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Pathway enrichment using a regularized regression approach

Bioconductor version: Release (3.19)

Compute pathway enrichment scores while accounting for term-term relations. This package uses a regularized multiple linear regression to regress differential expression p-values obtained from multi-condition experiments on a pathway membership matrix. By doing so, it is able to incorporate additional biological knowledge into the enrichment analysis and to estimate pathway enrichment scores more robustly.

Author: Kim Philipp Jablonski [aut, cre]

Maintainer: Kim Philipp Jablonski <kim.philipp.jablonski at>

Citation (from within R, enter citation("pareg")):


To install this package, start R (version "4.4") and enter:

if (!require("BiocManager", quietly = TRUE))


For older versions of R, please refer to the appropriate Bioconductor release.


To view documentation for the version of this package installed in your system, start R and enter:

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Pathway similarities HTML R Script
Reference Manual PDF


biocViews DifferentialExpression, GeneExpression, GraphAndNetwork, Network, NetworkEnrichment, Regression, Software, StatisticalMethod
Version 1.8.0
In Bioconductor since BioC 3.15 (R-4.2) (2 years)
License GPL-3
Depends R (>= 4.2), tensorflow (>= 2.2.0), tfprobability (>= 0.10.0)
Imports stats, tidyr, purrr, future, doFuture, foreach, doRNG, tibble, glue, tidygraph, igraph, proxy, dplyr, magrittr, ggplot2, ggraph, rlang, progress, Matrix, keras, nloptr, ggrepel, methods, DOSE, stringr, reticulate, logger, hms, devtools, basilisk
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Suggests knitr, rmarkdown, testthat (>= 2.1.0), BiocStyle, formatR, plotROC, PRROC, mgsa, topGO, msigdbr, betareg, fgsea, ComplexHeatmap, GGally, ggsignif, circlize, enrichplot, ggnewscale, tidyverse, cowplot, ggfittext, simplifyEnrichment, GSEABenchmarkeR, BiocParallel, ggupset, latex2exp,, GO.db
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Follow Installation instructions to use this package in your R session.

Source Package pareg_1.8.0.tar.gz
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macOS Binary (arm64) pareg_1.8.0.tgz
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