A Comparison of Filtering and Normalization Methods in the Statistical Analysis of Gene Expression Experiments

Both microarray and RNA-seq technologies are powerful tools which are commonly used in differential expression (DE) analysis. Gene expression levels are compared across treatment groups to determine which genes are differentially expressed. With both technologies, filtering and normalization are imp...

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Main Author:	Speicher, Mackenzie Rosa Marie
Format:	Others
Published:	North Dakota State University 2021
Subjects:	gene expression normalization methods
Online Access:	https://hdl.handle.net/10365/32041

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spelling	ndltd-ndsu.edu-oai-library.ndsu.edu-10365-320412021-10-02T17:09:20Z A Comparison of Filtering and Normalization Methods in the Statistical Analysis of Gene Expression Experiments Speicher, Mackenzie Rosa Marie gene expression normalization methods Both microarray and RNA-seq technologies are powerful tools which are commonly used in differential expression (DE) analysis. Gene expression levels are compared across treatment groups to determine which genes are differentially expressed. With both technologies, filtering and normalization are important steps in data analysis. In this thesis, real datasets are used to compare current analysis methods of two-color microarray and RNA-seq experiments. A variety of filtering, normalization and statistical approaches are evaluated. The results of this study show that although there is still no widely accepted method for the analysis of these types of experiments, the method chosen can largely impact the number of genes that are declared to be differentially expressed. 2021-08-23T20:17:13Z 2021-08-23T20:17:13Z 2020 text/thesis https://hdl.handle.net/10365/32041 NDSU policy 190.6.2 https://www.ndsu.edu/fileadmin/policy/190.pdf application/pdf North Dakota State University
collection	NDLTD
format	Others
sources	NDLTD
topic	gene expression normalization methods
spellingShingle	gene expression normalization methods Speicher, Mackenzie Rosa Marie A Comparison of Filtering and Normalization Methods in the Statistical Analysis of Gene Expression Experiments
description	Both microarray and RNA-seq technologies are powerful tools which are commonly used in differential expression (DE) analysis. Gene expression levels are compared across treatment groups to determine which genes are differentially expressed. With both technologies, filtering and normalization are important steps in data analysis. In this thesis, real datasets are used to compare current analysis methods of two-color microarray and RNA-seq experiments. A variety of filtering, normalization and statistical approaches are evaluated. The results of this study show that although there is still no widely accepted method for the analysis of these types of experiments, the method chosen can largely impact the number of genes that are declared to be differentially expressed.
author	Speicher, Mackenzie Rosa Marie
author_facet	Speicher, Mackenzie Rosa Marie
author_sort	Speicher, Mackenzie Rosa Marie
title	A Comparison of Filtering and Normalization Methods in the Statistical Analysis of Gene Expression Experiments
title_short	A Comparison of Filtering and Normalization Methods in the Statistical Analysis of Gene Expression Experiments
title_full	A Comparison of Filtering and Normalization Methods in the Statistical Analysis of Gene Expression Experiments
title_fullStr	A Comparison of Filtering and Normalization Methods in the Statistical Analysis of Gene Expression Experiments
title_full_unstemmed	A Comparison of Filtering and Normalization Methods in the Statistical Analysis of Gene Expression Experiments
title_sort	comparison of filtering and normalization methods in the statistical analysis of gene expression experiments
publisher	North Dakota State University
publishDate	2021
url	https://hdl.handle.net/10365/32041
work_keys_str_mv	AT speichermackenzierosamarie acomparisonoffilteringandnormalizationmethodsinthestatisticalanalysisofgeneexpressionexperiments AT speichermackenzierosamarie comparisonoffilteringandnormalizationmethodsinthestatisticalanalysisofgeneexpressionexperiments
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A Comparison of Filtering and Normalization Methods in the Statistical Analysis of Gene Expression Experiments

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