Package: dce 1.13.0

Kim Philipp Jablonski

dce: Pathway Enrichment Based on Differential Causal Effects

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.

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

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NEWS

# Install 'dce' in R:

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

Bug tracker:https://github.com/cbg-ethz/dce/issues

Datasets:

df_pathway_statistics - Biological pathway information.

On BioConductor:dce-1.13.0(bioc 3.20)dce-1.12.0(bioc 3.19)

bioconductor-package

19 exports 0.82 score 233 dependencies

Last updated 2 months agofrom:642d99a263

Exports:as_adjmat create_random_DAG dce dce_nb estimate_latent_count g2dag get_pathway_info get_pathways get_prediction_counts graph_union graph2df pcor permutation_test plot_network propagate_gene_edges resample_edge_weights simulate_data topologically_ordering trueEffects

Dependencies:abind amap AnnotationDbi apcluster askpass assertthat backports base64enc bdsmatrix BH Biobase BiocGenerics BiocManager Biostrings bit bit64 blob BoolNet BoutrosLab.plotting.general broom bslib cachem callr cellranger class cli clipr clue cluster codetools colorspace conflicted corpcor cpp11 crayon curl data.table DBI dbplyr deldir DEoptimR digest diptest dplyr dtplyr e1071 edgeR ellipse epiNEM evaluate expm fansi farver fastcluster fastICA fastmap flexclust flexmix FMStable fontawesome forcats fpc fs gargle gdata generics GenomeInfoDb GenomeInfoDbData ggdendro ggforce ggm ggplot2 ggraph ggrepel glm2 glue gmodels googledrive googlesheets4 graph graphite graphlayouts gridExtra gtable gtools harmonicmeanp haven hexbin highr hms htmltools httr ids igraph infotheo interp IRanges isoband jpeg jquerylib jsonlite KEGGREST kernlab knitr labeling latex2exp lattice latticeExtra lifecycle limma Linnorm lmtest locfit logger lubridate magrittr MASS mathjaxr Matrix MatrixModels matrixStats mclust memoise metap mgcv mime minet mnem mnormt modelr modeltools multcomp multtest munsell mutoss mvtnorm naturalsort nlme nnet numDeriv openssl org.Hs.eg.db pcalg permute pillar pkgconfig plogr plotrix plyr png polyclip ppcor prabclus prettyunits processx progress proxy ps purrr qqconf quantreg R6 ragg rappdirs RBGL rbibutils RColorBrewer Rcpp RcppArmadillo RcppEigen Rdpack readr readxl rematch rematch2 reprex reshape2 Rgraphviz rlang rmarkdown robustbase RSQLite rstudioapi Rtsne rvest S4Vectors sandwich sass scales selectr sfsmisc shadowtext sn snow snowfall SparseM statmod stringi stringr survival sys systemfonts textshaping TFisher TH.data tibble tidygraph tidyr tidyselect tidyverse timechange tinytex tsne tweenr tzdb UCSC.utils utf8 uuid vcd vctrs vegan viridis viridisLite vroom wesanderson withr xfun XML xml2 XVector yaml zlibbioc zoo

Get started

Kim Philipp Jablonski, Martin Pirkl

Rendered fromdce.Rmdusingknitr::rmarkdownon Jul 05 2024.

Last update: 2023-06-27
Started: 2021-01-20

Overview of pathway network databases

Kim Philipp Jablonski, Martin Pirkl

Rendered frompathway_databases.Rmdusingknitr::rmarkdownon Jul 05 2024.

Last update: 2022-03-29
Started: 2021-01-18

Help page	Topics
graph to adjacency	as_adjmat
Dce to data frame	as.data.frame.dce
Create random DAG (topologically ordered)	create_random_DAG
Differential Causal Effects - main function	dce dce,graphNEL-method dce,igraph-method dce,matrix-method
Differential Causal Effects for negative binomial data	dce_nb
Biological pathway information.	df_pathway_statistics
Estimate number of latent confounders Compute the true casual effects of a simulated dag	estimate_latent_count
Graph to DAG	g2dag
Dataframe containing meta-information of pathways in database	get_pathway_info
Easy pathway network access	get_pathways
Compute true positive/... counts	get_prediction_counts
Graph union	graph_union
Graph to data frame	graph2df
Partial correlation	pcor
Permutation test for (partial) correlation on non-Gaussian data	permutation_test
Plot network adjacency matrix	plot_network
Plot dce object	plot.dce
Remove non-gene nodes from pathway and reconnect nodes	propagate_gene_edges
Resample network edge weights	resample_edge_weights
costum rlm function	rlm_dce
Simulate data	simulate_data simulate_data,graphNEL-method simulate_data,igraph-method simulate_data,matrix-method
summary for rlm_dce	summary.rlm_dce
Topological ordering	topologically_ordering
Compute the true casual effects of a simulated dag	trueEffects

Package: dce 1.13.0

dce: Pathway Enrichment Based on Differential Causal Effects

Get started

Overview of pathway network databases

Citation

Development and contributors

Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)