Fig. 1.

ManiNetCluster Workflow. a Inputs: The inputs of ManiNetCluster are two gene expression datasets collected from different phenotypes, states or conditions. b Manifold approximation via neighborhood networks: ManiNetCluster constructs gene co-expression network using kNNGraph for each condition, connecting genes with similar expression level. This step aims to approximate the manifolds of the datasets. c Manifold learning for network alignment: Using manifold alignment and manifold warping methods to identify a common manifold, ManiNetCluster aligns two gene networks across conditions. The outcome of this step is a multilayer network consisting of two types of links: the inter-links (between the two co-expression neighborhood networks) showing the correspondence (e.g., shared genes) between the two datasets, and the intra-links showing the co-expression relationships. d Clustering aligned networks to reveal functional links between gene modules: The multilayer network is then clustered into modules, which have the following major types: (1) the conserved modules mainly consisting of the same or orthologous genes; (2) the condition-specific modules mainly containing genes from one network; (3) the cross-network linked modules consisting of different gene sets from each network and limited shared/orthologous genes