Fig. 1
From: multiWGCNA: an R package for deep mining gene co-expression networks in multi-trait expression data
Overview of the multiWGCNA workflow. (a) multiWGCNA requires a dataset with two sample traits, such as disease versus a secondary trait like time or space. From this data, co-expression networks are constructed using WGCNA. (b) Three levels of networks are constructed from this design: (1) the combined network, (2) the disease and wildtype (WT) networks, and (3) the secondary trait (ST) networks. From these networks, modules can be mapped both across levels (gray arrows) and within levels (white arrows). (c) For each network level, appropriate analysis is performed, including differential module expression, module preservation, and module dynamics