Package: GSEMA 0.99.3

GSEMA: Gene Set Enrichment Meta-Analysis

Performing the different steps of gene set enrichment meta-analysis. It provides different functions that allow the application of meta-analysis based on the combination of effect sizes from different pathways in different studies to obtain significant pathways that are common to all of them.

Authors:Juan Antonio Villatoro-García [aut, cre], Pablo Jurado-Bascón [aut], Pedro Carmona-Sáez [aut]

GSEMA_0.99.3.tar.gz
GSEMA_0.99.3.zip(r-4.7-any)GSEMA_0.99.3.zip(r-4.6-any)GSEMA_0.99.3.zip(r-4.5-any)
GSEMA_0.99.3.tgz(r-4.6-any)GSEMA_0.99.3.tgz(r-4.5-any)
GSEMA_0.99.3.tar.gz(r-4.7-any)GSEMA_0.99.3.tar.gz(r-4.6-any)
GSEMA_0.99.3.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
GSEMA/json (API)

# Install 'GSEMA' in R:

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

Bug tracker:https://github.com/juananvg/gsema/issues

Datasets:

GeneSets - GSEMA synthetic data
objectMApathSim - GSEMA synthetic data
study1Ex - GSEMA synthetic data
study1Pheno - GSEMA synthetic data
study2Ex - GSEMA synthetic data
study2Pheno - GSEMA synthetic data

On CRAN:

statisticalmethod genesetenrichment pathways

3.00 score 3 scripts 236 downloads 5 exports 153 dependencies

Last updated from:47fa3b86b9. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	OK	394
source / vignettes	OK	312
linux-release-x86_64	OK	400
macos-release-arm64	OK	329
macos-oldrel-arm64	OK	283
windows-devel	OK	328
windows-release	OK	412
windows-oldrel	OK	311
wasm-release	OK	197

Exports:calculateESpath createObjectMApath filteringPaths heatmapPaths metaAnalysisESpath

Dependencies:abind annotate AnnotationDbi askpass assorthead base64enc beachmat BH Biobase BiocFileCache BiocGenerics biocmake BiocParallel BiocSingular Biostrings bit bit64 blob bslib cachem cli codetools cpp11 crayon crosstalk curl data.table DBI dbplyr DelayedArray DelayedMatrixStats digest dir.expiry doParallel dplyr edgeR evaluate farver fastmap filelock fontawesome foreach formatR fs futile.logger futile.options generics GenomicRanges ggplot2 ggrepel glue graph GSEABase GSVA gtable h5mread HDF5Array highr hms htmltools htmlwidgets httr httr2 impute IRanges irlba isoband iterators jquerylib jsonlite KEGGREST knitr labeling lambda.r later lattice lazyeval lifecycle limma locfit magick magrittr mathjaxr Matrix MatrixGenerics matrixStats memoise memuse metadat metafor mime nlme numDeriv openssl otel pbapply pheatmap pillar pkgconfig plotly plyr png prettyunits progress promises purrr R6 rappdirs rbibutils RColorBrewer Rcpp Rdpack reshape reshape2 rhdf5 rhdf5filters Rhdf5lib rjson rlang rmarkdown RSQLite rsvd S4Arrays S4Vectors S7 sass ScaledMatrix scales Seqinfo SingleCellExperiment singscore snow SparseArray sparseMatrixStats SpatialExperiment statmod stringi stringr SummarizedExperiment sys tibble tidyr tidyselect tinytex utf8 vctrs viridisLite withr xfun XML xtable XVector yaml

Gene Set Enrichment Meta-Analysis with GSEMA package

Rendered fromGSEMA.Rmdusingknitr::rmarkdown

Last update: 2024-10-02
Started: 2024-09-26

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Calculation of Effects Sizes and their variance for the different Gene Sets and studies	calculateESpath
Creation of the object to use in gene set enrichment meta-analysis	createObjectMApath
Fuction for filtering gene sets with low expression	filteringPaths
Visualization of the gene set enrichment meta-analysis results	heatmapPaths
Performing Gene Set Enrichment Meta-analysis	metaAnalysisESpath
GSEMA synthetic data	GeneSets objectMApathSim simulatedData study1Ex study1Pheno study2Ex study2Pheno