High-throughput transcriptomics has provided a powerful new approach for studying host-pathogen interactions. While popular techniques such as differential expression and gene set enrichment analysis can yield informative results, they do not always make full use of information available in multi-condition experiments. Co-expression networks provide a novel way of analyzing these datasets which can lead to new discoveries that are not readily detectable using the more popular approaches.

While significant work has been done in recent years on the construction of coexpression networks, less is known about how to measure the quality of such networks. Here, I describe an approach for evaluating the quality of a co-expression network, based on enrichment of biological function across the network. The approach is used to measure the influence of various data transformations and algorithmic parameters on the resulting network quality, leading to several unexpected findings regarding commonly-used techniques, as well as to the development of a novel similarity metric used to assess the degree of co-expression between two genes. Next, I describe a simple approach for aggregating information across multiple network parameterizations, in order to arrive at a robust "consensus" co-expression network. This approach is used to generate independent host and parasite networks for two host-trypanosomatid transcriptomics datasets, resulting in the detection of both previously known disease pathways and novel gene networks potentially related to infection. Finally, a differential network analysis approach is developed and used to explore the impact of infection on the host co-expression network, and to elucidate shared transcriptional signatures of infection by different intracellular pathogens.

The approaches developed in this work provide a powerful set of tools and techniques for the rigorous generation and evaluation of co-expression networks, and have significant implications for co-expression network-based research. The application of these approaches to several host-pathogen systems demonstrates their utility for host-pathogen transcriptomics research, and has resulted in the creation of a number of valuable resources for understanding systems-levels processes that occur during the process of infection.