Update PPI-Analysis assets and code

This commit is contained in:
hyginn 2020-09-23 20:47:26 +10:00
parent 42ad74b2f2
commit 76b18a8a35
5 changed files with 224 additions and 45 deletions

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@ -332,5 +332,16 @@ selectChi2 <- function() {
}
# == 5.2 selectENSP() ====================================================
selectENSP <- function(x) {
oldSeed <- .Random.seed
set.seed(myStudentNumber)
x <- sample(x[order(x)])
.Random.seed <- oldSeed
cat(sprintf("seal: %s\n", seal(paste0(x,collapse=""))))
return(x)
}
# [END]

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@ -1,10 +1,5 @@
# tocID <- "BIN-PPI-Analysis.R"
#
# ---------------------------------------------------------------------------- #
# PATIENCE ... #
# Do not yet work wih this code. Updates in progress. Thank you. #
# boris.steipe@utoronto.ca #
# ---------------------------------------------------------------------------- #
#
# Purpose: A Bioinformatics Course:
# R code accompanying the BIN-PPI-Analysis unit.
@ -15,7 +10,8 @@
# Author: Boris Steipe (boris.steipe@utoronto.ca)
#
# Versions:
# 1.2 Deprecate save()/load() for saveRDS()/readRDS()
# 1.2 2020 Updates; Rewrite for new STRINg V11;
# Deprecate save()/load() for saveRDS()/readRDS()
# 1.1 Change from require() to requireNamespace(),
# use <package>::<function>() idiom throughout,
# use Biocmanager:: not biocLite()
@ -39,13 +35,13 @@
#TOC>
#TOC> Section Title Line
#TOC> ---------------------------------------------------------------
#TOC> 1 Setup and data 46
#TOC> 2 Functional Edges in the Human Proteome 82
#TOC> 2.1 Cliques 125
#TOC> 2.2 Communities 166
#TOC> 2.3 Betweenness Centrality 180
#TOC> 3 biomaRt 226
#TOC> 4 Task for submission 296
#TOC> 1 Setup and data 49
#TOC> 2 Functional Edges in the Human Proteome 85
#TOC> 2.1 Cliques 128
#TOC> 2.2 Communities 169
#TOC> 2.3 Betweenness Centrality 183
#TOC> 3 biomaRt 230
#TOC> 4 Task for submission 301
#TOC>
#TOC> ==========================================================================
@ -68,10 +64,10 @@ if (! requireNamespace("igraph", quietly = TRUE)) {
# from the STRING database. The full table has 8.5 million records, here is a
# subset of records with combined confidence scores > 980
# The selected set of edges with a confidence of > 980 is a dataframe with about
# 50,000 edges and 6,500 unique proteins. Incidentaly, that's about the size of
# The selected set of edges with a confidence of > 964 is a dataframe with about
# 50,000 edges and 8,400 unique proteins. Incidentaly, that's about the size of
# a fungal proteome. You can load the saved dataframe here (To read more about
# what the numbers mean, see http://www.ncbi.nlm.nih.gov/pubmed/15608232 ).
# what the scores mean, see http://www.ncbi.nlm.nih.gov/pubmed/15608232 ).
STRINGedges <- readRDS("./data/STRINGedges.rds")
@ -80,8 +76,8 @@ head(STRINGedges)
# Note that STRING has appended the tax-ID for Homo sapiens - 9606 - to the
# Ensemble transcript identifiers that start with ENSP. We'll remove them:
STRINGedges$protein1 <- gsub("^9606\\.", "", STRINGedges$protein1)
STRINGedges$protein2 <- gsub("^9606\\.", "", STRINGedges$protein2)
STRINGedges$a <- gsub("^9606\\.", "", STRINGedges$a)
STRINGedges$b <- gsub("^9606\\.", "", STRINGedges$b)
head(STRINGedges)
@ -91,9 +87,9 @@ head(STRINGedges)
# There are many possibilities to explore interesting aspects of biological
# networks, we will keep with some very simple procedures here but you have
# to be aware that this is barely scratching the surface of possibilites.
# to be aware that this is barely scratching the surface of possibilities.
# However, once the network exists in your computer, it is comparatively
# easy to find information nline about the many, many options to analyze.
# easy to find information online about the many, many options to analyze.
# Make a graph from this dataframe
@ -101,7 +97,7 @@ head(STRINGedges)
gSTR <- igraph::graph_from_data_frame(STRINGedges, directed = FALSE)
# CAUTION you DON'T want to plot a graph with 6,500 nodes and 50,000 edges -
# CAUTION you DON'T want to plot a graph with 8,000 nodes and 50,000 edges -
# layout of such large graphs is possible, but requires specialized code. Google
# for <layout large graphs> if you are curious. Also, consider what one can
# really learn from plotting such a graph ...
@ -119,7 +115,7 @@ plot(log10(as.numeric(names(freqRank)) + 1),
log10(as.numeric(freqRank)), type = "b",
pch = 21, bg = "#FEE0AF",
xlab = "log(Rank)", ylab = "log(frequency)",
main = "6,500 nodes from the human functional interaction network")
main = "8,400 nodes from the human functional interaction network")
# This looks very scale-free indeed.
@ -136,11 +132,11 @@ abline(regressionLine, col = "firebrick")
# Biological complexes often appear as cliques in interaction graphs.
igraph::clique_num(gSTR)
# The largest clique has 63 members.
# The largest clique has 81 members.
(C <- igraph::largest_cliques(gSTR)[[1]])
# Pick one of the proteins and find out what this fully connected cluster of 63
# Pick one of the proteins and find out what this fully connected cluster of 81
# proteins is (you can simply Google for any of the IDs). Is this expected?
# Plot this ...
@ -166,7 +162,7 @@ plot(R,
par(oPar)
# ... well: remember: a clique means every node is connected to every other
# node. We have 63 * 63 = 3,969 edges. This is what a matrix model of PPI
# node. We have 81 * 81 = 6,561 edges. This is what a matrix model of PPI
# networks looks like for large complexes.
@ -179,9 +175,9 @@ set.seed(NULL) # reset the RNG
igraph::modularity(gSTRclusters) # ... measures how separated the different
# membership types are from each other
tMem <- table(igraph::membership(gSTRclusters))
length(tMem) # More than 2000 communities identified
length(tMem) # About 700 communities identified
hist(tMem, breaks = 50, col = "skyblue") # most clusters are small ...
range(tMem) # ... but one has > 100 members
range(tMem) # ... but one has > 200 members
# == 2.3 Betweenness Centrality ============================================
@ -214,13 +210,14 @@ head(sBC)
# IDs...
(ENSPsel <- names(igraph::V(gSTR)[BCsel]))
# Task:
# =====
# IMPORTANT, IF YOU INTEND TO SUBMIT YOUR ANALYSIS FOR CREDIT
# We are going to use these IDs to produce some output for a submitted task:
# so I need you to personalize ENSPsel with the following
# three lines of code:
# therefore I need you to execute the following line, note the "seal" that this
# returns, and not change ENSPsel later:
set.seed(<myStudentNumber>) # enter your student number here
(ENSPsel <- sample(ENSPsel))
set.seed(NULL) # reset the random number generator
myENSPsel <- seal(ENSPsel)
# Next, to find what these proteins are...
@ -251,15 +248,15 @@ if (! requireNamespace("biomaRt", quietly = TRUE)) {
# browseVignettes("biomaRt") # available vignettes
# data(package = "biomaRt") # available datasets
# define which dataset to use ...
# define which dataset to use ... this takes a while for download
myMart <- biomaRt::useMart("ensembl", dataset="hsapiens_gene_ensembl")
# what filters are defined?
(filters <- biomaRt::listFilters(myMart))
( filters <- biomaRt::listFilters(myMart) )
# and what attributes can we filter for?
(attributes <- biomaRt::listAttributes(myMart))
( attributes <- biomaRt::listAttributes(myMart) )
# Soooo many options - let's look for the correct name of filters that are
@ -296,13 +293,13 @@ for (ID in ENSPsel) {
mart = myMart)
}
# So what are the proteins with the ten highest betweenness centralities?
# ... are you surprised? (I am! Really.)
# = 4 Task for submission =================================================
# Write a loop that will go through your personalized list of Ensemble IDs and
# for each ID:
# -- print the ID,
@ -310,11 +307,14 @@ for (ID in ENSPsel) {
# -- print the first row's wikigene description.
# -- print the first row's phenotype.
#
# Write your thoughts about this group of genes.
#
# (Hint, you can structure your loop in the same way as the loop that
# created CPdefs. )
# Place the R code for this loop and its output into your report if you are
# submitting a report for this unit. Please read the requirements carefully.
# Submit the "seal" for your ENSP vector, the ENSP vector itself, the R code
# for this loop and its output into your report if you are submitting
# 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
# we used above.
# == 4.3 Kolmogorov-Smirnov test for continuous distributions ==============
# == 4.3 Continuous distributions: Kolmogorov-Smirnov test ==============
# The Kolmogorov-Smirnov (KS) test is meant for continuous distributions, i.e.
# the probability it calculates assumes that the function values are all

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@ -0,0 +1,168 @@
# tocID <- "scripts/ABC-makeSTRINGedges.R"
#
# Create a subnetwork of high-confidence human STRING edges.
#
# Notes:
#
# The large source- datafile is NOT posted to github. If you want to
# experiment with the original data, download it and place it into your
# local ./data directory.
#
# STRING data source:
# Download page:
# https://string-db.org/cgi/download.pl?species_text=Homo+sapiens
# Data: (127.6 Mb)
# https://stringdb-static.org/download/protein.links.full.v11.0/9606.protein.links.full.v11.0.txt.gz
#
# Version: 1.0
#
# Date: 2020-09
# Author: Boris Steipe (boris.steipe@utoronto.ca)
#
# Versions:
# 1.0 Rewrite
#
# TODO:
#
# ==============================================================================
#TOC> ==========================================================================
#TOC>
#TOC> Section Title Line
#TOC> -------------------------------------------------
#TOC> 1 Initialize 44
#TOC> 2 Read STRING Data 51
#TOC> 3 Define cutoff and subset 63
#TOC> 4 Drop duplicates 103
#TOC> 5 Simple statistics 127
#TOC> 6 Write to file 160
#TOC>
#TOC> ==========================================================================
# = 1 Initialize ==========================================================
if (! requireNamespace("readr", quietly = TRUE)) {
install.packages("readr")
}
# = 2 Read STRING Data ====================================================
# Read STRING Data (needs to be downloaded from database, see URL in Notes)
# The .gz compressed version is 127.6MB, the uncompressed version is probably
# 848 Mb. Fortunately readr:: can read from compressed
# files, and does so automatically, based on the file extension.
( fn <- file.path("~", "9606.protein.links.full.v11.0.txt.gz") )
STR <- readr::read_delim(fn, delim = " ")
nrow(STR) # 11,759,454 rows
head(STR)
# = 3 Define cutoff and subset ============================================
# approximate distribution of combined_score
hist(sample(STR$combined_score, 10000), breaks = 50, col = "#6699FF")
# Let's table the counts >= 850 and plot them for better resolution.
myTb <- table(STR$combined_score[STR$combined_score >= 850])
is.unsorted(as.integer(names(myTb))) # Good - they are all in order
plot(myTb, type = "b", cex = 0.5, col = "#BB0000")
myTb[myTb == max(myTb)] # Apparently there is an algorithmic effect that
# frequently assigns a combined score of 0.900
# Let's plot these counts as cumulative sums, in reverse order, scaled
# as combined scores.
myX <- 1 - (1:length(myTb)) / 1000 # x-values, decreasing
plot(myX,
cumsum(myTb[length(myTb):1]), # cumulative sum, decreasing
xlim = c(1.0, 0.85), # reverse x-axis
type = "l",
main = "STRING interactions for 9606 (top 600,000)",
xlab = "combined_score",
ylab = "cumulative counts",
col = "#CC0000")
abline(h = seq(50000, sum(myTb), by = 50000), lwd = 0.5, col = "#DDDDFF")
# What's the cutoff for 100,000 edges?
which(cumsum(myTb[length(myTb):1]) >= 100000)[1] # p = 0.964
# confirm
sum(STR$combined_score >= 964) # 101,348
abline(v = 0.964, lwd = 0.5, col = "#DDDDFF")
# subset the table, and use only the protein IDs and the combined_score
STR <- STR[STR$combined_score >= 964,
c("protein1", "protein2", "combined_score")]
colnames(STR) <- c("a", "b", "score")
# = 4 Drop duplicates ====================================================
# identify duplicate interactions by creating keys in a defined alphabetical
# sort order, then checking for duplicated().
# e.g if we have (X:U, U:X), we change U:X to X:U and now find that
# (X:U, X:U) has a duplicate.
AB <- STR$a < STR$b # logical vector: genes we need to swap
tmp <- STR$b # copy column b
STR$b[AB] <- STR$a[AB] # copy a's into b
STR$a[AB] <- tmp[AB] # copy tmp's into a
all(STR$a >= STR$b) # confirm: TRUE
# now, make combined keys, like this:
paste0(STR$a[1:10], ":", STR$b[1:10])
tmp <- paste0(STR$a, ":", STR$b)
sum(duplicated(tmp)) # That's half of them ... i.e. STRING reports
# both a:b and b:a !
# drop all duplicated interactions from tmp
STR <- STR[ ! duplicated(tmp), ] # 50,674 interactions remain
# = 5 Simple statistics ===================================================
# how many unique genes?
length(unique(c(STR$a, STR$b))) # 8,445
# how many self-edges?
sum(STR$a == STR$b) # none
# log(rank) / log(frequency)
myTbl <- table(c(STR$a, STR$b))
myTbl <- myTbl[order(myTbl, decreasing = TRUE)]
hist(myTbl, breaks = 40, col = "#FFEEBB")
# number of singletons
sum(myTbl == 1) # almost a quarter
# maximum?
myTbl[which(myTbl == max(myTbl))] # 9606.ENSP00000360532: 465
# Google: CDC5L
# Zipf-plot
plot(log(1:length(myTbl)), log(as.numeric(myTbl)),
type = "b", cex = 0.7,
main = "STRINGedges - degrees",
xlab = "log(rank)",
ylab = "log(frequency)",
col = "#FFBB88")
sprintf("Average number of interactions: %5.2f",
nrow(STR) / length(unique(c(STR$a, STR$b))))
# = 6 Write to file =======================================================
saveRDS(STR, file = "./data/STRINGedges.rds")
# STRINGedges <- readRDS("./data/STRINGedges.rds") # use this to restore the
# object when needed
# [END]