Evaluation of Some Statistical Methods for the Identification of Differentially Expressed Genes

Authors

Andrew L. Haddon, Florida International UniversityFollow

Document Type

Thesis

Degree

Master of Science (MS)

Major/Program

Statistics

First Advisor's Name

Florence George

First Advisor's Committee Title

Committee Co-Chair

Second Advisor's Name

BM Golam Kibria

Second Advisor's Committee Title

Committee Co-Chair

Third Advisor's Name

Wensong Wu

Third Advisor's Committee Title

Committee Member

Keywords

statistics, biostatistics, microarray, genes, differentially expressed, SAM, fold change, samroc, bioinformatics

Date of Defense

3-24-2015

Abstract

Microarray platforms have been around for many years and while there is a rise of new technologies in laboratories, microarrays are still prevalent. When it comes to the analysis of microarray data to identify differentially expressed (DE) genes, many methods have been proposed and modified for improvement. However, the most popular methods such as Significance Analysis of Microarrays (SAM), samroc, fold change, and rank product are far from perfect. When it comes down to choosing which method is most powerful, it comes down to the characteristics of the sample and distribution of the gene expressions. The most practiced method is usually SAM or samroc but when the data tends to be skewed, the power of these methods decrease. With the concept that the median becomes a better measure of central tendency than the mean when the data is skewed, the tests statistics of the SAM and fold change methods are modified in this thesis. This study shows that the median modified fold change method improves the power for many cases when identifying DE genes if the data follows a lognormal distribution.

Identifier

FI15032117

Recommended Citation

Haddon, Andrew L., "Evaluation of Some Statistical Methods for the Identification of Differentially Expressed Genes" (2015). FIU Electronic Theses and Dissertations. 1913.
https://digitalcommons.fiu.edu/etd/1913