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Approaches to Find the Functionally Related Experiments Based on Enrichment Scores: Infinite Mixture Model Based Cluster Analysis for Gene Expression Data

Li, Qian
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1378113351

Abstract Details

2013, PhD, University of Cincinnati, Arts and Sciences: Mathematical Sciences.
DNA microarray is a widely used high-throughput technology to measure the expression level of tens of thousands of genes simultaneously. With increasing availability of microarray genomics data, various clustering algorithms have been explored to identify the latent patterns in gene expression data as well as discover disease subtypes. Interesting connections that can be founded correlating differential-expressed genes evidence to other biological information are very important in developing a full picture of the biological pathways as well as in giving insightful suggestions to the new conducted experiments. The abundant biological information we need to identify the disease signature is organized in the functional categories. Thus, relating the microarray experiments to the functional categories could lead to a better understanding of the underlying biological process and help develop targeted treatment to a specific disease. In this dissertation, we investigated several Dirichlet process mixture (DPM) model based clustering methods that explicitly account for interactions across the functional category enrichment scores for improved sample clustering. Our clustering method represents microarray data enrichment score profiles as multivariate Gaussian random variables with structured or unstructured correlation. Also we demonstrate by a simulation study that when correlation exist, our algorithm will outperform the other clustering algorithm assume independence. Furthermore, factor analysis based clustering procedure is developed to search for the correct underlying correlation pattern and we optimize the number of factors using the Metropolised Carlin and Chib method based model selection algorithm. In such a way, we reduce the number of parameters to be estimated in the unstructured covariance matrix model and also incorporate the unknown variance-covariance structure across different functional categories. The main contributions of our approaches are the ability to incorporate the correlation between the functional categories, as well as detect the latent factor structures. We apply this method to Juvenile Rheumatoid Arthritis (JRA) microarray data, and found our method has better predicting power of patients disease subtype over the other methods compared.
Siva Sivaganesan, Ph.D. (Committee Chair)
Seongho Song, Ph.D. (Committee Member)
Xia Wang, Ph.D. (Committee Member)
119 p.
Statistics
Microarray data; Clustering Analysis; Dirichlet process mixture model; Infinite Mixture model; Enrichment score; Functional category

Recommended Citations

Citations

  • Li, Q. (2013). Approaches to Find the Functionally Related Experiments Based on Enrichment Scores: Infinite Mixture Model Based Cluster Analysis for Gene Expression Data [Doctoral dissertation, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1378113351

    APA Style (7th edition)

  • Li, Qian. Approaches to Find the Functionally Related Experiments Based on Enrichment Scores: Infinite Mixture Model Based Cluster Analysis for Gene Expression Data. 2013. University of Cincinnati, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1378113351.

    MLA Style (8th edition)

  • Li, Qian. "Approaches to Find the Functionally Related Experiments Based on Enrichment Scores: Infinite Mixture Model Based Cluster Analysis for Gene Expression Data." Doctoral dissertation, University of Cincinnati, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1378113351

    Chicago Manual of Style (17th edition)

ucin1378113351
423
© 2013, some rights reserved.Creative Commons License Approaches to Find the Functionally Related Experiments Based on Enrichment Scores: Infinite Mixture Model Based Cluster Analysis for Gene Expression Data by Qian Li is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by University of Cincinnati and OhioLINK.