Cytoscape.js 2023 update: a graph theory library for visualization and analysis

Franz, Max; Lopes, Christian; Fong, Dylan; Kucera, Mike; Cheung, Manfred; Siper, Metin Can; Huck, Gerardo; Dong, Yue; Sumer, Onur; Bader, Gary D.

doi:10.1093/bioinformatics/btad031

Cited by 39 publications

(25 citation statements)

References 12 publications

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“… We envision specXplore’s functionalities to be used in an interactive local exploratory fashion, where the settings and their impact on local neighborhood can be evaluated seamlessly. This local exploration starts at a two-dimensional t-SNE overview figure rendered using dash and dash-cytoscape. ,, All other visualizations in specXplore make use of plotly . In its most basic form, the t-SNE map only contains a node for each feature, and a number of visually highlighted spike-in standard features.…”

Section: Materials and Methodsmentioning

confidence: 99%

“…This local exploration starts at a two-dimensional t-SNE overview figure rendered using dash and dash-cytoscape. 12 , 26 , 27 All other visualizations in specXplore make use of plotly. 28 In its most basic form, the t-SNE map only contains a node for each feature, and a number of visually highlighted spike-in standard features.…”

Section: Materials and Methodsmentioning

confidence: 99%

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Tailored Mass Spectral Data Exploration Using the SpecXplore Interactive Dashboard

Mildau,

Ehlers,

Oesterle

et al. 2024

Anal. Chem.

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Untargeted metabolomics promises comprehensive characterization of small molecules in biological samples. However, the field is hampered by low annotation rates and abstract spectral data. Despite recent advances in computational metabolomics, manual annotations and manual confirmation of in-silico annotations remain important in the field. Here, exploratory data analysis methods for mass spectral data provide overviews, prioritization, and structural hypothesis starting points to researchers facing large quantities of spectral data. In this research, we propose a fluid means of dealing with mass spectral data using specXplore, an interactive Python dashboard providing interactive and complementary visualizations facilitating mass spectral similarity matrix exploration. Specifically, specXplore provides a two-dimensional t-distributed stochastic neighbor embedding embedding as a jumping board for local connectivity exploration using complementary interactive visualizations in the form of partial network drawings, similarity heatmaps, and fragmentation overview maps. SpecXplore makes use of state-of-the-art ms2deepscore pairwise spectral similarities as a quantitative backbone while allowing fast changes of threshold and connectivity limitation settings, providing flexibility in adjusting settings to suit the localized node environment being explored. We believe that specXplore can become an integral part of mass spectral data exploration efforts and assist users in the generation of structural hypotheses for compounds of interest.

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Section: Materials and Methodsmentioning

confidence: 99%

Section: Materials and Methodsmentioning

confidence: 99%

Tailored Mass Spectral Data Exploration Using the SpecXplore Interactive Dashboard

Mildau,

Ehlers,

Oesterle

et al. 2024

Anal. Chem.

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“…By covering both aspects of clustering and pseudotemporal ordering it can model the underlying biological progression, assigning each cell a pseudotime value that reflects its position along the inferred trajectory. Also, Cytoscape and Enrichment Map are used to investigate and visualize the connection between the identified biological pathways (49,50).…”

Section: Analysis Of Scrna-seq Datamentioning

confidence: 99%

FateNet: an integration of dynamical systems and deep learning for cell fate prediction

Sadria,

Bury

2024

Preprint

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Understanding cellular decision-making, particularly its timing and impact on the biological system such as tissue health and function, is a fundamental challenge in biology and medicine. Existing methods for inferring fate decisions and cellular state dynamics from single-cell RNA sequencing data lack precision regarding decision points and broader tissue implications. Addressing this gap, we present FateNet, a computational approach integrating dynamical systems theory and deep learning to probe the cell decision-making process using scRNA-seq data. By leveraging information about normal forms and scaling behavior near tipping points common to many dynamical systems, FateNet accurately predicts cell decision occurrence and offers qualitative insights into the new state of the biological system. Also, through in-silico perturbation experiments, FateNet identifies key genes and pathways governing the differentiation process in hematopoiesis. Validated using different scRNA-seq data, FateNet emerges as a user-friendly and valuable tool for predicting critical points in biological processes, providing insights into complex trajectories.

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“…For protein-protein interaction (PPI) data, the present study utilized a human PPI database from STRING [27]. The PPI network was constructed using Cytoscape [28] for visualizing, modeling, and analyzing the integration of bimolecular interaction networks with high-throughput expression data and other molecular states.…”

Section: Protein-protein Interaction Network Analysis and Constructionmentioning

confidence: 99%

Interruption of p53-MDM2 Interaction by Nutlin-3a in Human Lymphoma Cell Models Initiates a Cell-Dependent Global Effect on Transcriptome and Proteome Level

Psatha,

Kollipara,

Drakos

et al. 2023

Cancers

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In most lymphomas, p53 signaling pathway is inactivated by various mechanisms independent to p53 gene mutations or deletions. In many cases, p53 function is largely regulated by alterations in the protein abundance levels by the action of E3 ubiquitin-protein ligase MDM2, targeting p53 to proteasome-mediated degradation. In the present study, an integrating transcriptomics and proteomics analysis was employed to investigate the effect of p53 activation by a small-molecule MDM2-antagonist, nutlin 3a, on three lymphoma cell models following p53 activation. Our analysis revealed a system-wide nutlin 3a-associated effect in all examined lymphoma types, identifying in total of 4037 differentially affected proteins involved in a plethora of pathways, with significant heterogeneity among lymphomas. Our findings include known p53-targets and novel p53 activation effects, involving transcription, translation, or degradation of protein components of pathways, such as a decrease in key members of PI3K/mTOR pathway, heat-shock response, and glycolysis, and an increase in key members of oxidative phoshosphorylation, autophagy and mitochondrial translation. Combined inhibition of HSP90 or PI3K/mTOR pathway with nutlin 3a-mediated p53-activation enhanced the apoptotic effects suggesting a promising strategy against human lymphomas. Integrated omic profiling after p53 activation offered novel insights on the regulatory role specific proteins and pathways may have in lymphomagenesis.

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Cytoscape.js 2023 update: a graph theory library for visualization and analysis

Cited by 39 publications

References 12 publications

Tailored Mass Spectral Data Exploration Using the SpecXplore Interactive Dashboard

Tailored Mass Spectral Data Exploration Using the SpecXplore Interactive Dashboard

FateNet: an integration of dynamical systems and deep learning for cell fate prediction

Interruption of p53-MDM2 Interaction by Nutlin-3a in Human Lymphoma Cell Models Initiates a Cell-Dependent Global Effect on Transcriptome and Proteome Level

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