Integrative Analysis of Many Weighted Co-Expression Networks Using Tensor Computation
Figure 1
Illustration of the tensor representation for multiple networks and a recurrent heavy subgraph.
(A) Microarray datasets are modeled as (B) a collection of co-expression networks; (C) These co-expression networks can be “stacked” together into (D) a third-order tensor such that each slice represents the adjacency matrix of one network. The weights of edges in the co-expression networks and their corresponding tensor elements are indicated by the color scale to the right of the figure. In (D), after reordering the tensor using the gene and network membership vectors, it becomes clear that the subtensor in the top-left corner of the tensor (formed by genes in networks
) corresponds to a recurrent heavy subgraph.