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On the Robustness of Graph-Based Clustering to Random Network Alterations

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 embedd...

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Autores principales: Stacey, R. Greg, Skinnider, Michael A., Foster, Leonard J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Society for Biochemistry and Molecular Biology 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7896145/
https://www.ncbi.nlm.nih.gov/pubmed/33592499
http://dx.doi.org/10.1074/mcp.RA120.002275
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author Stacey, R. Greg
Skinnider, Michael A.
Foster, Leonard J.
author_facet Stacey, R. Greg
Skinnider, Michael A.
Foster, Leonard J.
author_sort Stacey, R. Greg
collection PubMed
description 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.
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spelling pubmed-78961452021-03-19 On the Robustness of Graph-Based Clustering to Random Network Alterations Stacey, R. Greg Skinnider, Michael A. Foster, Leonard J. Mol Cell Proteomics Research 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. American Society for Biochemistry and Molecular Biology 2020-11-24 /pmc/articles/PMC7896145/ /pubmed/33592499 http://dx.doi.org/10.1074/mcp.RA120.002275 Text en © 2020 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Research
Stacey, R. Greg
Skinnider, Michael A.
Foster, Leonard J.
On the Robustness of Graph-Based Clustering to Random Network Alterations
title On the Robustness of Graph-Based Clustering to Random Network Alterations
title_full On the Robustness of Graph-Based Clustering to Random Network Alterations
title_fullStr On the Robustness of Graph-Based Clustering to Random Network Alterations
title_full_unstemmed On the Robustness of Graph-Based Clustering to Random Network Alterations
title_short On the Robustness of Graph-Based Clustering to Random Network Alterations
title_sort on the robustness of graph-based clustering to random network alterations
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7896145/
https://www.ncbi.nlm.nih.gov/pubmed/33592499
http://dx.doi.org/10.1074/mcp.RA120.002275
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