inst/doc/manual_execution.R

In pathfindR: Enrichment Analysis Utilizing Active Subnetworks

## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  eval = FALSE
)

## ----init_steps, eval=TRUE----------------------------------------------------
suppressPackageStartupMessages(library(pathfindR))
data(example_pathfindR_input)
head(example_pathfindR_input, 3)

## ----process------------------------------------------------------------------
# example_processed <- input_processing(
#   input = example_pathfindR_input, # the input: in this case, differential expression results
#   p_val_threshold = 0.05, # p value threshold to filter significant genes
#   pin_name_path = "Biogrid", # the name of the PIN to use for active subnetwork search
#   convert2alias = TRUE # boolean indicating whether or not to convert missing symbols to alias symbols in the PIN
# )

## ----gene_set-----------------------------------------------------------------
# # using "BioCarta" as our gene sets for enrichment
# biocarta_list <- fetch_gene_set(
#   gene_sets = "BioCarta",
#   min_gset_size = 10,
#   max_gset_size = 300
# )
# biocarta_gsets <- biocarta_list[[1]]
# biocarta_descriptions <- biocarta_list[[2]]

## ----snw_search---------------------------------------------------------------
# n_iter <- 10 ## number of iterations
# combined_res <- NULL ## to store the result of each iteration
# 
# for (i in 1:n_iter) {
#   ###### Active Subnetwork Search
#   snws_file <- paste0("active_snws_", i) # Name of output file
#   active_snws <- active_snw_search(
#     input_for_search = example_processed,
#     pin_name_path = "Biogrid",
#     snws_file = snws_file,
#     score_quan_thr = 0.8, # you may tweak these arguments for optimal filtering of subnetworks
#     sig_gene_thr = 0.02, # you may tweak these arguments for optimal filtering of subnetworks
#     search_method = "GR", # we suggest using GR
#     seedForRandom = i # setting seed to ensure reproducibility per iteration
#   )
# 
#   ###### Enrichment Analyses
#   current_res <- enrichment_analyses(
#     snws = active_snws,
#     sig_genes_vec = example_processed$GENE,
#     pin_name_path = "Biogrid",
#     genes_by_term = biocarta_gsets,
#     term_descriptions = biocarta_descriptions,
#     adj_method = "bonferroni",
#     enrichment_threshold = 0.05,
#     list_active_snw_genes = TRUE
#   ) # listing the non-input active snw genes in output
# 
#   ###### Combine results via `rbind`
#   combined_res <- rbind(combined_res, current_res)
# }

## ----post_proc----------------------------------------------------------------
# ###### Summarize Combined Enrichment Results
# summarized_df <- summarize_enrichment_results(combined_res,
#   list_active_snw_genes = TRUE
# )
# 
# ###### Annotate Affected Genes Involved in Each Enriched Term
# final_res <- annotate_term_genes(
#   result_df = summarized_df,
#   input_processed = example_processed,
#   genes_by_term = biocarta_gsets
# )

## ----vis_pws------------------------------------------------------------------
# visualize_terms(
#   result_df = final_res,
#   hsa_KEGG = FALSE, # boolean to indicate whether human KEGG gene sets were used for enrichment analysis or not
#   pin_name_path = "Biogrid"
# )

## ----enr_chart----------------------------------------------------------------
# enrichment_chart(final_res[1:10, ])