inst/doc/non_hs_analysis.R

In pathfindR: Enrichment Analysis Utilizing Active Subnetworks

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

## ----mmu_kegg-----------------------------------------------------------------
# gsets_list <- get_gene_sets_list(
#   source = "KEGG",
#   org_code = "mmu"
# )

## ----KEGG_save----------------------------------------------------------------
# mmu_kegg_genes <- gsets_list$gene_sets
# mmu_kegg_descriptions <- gsets_list$descriptions
# 
# ## Save both as RDS files for later use
# saveRDS(mmu_kegg_genes, "mmu_kegg_genes.RDS")
# saveRDS(mmu_kegg_descriptions, "mmu_kegg_descriptions.RDS")

## ----KEGG_load----------------------------------------------------------------
# mmu_kegg_genes <- readRDS("mmu_kegg_genes.RDS")
# mmu_kegg_descriptions <- readRDS("mmu_kegg_descriptions.RDS")

## ----process_PIN1-------------------------------------------------------------
# ## Downloading the STRING PIN file to tempdir
# url <- "https://stringdb-static.org/download/protein.links.v11.0/10090.protein.links.v11.0.txt.gz"
# path2file <- file.path(tempdir(check = TRUE), "STRING.txt.gz")
# download.file(url, path2file)
# 
# ## read STRING pin file
# mmu_string_df <- read.table(path2file, header = TRUE)
# 
# ## filter using combined_score cut-off value of 800
# mmu_string_df <- mmu_string_df[mmu_string_df$combined_score >= 800, ]
# 
# ## fix ids
# mmu_string_pin <- data.frame(
#   Interactor_A = sub("^10090\\.", "", mmu_string_df$protein1),
#   Interactor_B = sub("^10090\\.", "", mmu_string_df$protein2)
# )
# head(mmu_string_pin, 2)

## ----process_PIN2, eval=FALSE-------------------------------------------------
# # library(biomaRt)
# 
# mmu_ensembl <- useMart("ensembl", dataset = "mmusculus_gene_ensembl")
# 
# converted <- getBM(
#   attributes = c("ensembl_peptide_id", "mgi_symbol"),
#   filters = "ensembl_peptide_id",
#   values = unique(unlist(mmu_string_pin)),
#   mart = mmu_ensembl
# )
# mmu_string_pin$Interactor_A <- converted$mgi_symbol[match(mmu_string_pin$Interactor_A, converted$ensembl_peptide_id)]
# mmu_string_pin$Interactor_B <- converted$mgi_symbol[match(mmu_string_pin$Interactor_B, converted$ensembl_peptide_id)]
# mmu_string_pin <- mmu_string_pin[!is.na(mmu_string_pin$Interactor_A) & !is.na(mmu_string_pin$Interactor_B), ]
# mmu_string_pin <- mmu_string_pin[mmu_string_pin$Interactor_A != "" & mmu_string_pin$Interactor_B != "", ]
# 
# head(mmu_string_pin, 2)

## ----process_PIN3-------------------------------------------------------------
# # remove self interactions
# self_intr_cond <- mmu_string_pin$Interactor_A == mmu_string_pin$Interactor_B
# mmu_string_pin <- mmu_string_pin[!self_intr_cond, ]
# 
# # remove duplicated inteactions (including symmetric ones)
# mmu_string_pin <- unique(t(apply(mmu_string_pin, 1, sort))) # this will return a matrix object
# 
# mmu_string_pin <- data.frame(
#   A = mmu_string_pin[, 1],
#   pp = "pp",
#   B = mmu_string_pin[, 2]
# )

## ----process_PIN4-------------------------------------------------------------
# path2SIF <- file.path(tempdir(), "mmusculusPIN.sif")
# write.table(mmu_string_pin,
#   file = path2SIF,
#   col.names = FALSE,
#   row.names = FALSE,
#   sep = "\t",
#   quote = FALSE
# )
# path2SIF <- normalizePath(path2SIF)

## ----mmu_input_df, eval=TRUE--------------------------------------------------
knitr::kable(head(example_mmu_input))

## ----run----------------------------------------------------------------------
# example_mmu_output <- run_pathfindR(
#   input = example_mmu_input,
#   convert2alias = FALSE,
#   gene_sets = "Custom",
#   custom_genes = mmu_kegg_genes,
#   custom_descriptions = mmu_kegg_descriptions,
#   pin_name_path = path2SIF
# )

## ----enr_chart, echo=FALSE, eval=TRUE-----------------------------------------
enrichment_chart(example_mmu_output)

## ----output, eval=TRUE--------------------------------------------------------
knitr::kable(example_mmu_output)

## ----run2---------------------------------------------------------------------
# example_mmu_output <- run_pathfindR(
#   input = example_mmu_input,
#   convert2alias = FALSE,
#   gene_sets = "mmu_KEGG",
#   pin_name_path = "mmu_STRING"
# )