Update the ScCC network data
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data/scCCnet.rds
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data/scCCnet.rds
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@ -6,40 +6,70 @@
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# Boris Steipe for ABC learning units
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#
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# Notes:
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#
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# The large source- datafiles are NOT posted to github. If you want to
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# experiment with your own code, download them and place them into your
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# local ./data directory.
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#
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# STRING data source:
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# Download page: https://string-db.org/cgi/download.pl?species_text=Saccharomyces+cerevisiae
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# Data: (20.8 mb) https://string-db.org/download/protein.links.full.v10.5/4932.protein.links.full.v10.5.txt.gz
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# Download page:
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# https://string-db.org/cgi/download.pl?species_text=Saccharomyces+cerevisiae
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# Data: (20.1 mb)
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# https://stringdb-static.org/download/protein.links.full.v11.0/4932.protein.links.full.v11.0.txt.gz
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#
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# GOSlim data source:
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# Info page: http://www.geneontology.org/page/go-slim-and-subset-guide
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# Data: (3 mb) https://downloads.yeastgenome.org/curation/literature/go_slim_mapping.tab
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# GOSlim data source: (Note: this has moved from GO to SGD)
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# Info page: https://www.yeastgenome.org/downloads
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# Info page: http://sgd-archive.yeastgenome.org/curation/literature/
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# Data: (3 mb)
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# http://sgd-archive.yeastgenome.org/curation/literature/go_slim_mapping.tab
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#
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#
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# Version: 1.1
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# Version: 1.2
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#
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# Date: 2017 10 - 2019 01
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# Date: 2017-10 - 2020-09
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# Author: Boris Steipe (boris.steipe@utoronto.ca)
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#
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# Versions:
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# 1.2 2020 Update. GO Slim Yeast mow at SGD
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# 1.1 Change from require() to requireNamespace(),
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# use <package>::<function>() idiom throughout
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# 1.0 First code copied from 2016 material.
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#
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# TODO:
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#
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#
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# ==============================================================================
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# SRCDIR <- "./instructor"
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#TOC> ==========================================================================
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#TOC>
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#TOC> Section Title Line
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#TOC> ---------------------------------------------------------------
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#TOC> 1 INITIALIZE 58
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#TOC> 2 STRING FUNCTIONAL INTERACTION DATA 66
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#TOC> 3 GOSlim FUNCTIONAL ANNOTATIONS 96
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#TOC> 3.1 Intersect interactions and annotations 122
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#TOC> 4 DEFINE THE CELL-CYCLE NETWORK 128
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#TOC>
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#TOC> ==========================================================================
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# = 1 INITIALIZE ==========================================================
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SRCDIR <- "./data"
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if (! requireNamespace("readr", quietly = TRUE)) {
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install.packages("readr")
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}
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# STRING functional interaction data
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# = 2 STRING FUNCTIONAL INTERACTION DATA ==================================
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# Read STRING Data (needs to be downloaded from database, see URL in Notes)
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STR <- readr::read_delim("./data/4932.protein.links.full.v10.5.txt",
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delim = " ")
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# The .gz compressed version is 20MB, the uncompressed versioj is 110MB -
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# really not necessary to uncompress since readr:: can read from compressed
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# files, and does so automatically, based on the file extension.
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( fn <- file.path(SRCDIR, "4932.protein.links.full.v11.0.txt.gz") )
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STR <- readr::read_delim(fn, delim = " ")
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# Subset only IDs and combined_score column
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STR <- STR[ , c("protein1", "protein2", "combined_score")]
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@ -48,22 +78,26 @@ STR <- STR[ , c("protein1", "protein2", "combined_score")]
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# sum(STR$combined_score > 909) # 100270 edges
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# subset for 100,000 highest confidence edges
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STR <- STR[(STR$combined_score > 909), ]
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head(STR)
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# IDs are formatted like 4932.YAL005C ... drop the "4932." prefix
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STR$protein1 <- gsub("^4932\\.", "", STR$protein1)
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STR$protein2 <- gsub("^4932\\.", "", STR$protein2)
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# head(STR)
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head(STR)
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# get a vector of gene names in this list
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myIntxGenes <- unique(c(STR$protein1, STR$protein2)) # yeast systematic gene
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# names
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length(myIntxGenes)
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sample(myIntxGenes, 10) # choose 10 at random (sanity check)
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# GOSlim functional annotations
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# = 3 GOSlim FUNCTIONAL ANNOTATIONS =======================================
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#
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# Read GOSlim data (needs to be downloaded from database, see URL in Notes)
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( fn <- file.path(SRCDIR, "go_slim_mapping.tab") )
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Gsl <- readr::read_tsv("./data/go_slim_mapping.tab",
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Gsl <- readr::read_tsv(fn,
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col_names = c("ID",
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"name",
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"SGDId",
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@ -72,52 +106,57 @@ Gsl <- readr::read_tsv("./data/go_slim_mapping.tab",
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"termID",
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"status"))
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# head(Gsl)
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#
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head(Gsl)
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# What cell cycle names does it contain?
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# myGslTermNames <- unique(Gsl$termName) # 169 unique terms
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# myGslTermNames[grep("cycle", myGslTermNames)]
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myGslTermNames <- unique(Gsl$termName) # 169 unique terms
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myGslTermNames[grep("cycle", myGslTermNames)]
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# [1] "regulation of cell cycle" "mitotic cell cycle" "meiotic cell cycle"
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#
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# Choose "mitotic cell cycle" as the GOslim term to subset with
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#
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scCCgenes <- unique(Gsl$ID[Gsl$termName == "mitotic cell cycle"])
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# length(scCCgenes) # 324 genes annotated to that term
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length(scCCgenes) # 324 genes annotated to that term
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# sum(scCCgenes %in% myIntxGenes) # 294 of these have high-confidence
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# # interactions
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# == 3.1 Intersect interactions and annotations ============================
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sum(scCCgenes %in% myIntxGenes) # 307 of these have high-confidence
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# # functional interactions
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# = 4 DEFINE THE CELL-CYCLE NETWORK =======================================
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#
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# Define scCCnet ... the S. Cervisiae Cell Cycle network
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# Subset all rows for which BOTH genes are in the GOslim cell cycle set
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#
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scCCnet <- STR[(STR$protein1 %in% scCCgenes) &
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(STR$protein2 %in% scCCgenes), ]
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# How many genes are there?
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# length(unique(c(scCCnet$protein1, scCCnet$protein2))) #261
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length(unique(c(scCCnet$protein1, scCCnet$protein2))) #283
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# Each edge is listed twice - now remove duplicates.
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#
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# Step 1: make a vector: sort two names so the frist one is alphabetically
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# smaller han the second one. This brings the two names into a defined
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# Step 1: make a vector: sort two names so the fiRst one is alphabetically
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# smaller Than the second one. This brings the two names into a defined
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# order. Then concatenate them with a "." - the resulting string
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# is always the same, for any order. E.g. c("A", "B") gives "A.B"
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# and c("B", "A") also gives "A.B". This identifies duplicates.
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x <- apply(cbind(scCCnet$protein1,
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scCCnet$protein2),
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x <- apply(cbind(scCCnet$protein1, scCCnet$protein2),
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1,
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FUN = function(x) { return(paste(sort(x), collapse = ".")) })
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# head(x) # "YAL016W.YGR040W" "YAL016W.YOR014W" "YAL016W.YDL188C" ... etc.
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head(x) # "YAL016W.YGR040W" "YAL016W.YOR014W" "YAL016W.YDL188C" ... etc.
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# sum(duplicated(x)) # 1280
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sum(duplicated(x)) # 1453
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# Step 2: drop all rows that contain duplicates in x
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scCCnet <- scCCnet[! duplicated(x), ]
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# Confirm we didn't loose genes
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# length(unique(c(mySubnet$protein1, mySubnet$protein2))) # 261, no change
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# Network has 261 nodes, 1280 edges
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length(unique(c(scCCnet$protein1, scCCnet$protein2))) # 283, no change
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nrow(scCCnet)
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# Network has 283 nodes, 1453 edges
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saveRDS(scCCnet, file = "./data/scCCnet.rds")
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