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-4.5)GSEMA_0.99.3.zip(r-4.4)
GSEMA_0.99.3.tgz(r-4.4-any)
GSEMA_0.99.3.tar.gz(r-4.5-noble)GSEMA_0.99.3.tar.gz(r-4.4-noble)
GSEMA_0.99.3.tgz(r-4.4-emscripten)
GSEMA.pdf |GSEMA.html✨
GSEMA/json (API)
NEWS

# 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

3.90 score 3 scripts 154 downloads 5 exports 155 dependencies

Last updated 1 months agofrom:47fa3b86b9. Checks:OK: 4 WARNING: 1. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Nov 15 2024
R-4.5-win	OK	Nov 15 2024
R-4.5-linux	WARNING	Nov 15 2024
R-4.4-win	OK	Nov 15 2024
R-4.4-mac	OK	Nov 15 2024

Exports:calculateESpath createObjectMApath filteringPaths heatmapPaths metaAnalysisESpath

Dependencies:abind annotate AnnotationDbi askpass assorthead base64enc beachmat BH Biobase BiocFileCache BiocGenerics BiocParallel BiocSingular Biostrings bit bit64 blob bslib cachem cli codetools colorspace cpp11 crayon crosstalk curl data.table DBI dbplyr DelayedArray DelayedMatrixStats digest doParallel dplyr edgeR evaluate fansi farver fastmap filelock fontawesome foreach formatR fs futile.logger futile.options generics GenomeInfoDb GenomeInfoDbData GenomicRanges ggplot2 ggrepel glue graph GSEABase GSVA gtable HDF5Array highr hms htmltools htmlwidgets httr impute IRanges irlba isoband iterators jquerylib jsonlite KEGGREST knitr labeling lambda.r later lattice lazyeval lifecycle limma locfit magick magrittr MASS mathjaxr Matrix MatrixGenerics matrixStats memoise metadat metafor mgcv mime munsell nlme numDeriv openssl pbapply pheatmap pillar pkgconfig plogr 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 sass ScaledMatrix scales SingleCellExperiment singscore snow SparseArray sparseMatrixStats SpatialExperiment statmod stringi stringr SummarizedExperiment sys tibble tidyr tidyselect tinytex UCSC.utils utf8 vctrs viridisLite withr xfun XML xtable XVector yaml zlibbioc

Gene Set Enrichment Meta-Analysis with GSEMA package

Juan Antonio Villator-García, Pablo Jurado-Bascón, Pedro Carmona-Sáez

Rendered fromGSEMA.Rmdusingknitr::rmarkdownon Nov 15 2024.

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