DESCRIPTION was updatedannotate_pathway_DEGs(), calculate_pw_scores(), cluster_pathways(), fuzzy_pw_clustering(), hierarchical_pw_clustering(), visualize_pw_interactions() and visualize_pws() were renamed to annotate_term_DEGs(), score_terms(), cluster_enriched_terms(), fuzzy_term_clustering(), hierarchical_term_clustering(), visualize_term_interactions() and visualize_terms() respectivelyenriched_pathways.Rmd was renamed to enriched_terms.Rmd
term_gene_graph(), which creates a graph of enriched terms - involved genesenrichment() and enrichment_analyses() to get enrichment results fasterfetch_gene_set() for obtaining gene set data more easilymin_gset_size, max_gset_size in fetch_gene_set() and run_pathfindR())gaCrossover during active subnetwork search which controls the probability of a crossover in GA (default = 1, i.e. always perform crossover)testthat
create_kappa_matrix())mmu_kegg_genes & mmu_kegg_descriptions: mmu KEGG gene sets datamyeloma_input & myeloma_output: example mmu input and output datasig_gene_thr in subnetwork filtering via filterActiveSnws() now serves the threshold proportion of significant genes in the active subnetwork. e.g., if there are 100 significant genes and sig_gene_thr = 0.03, subnetwork that contain at least 3 (100 x 0.03) significant genes will be accepted for further analysispathview dependency by implementing colored pathway diagram visualization function using KEGGREST and KEGGgraph
hierarchical_term_clustering(), redefined the distance measure as 1 - kappa statistic
cluster_graph_vis() (during the calculations for additional node colors)cluster_graph_vis()
active_snw_search(), unnecessary warnings during active subnetwork search were removedenrichment_chart(), supplying fuzzy clustered results no longer raises an errorinput_testing() and input_processing() to ensure that both the initial input data frame and the processed input data frame for active subnetwork search contain at least 2 genes (to fix the corner case encountered in issue #17)enrichment_chart(), ensuring that bubble sizes displayed in the legend (proportional to # of DEGs) are integersenrichment_chart(), added the arguments num_bubbles (default is 4) to control number of bubbles displayed in the legend and even_breaks (default is TRUE) to indicate if even increments of breaks are requiredterm_gene_graph() (create the igraph object as an undirected graph for better auto layout)visualize_term_interactions(). The legend no longer displays “Non-input Active Snw. Genes” if they were not providedhuman_genes in run_pathfindR() and input_processing() was renamed as convert2alias
top_terms to enrichment_chart(), controlling the number top enriched terms to plot (default is 10)max_to_plot to visualize_hsa_KEGG() and to run_pathfindR(). This argument controls the number of pathways to be visualized (default is NULL, i.e. no filter). This was implemented not to slow down the runtime of run_pathfindR() as downloading the png files is slow.enriched_ters.Rmd
create_kappa_matrix() when chance is 1, the metric is turned into 0class(.) == * in cluster_graph_vis()