Update PPI-Analysis assets and code
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hyginn
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11
.utilities.R
11
.utilities.R
@ -332,5 +332,16 @@ selectChi2 <- function() {
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}
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# == 5.2 selectENSP() ====================================================
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selectENSP <- function(x) {
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oldSeed <- .Random.seed
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set.seed(myStudentNumber)
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x <- sample(x[order(x)])
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.Random.seed <- oldSeed
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cat(sprintf("seal: %s\n", seal(paste0(x,collapse=""))))
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return(x)
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}
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# [END]
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@ -1,10 +1,5 @@
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# tocID <- "BIN-PPI-Analysis.R"
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#
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# ---------------------------------------------------------------------------- #
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# PATIENCE ... #
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# Do not yet work wih this code. Updates in progress. Thank you. #
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# boris.steipe@utoronto.ca #
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# ---------------------------------------------------------------------------- #
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#
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# Purpose: A Bioinformatics Course:
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# R code accompanying the BIN-PPI-Analysis unit.
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@ -15,7 +10,8 @@
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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 Deprecate save()/load() for saveRDS()/readRDS()
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# 1.2 2020 Updates; Rewrite for new STRINg V11;
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# Deprecate save()/load() for saveRDS()/readRDS()
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# 1.1 Change from require() to requireNamespace(),
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# use <package>::<function>() idiom throughout,
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# use Biocmanager:: not biocLite()
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@ -39,13 +35,13 @@
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#TOC>
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#TOC> Section Title Line
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#TOC> ---------------------------------------------------------------
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#TOC> 1 Setup and data 46
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#TOC> 2 Functional Edges in the Human Proteome 82
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#TOC> 2.1 Cliques 125
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#TOC> 2.2 Communities 166
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#TOC> 2.3 Betweenness Centrality 180
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#TOC> 3 biomaRt 226
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#TOC> 4 Task for submission 296
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#TOC> 1 Setup and data 49
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#TOC> 2 Functional Edges in the Human Proteome 85
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#TOC> 2.1 Cliques 128
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#TOC> 2.2 Communities 169
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#TOC> 2.3 Betweenness Centrality 183
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#TOC> 3 biomaRt 230
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#TOC> 4 Task for submission 301
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#TOC>
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#TOC> ==========================================================================
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@ -68,10 +64,10 @@ if (! requireNamespace("igraph", quietly = TRUE)) {
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# from the STRING database. The full table has 8.5 million records, here is a
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# subset of records with combined confidence scores > 980
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# The selected set of edges with a confidence of > 980 is a dataframe with about
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# 50,000 edges and 6,500 unique proteins. Incidentaly, that's about the size of
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# The selected set of edges with a confidence of > 964 is a dataframe with about
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# 50,000 edges and 8,400 unique proteins. Incidentaly, that's about the size of
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# a fungal proteome. You can load the saved dataframe here (To read more about
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# what the numbers mean, see http://www.ncbi.nlm.nih.gov/pubmed/15608232 ).
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# what the scores mean, see http://www.ncbi.nlm.nih.gov/pubmed/15608232 ).
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STRINGedges <- readRDS("./data/STRINGedges.rds")
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@ -80,8 +76,8 @@ head(STRINGedges)
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# Note that STRING has appended the tax-ID for Homo sapiens - 9606 - to the
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# Ensemble transcript identifiers that start with ENSP. We'll remove them:
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STRINGedges$protein1 <- gsub("^9606\\.", "", STRINGedges$protein1)
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STRINGedges$protein2 <- gsub("^9606\\.", "", STRINGedges$protein2)
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STRINGedges$a <- gsub("^9606\\.", "", STRINGedges$a)
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STRINGedges$b <- gsub("^9606\\.", "", STRINGedges$b)
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head(STRINGedges)
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@ -91,9 +87,9 @@ head(STRINGedges)
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# There are many possibilities to explore interesting aspects of biological
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# networks, we will keep with some very simple procedures here but you have
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# to be aware that this is barely scratching the surface of possibilites.
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# to be aware that this is barely scratching the surface of possibilities.
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# However, once the network exists in your computer, it is comparatively
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# easy to find information nline about the many, many options to analyze.
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# easy to find information online about the many, many options to analyze.
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# Make a graph from this dataframe
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@ -101,7 +97,7 @@ head(STRINGedges)
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gSTR <- igraph::graph_from_data_frame(STRINGedges, directed = FALSE)
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# CAUTION you DON'T want to plot a graph with 6,500 nodes and 50,000 edges -
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# CAUTION you DON'T want to plot a graph with 8,000 nodes and 50,000 edges -
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# layout of such large graphs is possible, but requires specialized code. Google
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# for <layout large graphs> if you are curious. Also, consider what one can
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# really learn from plotting such a graph ...
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@ -119,7 +115,7 @@ plot(log10(as.numeric(names(freqRank)) + 1),
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log10(as.numeric(freqRank)), type = "b",
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pch = 21, bg = "#FEE0AF",
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xlab = "log(Rank)", ylab = "log(frequency)",
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main = "6,500 nodes from the human functional interaction network")
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main = "8,400 nodes from the human functional interaction network")
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# This looks very scale-free indeed.
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@ -136,11 +132,11 @@ abline(regressionLine, col = "firebrick")
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# Biological complexes often appear as cliques in interaction graphs.
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igraph::clique_num(gSTR)
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# The largest clique has 63 members.
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# The largest clique has 81 members.
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(C <- igraph::largest_cliques(gSTR)[[1]])
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# Pick one of the proteins and find out what this fully connected cluster of 63
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# Pick one of the proteins and find out what this fully connected cluster of 81
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# proteins is (you can simply Google for any of the IDs). Is this expected?
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# Plot this ...
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@ -166,7 +162,7 @@ plot(R,
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par(oPar)
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# ... well: remember: a clique means every node is connected to every other
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# node. We have 63 * 63 = 3,969 edges. This is what a matrix model of PPI
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# node. We have 81 * 81 = 6,561 edges. This is what a matrix model of PPI
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# networks looks like for large complexes.
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@ -179,9 +175,9 @@ set.seed(NULL) # reset the RNG
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igraph::modularity(gSTRclusters) # ... measures how separated the different
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# membership types are from each other
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tMem <- table(igraph::membership(gSTRclusters))
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length(tMem) # More than 2000 communities identified
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length(tMem) # About 700 communities identified
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hist(tMem, breaks = 50, col = "skyblue") # most clusters are small ...
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range(tMem) # ... but one has > 100 members
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range(tMem) # ... but one has > 200 members
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# == 2.3 Betweenness Centrality ============================================
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@ -214,13 +210,14 @@ head(sBC)
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# IDs...
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(ENSPsel <- names(igraph::V(gSTR)[BCsel]))
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# Task:
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# =====
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# IMPORTANT, IF YOU INTEND TO SUBMIT YOUR ANALYSIS FOR CREDIT
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# We are going to use these IDs to produce some output for a submitted task:
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# so I need you to personalize ENSPsel with the following
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# three lines of code:
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# therefore I need you to execute the following line, note the "seal" that this
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# returns, and not change ENSPsel later:
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set.seed(<myStudentNumber>) # enter your student number here
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(ENSPsel <- sample(ENSPsel))
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set.seed(NULL) # reset the random number generator
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myENSPsel <- seal(ENSPsel)
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# Next, to find what these proteins are...
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@ -251,15 +248,15 @@ if (! requireNamespace("biomaRt", quietly = TRUE)) {
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# browseVignettes("biomaRt") # available vignettes
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# data(package = "biomaRt") # available datasets
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# define which dataset to use ...
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# define which dataset to use ... this takes a while for download
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myMart <- biomaRt::useMart("ensembl", dataset="hsapiens_gene_ensembl")
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# what filters are defined?
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(filters <- biomaRt::listFilters(myMart))
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( filters <- biomaRt::listFilters(myMart) )
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# and what attributes can we filter for?
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(attributes <- biomaRt::listAttributes(myMart))
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( attributes <- biomaRt::listAttributes(myMart) )
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# Soooo many options - let's look for the correct name of filters that are
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@ -296,13 +293,13 @@ for (ID in ENSPsel) {
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mart = myMart)
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}
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# So what are the proteins with the ten highest betweenness centralities?
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# ... are you surprised? (I am! Really.)
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# = 4 Task for submission =================================================
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# Write a loop that will go through your personalized list of Ensemble IDs and
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# for each ID:
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# -- print the ID,
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@ -310,11 +307,14 @@ for (ID in ENSPsel) {
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# -- print the first row's wikigene description.
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# -- print the first row's phenotype.
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#
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# Write your thoughts about this group of genes.
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#
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# (Hint, you can structure your loop in the same way as the loop that
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# created CPdefs. )
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# Place the R code for this loop and its output into your report if you are
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# submitting a report for this unit. Please read the requirements carefully.
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# Submit the "seal" for your ENSP vector, the ENSP vector itself, the R code
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# for this loop and its output into your report if you are submitting
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# anything for credit for this unit. Please read the requirements carefully.
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@ -628,7 +628,7 @@ sum(divs < KLdiv(pmfL1, pmfL2)) # 933
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# we used above.
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# == 4.3 Kolmogorov-Smirnov test for continuous distributions ==============
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# == 4.3 Continuous distributions: Kolmogorov-Smirnov test ==============
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# The Kolmogorov-Smirnov (KS) test is meant for continuous distributions, i.e.
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# the probability it calculates assumes that the function values are all
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Binary file not shown.
168
scripts/ABC-makeSTRINGedges.R
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168
scripts/ABC-makeSTRINGedges.R
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@ -0,0 +1,168 @@
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# tocID <- "scripts/ABC-makeSTRINGedges.R"
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#
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# Create a subnetwork of high-confidence human STRING edges.
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#
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# Notes:
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#
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# The large source- datafile is NOT posted to github. If you want to
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# experiment with the original data, download it and place it 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:
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# https://string-db.org/cgi/download.pl?species_text=Homo+sapiens
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# Data: (127.6 Mb)
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# https://stringdb-static.org/download/protein.links.full.v11.0/9606.protein.links.full.v11.0.txt.gz
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#
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# Version: 1.0
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#
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# Date: 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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# 1.0 Rewrite
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#
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# TODO:
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#
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# ==============================================================================
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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 44
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#TOC> 2 Read STRING Data 51
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#TOC> 3 Define cutoff and subset 63
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#TOC> 4 Drop duplicates 103
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#TOC> 5 Simple statistics 127
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#TOC> 6 Write to file 160
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#TOC>
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#TOC> ==========================================================================
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# = 1 Initialize ==========================================================
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if (! requireNamespace("readr", quietly = TRUE)) {
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install.packages("readr")
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}
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# = 2 Read STRING Data ====================================================
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# Read STRING Data (needs to be downloaded from database, see URL in Notes)
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# The .gz compressed version is 127.6MB, the uncompressed version is probably
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# 848 Mb. Fortunately 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("~", "9606.protein.links.full.v11.0.txt.gz") )
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STR <- readr::read_delim(fn, delim = " ")
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nrow(STR) # 11,759,454 rows
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head(STR)
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# = 3 Define cutoff and subset ============================================
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# approximate distribution of combined_score
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hist(sample(STR$combined_score, 10000), breaks = 50, col = "#6699FF")
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# Let's table the counts >= 850 and plot them for better resolution.
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myTb <- table(STR$combined_score[STR$combined_score >= 850])
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is.unsorted(as.integer(names(myTb))) # Good - they are all in order
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plot(myTb, type = "b", cex = 0.5, col = "#BB0000")
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myTb[myTb == max(myTb)] # Apparently there is an algorithmic effect that
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# frequently assigns a combined score of 0.900
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# Let's plot these counts as cumulative sums, in reverse order, scaled
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# as combined scores.
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myX <- 1 - (1:length(myTb)) / 1000 # x-values, decreasing
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plot(myX,
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cumsum(myTb[length(myTb):1]), # cumulative sum, decreasing
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xlim = c(1.0, 0.85), # reverse x-axis
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type = "l",
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main = "STRING interactions for 9606 (top 600,000)",
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xlab = "combined_score",
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ylab = "cumulative counts",
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col = "#CC0000")
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abline(h = seq(50000, sum(myTb), by = 50000), lwd = 0.5, col = "#DDDDFF")
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# What's the cutoff for 100,000 edges?
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which(cumsum(myTb[length(myTb):1]) >= 100000)[1] # p = 0.964
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# confirm
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sum(STR$combined_score >= 964) # 101,348
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abline(v = 0.964, lwd = 0.5, col = "#DDDDFF")
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# subset the table, and use only the protein IDs and the combined_score
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STR <- STR[STR$combined_score >= 964,
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c("protein1", "protein2", "combined_score")]
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colnames(STR) <- c("a", "b", "score")
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# = 4 Drop duplicates ====================================================
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# identify duplicate interactions by creating keys in a defined alphabetical
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# sort order, then checking for duplicated().
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# e.g if we have (X:U, U:X), we change U:X to X:U and now find that
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# (X:U, X:U) has a duplicate.
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AB <- STR$a < STR$b # logical vector: genes we need to swap
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tmp <- STR$b # copy column b
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STR$b[AB] <- STR$a[AB] # copy a's into b
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STR$a[AB] <- tmp[AB] # copy tmp's into a
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all(STR$a >= STR$b) # confirm: TRUE
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# now, make combined keys, like this:
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paste0(STR$a[1:10], ":", STR$b[1:10])
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tmp <- paste0(STR$a, ":", STR$b)
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sum(duplicated(tmp)) # That's half of them ... i.e. STRING reports
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# both a:b and b:a !
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# drop all duplicated interactions from tmp
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STR <- STR[ ! duplicated(tmp), ] # 50,674 interactions remain
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# = 5 Simple statistics ===================================================
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# how many unique genes?
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length(unique(c(STR$a, STR$b))) # 8,445
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# how many self-edges?
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sum(STR$a == STR$b) # none
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# log(rank) / log(frequency)
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myTbl <- table(c(STR$a, STR$b))
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myTbl <- myTbl[order(myTbl, decreasing = TRUE)]
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hist(myTbl, breaks = 40, col = "#FFEEBB")
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# number of singletons
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sum(myTbl == 1) # almost a quarter
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# maximum?
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myTbl[which(myTbl == max(myTbl))] # 9606.ENSP00000360532: 465
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# Google: CDC5L
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# Zipf-plot
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plot(log(1:length(myTbl)), log(as.numeric(myTbl)),
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type = "b", cex = 0.7,
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main = "STRINGedges - degrees",
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xlab = "log(rank)",
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ylab = "log(frequency)",
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col = "#FFBB88")
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sprintf("Average number of interactions: %5.2f",
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nrow(STR) / length(unique(c(STR$a, STR$b))))
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# = 6 Write to file =======================================================
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saveRDS(STR, file = "./data/STRINGedges.rds")
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# STRINGedges <- readRDS("./data/STRINGedges.rds") # use this to restore the
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# object when needed
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# [END]
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