Showing 38 of total 38 results (show query)
bioc
RCy3:Functions to Access and Control Cytoscape
Vizualize, analyze and explore networks using Cytoscape via R. Anything you can do using the graphical user interface of Cytoscape, you can now do with a single RCy3 function.
Maintained by Alex Pico. Last updated 5 months ago.
visualizationgraphandnetworkthirdpartyclientnetwork
52.6 match 52 stars 13.39 score 628 scripts 15 dependentsbioc
RCX:R package implementing the Cytoscape Exchange (CX) format
Create, handle, validate, visualize and convert networks in the Cytoscape exchange (CX) format to standard data types and objects. The package also provides conversion to and from objects of iGraph and graphNEL. The CX format is also used by the NDEx platform, a online commons for biological networks, and the network visualization software Cytocape.
Maintained by Florian Auer. Last updated 5 months ago.
48.3 match 8 stars 6.28 score 10 scripts 1 dependents
ropensci
dendroNetwork:Create Networks of Dendrochronological Series using Pairwise Similarity
Creating dendrochronological networks based on the similarity between tree-ring series or chronologies. The package includes various functions to compare tree-ring curves building upon the 'dplR' package. The networks can be used to visualise and understand the relations between tree-ring curves. These networks are also very useful to estimate the provenance of wood as described in Visser (2021) <DOI:10.5334/jcaa.79> or wood-use within a structure/context/site as described in Visser and Vorst (2022) <DOI:10.1163/27723194-bja10014>.
Maintained by Ronald Visser. Last updated 1 months ago.
visualizationgraphandnetworkthirdpartyclientnetworkarchaeologydendrochronologydendroprovenancenetwork-analysistree-rings
14.1 match 7 stars 6.05 score 9 scripts
jhk0530
shinyCyJS:Create Interactive Network Visualizations in R and 'shiny'
Create Interactive Graph (Network) Visualizations. 'shinyCyJS' can be used in 'Shiny' apps or viewed from 'Rstudio' Viewer. 'shinyCyJS' includes API to build Graph model like node or edge with customized attributes for R. 'shinyCyJS' is built with 'cytoscape.js' and 'htmlwidgets' R package.
Maintained by Jinhwan Kim. Last updated 7 months ago.
16.1 match 8 stars 5.00 score 14 scripts 1 dependentsbioc
RCyjs:Display and manipulate graphs in cytoscape.js
Interactive viewing and exploration of graphs, connecting R to Cytoscape.js, using websockets.
Maintained by Paul Shannon. Last updated 5 months ago.
visualizationgraphandnetworkthirdpartyclient
11.0 match 4.68 score 48 scripts
cran
cyjShiny:Cytoscape.js Shiny Widget (cyjShiny)
Wraps cytoscape.js as a shiny widget. cytoscape.js <https://js.cytoscape.org/> is a Javascript-based graph theory (network) library for visualization and analysis. This package supports the visualization of networks with custom visual styles and several available layouts. Demo Shiny applications are provided in the package code.
Maintained by Augustin Luna. Last updated 2 years ago.
12.1 match 2.70 scorebioc
netZooR:Unified methods for the inference and analysis of gene regulatory networks
netZooR unifies the implementations of several Network Zoo methods (netzoo, netzoo.github.io) into a single package by creating interfaces between network inference and network analysis methods. Currently, the package has 3 methods for network inference including PANDA and its optimized implementation OTTER (network reconstruction using mutliple lines of biological evidence), LIONESS (single-sample network inference), and EGRET (genotype-specific networks). Network analysis methods include CONDOR (community detection), ALPACA (differential community detection), CRANE (significance estimation of differential modules), MONSTER (estimation of network transition states). In addition, YARN allows to process gene expresssion data for tissue-specific analyses and SAMBAR infers missing mutation data based on pathway information.
Maintained by Tara Eicher. Last updated 9 days ago.
networkinferencenetworkgeneregulationgeneexpressiontranscriptionmicroarraygraphandnetworkgene-regulatory-networktranscription-factors
3.6 match 105 stars 7.98 scorebioc
enrichViewNet:From functional enrichment results to biological networks
This package enables the visualization of functional enrichment results as network graphs. First the package enables the visualization of enrichment results, in a format corresponding to the one generated by gprofiler2, as a customizable Cytoscape network. In those networks, both gene datasets (GO terms/pathways/protein complexes) and genes associated to the datasets are represented as nodes. While the edges connect each gene to its dataset(s). The package also provides the option to create enrichment maps from functional enrichment results. Enrichment maps enable the visualization of enriched terms into a network with edges connecting overlapping genes.
Maintained by Astrid Deschรชnes. Last updated 5 months ago.
biologicalquestionsoftwarenetworknetworkenrichmentgocystocapefunctional-enrichment
4.8 match 5 stars 5.54 score 6 scripts
reimandlab
ActivePathways:Integrative Pathway Enrichment Analysis of Multivariate Omics Data
Framework for analysing multiple omics datasets in the context of molecular pathways, biological processes and other types of gene sets. The package uses p-value merging to combine gene- or protein-level signals, followed by ranked hypergeometric tests to determine enriched pathways and processes. Genes can be integrated using directional constraints that reflect how the input datasets are expected interact with one another. This approach allows researchers to interpret a series of omics datasets in the context of known biology and gene function, and discover associations that are only apparent when several datasets are combined. The recent version of the package is part of the following publication: Directional integration and pathway enrichment analysis for multi-omics data. Slobodyanyuk M^, Bahcheli AT^, Klein ZP, Bayati M, Strug LJ, Reimand J. Nature Communications (2024) <doi:10.1038/s41467-024-49986-4>.
Maintained by Juri Reimand. Last updated 8 months ago.
2.8 match 107 stars 8.61 score 35 scripts 2 dependentsbioc
fedup:Fisher's Test for Enrichment and Depletion of User-Defined Pathways
An R package that tests for enrichment and depletion of user-defined pathways using a Fisher's exact test. The method is designed for versatile pathway annotation formats (eg. gmt, txt, xlsx) to allow the user to run pathway analysis on custom annotations. This package is also integrated with Cytoscape to provide network-based pathway visualization that enhances the interpretability of the results.
Maintained by Catherine Ross. Last updated 5 months ago.
genesetenrichmentpathwaysnetworkenrichmentnetworkbioconductorenrichment
3.9 match 7 stars 5.32 score 10 scripts
peyronlab
ScriptMapR:R Script Visualization in Cytoscape
Displays the content of a R script into the 'Cytoscape' network-visualization app <https://cytoscape.org/>.
Maintained by Raphaรซl Bonnet. Last updated 4 years ago.
5.8 match 6 stars 3.48 score
plangfelder
WGCNA:Weighted Correlation Network Analysis
Functions necessary to perform Weighted Correlation Network Analysis on high-dimensional data as originally described in Horvath and Zhang (2005) <doi:10.2202/1544-6115.1128> and Langfelder and Horvath (2008) <doi:10.1186/1471-2105-9-559>. Includes functions for rudimentary data cleaning, construction of correlation networks, module identification, summarization, and relating of variables and modules to sample traits. Also includes a number of utility functions for data manipulation and visualization.
Maintained by Peter Langfelder. Last updated 6 months ago.
2.0 match 54 stars 9.65 score 5.3k scripts 32 dependentsbioc
graphite:GRAPH Interaction from pathway Topological Environment
Graph objects from pathway topology derived from KEGG, Panther, PathBank, PharmGKB, Reactome SMPDB and WikiPathways databases.
Maintained by Gabriele Sales. Last updated 5 months ago.
pathwaysthirdpartyclientgraphandnetworknetworkreactomekeggmetabolomicsbioinformaticsmirrorpathway-analysis
1.9 match 7 stars 10.17 score 122 scripts 21 dependentsbioc
transomics2cytoscape:A tool set for 3D Trans-Omic network visualization with Cytoscape
transomics2cytoscape generates a file for 3D transomics visualization by providing input that specifies the IDs of multiple KEGG pathway layers, their corresponding Z-axis heights, and an input that represents the edges between the pathway layers. The edges are used, for example, to describe the relationships between kinase on a pathway and enzyme on another pathway. This package automates creation of a transomics network as shown in the figure in Yugi.2014 (https://doi.org/10.1016/j.celrep.2014.07.021) using Cytoscape automation (https://doi.org/10.1186/s13059-019-1758-4).
Maintained by Kozo Nishida. Last updated 5 months ago.
networksoftwarepathwaysdataimportkegg
4.7 match 4.00 score 2 scriptsbioc
IntramiRExploreR:Predicting Targets for Drosophila Intragenic miRNAs
Intra-miR-ExploreR, an integrative miRNA target prediction bioinformatics tool, identifies targets combining expression and biophysical interactions of a given microRNA (miR). Using the tool, we have identified targets for 92 intragenic miRs in D. melanogaster, using available microarray expression data, from Affymetrix 1 and Affymetrix2 microarray array platforms, providing a global perspective of intragenic miR targets in Drosophila. Predicted targets are grouped according to biological functions using the DAVID Gene Ontology tool and are ranked based on a biologically relevant scoring system, enabling the user to identify functionally relevant targets for a given miR.
Maintained by Surajit Bhattacharya. Last updated 5 months ago.
softwaremicroarraygenetargetstatisticalmethodgeneexpressiongeneprediction
3.4 match 4.60 score 4 scripts
alextkalinka
linkcomm:Tools for Generating, Visualizing, and Analysing Link Communities in Networks
Link communities reveal the nested and overlapping structure in networks, and uncover the key nodes that form connections to multiple communities. linkcomm provides a set of tools for generating, visualizing, and analysing link communities in networks of arbitrary size and type. The linkcomm package also includes tools for generating, visualizing, and analysing Overlapping Cluster Generator (OCG) communities. Kalinka and Tomancak (2011) <doi:10.1093/bioinformatics/btr311>.
Maintained by Alex T. Kalinka. Last updated 4 years ago.
clusteringnetworksnetworks-biologyvisualizationcpp
1.8 match 7 stars 7.53 score 115 scripts 4 dependentsbioc
NetPathMiner:NetPathMiner for Biological Network Construction, Path Mining and Visualization
NetPathMiner is a general framework for network path mining using genome-scale networks. It constructs networks from KGML, SBML and BioPAX files, providing three network representations, metabolic, reaction and gene representations. NetPathMiner finds active paths and applies machine learning methods to summarize found paths for easy interpretation. It also provides static and interactive visualizations of networks and paths to aid manual investigation.
Maintained by Ahmed Mohamed. Last updated 4 months ago.
graphandnetworkpathwaysnetworkclusteringclassificationlibsbmllibxml2openblascpp
1.8 match 9 stars 6.56 score 9 scriptsbioc
GDCRNATools:GDCRNATools: an R/Bioconductor package for integrative analysis of lncRNA, mRNA, and miRNA data in GDC
This is an easy-to-use package for downloading, organizing, and integrative analyzing RNA expression data in GDC with an emphasis on deciphering the lncRNA-mRNA related ceRNA regulatory network in cancer. Three databases of lncRNA-miRNA interactions including spongeScan, starBase, and miRcode, as well as three databases of mRNA-miRNA interactions including miRTarBase, starBase, and miRcode are incorporated into the package for ceRNAs network construction. limma, edgeR, and DESeq2 can be used to identify differentially expressed genes/miRNAs. Functional enrichment analyses including GO, KEGG, and DO can be performed based on the clusterProfiler and DO packages. Both univariate CoxPH and KM survival analyses of multiple genes can be implemented in the package. Besides some routine visualization functions such as volcano plot, bar plot, and KM plot, a few simply shiny apps are developed to facilitate visualization of results on a local webpage.
Maintained by Ruidong Li. Last updated 5 months ago.
immunooncologygeneexpressiondifferentialexpressiongeneregulationgenetargetnetworkinferencesurvivalvisualizationgenesetenrichmentnetworkenrichmentnetworkrnaseqgokegg
2.0 match 5.64 score 44 scriptsbioc
BioNet:Routines for the functional analysis of biological networks
This package provides functions for the integrated analysis of protein-protein interaction networks and the detection of functional modules. Different datasets can be integrated into the network by assigning p-values of statistical tests to the nodes of the network. E.g. p-values obtained from the differential expression of the genes from an Affymetrix array are assigned to the nodes of the network. By fitting a beta-uniform mixture model and calculating scores from the p-values, overall scores of network regions can be calculated and an integer linear programming algorithm identifies the maximum scoring subnetwork.
Maintained by Marcus Dittrich. Last updated 5 months ago.
microarraydataimportgraphandnetworknetworknetworkenrichmentgeneexpressiondifferentialexpression
1.8 match 6.14 score 114 scripts 2 dependentsbioc
rScudo:Signature-based Clustering for Diagnostic Purposes
SCUDO (Signature-based Clustering for Diagnostic Purposes) is a rank-based method for the analysis of gene expression profiles for diagnostic and classification purposes. It is based on the identification of sample-specific gene signatures composed of the most up- and down-regulated genes for that sample. Starting from gene expression data, functions in this package identify sample-specific gene signatures and use them to build a graph of samples. In this graph samples are joined by edges if they have a similar expression profile, according to a pre-computed similarity matrix. The similarity between the expression profiles of two samples is computed using a method similar to GSEA. The graph of samples can then be used to perform community clustering or to perform supervised classification of samples in a testing set.
Maintained by Matteo Ciciani. Last updated 5 months ago.
geneexpressiondifferentialexpressionbiomedicalinformaticsclassificationclusteringgraphandnetworknetworkproteomicstranscriptomicssystemsbiologyfeatureextraction
1.8 match 4 stars 5.19 score 13 scriptsbioc
MetaboSignal:MetaboSignal: a network-based approach to overlay and explore metabolic and signaling KEGG pathways
MetaboSignal is an R package that allows merging, analyzing and customizing metabolic and signaling KEGG pathways. It is a network-based approach designed to explore the topological relationship between genes (signaling- or enzymatic-genes) and metabolites, representing a powerful tool to investigate the genetic landscape and regulatory networks of metabolic phenotypes.
Maintained by Andrea Rodriguez-Martinez. Last updated 5 months ago.
graphandnetworkgenesignalinggenetargetnetworkpathwayskeggreactomesoftware
1.9 match 4.90 score 8 scripts
yuanlonghu
immcp:Poly-Pharmacology Toolkit for Traditional Chinese Medicine Research
Toolkit for Poly-pharmacology Research of Traditional Chinese Medicine. Based on the biological descriptors and drug-disease interaction networks, it can analyze the potential poly-pharmacological mechanisms of Traditional Chinese Medicine and be used for drug-repositioning in Traditional Chinese Medicine.
Maintained by Yuanlong Hu. Last updated 2 years ago.
network-pharmacologypolypharmacologytraditional-chinese-medicine
1.9 match 5 stars 4.40 score 2 scripts
alexchristensen
SemNeT:Methods and Measures for Semantic Network Analysis
Implements several functions for the analysis of semantic networks including different network estimation algorithms, partial node bootstrapping (Kenett, Anaki, & Faust, 2014 <doi:10.3389/fnhum.2014.00407>), random walk simulation (Kenett & Austerweil, 2016 <http://alab.psych.wisc.edu/papers/files/Kenett16CreativityRW.pdf>), and a function to compute global network measures. Significance tests and plotting features are also implemented.
Maintained by Alexander P. Christensen. Last updated 2 years ago.
1.8 match 23 stars 4.51 score 28 scriptsbioc
Mergeomics:Integrative network analysis of omics data
The Mergeomics pipeline serves as a flexible framework for integrating multidimensional omics-disease associations, functional genomics, canonical pathways and gene-gene interaction networks to generate mechanistic hypotheses. It includes two main parts, 1) Marker set enrichment analysis (MSEA); 2) Weighted Key Driver Analysis (wKDA).
Maintained by Zeyneb Kurt. Last updated 5 months ago.
1.9 match 4.30 score 8 scriptsbioc
clipper:Gene Set Analysis Exploiting Pathway Topology
Implements topological gene set analysis using a two-step empirical approach. It exploits graph decomposition theory to create a junction tree and reconstruct the most relevant signal path. In the first step clipper selects significant pathways according to statistical tests on the means and the concentration matrices of the graphs derived from pathway topologies. Then, it "clips" the whole pathway identifying the signal paths having the greatest association with a specific phenotype.
Maintained by Paolo Martini. Last updated 5 months ago.
1.7 match 4.66 score 19 scriptsbioc
NCIgraph:Pathways from the NCI Pathways Database
Provides various methods to load the pathways from the NCI Pathways Database in R graph objects and to re-format them.
Maintained by Laurent Jacob. Last updated 5 months ago.
1.9 match 4.26 score 10 scripts 1 dependentsbioc
KEGGlincs:Visualize all edges within a KEGG pathway and overlay LINCS data
See what is going on 'under the hood' of KEGG pathways by explicitly re-creating the pathway maps from information obtained from KGML files.
Maintained by Shana White. Last updated 5 months ago.
networkinferencegeneexpressiondatarepresentationthirdpartyclientcellbiologygraphandnetworkpathwayskeggnetwork
1.8 match 4.00 score 3 scripts
mikehellstern
netgsa:Network-Based Gene Set Analysis
Carry out Network-based Gene Set Analysis by incorporating external information about interactions among genes, as well as novel interactions learned from data. Implements methods described in Shojaie A, Michailidis G (2010) <doi:10.1093/biomet/asq038>, Shojaie A, Michailidis G (2009) <doi:10.1089/cmb.2008.0081>, and Ma J, Shojaie A, Michailidis G (2016) <doi:10.1093/bioinformatics/btw410>
Maintained by Michael Hellstern. Last updated 3 years ago.
1.9 match 4 stars 3.75 score 28 scriptsbioc
PIUMA:Phenotypes Identification Using Mapper from topological data Analysis
The PIUMA package offers a tidy pipeline of Topological Data Analysis frameworks to identify and characterize communities in high and heterogeneous dimensional data.
Maintained by Mattia Chiesa. Last updated 5 months ago.
clusteringgraphandnetworkdimensionreductionnetworkclassification
1.3 match 4 stars 5.08 score 2 scripts
idslme
IDSL.FSA:Fragmentation Spectra Analysis (FSA)
The 'IDSL.FSA' package was designed to annotate standard .msp (mass spectra format) and .mgf (Mascot generic format) files using mass spectral entropy similarity, dot product (cosine) similarity, and normalized Euclidean mass error (NEME) followed by intelligent pre-filtering steps for rapid spectra searches. 'IDSL.FSA' also provides a number of modules to convert and manipulate .msp and .mgf files. The 'IDSL.FSA' workflow was integrated in the 'IDSL.CSA' and 'IDSL.NPA' packages introduced in <doi:10.1021/acs.analchem.3c00376>.
Maintained by Dinesh Barupal. Last updated 7 months ago.
fragmentation-spectramass-spectrometrymassbankmgfmgf-parsermspmsp-parserspectral-entropy
1.9 match 1 stars 3.48 score 2 dependentsbioc
regutools:regutools: an R package for data extraction from RegulonDB
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 provides researchers with 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.
Maintained by Joselyn Chavez. Last updated 3 months ago.
generegulationgeneexpressionsystemsbiologynetworknetworkinferencevisualizationtranscriptionbioconductorcdsbregulondb
1.3 match 4 stars 5.20 score 6 scripts
sergeitarasov
ontoFAST:Interactive Annotation of Characters with Biological Ontologies
Tools for annotating characters (character matrices) with anatomical and phenotype ontologies. Includes functions for visualising character annotations and creating simple queries using ontological relationships.
Maintained by Sergei Tarasov. Last updated 3 years ago.
annotationscharacter-matricescharactersontologyphylogenetics
2.0 match 2 stars 3.00 score 5 scripts
moseleybioinformaticslab
categoryCompare2:Meta-Analysis of High-Throughput Experiments Using Feature Annotations
Facilitates comparison of significant annotations (categories) generated on one or more feature lists. Interactive exploration is facilitated through the use of RCytoscape (heavily suggested).
Maintained by Robert M Flight. Last updated 5 months ago.
annotationgomultiplecomparisonpathwaysgeneexpressionbioconductorbioinformaticsgene-annotationgene-expressiongene-sets
2.3 match 1 stars 2.48 score 9 scripts
tgrimes
SeqNet:Generate RNA-Seq Data from Gene-Gene Association Networks
Methods to generate random gene-gene association networks and simulate RNA-seq data from them, as described in Grimes and Datta (2021) <doi:10.18637/jss.v098.i12>. Includes functions to generate random networks of any size and perturb them to obtain differential networks. Network objects are built from individual, overlapping modules that represent pathways. The resulting network has various topological properties that are characteristic of gene regulatory networks. RNA-seq data can be generated such that the association among gene expression profiles reflect the underlying network. A reference RNA-seq dataset can be provided to model realistic marginal distributions. Plotting functions are available to visualize a network, compare two networks, and compare the expression of two genes across multiple networks.
Maintained by Tyler Grimes. Last updated 4 years ago.
1.8 match 2.82 score 22 scripts 1 dependentsbioc
QUBIC:An R package for qualitative biclustering in support of gene co-expression analyses
The core function of this R package is to provide the implementation of the well-cited and well-reviewed QUBIC algorithm, aiming to deliver an effective and efficient biclustering capability. This package also includes the following related functions: (i) a qualitative representation of the input gene expression data, through a well-designed discretization way considering the underlying data property, which can be directly used in other biclustering programs; (ii) visualization of identified biclusters using heatmap in support of overall expression pattern analysis; (iii) bicluster-based co-expression network elucidation and visualization, where different correlation coefficient scores between a pair of genes are provided; and (iv) a generalize output format of biclusters and corresponding network can be freely downloaded so that a user can easily do following comprehensive functional enrichment analysis (e.g. DAVID) and advanced network visualization (e.g. Cytoscape).
Maintained by Yu Zhang. Last updated 5 months ago.
statisticalmethodmicroarraydifferentialexpressionmultiplecomparisonclusteringvisualizationgeneexpressionnetworkbioconductor-packagebioconductor-packagescppopenmp
0.5 match 3 stars 6.10 score 14 scripts 1 dependents
asa12138
MetaNet:Network Analysis for Omics Data
Comprehensive network analysis package. Calculate correlation network fastly, accelerate lots of analysis by parallel computing. Support for multi-omics data, search sub-nets fluently. Handle bigger data, more than 10,000 nodes in each omics. Offer various layout method for multi-omics network and some interfaces to other software ('Gephi', 'Cytoscape', 'ggplot2'), easy to visualize. Provide comprehensive topology indexes calculation, including ecological network stability.
Maintained by Chen Peng. Last updated 12 days ago.
dataimportnetwork analysisomicssoftwarevisualization
0.5 match 13 stars 5.51 score 9 scripts
hyu-ub
BayesNetBP:Bayesian Network Belief Propagation
Belief propagation methods in Bayesian Networks to propagate evidence through the network. The implementation of these methods are based on the article: Cowell, RG (2005). Local Propagation in Conditional Gaussian Bayesian Networks <https://www.jmlr.org/papers/v6/cowell05a.html>. For details please see Yu et. al. (2020) BayesNetBP: An R Package for Probabilistic Reasoning in Bayesian Networks <doi:10.18637/jss.v094.i03>. The optional 'cyjShiny' package for running the Shiny app is available at <https://github.com/cytoscape/cyjShiny>. Please see the example in the documentation of 'runBayesNetApp' function for installing 'cyjShiny' package from GitHub.
Maintained by Han Yu. Last updated 2 years ago.
bayesian-networksconditional-gaussiannetwork-inferenceprobabilistic-graphical-models
0.5 match 19 stars 3.98 score 3 scriptsbioc
DrugVsDisease:Comparison of disease and drug profiles using Gene set Enrichment Analysis
This package generates ranked lists of differential gene expression for either disease or drug profiles. Input data can be downloaded from Array Express or GEO, or from local CEL files. Ranked lists of differential expression and associated p-values are calculated using Limma. Enrichment scores (Subramanian et al. PNAS 2005) are calculated to a reference set of default drug or disease profiles, or a set of custom data supplied by the user. Network visualisation of significant scores are output in Cytoscape format.
Maintained by j. Saez-Rodriguez. Last updated 5 months ago.
microarraygeneexpressionclustering
0.5 match 3.30 score 8 scripts