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Investigating Gene Relationships in Microarray Expressions: Approaches Using Clustering Algorithms

Hasan, Mohammad Shabbir
http://rave.ohiolink.edu/etdc/view?acc_num=akron1376536496

Abstract Details

2013, Master of Science, University of Akron, Computer Science.
DNA Microarray technology has provided a very convenient way to concurrently investigate the expression levels of thousands of genes in a collection of related samples during different biological processes. Researchers from different disciplines such as computer science and biology have found it very much interesting and meaningful to group genes based on the similarity of their expression patterns. Different clustering algorithms such as hierarchical clustering, k-means clustering, self-organizing maps have been applied to group of genes with similar expression patterns. However each of these traditional clustering algorithms suffers from different limitations. Beside these clustering algorithms, there are some other algorithms to group similar items together. Ford Fulkerson algorithm which is based on maximum flow – minimum cut approach is one of them and it is widely used for community discovery in web graphs. In this research work, we aimed to group genes with similar expression pattern using two different approaches: one is k-means clustering combined with hierarchical clustering and another is maximum flow – minimum cut approach in association with Dijkstra’s algorithm to select source and sink node. We use a publicly available microarray data on Adenocarcinoma which is the most frequent type of non-small-cell cancers. This dataset is available in the Gene Expression Omnibus which is a public functional genomics data repository. This dataset contains samples of five different groups: normal tissue, tissues with EGFR mutation, tissues with KRAS mutation, tissues with EML4-ALK fusion and tissues with EGFR, KRAS, EML4-ALK negative cases. We investigate a number of representative genes from the group of normal tissue and from the group of KRAS mutation tissues which is also termed as KRAS positive groups in this study. We clustered the genes for both of these groups. Finally we used Gene Ontology database to find the change in the enrichment of molecular functions of the genes contained in each cluster discovered by the above mentioned approaches for both normal and KRAS positive dataset. We discovered that both of these approaches can group genes with similar expression pattern together and hence we proposed that these approaches can be used in future for clustering microarray data.
Zhong-Hui Duan, Dr. (Advisor)
Yingcai Xiao, Dr. (Committee Member)
Kathy Liszka, Dr. (Committee Member)
72 p.
Computer Science
Gene Relation, Microarray, Gene Expression, Clustering

Recommended Citations

Citations

  • Hasan, M. S. (2013). Investigating Gene Relationships in Microarray Expressions: Approaches Using Clustering Algorithms [Master's thesis, University of Akron]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=akron1376536496

    APA Style (7th edition)

  • Hasan, Mohammad Shabbir. Investigating Gene Relationships in Microarray Expressions: Approaches Using Clustering Algorithms. 2013. University of Akron, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=akron1376536496.

    MLA Style (8th edition)

  • Hasan, Mohammad Shabbir. "Investigating Gene Relationships in Microarray Expressions: Approaches Using Clustering Algorithms." Master's thesis, University of Akron, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=akron1376536496

    Chicago Manual of Style (17th edition)

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© 2013, all rights reserved.
This open access ETD is published by University of Akron and OhioLINK.