My everyday commute to uni is approximately 1 hour, so I try to use this time efficiently by reading relevant literature for my PhD project. Inspired by a paper from Kuchel et al. in 2010 I developed a network approach to integrate different -omics data, such as gene expression data (transcriptomics) and NMR and/or MS data (metabolomics). My initial approach is outlined in this blog post.
Creating a differential gene co-expression network
Suppose you have gene expression data from a disease group and a healthy control group, and you create a gene co-expression network (where nodes represent genes and edges represent high absolute correlation values), such that high correlated gene profiles in the control group are represented by green edges, red edges represent high correlations in the disease phenotype and black edges are drawn of two gene profiles are correlated in both, control and disease phenotype.