In last week’s group meeting, I introduced our network comparison method (Netdis) and presented some new results that enable the method to be applied to larger networks.
The most tractable methods for network comparison are those which compare at the level of the entire network using statistics that describe global properties, but these statistics are not sensitive enough to be able to reconstruct phylogeny or shed light on evolutionary processes. In contrast, there are several network alignment based methods that compare networks using the properties of the individual proteins (nodes) e.g. local network similarity and/or protein functional or sequence similarity. The aim of these methods is to identify matching proteins/nodes between networks and use these to identify exact or close sub-network matches. These methods are usually computationally intensive and tend to yield an alignment which contains only a relatively small proportion of the network, although this has been alleviated to some extent in more recent methods.
Thus, we do not follow the network alignment paradigm, but instead we take our lead from alignment-free sequence comparison methods that have been used to identify evolutionary relationships. Alignment-free methods based on k-tuple counts (also called k-grams or k-words) have been applied to construct trees from sequence data. A key feature is the standardisation of the counts to separate the signal from the background noise. Inspired by alignment-free sequence comparison we use subgraph counts instead of sequence homology or functional one-to-one matches to compare networks. Our proposed method, Netdis, compares the subgraph content not of the networks themselves but instead of the ensemble of all protein neighbourhoods (ego-networks) in each network, through an averaging many-to-many approach. The comparison between these ensembles is summarised in a Netdis value, which in turn is used as input for phylogenetic tree reconstruction.
Extensive tests on simulated and empirical data-sets show that it is not necessary to analyze all possible ego-networks within a network for Netdis to work. Our results indicate that in general, randomly sampling around 10% of egos in each network results in a very similar clustering of networks on average, compared to the tree with 100% sampling (Fig 1). This result has important implications for use-cases where eextremely large graphs are to be compared (e.g > 100,000 nodes). Related to the ego-nework sub-sampling idea is the notion of size-limiting the ego-networks that are to be analyzed by the algorithm. Our tests show that the vast majrity of ego-netowrks in most networks have a relatively low coverage of the overall network. Moreover, by introducing lower-size threshold on the egos, we observe better results on average. Together, this means a limited range of ego-network sizes to be analyzed for each network, which should lead to better statitical properties as the sub-sampling scheme is inspired by bootstrapping.