Date of Award

Summer 2002

Document Type

Thesis

Degree Name

Master of Science in Computational Biology - (M.S.)

Department

Federated Department of Biological Sciences

First Advisor

Michael Recce

Second Advisor

Peter P. Tolias

Third Advisor

Ronald Philip Hart

Abstract

There is a lot of systematic and specific variability in microarray experiments, this variability affects measured gene expression levels, leading to unreliable gene profiling or an heavy load of extra experiment to statistically confirm the data observed in one experiment.

The aim of this work is to systematically analyze, using statistics, the image derived from a cDNA microarray experiment to have a better understanding of this variability and thus a better confidence over the data obtained from an experiment.

Using technologies available at the Center for Applied Genomics, Newark, New Jersey. Selected images derived from different type of microarray experiments have been analyzed in statistical fashion to find answers about the variability of biological data. Statistical methods such regression have been applied to the whole image, print-tips and single spots; leading to answers, confirmations and new ideas about issues regarding analysis and reliability of microarrays experiments.

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