Unified Feature Association Networks through Integration of Transcriptomic and Proteomic Data

Transcriptomic and proteomic data from human cells infected with Dengue virus was used to infer a number of networks to determine which network inference methods were best for linking protiens and transcripts in the same network. GENIE3, a random forest method, was found to be the best and once inferred with this method networks were interrogated to gain knowledge regarding host pathogen interactions surrounding Dengue infection. Human U-937 cells were incubated with Dengue viral strains for varying amounts of time before both transcriptomic and proteomic data was collected.  Networks were inferred with 10 methods and methods were ranked by their ability to link proteins and transcripts as well as their accuracy in grouping transcripts and proteins of similar functions together.