Man pages for kpj/dce
Pathway Enrichment Based on Differential Causal Effects
| as_adjmat | graph to adjacency |
| as.data.frame.dce | Dce to data frame |
| create_random_DAG | Create random DAG (topologically ordered) |
| dce-methods | 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... |
| g2dag | Graph to DAG |
| get_pathway_info | Dataframe containing meta-information of pathways in database |
| get_pathways | Easy pathway network access |
| 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... |
| 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-methods | Simulate data |
| summary.rlm_dce | summary for rlm_dce |
| topologically_ordering | Topological ordering |
| trueEffects | Compute the true casual effects of a simulated dag |
