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# tocID <- "RPR_GEO2R.R"
#
# Purpose: A Bioinformatics Course:
# R code accompanying the RPR_GEO2R unit.
#
# Version: 1.3
#
# Date: 2017-09 - 2020-09
# Author: Boris Steipe <boris.steipe@utoronto.ca>
#
# Versions:
# 1.3 use saveRDS()/readRDS() rather than save()/load()
# 1.2 Change from require() to requireNamespace(),
# use <package>::<function>() idiom throughout,
# use Biocmanager:: not biocLite()
# 1.1 Add section on GPL annotations
# 1.0 Updates for BCH441 2017
# 0.1 First code copied from 2016 material.
#
#
# TODO:
#
#
# == DO NOT SIMPLY source() THIS FILE! =======================================
#
# If there are portions you don't understand, use R's help system, Google for an
# answer, or ask your instructor. Don't continue if you don't understand what's
# going on. That's not how it works ...
#
# TO SUBMIT FOR CREDIT....
#
# Note: to submit tasks for credit for this unit, report on the sections
# that have "Task ..." section headers, and report on the lines that are
# identified with #TASK> comments.
#
# ==============================================================================
#TOC> ==========================================================================
#TOC>
#TOC> Section Title Line
#TOC> --------------------------------------------------------------------------
#TOC> 1 Preparations 56
#TOC> 2 Loading a GEO Dataset 82
#TOC> 3 Column wise analysis - time points 152
#TOC> 3.1 Task - Comparison of experiments 158
#TOC> 3.2 Grouped Samples 205
#TOC> 4 Row-wise Analysis: Expression Profiles 240
#TOC> 4.1 Task - Read a table of features 275
#TOC> 4.2 Selected Expression profiles 323
#TOC> 5 Differential Expression 364
#TOC> 5.1 Final task: Gene descriptions 504
#TOC> 6 Improving on Discovery by Differential Expression 510
#TOC> 7 Annotation data 594
#TOC>
#TOC> ==========================================================================
# = 1 Preparations ========================================================
# To load and analyze GEO datasets we use a number of Bioconductor packages:
if (! requireNamespace("BiocManager", quietly = TRUE)) {
install.packages("BiocManager")
}
if (! requireNamespace("Biobase", quietly = TRUE)) {
BiocManager::install("Biobase")
}
# Package information:
# library(help = Biobase) # basic information
# browseVignettes("Biobase") # available vignettes
# data(package = "Biobase") # available datasets
if (! requireNamespace("GEOquery", quietly = TRUE)) {
BiocManager::install("GEOquery")
}
# Package information:
# library(help = GEOquery) # basic information
# browseVignettes("GEOquery") # available vignettes
# data(package = "GEOquery") # available datasets
# = 2 Loading a GEO Dataset ===============================================
# The R code below is adapted from the GEO2R scripts produced by GEO for GSE3635
# for the experiment conducted in the BIN-EXPR-GEO unit
# Version info: R 3.2.3, Biobase 2.30.0, GEOquery 2.40.0, limma 3.26.8
# R scripts generated Wed Jan 11 17:39:46 EST 2017
# Load series and platform data from GEO. The GEO server is a bit flakey and
# I have experienced outages over several hours. If the command below does
# not work for you, skip ahead to the fallback procedure.
GSE3635 <- GEOquery::getGEO("GSE3635", GSEMatrix =TRUE, getGPL=FALSE)
# Note: GEO2R scripts call the expression data set
# "gset" throughout ... in this script I give
# it the name "GSE3635" for clarity.
# Subset, if the data contains multiple experiments with different platforms
# since we should not be mixing data from different platforms. (That's
# unfortunately very hard to do correctly). The "platform" is the type of chip
# that was used, and what I selected online when this script was produced, so
# GEO2R knows about the correct platform ID. Technically, subsetting to just one
# platform is not necessary here because we _know_ that GSE3635 contains only
# data produced with the GPL1914 platform. But that's the way GEO2R scripts are
# setup be default since they have to be able to handle a variety of cases.
if (length(GSE3635) > 1) {
idx <- grep("GPL1914", attr(GSE3635, "names"))
} else {
idx <- 1
}
GSE3635 <- GSE3635[[idx]]
# FALLBACK
# ... in case the GEO server is not working, load the "GSE3635" object from
# the data directory:
#
# GSE3635 <- readRDS(file="./data/GSE3635.rds")
# Checkpoint ...
if (! exists("GSE3635")) {
stop("PANIC: GSE3635 was not loaded. Can't continue.")
}
# GSE3635 is an "Expression Set" - cf.
# https://bioconductor.org/packages/release/bioc/vignettes/Biobase/inst/doc/ExpressionSetIntroduction.pdf
# What does this contain?
help("ExpressionSet-class")
# Print it
GSE3635
# Access contents via methods:
Biobase::featureNames(GSE3635)[1:20] # Rows. What are these features?
Biobase::sampleNames(GSE3635)[1:10] # Columns. What are these columns?
# Access contents by subsetting:
( tmp <- GSE3635[12:17, 1:6] )
# Access data
Biobase::exprs(tmp) # exprs() gives us the actual expression values.
# == 3.1 Task - understanding the data ==================================
#TASK> What are the data values:
#TASK> ... in each cell?
#TASK> ... in each column?
#TASK> ... in each row?
# = 3 Column wise analysis - time points ==================================
# Get an overview of the distribution of values in individual columns
summary(Biobase::exprs(GSE3635)[ , 1])
summary(Biobase::exprs(GSE3635)[ , 4])
summary(Biobase::exprs(GSE3635)[ , 7])
# This allows us to com pare the columns, comment on the quality of the data,
# and get a sense for the distribution. We need to know how exactly these
# numbers were produced: obviously, if we don't know how those numbers were
# created in the first place, we would produce a major sin of Cargo Cult
# bioinformatics if we would analyze them.
# compare them in a a boxplot
cyclicPalette <- colorRampPalette(c("#14b4c9",
"#d2d1e6",
"#e66594",
"#d2d1e6",
"#14b4c9",
"#d2d1e6",
"#e66594",
"#d2d1e6",
"#14b4c9"))
tCols <- cyclicPalette(13)
boxplot(Biobase::exprs(GSE3635), col = tCols)
# == 3.1 Task - Comparison of experiments ==================================
#TASK> Study this boxplot. What's going on? Are these expression values?
#TASK> What do the numbers mean? (Summarize the process and computation
#TASK> that has gone i to the preprocessing. You need to understand why
#TASK> these columns all have the same mean and range.) Given what common
#TASK> sense tells you about the variability of experiments, do you
#TASK> believe your understanding is complete?
# Lets plot the distributions of values in a more fine-grained manner:
hT0 <- hist(Biobase::exprs(GSE3635)[ , 1], breaks = 100, col = tCols[1])
hT3 <- hist(Biobase::exprs(GSE3635)[ , 4], breaks = 100, col = tCols[4])
hT6 <- hist(Biobase::exprs(GSE3635)[ , 7], breaks = 100, col = tCols[7])
hT9 <- hist(Biobase::exprs(GSE3635)[ , 10], breaks = 100, col = tCols[10])
hT12 <- hist(Biobase::exprs(GSE3635)[ , 13], breaks = 100, col = tCols[13])
plot( hT0$mids, hT0$counts, type = "l", col = tCols[1], xlim = c(-0.5, 0.5))
points(hT3$mids, hT3$counts, type = "l", col = tCols[4])
points(hT6$mids, hT6$counts, type = "l", col = tCols[7])
points(hT9$mids, hT9$counts, type = "l", col = tCols[10])
points(hT12$mids, hT12$counts, type = "l", col = tCols[13])
legend("topright",
legend = c("hT0", "hT3", "hT6", "hT9", "hT12"),
col = tCols[c(1, 4, 7, 10, 13)],
lwd = 1)
#TASK> Study this plot. What does it tell you? Is there systematic, global
#TASK> change in the values over time? Within a cycle? Over the course of the
#TASK> experiment?
# == 3.2 Grouped Samples ===================================================
# This is the GEO2R code that produces the histogram you saw on the NCBI
# Website if you went through the BIN-EXPR-GEO unit.
# Group names for all samples in a series
gsms <- "0123450123450" # Each digit identifies one of the 13 columns
sml <- c()
for (i in 1:nchar(gsms)) {
sml[i] <- substr(gsms,i,i)
}
sml <- paste("G", sml, sep="") # set group names
# order samples by group
ex <- Biobase::exprs(GSE3635)[ , order(sml)]
sml <- sml[order(sml)]
fl <- as.factor(sml)
labels <- c("t0","t10","t20","t30","t40","t50") # these are the labels we
# assigned in the BIN-EXPR-GEO
# unit
# Set parameters and draw the plot. I changed this from the original GEO2R
# code which overwrote the global palette(). That's evil! Utility code
# should _never_ mess with global parameters!
GEOcols <- c("#dfeaf4", "#f4dfdf", "#f2cb98", "#dcdaa5",
"#dff4e4", "#f4dff4", "#AABBCC")
dev.new(width = 4 + dim(GSE3635)[[2]] / 5, height = 6) # plot into a new window
par(mar = c(2 + round(max(nchar(Biobase::sampleNames(GSE3635))) / 2), 4, 2, 1))
title <- paste ("GSE3635", '/', Biobase::annotation(GSE3635),
" grouped samples", sep ='')
boxplot(ex, boxwex = 0.6, notch = TRUE, main = title, outline=FALSE,
las = 2, col = GEOcols[fl])
legend("topleft", labels, fill = GEOcols, bty = "n")
# = 4 Row-wise Analysis: Expression Profiles ==============================
# What we did above was column-wise analysis and it gave us an idea about how
# our experiments relate to each other. To analyze individual genes, we need to
# do row-wise analyis.
#TASK> Try to answer the following questions:
#TASK> Are all rows genes?
#TASK> What identifiers are being used?
# (cf. https://sites.google.com/view/yeastgenome-help/community-help/nomenclature-conventions)
#TASK> Are all rows/genes unique?
#TASK> Are all yeast genes accounted for?
# These are crucially important questions but you can't answer the last
# question because you have no information other than the gene identifiers.
# However, any biological interpretation relies absolutely on understanding
# the semantics of the data. Merely manipulating abstract identifiers and
# numbers, that would surely be Cargo Cult bioinformatics.
# To answer these questions about the data semantics, I have provided the
# file "SGD_features.tab" in the data directory of this project. I have
# downloaded it from SGD
# (http://www.yeastgenome.org/download-data/curation), for a description of
# its contents see here:
file.show("./data/SGD_features.README.txt")
# Note: the file as downloaded from SGD actually crashed RStudio due to an
# unbalanced quotation mark which caused R to try and read the whole
# of the subsequent file into a single string. This was caused by an
# alias gene name (B"). I have removed this abomination
# by editing the file. The version in the ./data directory can be
# read without issues.
# Leets peek into the file:
readLines("./data/SGD_features.tab", n = 5)
# == 4.1 Task - Read a table of features ===================================
# This data file is rather typical of datasets that you will encounter "in the
# wild". To proceed, you need to write code to read it into an R-object. Develop
# the code in your script file according to the following specification:
#
# - read "./data/SGD_features.tab" into a data frame
# called "SGD_features"
# - remove unneeded columns - keep the following data columns:
# - Primary SGDID
# - Feature type
# - Feature qualifier
# - Feature name - (the systematic name !)
# - Standard gene name
# - Description
# - give the data frame meaningful column names:
# colnames(SGD_features) <- c("SGDID",
# "type",
# "qual",
# "sysName",
# "name",
# "description")
#
# - remove all rows that don't have a systematic name. (You'll have to check
# what's in cells that don't have a systematic name)
# - check that the systematic names are unique (Hint: use the duplicated()
# function.)
# - assign the systematic names as row names
# - confirm: are all rows of the expression data set represented in
# the feature table? Hint: use setdiff() to print all that
# are not.
# Example usage of setdiff():
# A <- c("duck", "crow", "gull", "tern")
# B <- c("gull", "rook", "tern", "kite", "myna")
#
# setdiff(A, B) # [1] "duck" "crow"
# setdiff(B, A) # [1] "rook" "kite" "myna"
# If some of the features in the expression set are not listed in the
# systematic names, you have to be aware of that, when you try to get
# more information on them. I presume they are missing because revisions
# of the yeast genome after these experiments were done showed that these
# genes did not actually exist.
# - confirm: how many / which genes in the feature table do not
# have expression data?
# How should we handle rows/columns that are missing or not unique?
# == 4.2 Selected Expression profiles ======================================
# The code below assumes that you have read ./data/SGD_features.tab and assigned
# the resulting data frame to SGD_features, with columns as specified above.
# Here is an expression profile for Mbp1.
gName <- "MBP1"
(iFeature <- which(SGD_features$name == gName))
(iExprs <- which(featureNames(GSE3635) == SGD_features$sysName[iFeature]))
plot(seq(0, 120, by = 10),
Biobase::exprs(GSE3635)[iExprs, ],
main = paste("Expression profile for", gName),
xlab = "time (min)",
ylab = "expression",
type = "b",
col= "maroon")
abline(h = 0, col = "#00000055")
abline(v = 60, col = "#00000055")
# Print the description
SGD_features$description[iFeature]
# Here is a list of gene names that may be involved in the cell cycle switch,
# and some genes that are controls (cf. BIN-SYS-Concepts):
# Turning it on
# Cdc14, Mbp1, Swi6, Swi4, Whi5, Cdc28, Cln1, Cln2, Cln3
# Turning it off
# Rad53, Cdc28, Clb1, Clb2, Clb6, Nrm1
# Housekeeping genes
# Act1, and Alg9
#TASK> Plot expression profiles for these genes and study them. What do you
#TASK> expect the profiles to look like, given the role of these genes? What
#TASK> do you find? (Hint: you will make your life much easier if you define
#TASK> a function that plots and prints descriptions with a gene name as input.
#TASK> Also: are the gene names in the feature table upper case, or lower case?
#TASK> Also: note that the absolute values of change are quite different.
#TASK> Also: note that some values may be outliers i.e. failed experiments.)
# = 5 Differential Expression =============================================
# GEO2R discovers the top differentially expressed expressed genes by
# using functions in the Bioconductor limma package.
if (! requireNamespace("limma", quietly = TRUE)) {
BiocManager::install("limma")
}
# Package information:
# library(help = limma) # basic information
# browseVignettes("limma") # available vignettes
# data(package = "limma") # available datasets
# The GEO2R limma code is virtually uncommented, and has not been written for
# clarity, but for being easily produced by a code-generator that is triggered
# by the parameters on the GEO Web-site. Most of it is actually dispensable for
# our purposes.
# In principle, the code goes through three steps:
# 1. Prepare the data
# 2. Define groups that are to be contrasted to define "Differential"
# 3. Find genes whose expression levels are significantly different across
# the groups
# 4. Format results.
# Biobase is a highly engineered package that is tightly integrated into
# the Bioconductor world - unfortunately that brings with it a somewhat
# undesirable level of computational overhead and dependencies. Using the
# package as we normally do - i.e. calling required functions with their
# explicit package prefix is therefore not advisable. There are generics
# that won't be propery dispatched. If you only need a small number of
# functions for a very specific context, you will probably get away with
# Biobase::<function>() - but even in the demonstration code of this script
# not everything works out of the box. We'll therefore load the library,
# but we'll (redundantly) use the prefix anyway so as to emphasize where
# the functions come from.
library(Biobase)
# We are recapitulating the experiment in which we assigned the 0, 10, 60 and
# 70 minute samples to one group, the 30, 40, 90 and 100 minute samples to
# another group, and calculated differential expression values between these
# two groups.
setA <- c(1, 2, 7, 8) # columns for set A
setB <- c(4, 5, 10, 11) # columns for set B
# We remove columns we don't need, and for simplicity put the experiments for
# group A in the first four columns, and the group B in the next four.
mySet <- GSE3635[ , c(setA, setB)]
# limma needs the column descriptions as factors
mySet$description <- as.factor(c(rep("A", 4), rep("B", 4)))
# Next we build the "design Matrix" for the statistical test:
myDesign <- model.matrix(~ description + 0, mySet)
colnames(myDesign) <- levels(mySet$description)
myDesign
# Now we can calculate the fit of all rows to a linear model that depends
# on the two groups as specified in the design:
myFit <- limma::lmFit(mySet, myDesign)
# Next we calculate the contrasts, given the fit ...
myCont.matrix <- limma::makeContrasts(A - B, levels = myDesign)
myFit2 <- limma::contrasts.fit(myFit, myCont.matrix)
# ... compute appropriate probabilites from a modified t-test
# (empirical Bayes) ...
myFit2 <- limma::eBayes(myFit2, 0.01)
# ... add the gene names to the fit - object ...
myFit2$genes <- featureNames(mySet)
# ... and pick the top N differentially expressed genes while controlling
# for multiple testing with a False Discovery Rate (fdr) correction. GEO2R
# gave us only the top 250 genes, but we might as well do 1000, just so we
# can be reasonable sure that our gens of interest are included.
N <- 1000
myTable <- limma::topTable(myFit2,
adjust.method = "fdr",
sort.by = "B",
number = N)
str(myTable)
# The gene names are now in the $ID column
# These are the top 10
write.table(myTable[1:10 , c("ID","P.Value","B")],
file = stdout(),
row.names = FALSE,
sep="\t")
# Let's see what we got: let's plot the full expression profiles (all 13
# columns) for the top ten genes from exprs(GSE3635).
plot(seq(0, 120, by = 10),
rep(0, 13),
type = "n",
ylim = c(-1, 1),
xlab = "time",
ylab = "log-ratio expression")
rect( 0, -2, 15, 2, col = "#dfeaf4", border = NA) # setA
rect( 55, -2, 75, 2, col = "#dfeaf4", border = NA) # setA
rect( 25, -2, 45, 2, col = "#f4dfdf", border = NA) # setB
rect( 85, -2, 105, 2, col = "#f4dfdf", border = NA) # setB
abline(h = 0, col = "#00000055")
for (i in 1:10) {
thisID <- myTable$ID[i]
points(seq(0, 120, by = 10), Biobase::exprs(GSE3635)[thisID, ], type = "b")
}
# Our guess that we might discover interesting genes by selecting groups A and B
# like we did was not bad. But limma knows nothing about the biology and though
# the expression profiles look good, there is no guarantee that these are the
# most biologically relevant genes. Significantly different in expression
# according to the groups we define is not necessarily the same as a cyclically
# varying gene, nor does it necessarily find the genes whose expression levels
# are _most_ different, i.e. if the variance of a highly, differentially
# expressed gene within a group is large, it may not be very significant. Also,
# we are not exploiting the fact that these values are time series.
# Nevertheless, we find genes for which we see a change in expression levels
# along two cell-cycles.
# Let's superimpose some "real" cell-cycle genes:
myControls <- c("Cdc14", "Mbp1", "Swi6", "Swi4", "Whi5", "Cln1", "Cln2", "Cln3")
for (name in toupper(myControls)) {
thisID <- SGD_features$sysName[which(SGD_features$name == name)]
points(seq(0, 120, by=10),
Biobase::exprs(GSE3635)[thisID, ],
type="b",
col="#AA0000")
}
# Indeed, the discovered gene profiles look much "cleaner" than the real cycle
# genes and this just means that differential expression in the way that we
# have performed it is an approximation to the biology.
# == 5.1 Final task: Gene descriptions =====================================
#TASK> Print the descriptions of the top ten differentially expressed genes
#TASK> and comment on what they have in common (or not).
# = 6 Improving on Discovery by Differential Expression ===================
# There are many ways to improve on purely statistical methods, if we have
# better ideas about the biology. I would just like to demonstrate one
# possibility here: calculate correlation values to a sample gene - such as Cln2
# for which we know that it is involved in the cell cycle. Let's plot it first:
gName <- "CLN2"
(iFeature <- which(SGD_features$name == gName))
(iExprs <- which(featureNames(GSE3635) == SGD_features$sysName[iFeature]))
Cln2Profile <- Biobase::exprs(GSE3635)[iExprs, ]
plot(seq(0, 120, by = 10),
Cln2Profile,
ylim = c(-1, 1),
main = paste("Expression profile for", gName),
xlab = "time (min)",
ylab = "expression",
type = "b",
pch = 16,
cex = 1.5,
lwd = 2,
col= "#40b886")
abline(h = 0, col = "#0000FF55")
abline(v = 60, col = "#0000FF55")
# Set up a vector of correlation values
myCorrelations <- numeric(nrow(Biobase::exprs(GSE3635)))
names(myCorrelations) <- Biobase::featureNames(GSE3635)
for (i in 1:length(myCorrelations)) {
myCorrelations[i] <- cor(Cln2Profile, Biobase::exprs(GSE3635)[i, ])
}
nTOP <- 20
myTopC <- order(myCorrelations, decreasing = TRUE)[1:nTOP]
# Number 1
(ID <- Biobase::featureNames(GSE3635)[myTopC[1]])
# Get information
SGD_features[which(SGD_features$sysName == ID), ]
# Of course: the highest correlation is Cln1 itself. This is our positive
# control for the experiment.
# Let's plot the rest
myPal <- colorRampPalette(c("#82f58d", "#E0F2E2", "#f6f6f6"))
for (i in 2:nTOP) {
ID <- Biobase::featureNames(GSE3635)[myTopC[i]]
points(seq(0, 120, by = 10),
Biobase::exprs(GSE3635)[ID, ],
type = "b",
cex = 0.8,
col= myPal(nTOP)[i])
print(SGD_features[which(SGD_features$sysName == ID),
c("name", "description")])
}
# Note that all of these genes are highly correlated with a known cell cycle
# gene, but because the absolute values of expression differences are not very
# large for some of them, they might not be picked up by any algorithm that is
# focussed on large differential expression changes. Do small relative changes
# mean small biological effects? Certainly not!
# And we haven't even looked at the anticorrelated genes yet...
nBOT <- nTOP
myBottomC <- order(myCorrelations, decreasing = FALSE)[1:nBOT] # bottom ten
myPal <- colorRampPalette(c("#ba112a", "#E3C8CC", "#ebebeb"))
for (i in 1:nBOT) {
ID <- Biobase::featureNames(GSE3635)[myBottomC[i]]
points(seq(0, 120, by = 10),
Biobase::exprs(GSE3635)[ID, ],
type = "b",
cex = 0.8,
col= myPal(nBOT)[i])
print(SGD_features[which(SGD_features$sysName == ID),
c("name", "description")])
}
# ... which are very interesting in their own right.
# What I hope you appreciate from this example is:
# - convenient general purpose methods exist that analyze expression data
# with sophisticated statistical methods;
# - the results of these methods depend on whether the statistics model
# the biology well;
# - you will draw Cargo Cult conclusions if you can't cast your biology
# of interest in terms of a model, "canned" solutions are unlikely
# to give you all the answers you need;
# - being able to write your own code gives you the freedom to experiment
# and explore. There is a learning curve - but the payoffs are
# significant.
# = 7 Annotation data =====================================================
#
# Loading feature data "by hand" as we've done above, is usually not necessary
# since GEO provides rich annotations in the GPL platform files, which are
# associated with its Gene Expression Sets files. In the code above,
# we used getGEO("GSE3635", GSEMatrix = TRUE, getGPL = FALSE), and the GPL
# annotations were not loaded. We could use getGPL = TRUE instead ...
GSE3635annot <- GEOquery::getGEO("GSE3635", GSEMatrix = TRUE, getGPL = TRUE)
GSE3635annot <- GSE3635annot[[1]]
# ... and the feature data is then available in the GSE3635@featureData@data
# slot:
str(GSE3635annot@featureData@data)
GSE3635annot@featureData@data[ 1:20 , ]
# ... from where you can access the columns you need, e.g. spotIDs and gene
# symbols:
myAnnot <- GSE3635annot@featureData@data[ , c("SPOT_ID", "Gene")]
str(myAnnot)
# ... Note that this is a data frame and it is easy to find things we
# might be looking for ...
myAnnot[which(myAnnot$Gene == "MBP1"), ]
# ... or identify rows that might give us trouble, such as probes that
# hybridize to more than one gene.
# Alternatively, we could have identified the GPL file for this set:
GSE3635@annotation # "GPL1914"
# ... and downloaded it directly from NCBI:
GPL1914 <- GEOquery::getGEO("GPL1914")
str(GPL1914)
# ... from which we can get the data - which is however NOT necessarily
# matched to the rows of our expression dataset.
# [END]
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