Some methods and models for analyzing time-series gene expression data

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.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2009

Author dc:creator

• Jammalamadaka, Arvind K. (Arvind Kumar), 1981-

Advisor dc:contributor.advisor

• David K. Gifford.

• Electrical Engineering and Computer Science.

Statement dc:rights

• M.I.T. theses are protected by 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.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/53278

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/53278