NDLI: Contribution to Statistical Techniques for Identifying Differentially Expressed Genes in Microarray Data

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Contribution to Statistical Techniques for Identifying Differentially Expressed Genes in Microarray Data

Content Provider	Semantic Scholar
Author	Hossain, Ahmed Zahid
Copyright Year	2011
Abstract	Contribution to Statistical Techniques for Identifying Differentially Expressed Genes in Microarray Data Ahmed Hossain Doctor of Philosophy Graduate Department of Dalla Lana School of Public Health University of Toronto 2011 With the development of DNA microarray technology, scientists can now measure the expression levels of thousands of genes (features or genomic biomarkers) simultaneously in one single experiment. Robust and accurate gene selection methods are required to identify differentially expressed genes across different samples for disease diagnosis or prognosis. The problem of identifying significantly differentially expressed genes can be stated as follows: Given gene expression measurements from an experiment of two (or more) conditions, find a subset of all genes having significantly different expression levels across these two (or more) conditions. Analysis of genomic data is challenging due to high dimensionality of data and low sample size. Currently several mathematical and statistical methods exist to identify significantly differentially expressed genes. The methods typically focus on gene by gene analysis within a parametric hypothesis testing framework. In this study, we propose three flexible procedures for analyzing microarray data. In the first method we propose a parametric method which is based on a flexible distribution, Generalized Logistic Distribution of Type II (GLDII), and an approximate likelihood ratio test (ALRT) is developed. Though the method considers gene-by-gene analysis, the ALRT method with distributional assumption GLDII appears to provide a
File Format	PDF HTM / HTML
Alternate Webpage(s)	https://tspace.library.utoronto.ca/bitstream/1807/29749/1/Hossain_Ahmed_201111_PhD_thesis.pdf.pdf
Language	English
Access Restriction	Open
Content Type	Text
Resource Type	Article

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