Package: vissE 1.13.0

Dharmesh D. Bhuva

vissE: Visualising Set Enrichment Analysis Results

This package enables the interpretation and analysis of results from a gene set enrichment analysis using network-based and text-mining approaches. Most enrichment analyses result in large lists of significant gene sets that are difficult to interpret. Tools in this package help build a similarity-based network of significant gene sets from a gene set enrichment analysis that can then be investigated for their biological function using text-mining approaches.

Authors:Dharmesh D. Bhuva [aut, cre], Ahmed Mohamed [ctb]

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vissE.pdf |vissE.html✨
vissE/json (API)
NEWS

# Install 'vissE' in R:

install.packages('vissE', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/davislaboratory/visse/issues

Datasets:

hgsc - The Hallmark collection from the MSigDB

On BioConductor:vissE-1.13.0(bioc 3.20)vissE-1.12.0(bioc 3.19)

bioconductor-package

11 exports 0.71 score 133 dependencies

Last updated 2 months agofrom:418c085f4c

Exports:bhuvad_theme characteriseGeneset computeMsigNetwork computeMsigOverlap computeMsigWordFreq findMsigClusters getMsigExclusionList plotGeneStats plotMsigNetwork plotMsigPPI plotMsigWordcloud

Dependencies:annotate AnnotationDbi AnnotationHub askpass BH Biobase BiocFileCache BiocGenerics BiocManager BiocVersion Biostrings bit bit64 blob cachem cli colorspace commonmark cpp11 crayon curl data.table DBI dbplyr digest dplyr dtt english ExperimentHub fansi farver fastmap fastmatch filelock generics GenomeInfoDb GenomeInfoDbData ggforce ggplot2 ggraph ggrepel ggwordcloud glue graph graphlayouts gridExtra gridtext GSEABase gtable httr hunspell igraph IRanges isoband ISOcodes jpeg jsonlite KEGGREST koRpus koRpus.lang.en labeling lattice lexicon lifecycle magrittr markdown MASS Matrix memoise mgcv mgsub mime msigdb munsell nlme NLP openssl org.Hs.eg.db org.Mm.eg.db pillar pkgconfig plogr plyr png polyclip purrr qdapRegex quanteda R6 rappdirs RColorBrewer Rcpp RcppArmadillo RcppEigen reshape2 rlang RSQLite S4Vectors scales scico slam SnowballC stopwords stringi stringr sylly sylly.en sys systemfonts syuzhet textclean textshape textstem tibble tidygraph tidyr tidyselect tm tweenr UCSC.utils utf8 vctrs viridis viridisLite withr xfun XML xml2 xtable XVector yaml zlibbioc zoo

vissE: Visualising Set Enrichment Analysis Results.

Dharmesh D. Bhuva

Rendered fromvissE.Rmdusingknitr::rmarkdownon Jun 24 2024.

Last update: 2022-03-17
Started: 2021-01-21

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Custom theme	bhuvad_theme
Functionally characterise a list of genes	characteriseGeneset
Compute a network using computed gene set overlap	computeMsigNetwork
Compute gene set overlap	computeMsigOverlap
Compute word frequencies for a single MSigDB collection	computeMsigWordFreq
Identify gene-set clusters from a gene-set overlap network	findMsigClusters
Exclusion list of words for MSigDB gene set text mining	getMsigExclusionList
The Hallmark collection from the MSigDB	hgsc
Plot gene statistics for clusters of gene sets	plotGeneStats
Plot a gene set overlap network	plotMsigNetwork
Plot PPI network for gene-set clusters identified using vissE	plotMsigPPI
Compute and plot word frequencies for multiple MSigDB collections	plotMsigWordcloud