bch441-work-abc-units/scripts/ABC-makeScCCnet.R

129 lines
4.4 KiB
R

# tocID <- "scripts/ABC-makeScCCnet.R"
#
# Create a subnetwork of high-confidence yeast genes with a "mitotic cell cycle"
# GOSlim annotation.
#
# Boris Steipe for ABC learning units
#
# Notes:
# STRING data source:
# Download page: https://string-db.org/cgi/download.pl?species_text=Saccharomyces+cerevisiae
# Data: (20.8 mb) https://string-db.org/download/protein.links.full.v10.5/4932.protein.links.full.v10.5.txt.gz
#
# GOSlim data source:
# Info page: http://www.geneontology.org/page/go-slim-and-subset-guide
# Data: (3 mb) https://downloads.yeastgenome.org/curation/literature/go_slim_mapping.tab
#
#
# Version: 1.1
#
# Date: 2017 10 - 2019 01
# Author: Boris Steipe (boris.steipe@utoronto.ca)
#
# Versions:
# 1.1 Change from require() to requireNamespace(),
# use <package>::<function>() idiom throughout
# 1.0 First code copied from 2016 material.
#
# TODO:
#
#
# ==============================================================================
if (! requireNamespace("readr", quietly = TRUE)) {
install.packages("readr")
}
# STRING functional interaction data
# Read STRING Data (needs to be downloaded from database, see URL in Notes)
STR <- readr::read_delim("./data/4932.protein.links.full.v10.5.txt",
delim = " ")
# Subset only IDs and combined_score column
STR <- STR[ , c("protein1", "protein2", "combined_score")]
# head(STR)
# sum(STR$combined_score > 909) # 100270 edges
# subset for 100,000 highest confidence edges
STR <- STR[(STR$combined_score > 909), ]
# IDs are formatted like 4932.YAL005C ... drop the "4932." prefix
STR$protein1 <- gsub("^4932\\.", "", STR$protein1)
STR$protein2 <- gsub("^4932\\.", "", STR$protein2)
# head(STR)
# get a vector of gene names in this list
myIntxGenes <- unique(c(STR$protein1, STR$protein2)) # yeast systematic gene
# names
# GOSlim functional annotations
#
# Read GOSlim data (needs to be downloaded from database, see URL in Notes)
Gsl <- readr::read_tsv("./data/go_slim_mapping.tab",
col_names = c("ID",
"name",
"SGDId",
"Ontology",
"termName",
"termID",
"status"))
# head(Gsl)
#
# What cell cycle names does it contain?
# myGslTermNames <- unique(Gsl$termName) # 169 unique terms
# myGslTermNames[grep("cycle", myGslTermNames)]
# [1] "regulation of cell cycle" "mitotic cell cycle" "meiotic cell cycle"
#
# Choose "mitotic cell cycle" as the GOslim term to subset with
#
scCCgenes <- unique(Gsl$ID[Gsl$termName == "mitotic cell cycle"])
# length(scCCgenes) # 324 genes annotated to that term
# sum(scCCgenes %in% myIntxGenes) # 294 of these have high-confidence
# # interactions
# Define scCCnet ... the S. Cervisiae Cell Cycle network
# Subset all rows for which BOTH genes are in the GOslim cell cycle set
scCCnet <- STR[(STR$protein1 %in% scCCgenes) &
(STR$protein2 %in% scCCgenes), ]
# How many genes are there?
# length(unique(c(scCCnet$protein1, scCCnet$protein2))) #261
# Each edge is listed twice - now remove duplicates.
#
# Step 1: make a vector: sort two names so the frist one is alphabetically
# smaller han the second one. This brings the two names into a defined
# order. Then concatenate them with a "." - the resulting string
# is always the same, for any order. E.g. c("A", "B") gives "A.B"
# and c("B", "A") also gives "A.B". This identifies duplicates.
x <- apply(cbind(scCCnet$protein1,
scCCnet$protein2),
1,
FUN = function(x) { return(paste(sort(x), collapse = ".")) })
# head(x) # "YAL016W.YGR040W" "YAL016W.YOR014W" "YAL016W.YDL188C" ... etc.
# sum(duplicated(x)) # 1280
# Step 2: drop all rows that contain duplicates in x
scCCnet <- scCCnet[! duplicated(x), ]
# Confirm we didn't loose genes
# length(unique(c(mySubnet$protein1, mySubnet$protein2))) # 261, no change
# Network has 261 nodes, 1280 edges
saveRDS(scCCnet, file = "./data/scCCnet.rds")
# scCCnet <- readRDS("./data/scCCnet.rds") # <<<- use this to restore the
# object when needed
# [END]