Abstract
The non-random interaction pattern of a protein–protein interaction network (PIN) is biologically informative, but its potentials have not been fully utilized in omics studies. Here, we propose a network-permutation-based association study (NetPAS) method that gauges the observed interactions between two sets of genes based on the comparison between permutation null models and the empirical networks. This enables NetPAS to evaluate relationships, constrained by network topology, between gene sets related to different phenotypes. We demonstrated the utility of NetPAS in 50 well-curated gene sets and comparison of association studies using Z-scores, modified Zʹ-scores, p-values and Jaccard indices. Using NetPAS, a weighted human disease network was generated from the association scores of 19 gene sets from OMIM. We also applied NetPAS in gene sets derived from gene ontology and pathway annotations and showed that NetPAS uncovered functional terms missed by DAVID and WebGestalt. Overall, we show that NetPAS can take topological constraints of molecular networks into account and offer new perspectives than existing methods.
Highlights
For the 50 hallmark sets (Fig. 2), the mean association Z-score excluding self-interactions (Fig. 2d) is 2.0
We demonstrate the utility of using Z-scores in NetPAS compared to using p-values or Jaccard-scores
NetPAS is useful in classifications of gene sets, including those associated with different diseases
Summary
For the 50 hallmark sets (Fig. 2), the mean association Z-score excluding self-interactions (Fig. 2d) is 2.0. The self-interactions for all gene sets have a significantly higher mean Z-score of 17.8. Because in the null models of present work all node degrees have been preserved, they have the same power-law distribution as those in the original PIN.
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