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Title: Computational high-throughput gene expression connectivity mapping and applications in cancer research
Author: Wen, Qing
ISNI:       0000 0004 6060 1377
Awarding Body: Queen's University Belfast
Current Institution: Queen's University Belfast
Date of Award: 2016
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Drug discovery and development have traditionally been long and costly processes. Modern biotechnologies have enabled high throughput gene expression profiling experiments on an unprecedented scale and consequently have generated a large volume of gene expression data. Facilitated by ’omics technologies and the expansion of publicly available data, gene expression connectivity mapping (GECM) is an advanced bioinformatics technique for establishing connections among genes, drugs, and diseases, which has proven to be a useful tool to accelerate the process of drug discovery. There remains an unmet need for optimised protocols in connectivity mapping. This research intends to formulate a standardised procedure for constructing high quality gene signatures from a user’s perspective and to provide comprehensive guidelines for researchers using GECM. Firstly, a gene-signature progression approach was developed to construct high quality gene signatures. This method was specially developed to handle large numbers of differentially expressed genes, when they are identified, and when there is a need to prioritize them to be included in the query signature. Secondly, the availability of multiple datasets for the same disease is a great advantage of public data repositories. As a further methodological enhancement to connectivity mapping, a simple but robust method using a signed and normalized scoring scheme was developed to compile combined/unified gene signatures using multiple datasets for the same diseases. Thirdly, the techniques used in the gene signature progression and multiple dataset approaches were integrated to present a comprehensive procedure for connectivity mapping, finishing with a drug class enrichment analysis. Overall, this research has resulted in some major methodological advancement for gene expression connectivity mapping and also presented successful case studies with novel discoveries demonstrating the utility of the new techniques.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available