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Friday, 24 June 2016

Principal Component Analysis with published CLL gene expression data (Herishanu et al, Blood 2011)

Over the last few years, my understanding of chronic lymphocytic leukaemia has developed. Leukaemic cell proliferation has been shown to be a key element of the disease. The site of CLL cell proliferation was an important question. A gene expression analysis published in Blood by Herishanu et al in 2011 showed a proliferative gene expression pattern in the leukeamic cells from the lymph nodes as distinct from other sites of the body (peripheral blood or bone marrow).

I've been exploring the gene expression data using R. As a first step, I've explored the principal component analysis similar to that shown in Figure 1A of the paper.



The PCA is convincing because the three groups of samples cluster among themselves.  The 3D plot is useful because the three components separate the groups while two dimensions do not.


There are other ways of visualising PCA plots...  Sources are indicated as part of the script. 



Here is the script that I used to analyse this:

SCRIPT START
# install.packages("RCurl", "scatterplot3d")

library(RCurl)
library(scatterplot3d)

# A few year's ago a gene expression profile paper was published in Blood by Herishanu et al
# that gave us a new insight into chronic lymphocytic leukamia. 
# http://www.bloodjournal.org/content/117/2/563.long
# I have written this script to explore the data 
# and to reproduce the principal component analysis. 

# I have downloaded the original CEL files from GEO
# they were placed in a folder and the expression values extracted 
# what's produced is Affymetrix IDs and log2 expression values 

# I have created two data sets
# we can reproduce the PCA workflow with a subset of the data is required (5000 genes selected randomly). 
# this will be faster to download and visualise. 
# get the data - needs connection to the internet
# to use remove the hashtag "#"
# x <- getURL("https://raw.githubusercontent.com/brennanpincardiff/RforBiochemists/master/data/herishanuMicroArrayDataSubset.tsv")

# here is a link for all the data (>50000 probes) 
x <- getURL("https://raw.githubusercontent.com/brennanpincardiff/RforBiochemists/master/data/herishanuMicroArrayData.tsv")

# turn the data into a useful data.frame
data <- read.table(text = x, header = TRUE, sep = "\t")

# read in a file with names of samples
# created this from cut and paste of web page combined with manipulation in Word & Excel
sampID <- read.csv("https://raw.githubusercontent.com/brennanpincardiff/RforBiochemists/master/data/Herishanu_Samp_ID.csv", header = FALSE)

str(sampID)
sampID$V1 <- as.character(sampID$V1)

# is order of column names same as list in sampID?
data.col.name <- colnames(data)
data.col.name <- gsub(".CEL", "", data.col.name)
sampID.name <- sampID$V1
all.equal(data.col.name, sampID.name)
# TRUE so that is useful. 


# change column names from chip to PB, BM and LN
# for peripheral blood, bone marrow and lymph node respectively
sampID$Name <- paste0(as.character(sampID$V3), as.character(sampID$V5))
colnames(data) <- sampID$Name

# how do the sample cluster?
# I first need to find the "distances" between the arrays
# and show those distances in the hierarchical cluster plot
# I can pile up all the commands in one
# for more of a step by step approach, see these scripts:

plot(hclust(dist(t(data))))



# this is interesting because all the patient samples cluter together!
# with bone marrow and peripheral blood closer together than LN in all cases. 
# this is what it says in the paper and is shown in the supplementary data. 

# What's needed is a way to normalise by patient - HOW??
# Quote from the paper:
# "In the 12 patients in whom all 3 compartments had been arrayed, 
# the patient effect on gene expression was subtracted 
# by mean centering the expression value of each gene across the 3 compartments 
# for each patient separately."

# the twelve with all 3 compartments: #1, #2, #3, #4, #8, #9, #10, #11, #12, #13, #25, #26

# so if I understand this correctly, I need to extract a patient sample
# then mean centre all three samples, 
# and repeat for all 12 samples

# let's explore this concept with just two samples to see if it works
# pick out data for patient #26
samp26 <- data[,grep("#26", sampID$Name)]

# pick out data for patient #25
samp25 <- data[,grep("#25", sampID$Name)]

# put them together
twosamp <- cbind(samp26, samp25)


# visualise with a box plot - look normalised
boxplot(twosamp)



plot(hclust(dist(t(twosamp))))


# cluster by patient

# I think I understand what they have done.
# mean centred for each gene.... i.e. by row in the data
# For us this is really each probeset
# http://gastonsanchez.com/how-to/2014/01/15/Center-data-in-R/

# for each of the values substract the rowMeans
samp25.s <- samp25 - rowMeans(samp25)
samp26.s <- samp26 - rowMeans(samp26)
twosamp.s <- cbind(samp26.s, samp25.s)

plot(hclust(dist(t(twosamp.s))))




# This seems to work and gives a different cluster diagram
# with LN coming together nicely.

# apply this to the whole data set.
# extract each patient cohort
# these twelve were: #1, #2, #3, #4, #8, #9, #10, #11, #12, #13, #25, #26
# do #1 and # 2 first then do the other automatically...

# pat # 1
colnames(data)
pat_1 <- cbind(data[,1], data[,27], data[,46])
colnames(pat_1) <- c("PB#1", "BM#1", "LN#1")
pat_1.s <- pat_1 - rowMeans(pat_1)
# pat # 2
pat_2 <- cbind(data[,2], data[,28], data[,47])
colnames(pat_2) <- c("PB#2", "BM#2", "LN#2")
pat_2.s <- pat_2 - rowMeans(pat_2)

data.n <- cbind(pat_1.s, pat_2.s)

data.reqd <- c("#3", "#4", "#8", "#9", "#10", "#11", "#12", "#13", "#25", "#26")
for(i in 1:length(data.reqd)){
  samp <- data[,grep(data.reqd[i], sampID$Name)]
  samp <- samp - rowMeans(samp)
  data.n <- cbind(data.n, samp)
}

# now have a file called dat.exp.n - normalised within patients.
colnames(data.n)
plot(hclust(dist(t(data.n))))



# nice cluster by region with good separation of LN samples
# some overlap within PB and LN


# extract cell site to use for colours in PCA plots
names <- colnames(data.n)
colourby <- gsub("#\\d+", "", colnames(data.n))
colourby <- gsub("\\.", "", colourby)
colourby <- gsub("\\d+", "", colourby)

# this does cluster the LN samples distinctly but there is still some overlap 
# of Peripheral Blood and Bone Marrow. 
# PCA from the paper looks nice and convincing
# http://www.r-bloggers.com/computing-and-visualizing-pca-in-r/
# uses function princomp()

exp.pca <- princomp(data.n)  # this function does the PCA
print(exp.pca)
plot(exp.pca, type = "l")

Plot of variance by component for CLL gene expression patterns



plot(princomp(data.n)$loadings)
A simple 2D plot of component 1 vs component 2



p <- princomp(data.n)
loadings <- p$loadings[]
p.variance.explained <- p$sdev^2 / sum(p$sdev^2)

# plot percentage of variance explained for each principal component    
barplot(100*p.variance.explained, las=2, xlab='', ylab='% Variance Explained')


Looking at the effects of each component as a bar plot.



#*****************************************************************
# 2-D Plot
#******************************************************************         
x <- loadings[,1]
y <- loadings[,2]
z <- loadings[,3]
cols <- as.factor(colourby)
cols <- gsub("PB", "green", cols)
cols <- gsub("BM", "red", cols)
cols <- gsub("LN", "blue", cols)

# pch = 24: triangle point-up
# pch = 22: square
# pch = 21: circle
symbols <- as.factor(colourby)
symbols <- gsub("PB", 24, symbols)
symbols <- gsub("BM", 21, symbols)
symbols <- gsub("LN", 22, symbols)
symbols <- as.numeric(symbols)


# plot loadings on the first and second principal components 
# identify sample by body location
plot(x, y, type='p', 
     pch=symbols, 
     xlab='Comp.1', ylab='Comp.2', 
     col = cols, main = "Principal Component Analysis - CLL cell gene expression")

A simple 2D plot of component 1 vs component 2
 Each sample is coloured by location
# add a legend to the top of the plot
legend("top",      # location
       bty="n",              # suppress legend box, shrink text 50%
       title="Body Location of sample",
       c("PB", "BM", "LN"), 
       pch=symbols, col = cols, horiz=TRUE)
# label up the points
text(x, y, colnames(data.n), col=cols, cex=.5, pos=4)

# Comment: lymph node samples separate nicely from other samples
# some overlap between bone marrow and peripheral blood 

#*****************************************************************
# 3-D Plot, for good examples of 3D plots
#******************************************************************                 
# plot all companies loadings on the first, second, and third principal components and highlight points according to the sector they belong
s3d = scatterplot3d(x, y, z, 
                    xlab='Comp.1', ylab='Comp.2', zlab='Comp.3', 
                    color=cols, pch = symbols,
                    main = "Principal Component Analysis in 3D \n CLL cell gene expression")
A 3D graph separates the different groups of samples. 
s3d.coords = s3d$xyz.convert(x, y, z)
text(s3d.coords$x, s3d.coords$y, 
     labels=colnames(data.n), col=cols, cex=.8, pos=4)

Labels can be added but I'm not sure they help in this example. 

# change the order and the angle to make it look a bit more like the figure
s3d = scatterplot3d(y, x, z, 
                    xlab='PC2', ylab='PC1', zlab='PC3',             
                    color=cols, pch = symbols,
                    angle=-25,
                    grid = FALSE,
                    main = "Principal Component Analysis in 3D \n CLL cell gene expression")

par(xpd=TRUE)    # allows legend outside the graph

# add legend
legend(10, -4.5,     # location
       bty="n",              # suppress legend box
       title="Site of sample",
       c("PB", "BM", "LN"), 
       pch=symbols, col = cols, horiz=TRUE)

# add source as text
text(4, -6.5, cex =0.7,
     "Source: Herishanu et al, Blood 2011 117:563-574; doi:10.1182/blood-2010-05-284984")


SCRIPT END

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