A generalised significance test for individual communities in networks

Kojaku, Sadamori; Masuda, Naoki

doi:10.1038/s41598-018-25560-z

Cited by 25 publications

(41 citation statements)

References 44 publications

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“…The package contains an implementation of the MINRES (minimal residual) algorithm 64 , which assigns each node in the perturbome to either the core or the periphery based on a singular value decomposition (SVD) of the adjacency matrix of the network. The statistical significance of the resulting assignment was tested using the ( q , s )-test 65 , which calculates a P value based on a comparison of the core–periphery structure within the perturbome to the core–periphery structure of 1000 randomized networks created using the configuration model.…”

Section: Methodsmentioning

confidence: 99%

Mapping the perturbome network of cellular perturbations

et al. 2019

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Drug combinations provide effective treatments for diverse diseases, but also represent a major cause of adverse reactions. Currently there is no systematic understanding of how the complex cellular perturbations induced by different drugs influence each other. Here, we introduce a mathematical framework for classifying any interaction between perturbations with high-dimensional effects into 12 interaction types. We apply our framework to a large-scale imaging screen of cell morphology changes induced by diverse drugs and their combination, resulting in a perturbome network of 242 drugs and 1832 interactions. Our analysis of the chemical and biological features of the drugs reveals distinct molecular fingerprints for each interaction type. We find a direct link between drug similarities on the cell morphology level and the distance of their respective protein targets within the cellular interactome of molecular interactions. The interactome distance is also predictive for different types of drug interactions.

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Section: Methodsmentioning

confidence: 99%

Mapping the perturbome network of cellular perturbations

et al. 2019

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“…The modularity maximisation for finding communities in networks also shares this shortcoming 43 . To discuss the CP structure at different resolutions, here we extend the algorithm 6,20 using multiresolution methods 21,22 . In the new algorithm presented in this study, we seek CP pairs by maximisingwhere γ ( γ ≥ 0) is a resolution parameter that controls the effect of the null model term (i.e, ).…”

Section: Methodsmentioning

confidence: 99%

“…We used a label switching heuristic to maximise in our previous algorithms 6,20 . In our preliminary analysis, we found that the label switching heuristic in the present case detected multiple CP pairs in the GLSN for γ = 0, whereas a single CP pair is natural anticipation in this case.…”

Section: Methodsmentioning

confidence: 99%

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Multiscale core-periphery structure in a global liner shipping network

Kojaku

Xia

et al. 2019

Sci Rep

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Maritime transport accounts for a majority of trades in volume, of which 70% in value is carried by container ships that transit regular routes on fixed schedules in the ocean. In the present paper, we analyse a data set of global liner shipping as a network of ports. In particular, we construct the network of the ports as the one-mode projection of a bipartite network composed of ports and ship routes. Like other transportation networks, global liner shipping networks may have core-periphery structure, where a core and a periphery are groups of densely and sparsely interconnected nodes, respectively. Core-periphery structure may have practical implications for understanding the robustness, efficiency and uneven development of international transportation systems. We develop an algorithm to detect core-periphery pairs in a network, which allows one to find core and peripheral nodes on different scales and uses a configuration model that accounts for the fact that the network is obtained by the one-mode projection of a bipartite network. We also found that most ports are core (as opposed to peripheral) ports and that ports in some countries in Europe, America and Asia belong to a global core-periphery pair across different scales, whereas ports in other countries do not.

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“…However, this method leads to a significant reduction of resolution that impairs biological interpretation of the discovered communities [23]. Alternatively, the statistical significance of individual communities can be estimated by comparing them with structures formed in random networks with the same degree characteristics as the original network [13,24,25]. These random networks are typically created using generative models.…”

Section: Introductionmentioning

confidence: 99%

Generating Ensembles of Gene Regulatory Networks to Assess Robustness of Disease Modules

Lim

Chen

Grant

et al. 2020

Preprint

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The use of biological networks such as protein-protein interaction and transcriptional regulatory networks is becoming an integral part of biological research in the genomics era. However, these networks are not static, and during phenotypic transitions like disease onset, they can acquire new "communities" of genes that carry out key cellular processes. Changes in community structure can be detected by maximizing a modularity-based score, but because biological systems and network inference algorithms are inherently noisy, it remains a challenge to determine whether these changes represent real cellular responses or whether they appeared by random chance. Here, we introduce Constrained Random Alteration of Network Edges (CRANE), a computational method that samples networks with fixed node strengths to identify a null distribution and assess the robustness of observed changes in network structure. In contrast with other approaches, such as consensus clustering or established network generative models, CRANE produces more biologically realistic results and performs better in simulations. When applied to breast and ovarian cancer networks, CRANE improves the recovery of cancer-relevant GO terms while reducing the signal from non-specific housekeeping processes. CRANE is a general tool that can be applied in tandem with a variety of stochastic community detection methods to evaluate the veracity of their results.

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A generalised significance test for individual communities in networks

Cited by 25 publications

References 44 publications

Mapping the perturbome network of cellular perturbations

Mapping the perturbome network of cellular perturbations

Multiscale core-periphery structure in a global liner shipping network

Generating Ensembles of Gene Regulatory Networks to Assess Robustness of Disease Modules

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