Date of Award

Spring 2006

Document Type

Thesis

Degree Name

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

Department

Computer Science

First Advisor

Michael Recce

Second Advisor

Qun Ma

Third Advisor

Usman W. Roshan

Abstract

DNA microarrays permit us to study the expression of thousands of genes simultaneously. They are now used in many different contexts to compare mRNA levels between two or more samples of cells. Microarray experiments typically give us expression measurements on a large number of genes. Increasing popularity of microarray technology has resulted in a number of tests being proposed to detect differentials expression.

The purpose of study is to compare the parametric, non parametric and permutation tests when applied to microarray data for differential expression analysis. t test (parametric), Mann Whitney test (nonparametric) and Significance of analysis (permutation ) test are compared. The study focused on comparison of tests based on the ranking of genes by different tests. Biological and simulation data was used to test compare the performance of statistical tests. The result shows that the SAM test outperform the other two tests, under Normal as well as Lognormal data simulation in case of both low and high number of replicates. Application to simulated data also brings out the fact that with increase in number of replicates the performance all the tests improves.

Share

COinS