RCy3: Network biology using Cytoscape from within R

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

doi:10.12688/f1000research.20887.3

Cited by 99 publications

(66 citation statements)

References 12 publications

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“…All pathway features and their significantly associated metabolites are visualized by a bipartite graph with Cytoscape style. Cytoscape modules are imported in Lilikoi by the RCy3 R package [ 35 ].…”

Section: Methodsmentioning

confidence: 99%

Lilikoi V2.0: a deep learning–enabled, personalized pathway-based R package for diagnosis and prognosis predictions using metabolomics data

et al. 2021

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Background previously we developed Lilikoi, a personalized pathway-based method to classify diseases using metabolomics data. Given the new trends of computation in the metabolomics field, it is important to update Lilikoi software. Results here we report the next version of Lilikoi as a significant upgrade. The new Lilikoi v2.0 R package has implemented a deep learning method for classification, in addition to popular machine learning methods. It also has several new modules, including the most significant addition of prognosis prediction, implemented by Cox-proportional hazards model and the deep learning–based Cox-nnet model. Additionally, Lilikoi v2.0 supports data preprocessing, exploratory analysis, pathway visualization, and metabolite pathway regression. Conculsion Lilikoi v2.0 is a modern, comprehensive package to enable metabolomics analysis in R programming environment.

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Section: Methodsmentioning

confidence: 99%

Lilikoi V2.0: a deep learning–enabled, personalized pathway-based R package for diagnosis and prognosis predictions using metabolomics data

et al. 2021

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“… 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 – 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: Use Casesmentioning

confidence: 99%

“… 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 train-test splits.…”

Section: Use Casesmentioning

confidence: 99%

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

et al. 2021

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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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“…Chemical data of interacting lipid molecules was obtained from Lipid Maps ® Lipidomics Gateway (https://lipidmaps.org/ ). ALP and LDP network were visualized in Cytoscape 3.7.2 (Shannon et al, 2003) using the RCyc3 package (Gustavsen et al, 2019) (Suppl. Fig.…”

mentioning

confidence: 99%

“…The selected clusters were visualized in Cytoscape 3.7.2 (Shannon et al, 2003) using the R package RCyc3 (Gustavsen et al, 2019) (Suppl. Fig.…”

mentioning

confidence: 99%

Rethinking the role of lipids in lager yeast cells during beer fermentation from a transcriptome and systems biology perspective

Bonatto

2020

Preprint

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Brewing lager yeasts (Saccharomyces pastorianus) should deal with stressful conditions during beer fermentation, including ethanol toxicity. In response to ethanol toxicity, different biological mechanisms are modulated, including lipid biosynthesis. It is well known that during beer fermentation, the composition of yeast membranes change in response to ethanol toxicity, making it less fluid and permeable. Additionally, neutral lipids and lipid droplets (LDs) are produced in response to ethanol toxicity. LDs are membranous organelles that transport lipids and proteins, acting as a hub for inter-organellar communication and module the activity of mechanisms necessary for ethanol tolerance, like proteostasis and autophagy. Unfortunately, little is known about the interplay of lipid biosynthesis, proteostasis, and autophagy in lager cells during beer fermentation. Thus, a transcriptome single and meta-analysis using publically available DNA microarray data of lager yeast cells in conditions of beer fermentation and cell biomass propagation was used to select all the upregulated genes associated to autophagy, lipid biosynthesis, and proteostasis (ALP) during beer fermentation. Following transcriptome data collection, a top-down systems biology analyses was applied with the design of an ALP-associated shortest pathway protein-protein network (ALP network), selection of central nodes and communities within ALP network, and the observation of the overrepresented biological processes and cellular components by gene ontology (GO) analysis. The transcriptome results show the upregulation of 204 non redundant ALP genes in conditions of beer fermentation, whose respective proteins interact in a shortest pathway ALP network. Thirteen communities where selected from ALP network containing overrepresented processes and components like mitophagy, cytoplasm-to-vacuole transport, pieacemeal micrautophagy of the nucleus, endoplasmic reticulum (ER) stress, ergosterol and lipid biosynthesis, LDs, ER membrane, phagophore assembly, among others. These results indicated that ethanol tolerance in lager yeasts could be due to the modulation of proteostasis and different forms 2 of autophagy by lipid biosynthesis and LDs, and thus extending the importance of lipids for beer fermentation.

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RCy3: Network biology using Cytoscape from within R

Cited by 99 publications

References 12 publications

Lilikoi V2.0: a deep learning–enabled, personalized pathway-based R package for diagnosis and prognosis predictions using metabolomics data

Lilikoi V2.0: a deep learning–enabled, personalized pathway-based R package for diagnosis and prognosis predictions using metabolomics data

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

Rethinking the role of lipids in lager yeast cells during beer fermentation from a transcriptome and systems biology perspective

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