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Active Module Discovery: Integrated Approaches of Gene Co-Expression and PPI Networks and MicroRNA Data

Hatem, Ayat
http://rave.ohiolink.edu/etdc/view?acc_num=osu1398949621

Abstract Details

2014, Doctor of Philosophy, Ohio State University, Electrical and Computer Engineering.
Integrating protein-protein interaction (PPI) networks with gene expression data to extract active modules is shown to be promising in detecting meaningful biomarkers for cancer and other diseases. However, current algorithms suffer from many drawbacks such as focusing only on the highly differentially expressed genes, analyzing dependencies between genes in the PPI network only; totally neglecting the genes whose interactions are not known yet, and finally using mRNA gene expression data; ignoring other types of data such as gene mutation information and microRNAs expressions. In addition, lately, using the next generation sequencing technology to sequence the mRNA (RNA-Seq) has become the new standard for gene expression. However, existing algorithms either cannot handle the RNA-Seq data, or they return large modules which are hard to analyze. Therefore, we need new approaches to address the current drawbacks while utilizing and integrating the RNA-Seq data to the module discovery process. This work explores some of the drawbacks of current active module discovery algorithms. We first discuss the differences between RNA-Seq data and microarray data. With experimental evidence, we show that RNA-Seq is more powerful than microarray in providing better active modules at the expense of generating larger ones. Therefore, new approaches are needed to handle RNA-Seq data. Afterwards, we present a new workflow, PRASE, that is specifically designed to handle and obtain better active modules while using RNA-Seq data. PRASE employs a variation of the famous PageRank algorithm to preprocess the gene expression p-values. Then, it applies a scaling function to construct new p-values for the genes. Such new p-values redefine the importance of the genes: a gene is important not only based on its own value but also based on the values of the surrounding genes, thus, boosting the importance of genes that might not be differentially expressed from the p-value perspective. Finally, PRASE uses the new p-values with the existing active module discovery algorithms to extract the final modules. We applied our workflow on colorectal cancer, oligodendroglioma tumor, and breast cancer datasets. Using PRASE, we obtain more specialized modules which contain information that is overlooked by existing algorithms. Finally, we present our novel microRNA-mRNA integration technique, Mica, that efficiently integrates microRNA and mRNA expressions with the PPI network to discover more disease-specific active modules. The novelty of Mica lies in the early integration of microRNA expression with mRNA expression to better highlight the indirect dependencies between genes. We applied Mica on microRNA-Seq and mRNA-Seq data sets of 699 invasive ductal carcinoma samples and 150 invasive lobular carcinoma samples from the Cancer Genome Atlas Project (TCGA). The Mica modules unravel new and interesting dependencies between the genes and miRNAs. Additionally, the modules accurately differentiate between case and control samples while being highly enriched with disease-specific pathways and genes.
Umit V. Catalyurek (Advisor)
Yuejie Chi (Committee Member)
Kun Huang (Committee Member)
Fusun Ozguner (Committee Member)
190 p.
Bioinformatics; Computer Science

Recommended Citations

Citations

  • Hatem, A. (2014). Active Module Discovery: Integrated Approaches of Gene Co-Expression and PPI Networks and MicroRNA Data [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1398949621

    APA Style (7th edition)

  • Hatem, Ayat. Active Module Discovery: Integrated Approaches of Gene Co-Expression and PPI Networks and MicroRNA Data. 2014. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1398949621.

    MLA Style (8th edition)

  • Hatem, Ayat. "Active Module Discovery: Integrated Approaches of Gene Co-Expression and PPI Networks and MicroRNA Data." Doctoral dissertation, Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1398949621

    Chicago Manual of Style (17th edition)

osu1398949621
637
© 2014, all rights reserved.
This open access ETD is published by The Ohio State University and OhioLINK.