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Pathway Enrichment Based on Differential Causal Effects

Bioconductor version: Release (3.19)

Compute differential causal effects (dce) on (biological) networks. Given observational samples from a control experiment and non-control (e.g., cancer) for two genes A and B, we can compute differential causal effects with a (generalized) linear regression. If the causal effect of gene A on gene B in the control samples is different from the causal effect in the non-control samples the dce will differ from zero. We regularize the dce computation by the inclusion of prior network information from pathway databases such as KEGG.

Author: Kim Philipp Jablonski [aut, cre] , Martin Pirkl [aut]

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

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


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Overview of pathway network databases HTML R Script
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biocViews DifferentialExpression, GeneExpression, GraphAndNetwork, KEGG, Network, NetworkEnrichment, Regression, Software, StatisticalMethod
Version 1.12.0
In Bioconductor since BioC 3.13 (R-4.1) (3 years)
License GPL-3
Depends R (>= 4.1)
Imports stats, methods, assertthat, graph, pcalg, purrr, tidyverse, Matrix, ggraph, tidygraph, ggplot2, rlang, expm, MASS, edgeR, epiNEM, igraph, metap, mnem, naturalsort, ppcor, glm2, graphite, reshape2, dplyr, magrittr, glue, Rgraphviz, harmonicmeanp,, logger, shadowtext
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Source Package dce_1.12.0.tar.gz
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