RCy3: Network Biology using Cytoscape from within R

Gustavsen, Julia; Pai, Shraddha; Isserlin, Ruth; Demchak, Barry; Pico, Alexander R.

doi:10.1101/793166

Cited by 29 publications

(27 citation statements)

References 11 publications

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“…The metabolite-metabolite correlation matrix was used to construct a network visualization to explore similarly affected metabolites. The correlation networks were constructed in Cytoscape (version 3.7.2) 75 using the RCy3 package (version 2.6.3) 76 and aMatReader (version 1.1.3) 77 . Only Spearman's correlations above 0.9 were included in the network, and nodes were overlaid with descriptive statistics including q-value and log-fold change (logFC).…”

Section: Cluster 3 Metabolites (N = 5)mentioning

confidence: 99%

An enriched biosignature of gut microbiota-dependent metabolites characterizes maternal plasma in a mouse model of fetal alcohol spectrum disorder

Virdee

Saini

Kay

et al. 2021

Sci Rep

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Prenatal alcohol exposure (PAE) causes permanent cognitive disability. The enteric microbiome generates microbial-dependent products (MDPs) that may contribute to disorders including autism, depression, and anxiety; it is unknown whether similar alterations occur in PAE. Using a mouse PAE model, we performed untargeted metabolome analyses upon the maternal–fetal dyad at gestational day 17.5. Hierarchical clustering by principal component analysis and Pearson’s correlation of maternal plasma (813 metabolites) both identified MDPs as significant predictors for PAE. The majority were phenolic acids enriched in PAE. Correlational network analyses revealed that alcohol altered plasma MDP-metabolite relationships, and alcohol-exposed maternal plasma was characterized by a subnetwork dominated by phenolic acids. Twenty-nine MDPs were detected in fetal liver and sixteen in fetal brain, where their impact is unknown. Several of these, including 4-ethylphenylsulfate, oxindole, indolepropionate, p-cresol sulfate, catechol sulfate, and salicylate, are implicated in other neurological disorders. We conclude that MDPs constitute a characteristic biosignature that distinguishes PAE. These MDPs are abundant in human plasma, where they influence physiology and disease. Their altered abundance here may reflect alcohol’s known effects on microbiota composition and gut permeability. We propose that the maternal microbiome and its MDPs are a previously unrecognized influence upon the pathologies that typify PAE.

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Section: Cluster 3 Metabolites (N = 5)mentioning

confidence: 99%

An enriched biosignature of gut microbiota-dependent metabolites characterizes maternal plasma in a mouse model of fetal alcohol spectrum disorder

Virdee

Saini

Kay

et al. 2021

Sci Rep

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“…We used the R package igraph (Csardi and Nepusz, 2006) to convert the CoRegNet object (coregulatory network) and the RA map SIF file into separate graphs. Then, we merged both networks using igraph, and imported the network into Cytoscape using the RCy3 R/ Bioconductor package (Gustavsen et al, 2019) forming the global RA-specific network.…”

Section: Global Ra Network Inferencementioning

confidence: 99%

Inference of an integrative, executable network for Rheumatoid Arthritis combining data-driven machine learning approaches and a state-of-the-art mechanistic disease map

Miagoux

Mezquita

Singh

et al. 2021

Preprint

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MotivationRheumatoid arthritis (RA) is a multifactorial autoimmune disease that causes chronic inflammation of the joints. RA is considered a complex disease as it involves various genetic, environmental, and epigenetic factors. Systems biology approaches provide the means to study complex diseases by integrating different layers of biological information. Combining multiple data types can help compensate for missing or conflicting information and limit the possibility of false positives. In this approach, we integrate three different biological layers (gene expression, signalling cascades, mutations), obtained by bottom-up and top-down methods to build an integrative, disease-specific network. The goal behind this endeavour is to see if we can unravel mechanisms governing the regulation of key genes identified as mutation carriers in RA and derive patient-specific models to gain more insights into the disease heterogeneity.ResultsIn this work, we combine biological data relevant to Rheumatoid Arthritis, in the form of a global, integrative network. We first make use of publicly available transcriptomic datasets (peripheral blood) relative to RA and machine learning to create an RA specific transcription factor (TF) co-regulatory network. The TF cooperativity network is subsequently enriched in signalling cascades and upstream regulators using prior knowledge encoded in a state-of-the-art, RA-specific molecular map. Lastly, a list of RA specific variants highlights key genes associated with known disease mutations.AvailabilityDatasets used for the analysis are publicly available. All scripts used to generate results and the Shiny app will be freely accessible after peer-reviewed publication.

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“…Here we define predictive features as those scoring 2 out of 2 in all train/test splits. This network is generated by running the plotEmap() function, which uses the RCy3 Bioconductor package to programmatically call Cytoscape network visualization software from within R, to run the EnrichmentMap app [5][6][7] . Nodes show pathways features that scored a minimum of 9 out of 10 in feature selection, in at least 70% of train/test splits; node fill indicates feature score.…”

Section: Plotemap(gmtfiles[[1]] Nodeattrfiles[[1]] Groupclusters=trmentioning

confidence: 99%

“…Integrated patient similarity network, generated by combining all networks that consistently pass feature selection. This network is generated by calling plotIntegratedPatientNetwork() and uses RCy3 to programmatically generate the network in Cytoscape 7,8 . This network uses features that scored 2 out of 2 in all traintest splits.…”

Section: Plotemap(gmtfiles[[1]] Nodeattrfiles[[1]] Groupclusters=trmentioning

confidence: 99%

netDx: Software for building interpretable patient classifiers by multi-'omic data integration using patient similarity networks

et al. 2021

Self Cite

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Patient classification based on clinical and genomic data will further the goal of precision medicine. Interpretability is of particular relevance for models based on genomic data, where sample sizes are relatively small (in the hundreds), increasing overfitting risk netDx is a machine learning method to integrate multi-modal patient data and build a patient classifier. Patient data are converted into networks of patient similarity, which is intuitive to clinicians who also use patient similarity for medical diagnosis. Features passing selection are integrated, and new patients are assigned to the class with the greatest profile similarity. netDx has excellent performance, outperforming most machine-learning methods in binary cancer survival prediction. It handles missing data – a common problem in real-world data – without requiring imputation. netDx also has excellent interpretability, with native support to group genes into pathways for mechanistic insight into predictive features. The netDx Bioconductor package provides multiple workflows for users to build custom patient classifiers. It provides turnkey functions for one-step predictor generation from multi-modal data, including feature selection over multiple train/test data splits. Workflows offer versatility with custom feature design, choice of similarity metric; speed is improved by parallel execution. Built-in functions and examples allow users to compute model performance metrics such as AUROC, AUPR, and accuracy. netDx uses RCy3 to visualize top-scoring pathways and the final integrated patient network in Cytoscape. Advanced users can build more complex predictor designs with functional building blocks used in the default design. Finally, the netDx Bioconductor package provides a novel workflow for pathway-based patient classification from sparse genetic data.

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RCy3: Network Biology using Cytoscape from within R

Cited by 29 publications

References 11 publications

An enriched biosignature of gut microbiota-dependent metabolites characterizes maternal plasma in a mouse model of fetal alcohol spectrum disorder

An enriched biosignature of gut microbiota-dependent metabolites characterizes maternal plasma in a mouse model of fetal alcohol spectrum disorder

Inference of an integrative, executable network for Rheumatoid Arthritis combining data-driven machine learning approaches and a state-of-the-art mechanistic disease map

netDx: Software for building interpretable patient classifiers by multi-'omic data integration using patient similarity networks

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