RCy3: Network biology using Cytoscape from within R

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

doi:10.12688/f1000research.20887.1

Cited by 33 publications

(31 citation statements)

References 12 publications

(3 reference statements)

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“…Furthermore, we exemplify in the documentation how regutools queries can be integrated with other data analysis methods available in Bioconductor. For example, we demonstrate how DNA elements can be visualized in genome graphs using the package Gviz and how regulatory networks can be visualized using the R package RCy3 (Hahne and Ivanek, 2016;Gustavsen et al, 2019).…”

Section: Methodsmentioning

confidence: 99%

Programmatic access to bacterial regulatory networks withregutools

Chávez

Barberena-Jonas²,

Sotelo-Fonseca³

et al. 2020

Preprint

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RegulonDB has collected, harmonized and centralized data from hundreds of experiments for nearly two decades and is considered a point of reference for transcriptional regulation in Escherichia coli K12. Here, we present the regutools R package to facilitate programmatic access to RegulonDB data in computational biology. regutools gives researchers the possibility of writing reproducible workflows with automated queries to RegulonDB. The regutools package serves as a bridge between RegulonDB data and the Bioconductor ecosystem by reusing the data structures and statistical methods powered by other Bioconductor packages. We demonstrate the integration of regutools with Bioconductor by analyzing transcription factor DNA binding sites and transcriptional regulatory networks from RegulonDB. We anticipate that regutools will serve as a useful building block in our progress to further our understanding of gene regulatory networks. Availability and Implementation: regutools is an R package available through Bioconductor at bioconductor.org/packages/regutools. Contact: github.com/ComunidadBioInfo/regutools,

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Section: Methodsmentioning

confidence: 99%

Programmatic access to bacterial regulatory networks withregutools

Chávez

Barberena-Jonas²,

Sotelo-Fonseca³

et al. 2020

Preprint

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“…Pearson's correlations and their significance were calculated between log-transformed cyto-and chemokine levels, separately for controls and FEP patients, using package Hmisc v4.2-0 (function rcorr) 36 in the software environment R 3.6.1 37 . Undirected correlation networks were created in R with the igraph package v1.2.4 (function make_undirected_graph) 38 and exported to Cytoscape v3.7.1 39 for further editing using the RCy3 package 40 .…”

Section: Statistical Analyses Of Serum Concentrations Of Cytokines Cmentioning

confidence: 99%

Elevated serum chemokine CCL22 levels in first-episode psychosis: associations with symptoms, peripheral immune state and in vivo brain glial cell function

et al. 2020

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Several lines of research support immune system dysregulation in psychotic disorders. However, it remains unclear whether the immunological marker alterations are stable and how they associate with brain glial cell function. This longitudinal study aimed at investigating whether peripheral immune functions are altered in the early phases of psychotic disorders, whether the changes are associated with core symptoms, remission, brain glial cell function, and whether they persist in a one-year follow-up. Two independent cohorts comprising in total of 129 first-episode psychosis (FEP) patients and 130 controls were assessed at baseline and at the one-year follow-up. Serum cyto-/ chemokines were measured using a 38-plex Luminex assay. The FEP patients showed a marked increase in chemokine CCL22 levels both at baseline (p < 0.0001; Cohen's d = 0.70) and at the 12-month follow-up (p = 0.0007) compared to controls. The group difference remained significant (p = 0.0019) after accounting for relevant covariates including BMI, smoking, and antipsychotic medication. Elevated serum CCL22 levels were significantly associated with hallucinations (ρ = 0.20) and disorganization (ρ = 0.23), and with worse verbal performance (ρ = −0.23). Brain glial cell activity was indexed with positron emission tomography and the translocator protein radiotracer [ 11 C]PBR28 in subgroups of 15 healthy controls and 14 FEP patients with serum CCL22/CCL17 measurements. The distribution volume (V T) of [ 11 C] PBR28 was lower in patients compared to controls (p = 0.026; Cohen's d = 0.94) without regionally specific effects, and was inversely associated with serum CCL22 and CCL17 levels (p = 0.036). Our results do not support the over-active microglia hypothesis of psychosis, but indicate altered CCR4 immune signaling in early psychosis with behavioral correlates possibly mediated through cross-talk between chemokine networks and dysfunctional or a decreased number of glial cells.

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“…Enrichment map shows consistently high-scoring pathway features when running the breast tumour binary classifier with real-world parameters. 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: Summary(out)mentioning

confidence: 99%

“…psn <-suppressMessages( plotIntegratedPatientNetwork( brca, groupList=g2, makeNetFunc=makeNets, aggFun="MEAN",topX=0.08, numCores=1L,calcShortestPath=TRUE, showStats=FALSE, verbose=FALSE, plotCytoscape=FALSE) ) Figure 7. 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: Visualize Integrated Patient Similarity Network Based On Topmentioning

confidence: 99%

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

et al. 2020

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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 33 publications

References 12 publications

Programmatic access to bacterial regulatory networks withregutools

Programmatic access to bacterial regulatory networks withregutools

Elevated serum chemokine CCL22 levels in first-episode psychosis: associations with symptoms, peripheral immune state and in vivo brain glial cell function

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

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