Package: enrichit 0.2.0

Guangchuang Yu

enrichit: 'C++' Implementations of Functional Enrichment Analysis

Fast implementations of functional enrichment analysis methods using 'C++' via 'Rcpp'. Currently provides Over-Representation Analysis (ORA), Gene Set Enrichment Analysis (GSEA), Weighted Enrichment Analysis for ORA and GSEA, Network-based Set Enrichment Analysis (NSEA), multi-layer network-based enrichment, and multi-omics integration workflows. Additional features include early fusion at the feature level, late fusion at the pathway level, multi-omics contribution tracing, topology-aware explanation helpers, Bayesian term selection, and extremely fast Random Walk with Restart (RWR) using 'RcppEigen'. The enrichment methods build on GSEA by Subramanian et al. (2005) <doi:10.1073/pnas.0506580102>, the multilevel strategy derived from 'fgsea' by Korotkevich et al. (2021) <doi:10.1101/060012>, and network-based enrichment ideas described by Glaab et al. (2012) <doi:10.1093/bioinformatics/bts389>.

Authors:Guangchuang Yu [aut, cre]

enrichit_0.2.0.tar.gz
enrichit_0.2.0.tar.gz(r-4.7-arm64)enrichit_0.2.0.tar.gz(r-4.7-x86_64)enrichit_0.2.0.tar.gz(r-4.6-arm64)enrichit_0.2.0.tar.gz(r-4.6-x86_64)
enrichit_0.2.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
enrichit/json (API)

# Install 'enrichit' in R:
install.packages('enrichit', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))
Uses libs:
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

cpp

6.54 score 65 packages 21 scripts 19k downloads 30 exports 10 dependencies

Last updated from:fb787911b6. Checks:6 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-arm64OK247
linux-devel-x86_64OK221
source / vignettesOK306
linux-release-arm64OK200
linux-release-x86_64OK278
wasm-releaseOK225

Exports:aggregate_enrichmentaggregate_omicsbayes_enrichbayes_summaryclassify_omics_patterncollapse_multilayer_scoresEXTID2NAMEextract_mnsea_subnetworkgeneIDgeneInCategoryget_mnsea_contributionget_omics_contributiongseagsea_gsongseaScoresgsfilterharmonize_idsmnseamnsea_gsonnseansea_gsonoraora_gsonprepare_multilayer_networkprepare_networkpropagate_multilayerselect_features_for_orasetReadableshowsummary

Dependencies:clidigestfslatticeMatrixrappdirsRcppRcppEigenrlangyulab.utils

Readme and manuals

Help Manual

Help pageTopics
Aggregate multiple enrichment results (Late Fusion)aggregate_enrichment
Aggregate multi-omics gene/protein-level statisticsaggregate_omics
Bayesian term selection for enrichment resultsbayes_enrich
Summarize Bayesian enrichment resultsbayes_summary
Classify pathway-level multi-omics patternsclassify_omics_pattern
Collapse multi-layer diffusion scorescollapse_multilayer_scores
Class "compareClusterResult" This class represents the comparison result of gene clusters by GO categories at specific level or GO enrichment analysis.compareClusterResult-class plot,compareClusterResult-method show,compareClusterResult-method summary,compareClusterResult-method
Common parameters for enrichit functionsenrichit_params
Class "enrichResult" This class represents the result of enrichment analysis.enrichResult-class show,enrichResult-method summary,enrichResult-method
EXTID2NAMEEXTID2NAME
Extract pathway subnetwork data from a 'mnseaResult'extract_mnsea_subnetwork
geneID genericgeneID
geneInCategory genericgeneInCategory
Get cached contribution tables from a 'mnseaResult'get_mnsea_contribution
Get gene-level omics contribution for a specific pathwayget_omics_contribution
Gene Set Enrichment Analysis (GSEA)gsea
gsea_gsongsea_gson
Class "gseaResult" This class represents the result of GSEA analysisgseahResult-class gseaResult-class show,gseaResult-method summary,gseaResult-method
Calculate GSEA Running Enrichment ScoresgseaScores
gsfiltergsfilter
Harmonize feature IDs to a target spaceharmonize_ids
Multi-layer Network-based Gene Set Enrichment Analysismnsea
Multi-layer NSEA using a GSON objectmnsea_gson
Class "mnseaResult" This class represents the result of multi-layer Network-based Set Enrichment Analysis.mnseaResult-class
Network-based Gene Set Enrichment Analysisnsea
Network-based GSEA using a GSON objectnsea_gson
Class "nseaResult" This class represents the result of Network-based Set Enrichment Analysis (NSEA).nseaResult-class
Over-Representation Analysis (ORA)ora
ora-gsonora_gson
Prepare multi-layer network for repeated propagationprepare_multilayer_network
Prepare network for repeated NSEA runsprepare_network
Propagate signals on a multi-layer networkpropagate_multilayer
Select features for ORAselect_features_for_ora
setReadablesetReadable
show methodshow show,mnseaResult-method show,nseaResult-method
summary methodsummary