Comparison of simultaneous testing of multiple hypotheses in microarray experiments
LE3. A278 2009
Bachelor of Science
Mathematics and Statistics
Mathematics & Statistics
Microarray applications in scientific research have thrived since DNA microarray technology has been introduced to many fields, especially in biomedical fields. In a typical microarray experiment, thousands of genes are measured at the same time and in this case large multiplicity problems occur. In any hypothesis-testing situation, there are two types of errors committed: Type I error which consist of rejecting the null hypothesis H0 when it is true and Type II error which involves not rejecting H0 when H0 is false. When many hypotheses are tested and each test has a specified Type I error probability, the possibility of committing Type I error increases greatly with the number of hypotheses. In this context, the null hypothesis of no differential expression should be tested carefully for each gene. Because the numbers of genes are in thousands or more, controlling for errors in this multiple testing situation is very important. In this thesis, we will discuss and apply different approaches to microarray datasets in order to find those genes with differential expression.
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