Evaluation of Some Statistical Methods for the Identification of Differentially Expressed Genes
Master of Science (MS)
First Advisor's Name
First Advisor's Committee Title
Second Advisor's Name
BM Golam Kibria
Second Advisor's Committee Title
Third Advisor's Name
Third Advisor's Committee Title
statistics, biostatistics, microarray, genes, differentially expressed, SAM, fold change, samroc, bioinformatics
Date of Defense
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.
Haddon, Andrew L., "Evaluation of Some Statistical Methods for the Identification of Differentially Expressed Genes" (2015). FIU Electronic Theses and Dissertations. 1913.
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