inst/doc/intro_vignette.R

In pathfindR: Enrichment Analysis Utilizing Active Subnetworks

## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  eval = FALSE,
  fig.width = 7, fig.height = 7, fig.align = "center"
)
suppressPackageStartupMessages(library(pathfindR))

## ----load_pkg, eval=TRUE------------------------------------------------------
library(pathfindR)
knitr::kable(head(example_pathfindR_input))

## ----run_pathfindR------------------------------------------------------------
# output_df <- run_pathfindR(example_pathfindR_input)

## ----change_input_thr---------------------------------------------------------
# output_df <- run_pathfindR(example_pathfindR_input, p_val_threshold = 0.01)

## ----change_out_dir-----------------------------------------------------------
# output_df <- run_pathfindR(example_pathfindR_input, output_dir = "this_is_my_output_directory")

## ----change_out_dir2----------------------------------------------------------
# output_df <- run_pathfindR(example_pathfindR_input, output_dir = "~/Desktop/my_dir")

## ----change_gset1-------------------------------------------------------------
# output_df <- run_pathfindR(example_pathfindR_input, gene_sets = "GO-MF")

## ----change_gset2-------------------------------------------------------------
# ## Including more terms for enrichment analysis
# output_df <- run_pathfindR(example_pathfindR_input,
#   gene_sets = "GO-MF",
#   min_gset_size = 5,
#   max_gset_size = 500
# )

## ----change_enr_threshold-----------------------------------------------------
# output_df <- run_pathfindR(example_pathfindR_input,
#   adj_method = "fdr",
#   enrichment_threshold = 0.01
# )

## ----change_PIN1--------------------------------------------------------------
# output_df <- run_pathfindR(example_pathfindR_input, pin_name_path = "IntAct")

## ----change_PIN2--------------------------------------------------------------
# # to use an external PIN of your choice
# output_df <- run_pathfindR(example_pathfindR_input, pin_name_path = "/path/to/myPIN.sif")

## ----change_method------------------------------------------------------------
# # for simulated annealing:
# output_df <- run_pathfindR(example_pathfindR_input, search_method = "SA")
# # for genetic algorithm:
# output_df <- run_pathfindR(example_pathfindR_input, search_method = "GA")

## ----change_n_iters-----------------------------------------------------------
# output_df <- run_pathfindR(example_pathfindR_input, iterations = 25)

## ----change_n_proc------------------------------------------------------------
# # if not set, `n_processes` defaults to (number of detected cores - 1)
# output_df <- run_pathfindR(example_pathfindR_input, iterations = 5, n_processes = 2)

## ----example_out, eval=TRUE---------------------------------------------------
knitr::kable(head(example_pathfindR_output, 2))

## ----encrichment_plot_shown---------------------------------------------------
# # change number of top terms plotted (default = 10)
# enrichment_chart(
#   result_df = example_pathfindR_output,
#   top_terms = 15
# )

## ----KEGG_vis-----------------------------------------------------------------
# input_processed <- input_processing(example_pathfindR_input)
# gg_list <- visualize_terms(
#   result_df = example_pathfindR_output,
#   input_processed = input_processed,
#   is_KEGG_result = TRUE
# )  # this function returns a list of ggraph objects (named by Term ID)
# 
# # save one of the plots as PDF image
# ggplot2::ggsave(
#   "hsa04911_diagram.pdf",   # path to output, format is determined by extension
#   gg_list$hsa04911,         # what to plot
#   width = 5,                 # adjust width
#   height = 5                # adjust height
# )

## ----nonKEGG_viss-------------------------------------------------------------
# input_processed <- input_processing(example_pathfindR_input)
# gg_list <- visualize_terms(
#   result_df = example_pathfindR_output,
#   input_processed = input_processed,
#   is_KEGG_result = FALSE,
#   pin_name_path = "Biogrid"
# )  # this function returns a list of ggraph objects (named by Term ID)
# 
# # save one of the plots as PDF image
# ggplot2::ggsave(
#   "diabetic_cardiomyopathy_interactions.pdf",   # path to output, format is determined by extension
#   gg_list$hsa04911,                             # what to plot
#   width = 10,                                    # adjust width
#   height = 6                                    # adjust height
# )

## ----hierarchical0------------------------------------------------------------
# example_pathfindR_output_clustered <- cluster_enriched_terms(example_pathfindR_output, plot_dend = FALSE, plot_clusters_graph = FALSE)

## ----hierarchical1, eval=TRUE-------------------------------------------------
## First 2 rows of clustered data frame
knitr::kable(head(example_pathfindR_output_clustered, 2))
## The representative terms
knitr::kable(example_pathfindR_output_clustered[example_pathfindR_output_clustered$Status == "Representative", ])

## ----hierarchical2, eval=TRUE-------------------------------------------------
# plotting only selected clusters for better visualization
selected_clusters <- subset(example_pathfindR_output_clustered, Cluster %in% 5:7)
enrichment_chart(selected_clusters, plot_by_cluster = TRUE)

## ----fuzzy--------------------------------------------------------------------
# clustered_fuzzy <- cluster_enriched_terms(example_pathfindR_output, method = "fuzzy")

## ----scores-------------------------------------------------------------------
# ## Vector of "Case" IDs
# cases <- c(
#   "GSM389703", "GSM389704", "GSM389706", "GSM389708",
#   "GSM389711", "GSM389714", "GSM389716", "GSM389717",
#   "GSM389719", "GSM389721", "GSM389722", "GSM389724",
#   "GSM389726", "GSM389727", "GSM389730", "GSM389731",
#   "GSM389733", "GSM389735"
# )
# 
# ## Calculate scores for representative terms
# ## and plot heat map using term descriptions
# representative_df <- example_pathfindR_output_clustered[example_pathfindR_output_clustered$Status == "Representative", ]
# score_matrix <- score_terms(
#   enrichment_table = representative_df,
#   exp_mat = example_experiment_matrix,
#   cases = cases,
#   use_description = TRUE, # default FALSE
#   label_samples = FALSE, # default = TRUE
#   case_title = "RA", # default = "Case"
#   control_title = "Healthy", # default = "Control"
#   low = "#f7797d", # default = "green"
#   mid = "#fffde4", # default = "black"
#   high = "#1f4037" # default = "red"
# )

## ----compare2res, eval=TRUE, fig.height=4, fig.width=8------------------------
combined_df <- combine_pathfindR_results(
  result_A = example_pathfindR_output,
  result_B = example_comparison_output,
  plot_common = FALSE
)

## ----custom_prep, eval=TRUE---------------------------------------------------
## CREB target genes
CREB_target_genes <- normalizePath(system.file("extdata/CREB.txt", package = "pathfindR"))
CREB_target_genes <- readLines(CREB_target_genes)[-c(1, 2)] # skip the first two lines

## MYC target genes
MYC_target_genes <- normalizePath(system.file("extdata/MYC.txt", package = "pathfindR"))
MYC_target_genes <- readLines(MYC_target_genes)[-c(1, 2)] # skip the first two lines

## Prep for use
custom_genes <- list(TF1 = CREB_target_genes, TF2 = MYC_target_genes)
custom_descriptions <- c(TF1 = "CREB target genes", TF2 = "MYC target genes")

## ----custom_input, eval=TRUE--------------------------------------------------
set.seed(123)

## Select 40 random genes from MYC gene sets and 10 from CREB gene sets
selected_genes <- sample(MYC_target_genes, 40)
selected_genes <- c(
  selected_genes,
  sample(CREB_target_genes, 10)
)

## Assign random p value between 0.001 and 0.05 for each selected gene
rand_p_vals <- sample(seq(0.001, 0.05, length.out = 5),
  size = length(selected_genes),
  replace = TRUE
)

example_pathfindR_input <- data.frame(
  Gene_symbol = selected_genes,
  p_val = rand_p_vals
)
knitr::kable(head(example_pathfindR_input))

## ----custom_run---------------------------------------------------------------
# example_custom_genesets_result <- run_pathfindR(
#   example_pathfindR_input,
#   gene_sets = "Custom",
#   custom_genes = custom_genes,
#   custom_descriptions = custom_descriptions,
#   min_gset_size = 1,   # do not limit the gene set size for demo
#   max_gset_size = Inf, # do not limit the gene set size for demo
# )
# 
# knitr::kable(example_custom_genesets_result)

## ----custom_result1, eval=TRUE, echo=FALSE------------------------------------
knitr::kable(example_custom_genesets_result)