# Pathway Enrichment Based on Differential Causal Effects

## Documentation for package ‘dce’ version 1.6.0

- DESCRIPTION file.
- User guides, package vignettes and other documentation.

## Help Pages

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