The reduced cost of sequencing has made it feasible to acquire multi-tissue site expression data from the same patient. In the field of cancer research, this has caused an accumulation of cancer type specific tumor with matched adjacent normal expression data sets. Co-expression network analysis is a common technique used to analyze expression data; however, it is unknown whether integrating multi-tissue site data into network construction or constructing pan-cancer networks will improve gene function prediction. One method of evaluating network performance relies on semantic similarity scores; however, computing these scores is computationally intensive. Here, I develop a GPU implementation of a commonly used semantic similarity measure and evaluate its performance compared to CPU-based approaches. Next, I explore whether constructing co-expression networks using the ratio of tumor to match adjacent normal mRNA or a pan-cancer consensus network produces superior performance compared to networks constructed with tumor expression alone. The findings presented here indicate that the GPU-based approach offers significant performance improvement over CPU-based approaches. However, the ratio- and pan-cancer networks produce only a modest improvement over tumor-based networks.
| Identifer | oai:union.ndltd.org:VANDERBILT/oai:VANDERBILTETD:etd-11132017-115307 |
| Date | 21 November 2017 |
| Creators | Greer, Michael J. |
| Contributors | Bing Zhang, Alissa Weaver, Qi Liu |
| Publisher | VANDERBILT |
| Source Sets | Vanderbilt University Theses |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.library.vanderbilt.edu/available/etd-11132017-115307/ |
| Rights | restricted, I hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to Vanderbilt University or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report. |
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