Doctoral Thesis
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High-throughput expression data have become the norm in molecular biology research. However, the analysis of expression data is statistically and computationally challenging and has not kept up with their generation. This has resulted in large amounts of unexplored data in public repositories. After pre-processing and quality control, the typical gene expression analysis workflow follows two main steps. First, the complexity of the data is reduced by removing the genes that are redundant or irrelevant for the biological question that motivated the experiment, using a feature selection method. Second, relevant genes are investigated to extract biological information that could aid in the interpretation of the results. Different methods, such as functional annotation, clustering, network analysis, and/or combinations thereof are useful for the latter purpose. Here, I investigated and presented solutions to three problems encountered in the expression data analysis workflow. First, I worked on reducing complexity of high-throughput expression data by selecting relevant genes in the context of the sample classification problem. The sample classification problem aims to assign unknown samples into one of the known classes, such as healthy and diseased. For this purpose, I developed the relative signal-to-noise ratio (rSNR), a novel feature selection method which was shown to perform significantly better than other methods with similar objectives. Second, to better understand complex phenotypes using high-throughput expression data, I developed a pipeline to identify the underlying biological units, as well as their interactions. These biological units were assumed to be represented by groups of genes working in synchronization to perform a given function or participate in common biological processes or pathways. Thus, to identify biological units, those genes that had been identified as relevant to the phenotype under consideration through feature selection methods were clustered based on both their functional annotations and expression profiles. Relationships between the associated biological functions, processes, and/or pathways were investigated by means of a co-expression network. The developed pipeline provides a new perspective to the analysis of high-throughput expression data by investigating interactions between biological units. Finally, I contributed to a project where a network describing pluripotency in mouse was used to infer the corresponding network in human. Biological networks are context-specific. Combining network information with high-throughput expression data can explain the control mechanisms underlying changes and maintenance of complex phenotypes. The human network was constructed on the basis of orthology between mouse and human genes and proteins. It was validated with available data in the literature. The methods and strategies proposed here were mainly trained and tested on microarray expression data. However, they can be easily adapted to next-generation sequencing and proteomics data.