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Methods for Systematic Exploratory Analysis of Gene Expression Data with Applications to Cancer Genomics

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Date
2017
Author
Wagner, Florian
Advisor
Dave, Sandeep S
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Abstract

Advances in technologies for gene expression profiling have resulted in an unprecedented abundance of gene expression data. However, computational methods available for the exploratory analysis of such data are limited in their ability to generate an interpretable overview of biologically relevant similarities and differences among samples. This work first introduces the XL-mHG test, a sensitive and specific hypothesis test for detecting gene set enrichment, and discusses its algorithmic and statistical properties. It further introduces GO-PCA, a method for exploratory analysis of gene expression data using prior knowledge. The XL-mHG test serves as a building block for GO-PCA. The output of GO-PCA consists of functional expression signatures, designed to provide an interpretable representation of biologically meaningful variation in the data. The power and versatility of the method is demonstrated on heterogeneous human and mouse expression data. Finally, applications of the proposed methods to carcinoma and lymphoma expression data aim to demonstrate their clinical relevance. The effective utilization of prior knowledge in the exploratory analysis of gene expression data through carefully designed computational methods is essential for successfully harnessing the power of current and future platforms for gene expression profiling, with the aim of generating clinically relevant insights into complex diseases such as cancer.

Type
Dissertation
Department
Computational Biology and Bioinformatics
Subject
Biology
Computer science
Bioinformatics
algorithms
cancer genomics
exploratory data analysis
gene expression
nonparametric statistics
transcriptomics
Permalink
https://hdl.handle.net/10161/14375
Citation
Wagner, Florian (2017). Methods for Systematic Exploratory Analysis of Gene Expression Data with Applications to Cancer Genomics. Dissertation, Duke University. Retrieved from https://hdl.handle.net/10161/14375.
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This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 United States License.

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