From: BicNET: Flexible module discovery in large-scale biological networks using biclustering
Approach | Method | Solution aspects and concerns | Efficiency |
---|---|---|---|
Clustering (exhaustive and non-overlapping node coverage) | k-Means | Majority of clusters show loose connectedness; High variation on the size of modules (1-to-3 clusters covering almost all nodes and the remaining clusters being statistically non-significant [66]) | Efficiency problems for networks with >100.000 interactions |
Spectral | Able to isolate modules where the degree of connectedness is approximately constant per module; Only a small subset of clusters is relevant (medium-to-high degree of connectedness) | Medusa implementation only scales for networks with <10.000 interactions | |
Affinity propagation | The clusters collected from (small samples of) the target biological networks show a generalized lack of biological relevance | Time and memory bottlenecks for small nets (<1000 interactions) | |
Clustering (non-exhaustive and possibly overlapping node coverage) | CPMw (weighted k-clique percolation) | Intolerance to noise; Intractably large solutions (explosion of similar clusters) with strict coherency criterion (k-clique); Dependence on parameters (e.g. k, intensity level) | Only scales for nets with <5000 nodes (5–10 % density). Bottlenecks for the target biological data even when removing >95 % interactions |
Biclustering (bi-sets of nodes) | Hypercliques (unweighted) | Intolerant to missing interactions; Large number of highly similar modules; Dense coherency only | BicNET implementation efficient for large networks (>10000 nodes) with density up to 25 % |
Hypercliques (differential) | Intolerant to noise and the prone item-boundaries problem during the selection of differential weights; Dense coherency only | BicNET implementation scales for large dense networks | |
BicNET (dense assumption) | Focus on dissimilar modules robust to noise and missings, with possibly distinct forms of coherency strength (|L| \(\in\){1,2,3,5}) | Efficiency bounded by the search for unweigthed hypercliques (|L|=1) |