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Understanding pathways

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Title: Understanding pathways
Authors: Soh, Donny
Item Type: Thesis or dissertation
Abstract: The challenge with todays microarray experiments is to infer biological conclusions from them. There are two crucial difficulties to be surmounted in this challenge:(1) A lack of suitable biological repository that can be easily integrated into computational algorithms. (2) Contemporary algorithms used to analyze microarray data are unable to draw consistent biological results from diverse datasets of the same disease. To deal with the first difficulty, we believe a core database that unifies available biological repositories is important. Towards this end, we create a unified biological database from three popular biological repositories (KEGG, Ingenuity and Wikipathways). This database provides computer scientists the flexibility of easily integrating biological information using simple API calls or SQL queries. To deal with the second difficulty of deriving consistent biological results from the experiments, we first conceptualize the notion of “subnetworks”, which refers to a connected portion in a biological pathway. Then we propose a method that identifies subnetworks that are consistently expressed by patients of he same disease phenotype. We test our technique on independent datasets of several diseases, including ALL, DMD and lung cancer. For each of these diseases, we obtain two independent microarray datasets produced by distinct labs on distinct platforms. In each case, our technique consistently produces overlapping lists of significant nontrivial subnetworks from two independent sets of microarray data. The gene-level agreement of these significant subnetworks is between 66.67% to 91.87%. In contrast, when the same pairs of microarray datasets were analysed using GSEA and t-test, this percentage fell between 37% to 55.75% (GSEA) and between 2.55% to 19.23% (t-test). Furthermore, the genes selected using GSEA and t-test do not form subnetworks of substantial size. Thus it is more probable that the subnetworks selected by our technique can provide the researcher with more descriptive information on the portions of the pathway which actually associates with the disease. Keywords: pathway analysis, microarray
Issue Date: 2011
Date Awarded: Mar-2011
URI: http://hdl.handle.net/10044/1/6399
DOI: https://doi.org/10.25560/6399
Supervisor: Wong, Limsoon
Guo, Yike
Sponsor/Funder: A*STAR
Author: Soh, Donny
Funder's Grant Number: 072 101 0016
Department: Computing
Publisher: Imperial College London
Qualification Level: Doctoral
Qualification Name: Doctor of Philosophy (PhD)
Appears in Collections:Computing PhD theses



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