Sharing and Specificity of Co-expression Networks across 35 Human Tissues
Fig 2
An illustration of our algorithm for hypothetical tissues (1, 2, 3, 4) and genes (A, B, C).
The tree represents the hierarchy over tissues 1–4. For each tissue and each internal node in the hierarchy, gene networks over three genes (A, B, and C) are represented by circles (genes) and edges. a) Learning the hierarchy: tissues 1 and 2 are clustered together because A, B, and C have high mean expression levels in both tissues (green) and low levels in tissues 3 and 4 (red). b) co-expression networks are learned in each tissue independently. Edge AB is shared across three tissues; BC and AC only appear in one tissue. c) Networks are learned for each internal node in the hierarchy, representing an “average” of the child node networks, allowing similar tissues to share knowledge. The child node networks are re-learned and encouraged to be similar to their parents; this repeats until convergence. d) The final networks. Edge AB is now present in all 4 tissues; similarly, AC now appears in tissues 1 and 2, and edge BC in tissues 3 and 4.