Implemented the (per subject) pathway scoring function calculate_pw_scores and the function to plot the heatmap of pathway scores per subject plot_scores.
Added the auto parameter to choose_clusters. When auto == TRUE (default), the function chooses the optimal number of clusters k automatically, as the value which maximizes the average silhouette width. It then returns a data frame with the cluster assignments and the representative/member statuses of each pathway.
Added the Fold_Enrichment column to the resulting data frame of enrichment, and as a corollary to the resulting data frame of run_pathfindR.
Added the option bubble to plot a bubble chart displaying the enrichment results in run_pathfindR using the helper function enrichment_chart. To plot the bubble chart set bubble = TRUE in run_pathfindR or use enrichment_chart(your_result_df).
Add the parameter silent_option to run_pathfindR. When silent_option == TRUE (default), the console outputs during active subnetwork search are printed to a file named “console_out.txt”. If silent_option == FALSE, the output is printed on the screen. Default was set to TRUE because multiple console outputs are simultaneously printed when running in parallel.
Added the list_active_snw_genes parameter to run_pathfindR. When list_active_snw_genes == TRUE, the function adds the column non_DEG_Active_Snw_Genes, which reports the non-DEG active subnetwork genes for the active subnetwork which was enriched for the given pathway with the lowest p value.
Added the data RA_clustered, which is the example output of the clustering workflow.
In the function, run_pathfindR added the option to specify the argument output_dir which specifies the directory to be created under the current working directory for storing the result HTML files. output_dir is “pathfindR_Results” by default.
run_pathfindR now checks whether the output directory (output_dir) already exists and if it exists, stops and displays an error message. This was implemented to prevent writing over existing results.
genes_table.html now contains a second table displaying the input gene symbols for which there were no interactions in the PIN.
run_pathfindR. For this, the gene_sets argument should be set to “Custom” and custom_genes and custom_pathways should be provided.calculate_pw_scores where if there was one DEG, subsetting the experiment matrix failedcalculate_pw_scores. If there is none, the pathway is skipped.calculate_pw_scores, if cases are provided, the pathways are reordered before plotting the heat map and returning the matrix according to their activity in cases. This way, “up” pathways are grouped together, same for “down” pathways.calculate_pwd, if a pathway has perfect overlap with other pathways, change the correlation value with 1 instead of NA.choose_clusters, if result_df has less than 3 pathways, do not perform clustering.run_pathfindR checks whether the output directory (output_dir) already exists and if it exists, now appends “(1)” to output_dir and displays a warning message. This was implemented to prevent writing over existing results.run_pathfindR, recursive creation for the output directory (output_dir) is now supported.run_pathfindR, if no pathways are found, the function returns an empty data frame instead of raising an error.plot_scores, added the argument label_cases to indicate whether or not to label the cases in the pathway scoring heatmap plot. Also added the argument case_control_titles which allows the user to change the default “Case” and “Control” headers. Also added the arguments low and high used to change the low and high end colors of the scoring color gradient.plot_scores, reversed the color gradient to match the coloring scheme used by pathview (i.e. red for positive values, green for negative values)parseActiveSnwSearch, replaced score_thr by score_quan_thr. This was done so that the scoring filter for active subnetworks could be performed based on the distribution of the current active subnetworks and not using a constant empirical score value threshold.parseActiveSnwSearch, increased sig_gene_thr from 2 to 10 as we observed in most of the cases, this resulted in faster runs with comparable results.choose_clusters, added the argument p_val_threshold to be used as p value threshold for filtering the enriched pathways prior to clustering.