Fuzzy-FishNET: A highly reproducible protein complex-based approach for feature selection in comparative proteomics

Abstract

Background: The hypergeometric enrichment analysis approach typically fares poorly in feature-selection stability due to its upstream reliance on the t-test to generate differential protein lists before testing for enrichment on a protein complex, subnetwork or gene group. Methods: Swapping the t-test in favour of a fuzzy rank-based weight system similar to that used in network-based methods like Quantitative Proteomics Signature Profiling (QPSP), Fuzzy SubNets (FSNET) and paired FSNET (PFSNET) produces dramatic improvements. Results: This approach, Fuzzy-FishNET, exhibits high precision-recall over three sets of simulated data (with simulated protein complexes) while excelling in feature-selection reproducibility on real data (based on evaluation with real protein complexes). Overlap comparisons with PFSNET shows Fuzzy-FishNET selects the most significant complexes, which are also strongly class-discriminative. Cross-validation further demonstrates Fuzzy-FishNET selects class-relevant protein complexes. Conclusions: Based on evaluation with simulated and real datasets, Fuzzy-FishNET is a significant upgrade of the traditional hypergeometric enrichment approach and a powerful new entrant amongst comparative proteomics analysis methods.