-11-09T01:16:48.000
dc.date.accessioned	2021-02-26T08:56:46Z
dc.date.available	2021-02-26T08:56:46Z
dc.date.copyright	Thu Jan 01 00:00:00 UTC 2004
dc.date.issued	2004-01-01
dc.description.abstract	<p>Genes are parts of the genome which encode for proteins in an organism. Proteins play an important part in many biologicl processes in any organism. Measuring expression level of a gene helps biologists estimate the amount of protein produced by that gene. Microarrays can be used to measure the expression levels of thousands of genes in a single experiment. Using additional techniques such as clustering various correlations among genes of interest can be found. The most commonly used clustering technique for microarray data analysis is hierarchical clustering. Various metrics such ad Euclidean, Manhattan, Pearson correlation coefficient have been used to measure (dis)similarity between genes. A commonly used software for hierarchical clustering based on Pearson correlation coefficient takes O(N[Arrow pointing up]3) for clustering N genes, even though there are algorithms which can reduce the runtime to O(N[Arrow pointing up]2). In this thesis, we show how the runtime can be reduced to O(N log N) by using a geometric interpretation of the Pearson correlation coeffcient and show that it is optimal.</p>
dc.format.mimetype	application/pdf
dc.identifier	archive/lib.dr.iastate.edu/rtd/20679/
dc.identifier.articleid	21678
dc.identifier.contextkey	20115128
dc.identifier.doi	https://doi.org/10.31274/rtd-20201107-236
dc.identifier.s3bucket	isulib-bepress-aws-west
dc.identifier.submissionpath	rtd/20679
dc.identifier.uri	https://dr.lib.iastate.edu/handle/20.500.12876/98046
dc.language.iso	en
dc.source.bitstream	archive/lib.dr.iastate.edu/rtd/20679/Komarina_ISU_2004_K66.pdf\|\|\|Fri Jan 14 22:26:59 UTC 2022
dc.subject.keywords	Electrical and computer engineering
dc.subject.keywords	Computer engineering
dc.title	Algorithms for hierarchical clustering of gene expression data
dc.type	article
dc.type.genre	thesis
dspace.entity.type	Publication
relation.isOrgUnitOfPublication	a75a044c-d11e-44cd-af4f-dab1d83339ff
thesis.degree.discipline	Computer Engineering
thesis.degree.level	thesis
thesis.degree.name	Master of Science

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