The code and approaches that I share here are those I am using to analyze TCGA methylation data. At the bottom of the page, you can find references used to make this tutorial. If you are coming from a computer background, please bear with a geneticist who tried to write code :). Your feedback worth a lot to me.

The three main analysis of methylation data are covered:

1-differential methylation analysis

2- differential variability analysis

3- integrative analysis

Fo codes and files you can check my GitHub. For this post, I used publicly available data from TCGA bladder cancer (TCGA-BLCA) for methylation and gene expression analysis.

Workspace preparation

#______________loading packages__________________#

#increase memory size
memory.limit(size = 28000) # do this on your own risk!

Data download and preparation

#_________ DNA methylation Download____________#
# DNA methylation aligned to hg19
query_met <- GDCquery(project= "TCGA-BLCA", 
                           data.category = "DNA methylation", 
                           platform = "Illumina Human Methylation 450", 
                           legacy = TRUE)
#putting files togathers
data.met <- GDCprepare(query_met)
#saving the met object
saveRDS(object = data.met,
        file = "data.met.RDS",
        compress = FALSE)
# loading saved session: Once you saved your data, you can load it into your environment: 
data.met = readRDS(file = "data.met.RDS")
# met matrix
met <-
# clinical data
clinical <- data.frame([email protected])

#___________inspectiong methylation data_______________#

# get the 450k annotation data
ann450k <- getAnnotation(IlluminaHumanMethylation450kanno.ilmn12.hg19)

## remove probes with NA <- == 0)
#103553 382024 
# chose those has not NA values in rows
probe <-[ == 0]
met <- met[row.names(met) %in% names(probe), ]

## remove probes that match to chromosome  X and Y 
keep <- !(row.names(met) %in% ann450k$Name[ann450k$chr %in% c("chrX","chrY")])
met <- met[keep, ]
rm(keep) # remove no further needed probes.

## remove SNPs overlapped probe$Probe_rs))
# probes without snp
no.snp.probe <- ann450k$Name[$Probe_rs)]

snp.probe <- ann450k[!$Probe_rs), ]
#snps with maf <= 0.05
snp5.probe <- snp.probe$Name[snp.probe$Probe_maf <= 0.05]

# filter met
met <- met[row.names(met) %in% c(no.snp.probe, snp5.probe), ]

#remove no-further needed dataset
rm(no.snp.probe, probe,, snp.probe, snp5.probe)

## Removing probes that have been demonstrated to map to multiple places in the genome.
# list adapted from

crs.reac <- read.csv("cross_reactive_probe.chen2013.csv")
crs.reac <- crs.reac$TargetID[-1]

# filtre met
met <- met[ -which(row.names(met) %in% crs.reac), ]
bval <- met

## converting beta values to m_values
## m = log2(beta/1-beta)
mval <- t(apply(met, 1, function(x) log2(x/(1-x))))
# save data sets
#saveRDS(mval, file = "mval.RDS", compress = FALSE)
#saveRDS (bval, file = "bval.RDS", compress = FALSE)
#mval <- readRDS("mval.RDS")
#bval <- readRDS("bval.RDS")

Differential methylation analysis

Different methylated CpGs (DMC) or Probe-wise differential methylation analysis

Here we are interested to find probes that are differentially methylated between low-grade and high-grade bladder cancer. In the clinical dataset, there is column paper_Histologic.grade that provides data on tumor grade. Also as one can see, most low-grade bladder cancer samples belong to the luminal papillary subtype.

I have to make it clear that for assigning "low-grade" or "high-grade" to a sample I relied on the clinical data published by A Gordon Robertson , however, it is acknowledged that those samples in Ta stage should be considered as "low-grade", samples with T1 and upper stages are "high-grade". By the way, this would not affect a bioinformatic analysis!

table(clinical$paper_Histologic.grade, clinical$paper_mRNA.cluster)

#             Basal_squamous Luminal Luminal_infiltrated Luminal_papillary  ND Neuronal
#  High Grade            143      28                  79               120   4       20
#  Low Grade               1       0                   0                20   0        0
#  ND                      0       0                   1                 2   0        0

# filtering and grouping
clinical <- clinical[, c("barcode", "paper_Histologic.grade", "paper_mRNA.cluster")]
clinical <- na.omit(clinical)
clinical <- clinical[-which(clinical$paper_mRNA.cluster == "ND"), ]
clinical <- clinical[-which(clinical$paper_Histologic.grade == "ND"), ]
clinical <- clinical[which(clinical$paper_mRNA.cluster == "Luminal_papillary"), ]
barcode <- clinical$barcode

# removing samples from meth matrixes
bval <- bval[, colnames(bval) %in% barcode]
mval <- mval[, colnames(mval) %in% barcode]

# Making sure about samples in clinical and matrixes and their order
table(colnames(mval) %in% row.names(clinical))
table(colnames(bval) %in% row.names(clinical))
all(row.names(clinical) == colnames(bval))
all(row.names(clinical) == colnames(mval))

#Making grouping variable
clinical$paper_Histologic.grade <- as.factor(clinical$paper_Histologic.grade)
clinical$paper_Histologic.grade <- relevel(clinical$paper_Histologic.grade, ref = "Low Grade")

#_____________ DMC analysis________________#
design <- model.matrix(~ paper_Histologic.grade, data = clinical)
# fit the linear model 
fit <- lmFit(mval, design)
fit2 <- eBayes(fit)

# extracting significantly methylated probes
deff.meth = topTable(fit2, coef=ncol(design),"p",number = nrow(mval), adjust.method = "BY")
# Visualization
# plot the top 10 most significantly differentially methylated CpGs 
sapply(rownames(deff.meth)[1:10], function(cpg){
  plotCpg(bval, cpg=cpg, pheno=clinical$paper_Histologic.grade, ylab = "Beta values")

enter image description here

# making a volcano plot
#making dataset
dat <- data.frame(foldchange = fit[["coefficients"]][,2], logPvalue =  -log10(fit2[["p.value"]][,2]))
dat$threshold <- as.factor(abs(dat$foldchange) < 0.4)

cols <- c("TRUE" = "grey", "FALSE" = "blue")
ggplot(data=dat, aes(x=foldchange, y = logPvalue, color=threshold)) +
  geom_point(alpha=.6, size=1.2) +
  scale_colour_manual(values = cols) +
  geom_vline(xintercept = 0.4, colour="#990000", linetype="dashed") + 
  geom_vline(xintercept = - 0.4, colour="#990000", linetype="dashed") +
  theme(legend.position="none") +
  xlab("Fold Change") +
  ylab("-log10 p value") +
  theme_bw() +
  theme(legend.position = "none")

enter image description here

Differentially methylated regions (DMRs) analysis

# setting some annotation
myAnnotation <- cpg.annotate(object = mval, datatype = "array", 
                             what = "M", 
                             analysis.type = "differential", 
                             design = design, 
                             contrasts = FALSE, 
                             coef = "paper_Histologic.gradeHigh Grade", 
                             arraytype = "450K",
                             fdr = 0.001)

# DMR analysis
DMRs <- dmrcate(myAnnotation, lambda=1000, C=2)
results.ranges <- extractRanges(DMRs)

# visualization
dmr.table <- data.frame(results.ranges)

# setting up variable for grouping and color

pal <- brewer.pal(8,"Dark2")
groups <- pal[1:length(unique(clinical$paper_Histologic.grade))]
names(groups) <- levels(factor(clinical$paper_Histologic.grade))

#setting up the genomic region 
gen <- "hg19"
# the index of the DMR that we will plot 
dmrIndex <- 2
# coordinates are stored under results.ranges[dmrIndex]

chrom <- as.character(seqnames(results.ranges[dmrIndex]))
start <- as.numeric(start(results.ranges[dmrIndex]))
end <- as.numeric(end(results.ranges[dmrIndex]))

# add 25% extra space to plot
minbase <- start - (0.25*(end-start))
maxbase <- end + (0.25*(end-start))

# defining CpG islands track
# download cpgislands for chromosome number 6 from ucsc
chr6.cpg <- read.csv("chr6-cpg.csv")

islandData <- GRanges(seqnames=Rle(chr6.cpg[,1]), 

# DNAseI hypersensitive sites track
#downloaded from ucsc
chr6.dnase <- read.csv("chr6-dnase.csv")

dnaseData <- GRanges(seqnames=chr6.dnase[,1],
                     ranges=IRanges(start=chr6.dnase[,2], end=chr6.dnase[,3]),

#Setting up the ideogram, genome and RefSeq tracks 

iTrack <- IdeogramTrack(genome = gen, chromosome = chrom, name=paste0(chrom))
gTrack <- GenomeAxisTrack(col="black", cex=1, name="", fontcolor="black")
rTrack <- UcscTrack(genome=gen, chromosome=chrom, track="NCBI RefSeq", 
                    from=minbase, to=maxbase, trackType="GeneRegionTrack", 
                    rstarts="exonStarts", rends="exonEnds", gene="name", 
                    symbol="name2", transcript="name", strand="strand", 
                    fill="darkblue",stacking="squish", name="RefSeq", 
                    showId=TRUE, geneSymbol=TRUE)

#Ensure that the methylation data is ordered by chromosome and base position.

ann450kOrd <- ann450k[order(ann450k$chr,ann450k$pos),]
bvalOrd <- bval[match(ann450kOrd$Name,rownames(bval)),]

#Create the data tracks:
# create genomic ranges object from methylation data
cpgData <- GRanges(seqnames=Rle(ann450kOrd$chr),
                   ranges=IRanges(start=ann450kOrd$pos, end=ann450kOrd$pos),

# methylation data track
methTrack <- DataTrack(range=cpgData, 
                       groups=clinical$paper_Histologic.grade, # change this if your groups are diffrent
                       genome = gen,
                       name="DNA Meth.n(beta value)",

# CpG island track
islandTrack <- AnnotationTrack(range=islandData, genome=gen, name="CpG Is.", 

# DNaseI hypersensitive site data track
dnaseTrack <- DataTrack(range=dnaseData, genome=gen, name="DNAseI", 
                        type="gradient", chromosome=chrom)

# DMR position data track
dmrTrack <- AnnotationTrack(start=start, end=end, genome=gen, name="DMR", 

# Set up the track list and indicate the relative sizes of the different tracks. 
# Finally, draw the plot using the plotTracks function
tracks <- list(iTrack, gTrack, methTrack, dmrTrack, islandTrack, dnaseTrack,
sizes <- c(2,2,5,2,2,2,3) # set up the relative sizes of the tracks
# to save figure and scaling graphic device
tiff( filename = "dmr.tiff", width = 15, height = 10, units = "in", res = 400)
plotTracks(tracks, from=minbase, to=maxbase, showTitle=TRUE, add53=TRUE, 
           add35=TRUE, grid=TRUE, lty.grid=3, sizes = sizes, length(tracks))

enter image description here


1- A cross-package Bioconductor workflow for analysing methylation array data

2- Integrative analysis of DNA methylation and gene expression identified cervical cancer-specific diagnostic biomarkers

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