Py3plex toolkit for visualization and analysis of multilayer networks

Škrlj, Blaž; Kralj, Jan; Lavrač, Nada

doi:10.1007/s41109-019-0203-7

Cited by 15 publications

(8 citation statements)

References 40 publications

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Section: Evaluation Of the Methodologymentioning

confidence: 99%

“…After the application of a new modelling method to the transcriptional data of these samples, several communities of genes from different metabolic pathways were formed and visualised with Py3plex [43] (Figure 5). There is growing evidence that the phytoplasma infection of grapevines is characterised by severely affected photosynthesis and carbohydrate metabolism pathways [16,38].…”

Section: Recovery Of Empirically Validated Community Informationmentioning

confidence: 99%

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New Cross-Talks between Pathways Involved in Grapevine Infection with ‘Candidatus Phytoplasma solani’ Revealed by Temporal Network Modelling

Škrlj

Pompe-Novak

Brader

et al. 2021

Plants

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Understanding temporal biological phenomena is a challenging task that can be approached using network analysis. Here, we explored whether network reconstruction can be used to better understand the temporal dynamics of bois noir, which is associated with ‘Candidatus Phytoplasma solani’, and is one of the most widespread phytoplasma diseases of grapevine in Europe. We proposed a methodology that explores the temporal network dynamics at the community level, i.e., densely connected subnetworks. The methodology offers both insights into the functional dynamics via enrichment analysis at the community level, and analyses of the community dissipation, as a measure that accounts for community degradation. We validated this methodology with cases on experimental temporal expression data of uninfected grapevines and grapevines infected with ‘Ca. P. solani’. These data confirm some known gene communities involved in this infection. They also reveal several new gene communities and their potential regulatory networks that have not been linked to ‘Ca. P. solani’ to date. To confirm the capabilities of the proposed method, selected predictions were empirically evaluated.

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Section: Evaluation Of the Methodologymentioning

confidence: 99%

Section: Recovery Of Empirically Validated Community Informationmentioning

confidence: 99%

New Cross-Talks between Pathways Involved in Grapevine Infection with ‘Candidatus Phytoplasma solani’ Revealed by Temporal Network Modelling

Škrlj

Pompe-Novak

Brader

et al. 2021

Plants

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“…( 2008 ), the Louvain algorithm implementation, as well as a wrapper for the InfoMap binary can be found in Skrlj et al. ( 2019c ), Škrlj et al. ( 2019b ).…”

Section: Quantitative Evaluation Of Scdmentioning

confidence: 99%

“…We intentionally didn't use GPU to demonstrate that no specialized hardware is needed to obtain competitive results. The LabelPropagation baseline was implemented using Hagberg et al (2008), the Louvain algorithm implementation, as well as a wrapper for the InfoMap binary can be found in Skrlj et al (2019c), Škrlj et al (2019b). The validRange for the empirical evaluation was set to the interval [5, |N|, 10].…”

Section: Technical Detailsmentioning

confidence: 99%

Embedding-based Silhouette community detection

2020

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Mining complex data in the form of networks is of increasing interest in many scientific disciplines. Network communities correspond to densely connected subnetworks, and often represent key functional parts of real-world systems. This paper proposes the embedding-based Silhouette community detection (SCD), an approach for detecting communities, based on clustering of network node embeddings, i.e. real valued representations of nodes derived from their neighborhoods. We investigate the performance of the proposed SCD approach on 234 synthetic networks, as well as on a real-life social network. Even though SCD is not based on any form of modularity optimization, it performs comparably or better than state-of-the-art community detection algorithms, such as the InfoMap and Louvain. Further, we demonstrate that SCD’s outputs can be used along with domain ontologies in semantic subgroup discovery, yielding human-understandable explanations of communities detected in a real-life protein interaction network. Being embedding-based, SCD is widely applicable and can be tested out-of-the-box as part of many existing network learning and exploration pipelines.

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“…Pymnet is a python library which can produce high quality static images of multilayer and multiplex networks and therefore, familiarity with Python programming language is required. Similarly, Py3plex ( 17 ) a python library, which handles multilayered networks and enables common operations such as aggregation, slicing, indexing and traversal but lacks interactivity and fails to solve the edge overlapping problem. Mully ( 18 ) is an R package to create, modify and visualize multilayered graphs but lacks interactivity too.…”

Section: Introductionmentioning

confidence: 99%

Arena3Dweb: interactive 3D visualization of multilayered networks

Karatzas

Baltoumas

Panayiotou

et al. 2021

Nucleic Acids Research

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Efficient integration and visualization of heterogeneous biomedical information in a single view is a key challenge. In this study, we present Arena3D web , the first, fully interactive and dependency-free, web application which allows the visualization of multilayered graphs in 3D space. With Arena3D web , users can integrate multiple networks in a single view along with their intra- and inter-layer connections. For clearer and more informative views, users can choose between a plethora of layout algorithms and apply them on a set of selected layers either individually or in combination. Users can align networks and highlight node topological features, whereas each layer as well as the whole scene can be translated, rotated and scaled in 3D space. User-selected edge colors can be used to highlight important paths, while node positioning, coloring and resizing can be adjusted on-the-fly. In its current version, Arena3D web supports weighted and unweighted undirected graphs and is written in R, Shiny and JavaScript. We demonstrate the functionality of Arena3D web using two different use-case scenarios; one regarding drug repurposing for SARS-CoV-2 and one related to GPCR signaling pathways implicated in melanoma. Arena3D web is available at http://bib.fleming.gr:3838/Arena3D or http://bib.fleming.gr/Arena3D .

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Py3plex toolkit for visualization and analysis of multilayer networks

Cited by 15 publications

References 40 publications

New Cross-Talks between Pathways Involved in Grapevine Infection with ‘Candidatus Phytoplasma solani’ Revealed by Temporal Network Modelling

New Cross-Talks between Pathways Involved in Grapevine Infection with ‘Candidatus Phytoplasma solani’ Revealed by Temporal Network Modelling

Embedding-based Silhouette community detection

Arena3Dweb: interactive 3D visualization of multilayered networks

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