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enrichplot:Visualization of Functional Enrichment Result
The 'enrichplot' package implements several visualization methods for interpreting functional enrichment results obtained from ORA or GSEA analysis. It is mainly designed to work with the 'clusterProfiler' package suite. All the visualization methods are developed based on 'ggplot2' graphics.
Maintained by Guangchuang Yu. Last updated 3 months ago.
annotationgenesetenrichmentgokeggpathwayssoftwarevisualizationenrichment-analysispathway-analysis
239 stars 15.71 score 3.1k scripts 58 dependents
igordot
msigdbr:MSigDB Gene Sets for Multiple Organisms in a Tidy Data Format
Provides the 'Molecular Signatures Database' (MSigDB) gene sets typically used with the 'Gene Set Enrichment Analysis' (GSEA) software (Subramanian et al. 2005 <doi:10.1073/pnas.0506580102>, Liberzon et al. 2015 <doi:10.1016/j.cels.2015.12.004>, Castanza et al. 2023 <doi:10.1038/s41592-023-02014-7>) as an R data frame. The package includes the human genes as listed in MSigDB as well as the corresponding symbols and IDs for frequently studied model organisms such as mouse, rat, pig, fly, and yeast.
Maintained by Igor Dolgalev. Last updated 13 days ago.
enrichment-analysisgene-setsgenomicsgseamsigdbpathway-analysispathways
73 stars 12.20 score 3.6k scripts 20 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
8 stars 10.24 score 122 scripts 21 dependentsbioc
GeneTonic:Enjoy Analyzing And Integrating The Results From Differential Expression Analysis And Functional Enrichment Analysis
This package provides functionality to combine the existing pieces of the transcriptome data and results, making it easier to generate insightful observations and hypothesis. Its usage is made easy with a Shiny application, combining the benefits of interactivity and reproducibility e.g. by capturing the features and gene sets of interest highlighted during the live session, and creating an HTML report as an artifact where text, code, and output coexist. Using the GeneTonicList as a standardized container for all the required components, it is possible to simplify the generation of multiple visualizations and summaries.
Maintained by Federico Marini. Last updated 3 months ago.
guigeneexpressionsoftwaretranscriptiontranscriptomicsvisualizationdifferentialexpressionpathwaysreportwritinggenesetenrichmentannotationgoshinyappsbioconductorbioconductor-packagedata-explorationdata-visualizationfunctional-enrichment-analysisgene-expressionpathway-analysisreproducible-researchrna-seq-analysisrna-seq-datashinytranscriptomeuser-friendly
77 stars 8.28 score 37 scripts 1 dependentsbioc
mitch:Multi-Contrast Gene Set Enrichment Analysis
mitch is an R package for multi-contrast enrichment analysis. At it’s heart, it uses a rank-MANOVA based statistical approach to detect sets of genes that exhibit enrichment in the multidimensional space as compared to the background. The rank-MANOVA concept dates to work by Cox and Mann (https://doi.org/10.1186/1471-2105-13-S16-S12). mitch is useful for pathway analysis of profiling studies with one, two or more contrasts, or in studies with multiple omics profiling, for example proteomic, transcriptomic, epigenomic analysis of the same samples. mitch is perfectly suited for pathway level differential analysis of scRNA-seq data. We have an established routine for pathway enrichment of Infinium Methylation Array data (see vignette). The main strengths of mitch are that it can import datasets easily from many upstream tools and has advanced plotting features to visualise these enrichments.
Maintained by Mark Ziemann. Last updated 13 hours ago.
geneexpressiongenesetenrichmentsinglecelltranscriptomicsepigeneticsproteomicsdifferentialexpressionreactomednamethylationmethylationarraydataimportgene-regulationgene-seq-analysispathway-analysis
16 stars 7.11 score 15 scriptsbioc
BioCor:Functional similarities
Calculates functional similarities based on the pathways described on KEGG and REACTOME or in gene sets. These similarities can be calculated for pathways or gene sets, genes, or clusters and combined with other similarities. They can be used to improve networks, gene selection, testing relationships...
Maintained by Lluís Revilla Sancho. Last updated 5 months ago.
statisticalmethodclusteringgeneexpressionnetworkpathwaysnetworkenrichmentsystemsbiologybioconductor-packagesbioinformaticsfunctional-similaritygenegene-setspathway-analysissimilaritysimilarity-measurement
14 stars 6.47 score