Some methods and models for analyzing time-series gene expression data
Name
547364390-MIT.pdf
Description
Full printable version
Size
19.98 MB
Format
Adobe PDF
Checksum (MD5)
c10ce5e2d8dfe73c8697c17c7f67fccd
Author(s)
Jammalamadaka, Arvind K. (Arvind Kumar), 1981-
Advisor(s)
David K. Gifford.
Date Issued
2009
Publisher
Massachusetts Institute of Technology
Abstract
Experiments in a variety of fields generate data in the form of a time-series. Such time-series profiles, collected sometimes for tens of thousands of experiments, are a challenge to analyze and explore. In this work, motivated by gene expression data, we provide several methods and models for such analysis. The methods developed include new clustering techniques based on nonparametric Bayesian procedures, and a confirmatory methodology to validate that the clusters produced by any of these methods have statistically different mean paths.
Description
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 199-203).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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copyright. They may be viewed from this source for any purpose, but
reproduction or distribution in any format is prohibited without written
permission. See provided URL for inquiries about permission.
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