Showing 13 of total 13 results (show query)
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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 18 days ago.
graphandnetworknetworkpathwayssoftwarethirdpartyclientdataimportdatarepresentationgenesignalinggeneregulationsystemsbiologytranscriptomicssinglecellannotationkeggcomplexesenzyme-ptmnetworksnetworks-biologyomnipathproteinsquarto
10.0 match 126 stars 9.90 score 226 scripts 2 dependentsbioc
pathwayPCA:Integrative Pathway Analysis with Modern PCA Methodology and Gene Selection
pathwayPCA is an integrative analysis tool that implements the principal component analysis (PCA) based pathway analysis approaches described in Chen et al. (2008), Chen et al. (2010), and Chen (2011). pathwayPCA allows users to: (1) Test pathway association with binary, continuous, or survival phenotypes. (2) Extract relevant genes in the pathways using the SuperPCA and AES-PCA approaches. (3) Compute principal components (PCs) based on the selected genes. These estimated latent variables represent pathway activities for individual subjects, which can then be used to perform integrative pathway analysis, such as multi-omics analysis. (4) Extract relevant genes that drive pathway significance as well as data corresponding to these relevant genes for additional in-depth analysis. (5) Perform analyses with enhanced computational efficiency with parallel computing and enhanced data safety with S4-class data objects. (6) Analyze studies with complex experimental designs, with multiple covariates, and with interaction effects, e.g., testing whether pathway association with clinical phenotype is different between male and female subjects. Citations: Chen et al. (2008) <https://doi.org/10.1093/bioinformatics/btn458>; Chen et al. (2010) <https://doi.org/10.1002/gepi.20532>; and Chen (2011) <https://doi.org/10.2202/1544-6115.1697>.
Maintained by Gabriel Odom. Last updated 5 months ago.
copynumbervariationdnamethylationgeneexpressionsnptranscriptiongenepredictiongenesetenrichmentgenesignalinggenetargetgenomewideassociationgenomicvariationcellbiologyepigeneticsfunctionalgenomicsgeneticslipidomicsmetabolomicsproteomicssystemsbiologytranscriptomicsclassificationdimensionreductionfeatureextractionprincipalcomponentregressionsurvivalmultiplecomparisonpathways
10.0 match 11 stars 7.74 score 42 scriptsbioc
cosmosR:COSMOS (Causal Oriented Search of Multi-Omic Space)
COSMOS (Causal Oriented Search of Multi-Omic Space) is a method that integrates phosphoproteomics, transcriptomics, and metabolomics data sets based on prior knowledge of signaling, metabolic, and gene regulatory networks. It estimated the activities of transcrption factors and kinases and finds a network-level causal reasoning. Thereby, COSMOS provides mechanistic hypotheses for experimental observations across mulit-omics datasets.
Maintained by Attila Gabor. Last updated 5 months ago.
cellbiologypathwaysnetworkproteomicsmetabolomicstranscriptomicsgenesignalingdata-integrationmetabolomic-datanetwork-modellingphosphoproteomics
10.0 match 59 stars 7.22 score 35 scriptsbioc
RankProd:Rank Product method for identifying differentially expressed genes with application in meta-analysis
Non-parametric method for identifying differentially expressed (up- or down- regulated) genes based on the estimated percentage of false predictions (pfp). The method can combine data sets from different origins (meta-analysis) to increase the power of the identification.
Maintained by Francesco Del Carratore. Last updated 5 months ago.
differentialexpressionstatisticalmethodsoftwareresearchfieldmetabolomicslipidomicsproteomicssystemsbiologygeneexpressionmicroarraygenesignaling
10.0 match 6.46 score 81 scripts 6 dependentsbioc
signifinder:Collection and implementation of public transcriptional cancer signatures
signifinder is an R package for computing and exploring a compendium of tumor signatures. It allows to compute a variety of signatures, based on gene expression values, and return single-sample scores. Currently, signifinder contains more than 60 distinct signatures collected from the literature, relating to multiple tumors and multiple cancer processes.
Maintained by Stefania Pirrotta. Last updated 2 months ago.
geneexpressiongenetargetimmunooncologybiomedicalinformaticsrnaseqmicroarrayreportwritingvisualizationsinglecellspatialgenesignaling
10.0 match 7 stars 6.40 score 15 scriptsbioc
escape:Easy single cell analysis platform for enrichment
A bridging R package to facilitate gene set enrichment analysis (GSEA) in the context of single-cell RNA sequencing. Using raw count information, Seurat objects, or SingleCellExperiment format, users can perform and visualize ssGSEA, GSVA, AUCell, and UCell-based enrichment calculations across individual cells.
Maintained by Nick Borcherding. Last updated 2 months ago.
softwaresinglecellclassificationannotationgenesetenrichmentsequencinggenesignalingpathways
10.0 match 5.92 score 138 scriptsbioc
EGSEA:Ensemble of Gene Set Enrichment Analyses
This package implements the Ensemble of Gene Set Enrichment Analyses (EGSEA) method for gene set testing. EGSEA algorithm utilizes the analysis results of twelve prominent GSE algorithms in the literature to calculate collective significance scores for each gene set.
Maintained by Monther Alhamdoosh. Last updated 5 months ago.
immunooncologydifferentialexpressiongogeneexpressiongenesetenrichmentgeneticsmicroarraymultiplecomparisononechannelpathwaysrnaseqsequencingsoftwaresystemsbiologytwochannelmetabolomicsproteomicskegggraphandnetworkgenesignalinggenetargetnetworkenrichmentnetworkclassification
10.0 match 5.81 score 64 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
10.0 match 4.90 score 8 scriptsbioc
sSNAPPY:Single Sample directioNAl Pathway Perturbation analYsis
A single sample pathway perturbation testing method for RNA-seq data. The method propagates changes in gene expression down gene-set topologies to compute single-sample directional pathway perturbation scores that reflect potential direction of change. Perturbation scores can be used to test significance of pathway perturbation at both individual-sample and treatment levels.
Maintained by Wenjun Liu. Last updated 5 months ago.
softwaregeneexpressiongenesetenrichmentgenesignaling
10.0 match 1 stars 4.83 score 15 scriptsbioc
nempi:Inferring unobserved perturbations from gene expression data
Takes as input an incomplete perturbation profile and differential gene expression in log odds and infers unobserved perturbations and augments observed ones. The inference is done by iteratively inferring a network from the perturbations and inferring perturbations from the network. The network inference is done by Nested Effects Models.
Maintained by Martin Pirkl. Last updated 5 months ago.
softwaregeneexpressiondifferentialexpressiondifferentialmethylationgenesignalingpathwaysnetworkclassificationneuralnetworknetworkinferenceatacseqdnaseqrnaseqpooledscreenscrisprsinglecellsystemsbiology
10.0 match 2 stars 4.60 score 2 scriptsbioc
ASURAT:Functional annotation-driven unsupervised clustering for single-cell data
ASURAT is a software for single-cell data analysis. Using ASURAT, one can simultaneously perform unsupervised clustering and biological interpretation in terms of cell type, disease, biological process, and signaling pathway activity. Inputting a single-cell RNA-seq data and knowledge-based databases, such as Cell Ontology, Gene Ontology, KEGG, etc., ASURAT transforms gene expression tables into original multivariate tables, termed sign-by-sample matrices (SSMs).
Maintained by Keita Iida. Last updated 5 months ago.
geneexpressionsinglecellsequencingclusteringgenesignalingcpp
10.0 match 4.32 score 21 scriptsbioc
wppi:Weighting protein-protein interactions
Protein-protein interaction data is essential for omics data analysis and modeling. Database knowledge is general, not specific for cell type, physiological condition or any other context determining which connections are functional and contribute to the signaling. Functional annotations such as Gene Ontology and Human Phenotype Ontology might help to evaluate the relevance of interactions. This package predicts functional relevance of protein-protein interactions based on functional annotations such as Human Protein Ontology and Gene Ontology, and prioritizes genes based on network topology, functional scores and a path search algorithm.
Maintained by Ana Galhoz. Last updated 5 months ago.
graphandnetworknetworkpathwayssoftwaregenesignalinggenetargetsystemsbiologytranscriptomicsannotationgene-ontologygene-prioritizationhuman-phenotype-ontologyomnipathppi-networksrandom-walk-with-restartquarto
10.0 match 1 stars 4.30 score 4 scriptsbioc
hipathia:HiPathia: High-throughput Pathway Analysis
Hipathia is a method for the computation of signal transduction along signaling pathways from transcriptomic data. The method is based on an iterative algorithm which is able to compute the signal intensity passing through the nodes of a network by taking into account the level of expression of each gene and the intensity of the signal arriving to it. It also provides a new approach to functional analysis allowing to compute the signal arriving to the functions annotated to each pathway.
Maintained by Marta R. Hidalgo. Last updated 5 months ago.
pathwaysgraphandnetworkgeneexpressiongenesignalinggo
10.0 match 3.62 score 42 scripts