Mol Cell Proteomics. 2021;20:100002. doi: 10.1074/mcp.RA120.002275. Epub 2020 Nov 24.
On the Robustness of Graph-Based Clustering to Random Network Alterations.
Molecular & cellular proteomics : MCP
R Greg Stacey, Michael A Skinnider, Leonard J Foster
Affiliations
Affiliations
- Michael Smith Laboratories, University of British Columbia, Vancouver, Canada. Electronic address: [email protected].
- Michael Smith Laboratories, University of British Columbia, Vancouver, Canada.
- Michael Smith Laboratories, University of British Columbia, Vancouver, Canada; Department of Biochemistry, University of British Columbia, Vancouver, Canada.
PMID: 33592499
PMCID: PMC7896145 DOI: 10.1074/mcp.RA120.002275
Abstract
Biological functions emerge from complex and dynamic networks of protein-protein interactions. Because these protein-protein interaction networks, or interactomes, represent pairwise connections within a hierarchically organized system, it is often useful to identify higher-order associations embedded within them, such as multimember protein complexes. Graph-based clustering techniques are widely used to accomplish this goal, and dozens of field-specific and general clustering algorithms exist. However, interactomes can be prone to errors, especially when inferred from high-throughput biochemical assays. Therefore, robustness to network-level noise is an important criterion. Here, we tested the robustness of a range of graph-based clustering algorithms in the presence of noise, including algorithms common across domains and those specific to protein networks. Strikingly, we found that all of the clustering algorithms tested here markedly amplified network-level noise. Randomly rewiring only 1% of network edges yielded more than a 50% change in clustering results. Moreover, we found the impact of network noise on individual clusters was not uniform: some clusters were consistently robust to injected noise, whereas others were not. Therefore we developed the clust.perturb R package and Shiny web application to measure the reproducibility of clusters by randomly perturbing the network. We show that clust.perturb results are predictive of real-world cluster stability: poorly reproducible clusters as identified by clust.perturb are significantly less likely to be reclustered across experiments. We conclude that graph-based clustering amplifies noise in protein interaction networks, but quantifying the robustness of a cluster to network noise can separate stable protein complexes from spurious associations.
Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.
Keywords: Biochemistry; Bioinformatics; Graph-based clustering; Interactome; Noise; Proteomics; Robust
Conflict of interest statement
Conflict of interest The authors declare no competing interests.
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