Showing 33 of total 33 results (show query)
egeulgen
pathfindR:Enrichment Analysis Utilizing Active Subnetworks
Enrichment analysis enables researchers to uncover mechanisms underlying a phenotype. However, conventional methods for enrichment analysis do not take into account protein-protein interaction information, resulting in incomplete conclusions. 'pathfindR' is a tool for enrichment analysis utilizing active subnetworks. The main function identifies active subnetworks in a protein-protein interaction network using a user-provided list of genes and associated p values. It then performs enrichment analyses on the identified subnetworks, identifying enriched terms (i.e. pathways or, more broadly, gene sets) that possibly underlie the phenotype of interest. 'pathfindR' also offers functionalities to cluster the enriched terms and identify representative terms in each cluster, to score the enriched terms per sample and to visualize analysis results. The enrichment, clustering and other methods implemented in 'pathfindR' are described in detail in Ulgen E, Ozisik O, Sezerman OU. 2019. 'pathfindR': An R Package for Comprehensive Identification of Enriched Pathways in Omics Data Through Active Subnetworks. Front. Genet. <doi:10.3389/fgene.2019.00858>.
Maintained by Ege Ulgen. Last updated 26 days ago.
active-subnetworksenrichmentpathwaypathway-enrichment-analysissubnetwork
41.4 match 186 stars 10.13 score 138 scripts
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
5.9 match 52 stars 13.39 score 628 scripts 15 dependentsbioc
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 8 days ago.
networkinferencenetworkgeneregulationgeneexpressiontranscriptionmicroarraygraphandnetworkgene-regulatory-networktranscription-factors
6.6 match 105 stars 7.98 scorebioc
OmnipathR:OmniPath web service client and more
A client for the OmniPath web service (https://www.omnipathdb.org) and many other resources. It also includes functions to transform and pretty print some of the downloaded data, functions to access a number of other resources such as BioPlex, ConsensusPathDB, EVEX, Gene Ontology, Guide to Pharmacology (IUPHAR/BPS), Harmonizome, HTRIdb, Human Phenotype Ontology, InWeb InBioMap, KEGG Pathway, Pathway Commons, Ramilowski et al. 2015, RegNetwork, ReMap, TF census, TRRUST and Vinayagam et al. 2011. Furthermore, OmnipathR features a close integration with the NicheNet method for ligand activity prediction from transcriptomics data, and its R implementation `nichenetr` (available only on github).
Maintained by Denes Turei. Last updated 17 days ago.
graphandnetworknetworkpathwayssoftwarethirdpartyclientdataimportdatarepresentationgenesignalinggeneregulationsystemsbiologytranscriptomicssinglecellannotationkeggcomplexesenzyme-ptmnetworksnetworks-biologyomnipathproteinsquarto
5.1 match 126 stars 9.90 score 226 scripts 2 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.
5.6 match 8 stars 6.28 score 10 scripts 1 dependentsbioc
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
5.6 match 6.14 score 114 scripts 2 dependents
kechrislab
SmCCNet:Sparse Multiple Canonical Correlation Network Analysis Tool
A canonical correlation based framework (SmCCNet) designed for the construction of phenotype-specific multi-omics networks. This framework adeptly integrates single or multiple omics data types along with a quantitative or binary phenotype of interest. It offers a streamlined setup process that can be tailored manually or configured automatically, ensuring a flexible and user-friendly experience.
Maintained by Weixuan Liu. Last updated 11 months ago.
5.1 match 28 stars 6.40 score 30 scriptsbioc
pathlinkR:Analyze and interpret RNA-Seq results
pathlinkR is an R package designed to facilitate analysis of RNA-Seq results. Specifically, our aim with pathlinkR was to provide a number of tools which take a list of DE genes and perform different analyses on them, aiding with the interpretation of results. Functions are included to perform pathway enrichment, with muliplte databases supported, and tools for visualizing these results. Genes can also be used to create and plot protein-protein interaction networks, all from inside of R.
Maintained by Travis Blimkie. Last updated 3 months ago.
genesetenrichmentnetworkpathwaysreactomernaseqnetworkenrichmentbioinformaticsnetworkspathway-enrichment-analysisvisualization
4.8 match 26 stars 6.62 score 2 scriptsbioc
pandaR:PANDA Algorithm
Runs PANDA, an algorithm for discovering novel network structure by combining information from multiple complementary data sources.
Maintained by Joseph N. Paulson. Last updated 5 months ago.
statisticalmethodgraphandnetworkmicroarraygeneregulationnetworkinferencegeneexpressiontranscriptionnetwork
6.6 match 3.30 score 8 scriptsbioc
netresponse:Functional Network Analysis
Algorithms for functional network analysis. Includes an implementation of a variational Dirichlet process Gaussian mixture model for nonparametric mixture modeling.
Maintained by Leo Lahti. Last updated 5 months ago.
cellbiologyclusteringgeneexpressiongeneticsnetworkgraphandnetworkdifferentialexpressionmicroarraynetworkinferencetranscription
3.8 match 3 stars 5.64 score 21 scriptsbioc
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
3.1 match 9 stars 6.56 score 9 scripts
guido-s
netmeta:Network Meta-Analysis using Frequentist Methods
A comprehensive set of functions providing frequentist methods for network meta-analysis (Balduzzi et al., 2023) <doi:10.18637/jss.v106.i02> and supporting Schwarzer et al. (2015) <doi:10.1007/978-3-319-21416-0>, Chapter 8 "Network Meta-Analysis": - frequentist network meta-analysis following Rรผcker (2012) <doi:10.1002/jrsm.1058>; - additive network meta-analysis for combinations of treatments (Rรผcker et al., 2020) <doi:10.1002/bimj.201800167>; - network meta-analysis of binary data using the Mantel-Haenszel or non-central hypergeometric distribution method (Efthimiou et al., 2019) <doi:10.1002/sim.8158>, or penalised logistic regression (Evrenoglou et al., 2022) <doi:10.1002/sim.9562>; - rankograms and ranking of treatments by the Surface under the cumulative ranking curve (SUCRA) (Salanti et al., 2013) <doi:10.1016/j.jclinepi.2010.03.016>; - ranking of treatments using P-scores (frequentist analogue of SUCRAs without resampling) according to Rรผcker & Schwarzer (2015) <doi:10.1186/s12874-015-0060-8>; - split direct and indirect evidence to check consistency (Dias et al., 2010) <doi:10.1002/sim.3767>, (Efthimiou et al., 2019) <doi:10.1002/sim.8158>; - league table with network meta-analysis results; - 'comparison-adjusted' funnel plot (Chaimani & Salanti, 2012) <doi:10.1002/jrsm.57>; - net heat plot and design-based decomposition of Cochran's Q according to Krahn et al. (2013) <doi:10.1186/1471-2288-13-35>; - measures characterizing the flow of evidence between two treatments by Kรถnig et al. (2013) <doi:10.1002/sim.6001>; - automated drawing of network graphs described in Rรผcker & Schwarzer (2016) <doi:10.1002/jrsm.1143>; - partial order of treatment rankings ('poset') and Hasse diagram for 'poset' (Carlsen & Bruggemann, 2014) <doi:10.1002/cem.2569>; (Rรผcker & Schwarzer, 2017) <doi:10.1002/jrsm.1270>; - contribution matrix as described in Papakonstantinou et al. (2018) <doi:10.12688/f1000research.14770.3> and Davies et al. (2022) <doi:10.1002/sim.9346>; - subgroup network meta-analysis.
Maintained by Guido Schwarzer. Last updated 13 hours ago.
meta-analysisnetwork-meta-analysisrstudio
1.7 match 33 stars 11.82 score 199 scripts 10 dependentsegeulgen
pathfindR.data:Data Package for 'pathfindR'
This is a data-only package, containing data needed to run the CRAN package 'pathfindR', a package for enrichment analysis utilizing active subnetworks. This package contains protein-protein interaction network data, data related to gene sets and example input/output data.
Maintained by Ege Ulgen. Last updated 11 months ago.
4.5 match 4.21 score 1 scripts 1 dependentsbioc
RITAN:Rapid Integration of Term Annotation and Network resources
Tools for comprehensive gene set enrichment and extraction of multi-resource high confidence subnetworks. RITAN facilitates bioinformatic tasks for enabling network biology research.
Maintained by Michael Zimmermann. Last updated 5 months ago.
qualitycontrolnetworknetworkenrichmentnetworkinferencegenesetenrichmentfunctionalgenomicsgraphandnetwork
3.5 match 5.40 score 9 scripts
syedhaider5
SIMMS:Subnetwork Integration for Multi-Modal Signatures
Algorithms to create prognostic biomarkers using biological genesets or networks.
Maintained by Syed Haider. Last updated 3 years ago.
7.6 match 2.30 score 20 scripts
cogdisreslab
PCSF:Network-based interpretation of highthroughput data
The PCSF package performs an integrated analysis of highthroughput data using the interaction networks as a template, and interprets the biological landscape of interaction networks with respect to the data, which potentially leads to predictions of functional units. It also interactively visualize the resulting subnetwork with functional enrichment analysis.
Maintained by Murodzhon Akhmedov. Last updated 6 years ago.
6.1 match 2.76 score 38 scripts 1 dependentsbioc
MSstatsBioNet:Network Analysis for MS-based Proteomics Experiments
A set of tools for network analysis using mass spectrometry-based proteomics data and network databases. The package takes as input the output of MSstats differential abundance analysis and provides functions to perform enrichment analysis and visualization in the context of prior knowledge from past literature. Notably, this package integrates with INDRA, which is a database of biological networks extracted from the literature using text mining techniques.
Maintained by Anthony Wu. Last updated 1 months ago.
immunooncologymassspectrometryproteomicssoftwarequalitycontrolnetworkenrichmentnetwork
3.4 match 4.85 score 3 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
3.1 match 4.90 score 8 scripts
htx-r
crossnma:Cross-Design & Cross-Format Network Meta-Analysis and Regression
Network meta-analysis and meta-regression (allows including up to three covariates) for individual participant data, aggregate data, and mixtures of both formats using the three-level hierarchical model. Each format can come from randomized controlled trials or non-randomized studies or mixtures of both. Estimates are generated in a Bayesian framework using JAGS. The implemented models are described by Hamza et al. 2023 <DOI:10.1002/jrsm.1619>.
Maintained by Guido Schwarzer. Last updated 4 months ago.
3.4 match 1 stars 4.29 score 13 scripts
songw01
MEGENA:Multiscale Clustering of Geometrical Network
Co-Expression Network Analysis by adopting network embedding technique. Song W.-M., Zhang B. (2015) Multiscale Embedded Gene Co-expression Network Analysis. PLoS Comput Biol 11(11): e1004574. <doi: 10.1371/journal.pcbi.1004574>.
Maintained by Won-Min Song. Last updated 1 years ago.
2.0 match 49 stars 6.82 score 45 scripts 1 dependents
cran
JGL:Performs the Joint Graphical Lasso for Sparse Inverse Covariance Estimation on Multiple Classes
The Joint Graphical Lasso is a generalized method for estimating Gaussian graphical models/ sparse inverse covariance matrices/ biological networks on multiple classes of data. We solve JGL under two penalty functions: The Fused Graphical Lasso (FGL), which employs a fused penalty to encourage inverse covariance matrices to be similar across classes, and the Group Graphical Lasso (GGL), which encourages similar network structure between classes. FGL is recommended over GGL for most applications. Reference: Danaher P, Wang P, Witten DM. (2013) <doi:10.1111/rssb.12033>.
Maintained by Patrick Danaher. Last updated 1 years ago.
5.0 match 1 stars 2.65 score 1 dependentscran
crosstalkr:Analysis of Graph-Structured Data with a Focus on Protein-Protein Interaction Networks
Provides a general toolkit for drug target identification. We include functionality to reduce large graphs to subgraphs and prioritize nodes. In addition to being optimized for use with generic graphs, we also provides support to analyze protein-protein interactions networks from online repositories. For more details on core method, refer to Weaver et al. (2021) <https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008755>.
Maintained by Davis Weaver. Last updated 10 months ago.
4.7 match 2.70 score
druegamer
deepregression:Fitting Deep Distributional Regression
Allows for the specification of semi-structured deep distributional regression models which are fitted in a neural network as proposed by Ruegamer et al. (2023) <doi:10.18637/jss.v105.i02>. Predictors can be modeled using structured (penalized) linear effects, structured non-linear effects or using an unstructured deep network model.
Maintained by David Ruegamer. Last updated 3 months ago.
5.1 match 2.28 score 63 scripts 1 dependentsbioc
gatom:Finding an Active Metabolic Module in Atom Transition Network
This package implements a metabolic network analysis pipeline to identify an active metabolic module based on high throughput data. The pipeline takes as input transcriptional and/or metabolic data and finds a metabolic subnetwork (module) most regulated between the two conditions of interest. The package further provides functions for module post-processing, annotation and visualization.
Maintained by Alexey Sergushichev. Last updated 5 months ago.
geneexpressiondifferentialexpressionpathwaysnetwork
1.8 match 6 stars 5.26 score 8 scripts
aljensen89
CommKern:Network-Based Communities and Kernel Machine Methods
Analysis of network community objects with applications to neuroimaging data. There are two main components to this package. The first is the hierarchical multimodal spinglass (HMS) algorithm, which is a novel community detection algorithm specifically tailored to the unique issues within brain connectivity. The other is a suite of semiparametric kernel machine methods that allow for statistical inference to be performed to test for potential associations between these community structures and an outcome of interest (binary or continuous).
Maintained by Alexandria Jensen. Last updated 2 years ago.
1.8 match 4.11 score 26 scriptsbioc
MWASTools:MWASTools: an integrated pipeline to perform metabolome-wide association studies
MWASTools provides a complete pipeline to perform metabolome-wide association studies. Key functionalities of the package include: quality control analysis of metabonomic data; MWAS using different association models (partial correlations; generalized linear models); model validation using non-parametric bootstrapping; visualization of MWAS results; NMR metabolite identification using STOCSY; and biological interpretation of MWAS results.
Maintained by Andrea Rodriguez-Martinez. Last updated 5 months ago.
metabolomicslipidomicscheminformaticssystemsbiologyqualitycontrol
1.8 match 3.78 score 5 scripts 1 dependentscran
ILSM:Analyze Interconnection Structure of Multilayer Interaction Networks
In view of the analysis of the structural characteristics of the multilayer network has been complete, however, there is still a lack of a unified operation that can quickly obtain the corresponding characteristics of the multilayer network. To solve this insufficiency, 'ILSM' was designed for supporting calculating such metrics of multilayer networks by functions of this R package.
Maintained by WeiCheng Sun. Last updated 6 months ago.
1.9 match 3.30 score
hanjunwei-lab
ProgModule:Identification of Prognosis-Related Mutually Exclusive Modules
A novel tool to identify candidate driver modules for predicting the prognosis of patients by integrating exclusive coverage of mutations with clinical characteristics in cancer.
Maintained by Junwei Han. Last updated 3 months ago.
1.2 match 3.70 score 1 scriptsbioc
martini:GWAS Incorporating Networks
martini deals with the low power inherent to GWAS studies by using prior knowledge represented as a network. SNPs are the vertices of the network, and the edges represent biological relationships between them (genomic adjacency, belonging to the same gene, physical interaction between protein products). The network is scanned using SConES, which looks for groups of SNPs maximally associated with the phenotype, that form a close subnetwork.
Maintained by Hector Climente-Gonzalez. Last updated 5 months ago.
softwaregenomewideassociationsnpgeneticvariabilitygeneticsfeatureextractiongraphandnetworknetworkbioinformaticsgenomicsgwasnetwork-analysissnpssystems-biologycpp
0.5 match 4 stars 6.16 score 30 scriptsbioc
FELLA:Interpretation and enrichment for metabolomics data
Enrichment of metabolomics data using KEGG entries. Given a set of affected compounds, FELLA suggests affected reactions, enzymes, modules and pathways using label propagation in a knowledge model network. The resulting subnetwork can be visualised and exported.
Maintained by Sergio Picart-Armada. Last updated 5 months ago.
softwaremetabolomicsgraphandnetworkkegggopathwaysnetworknetworkenrichment
0.5 match 4.41 score 32 scriptsbioc
SMITE:Significance-based Modules Integrating the Transcriptome and Epigenome
This package builds on the Epimods framework which facilitates finding weighted subnetworks ("modules") on Illumina Infinium 27k arrays using the SpinGlass algorithm, as implemented in the iGraph package. We have created a class of gene centric annotations associated with p-values and effect sizes and scores from any researchers prior statistical results to find functional modules.
Maintained by Neil Ari Wijetunga. Last updated 5 months ago.
immunooncologydifferentialmethylationdifferentialexpressionsystemsbiologynetworkenrichmentgenomeannotationnetworksequencingrnaseqcoverage
0.5 match 1 stars 4.26 score 13 scriptsbioc
MODA:MODA: MOdule Differential Analysis for weighted gene co-expression network
MODA can be used to estimate and construct condition-specific gene co-expression networks, and identify differentially expressed subnetworks as conserved or condition specific modules which are potentially associated with relevant biological processes.
Maintained by Dong Li. Last updated 5 months ago.
geneexpressionmicroarraydifferentialexpressionnetwork
0.5 match 3.30 score 9 scripts
