DMR
Elouan Bethuel
28 May, 2024
WGBSFlow/Methylator pipeline version XXX
Developed and maintained at the BiBs platform (Epigenetics and Cell Fate Center - UMR7216) by Olivier Kirsh, Elouan Bethuel and Magali Hennion.
Please send feedback to: bibs@parisepigenetics.com.
Don’t forget to read and sign our charter.
Remember to cite pipeline authors in your publication.
1 R packages requirements
Methylator runs R scripts and uses : - MethylKit package to handle DNA methylation data.
- bsseq package to infere differentially methylated regions (DMRs) from bisulfite sequencing
Conda within a Singularity/Apptainer image handles R version and package dependencies for improved reproducibility. Refer to the ‘R session information’ section for version details.
# load packages
library(BiocParallel)
library(ggplot2)
library(dplyr)
library(tidyr)
library(GenomicFeatures)
library(annotatr)
library(FactoMineR)
library(factoextra)
library(bsseq)
library(yaml)
library(dmrseq)
library(plyranges)
library(annotatr)
library(GenomicRanges)
library(stringr)
library(methylKit)2 Configuration and settings
Read analysis parameters from ‘config_wgbs.yaml’ or ‘config_nanopore.yaml’ and extract sample names and groups from the corresponding metadata file.
# load the config file
yaml.file <- yaml.load_file('config_ongoing_run.yaml')
# extract the information from the yaml file
ORG <- yaml.file$ASSEMBLY
DESTRAND<- yaml.file$DESTRAND
MINCOV <- yaml.file$MINCOV
COV.PERC <- yaml.file$COV.PERC
DMR_TYPE <- yaml.file$DMR_TYPE
MIN_CPG <- yaml.file$MIN_CPG
MAX_GAP <- yaml.file$MAX_GAP
CUTOFF <- yaml.file$CUTOFF
FDR <- yaml.file$FDR
UNITE <- yaml.file$UNITE
# extract the metadata
meta <- read.table(yaml.file$METAFILE, sep='\t', header=T)
knitr::kable(meta, "pipe")| sample | group |
|---|---|
| SRR9016926_sub500000_chr19 | WT |
| SRR9016927_sub500000_chr19 | 1KO |
| SRR9016928_sub500000_chr19 | DKO |
| SRR11806587_sub500000_chr19 | WT |
| SRR11806588_sub500000_chr19 | 1KO |
| SRR11806589_sub500000_chr19 | DKO |
# set colors
full.color = "#de0000"
high.color = "#e17e65"
mid.color = "#c6c6c6"
low.color = "#7c6fac"
unmeth.color = "#0f208f"
hyper.color = "#de0000"
hypo.color = "#0f208f"
not.color = "#c6c6c6"3 Configuration file parameters
- Minimum coverage - MINCOV : 5 à voir si cérer aussi une variable sépciale en DMR
- Discard the bases that have more than COV.PERC the percentile of coverage - COV.PERC : 99.9
- Minimum number of CpGs to consider for a candidate DMR : 5
- Maximum number of bp in between neighboring CpGs to be included in the same DMR : 1000
- Significant difference threshold to consider a CpG as a candidate for inferring a DMR: 0.05
- Qvalue for significant differential methylation regions : 0.05
- Type of DMR selection (regions or blocks) : regions
4 Load RData
Methylator takes primary DNA methylation data and generates one RData environment with one MethylRaw object per sample.
The MethylRaw object stores the following for each CpG:
- Genomic coordinates (Chrom, Start, End, Strand)
- Coverage (number of reads)
- Number of methylated and unmethylated cytosines (for computing the methylation status)
It contains the total number of CpG, sample_id, genome assembly and methylation context (CpG).
5 Convert methylkit to bsseq object
The dmrsseq package manipulates bsseq objects to infer DMRs,
It is possible to read files produced by Bismark’s methylation extractor (.gz or .bz2 )
But the Methylkit package offers numerous functions for filtering and unifying samples.
So we prefer to start with a Methylkit object and convert it into a bsseq object.
## An object of type 'BSseq' with
## 6 methylation loci
## 4 samples
## has not been smoothed
## All assays are in-memory6 Plot distribution of methylation values and coverage
Beta values can be interpreted as the proportion of methylation at a given locus.
Beta values are continuous variables between 0 (CpG site always found unmethylated in sample DNA) and 1 (CpG site always found methylated in sample DNA)
6.1 Beta values by group
plotEmpiricalDistribution(bs, testCovariate="group")
6.2 Beta values by sample
plotEmpiricalDistribution(bs, testCovariate="sample")
6.3 Coverage plot by group
bs_cov <- as.data.frame(getCoverage(bs, type = "Cov"))
colnames(bs_cov) <- meta_cond$group
bs_cov_rsh <- pivot_longer(bs_cov, cols = meta_cond$group, names_to = "samples", values_to = "CpG_cov")
plt_cov <- ggplot(data = bs_cov_rsh, aes(x = CpG_cov, color = samples)) +
geom_line(stat = "count", size = 1.5) +
coord_cartesian(xlim = c(0, 40)) +
ggtitle("CpG coverage by group") + theme_classic(base_size = 14)
plt_cov
6.4 Coverage plot by sample
bs_cov <- as.data.frame(getCoverage(bs, type = "Cov"))
colnames(bs_cov) <- meta_cond$sample
bs_cov_rsh <- pivot_longer(bs_cov, cols = meta_cond$sample, names_to = "samples", values_to = "CpG_cov")
plt_cov <- ggplot(data = bs_cov_rsh, aes(x = CpG_cov, color = samples)) +
geom_line(stat = "count", size = 1.5) +
coord_cartesian(xlim = c(0, 40)) +
scale_x_continuous(breaks = seq(0, 40, 5)) +
ggtitle("CpG coverage by sample") + theme_classic()
plt_cov
7 Build annotations
When DMRs are inferred and then displayed with dmrseq, annotations can be associated with them
(genes or CpG islands present in the region or nearby) using an annoTrack object.
To build annoTrack we used the “annotation” object produced with the ‘Annotatr.R’ script using the annotar package
annotatr
The annoTrack object looks like this:
annoTrack <- GenomicRanges::GRangesList(CpG = annotations , Genes = annotations[annotations$type == paste0(ORG,"_genes")], compress = FALSE)
annoTrack$CpG$type <- ifelse(annoTrack$CpG$type == paste0(ORG,"_cpg_islands") , "islands",
ifelse(annoTrack$CpG$type == paste0(ORG,"_cpg_shores") , "shores",
ifelse(annoTrack$CpG$type == paste0(ORG,"_cpg_shelves"), "shelves" , "inter")))
annoTrack$Genes$symbol <- annoTrack$Genes$gene_id
head(annoTrack)## GRangesList object of length 2:
## $CpG
## GRanges object with 84476 ranges and 5 metadata columns:
## seqnames ranges strand | id tx_id
## <Rle> <IRanges> <Rle> | <character> <character>
## [1] chr19 3946193-3946269 - | introns:1 <NA>
## [2] chr19 3946448-3946603 - | introns:2 <NA>
## [3] chr19 3946722-3946794 - | introns:3 <NA>
## [4] chr19 3946903-3947037 - | introns:4 <NA>
## [5] chr19 3947231-3947467 - | introns:5 <NA>
## ... ... ... ... . ... ...
## [84472] chr19 5250832-5256331 - | gene:263 ENSMUSG00002076406.1
## [84473] chr19 6189762-6195261 + | gene:264 ENSMUSG00002076495.1
## [84474] chr19 5848614-5854113 - | gene:265 ENSMUSG00002076626.1
## [84475] chr19 5894566-5900065 - | gene:266 ENSMUSG00002076763.1
## [84476] chr19 5893966-5899465 - | gene:267 ENSMUSG00002076852.1
## gene_id symbol type
## <character> <logical> <character>
## [1] <NA> <NA> inter
## [2] <NA> <NA> inter
## [3] <NA> <NA> inter
## [4] <NA> <NA> inter
## [5] <NA> <NA> inter
## ... ... ... ...
## [84472] ENSMUSG00002076406.1 <NA> inter
## [84473] ENSMUSG00002076495.1 <NA> inter
## [84474] ENSMUSG00002076626.1 <NA> inter
## [84475] ENSMUSG00002076763.1 <NA> inter
## [84476] ENSMUSG00002076852.1 <NA> inter
## -------
## seqinfo: 26 sequences from mm39 genome
##
## $Genes
## GRanges object with 267 ranges and 5 metadata columns:
## seqnames ranges strand | id tx_id
## <Rle> <IRanges> <Rle> | <character> <character>
## [1] chr19 3946050-3957133 - | gene:1 ENSMUSG00000001750.17
## [2] chr19 5738770-5752893 + | gene:2 ENSMUSG00000004054.10
## [3] chr19 4320208-4333165 - | gene:3 ENSMUSG00000005986.17
## [4] chr19 4850597-4861662 - | gene:4 ENSMUSG00000006456.11
## [5] chr19 4911244-4927937 - | gene:5 ENSMUSG00000006457.5
## ... ... ... ... . ... ...
## [263] chr19 5251226-5251331 - | gene:263 ENSMUSG00002076406.1
## [264] chr19 6194762-6194881 + | gene:264 ENSMUSG00002076495.1
## [265] chr19 5849028-5849113 - | gene:265 ENSMUSG00002076626.1
## [266] chr19 5894944-5895065 - | gene:266 ENSMUSG00002076763.1
## [267] chr19 5894314-5894465 - | gene:267 ENSMUSG00002076852.1
## gene_id symbol type
## <character> <character> <character>
## [1] ENSMUSG00000001750.17 ENSMUSG00000001750.17 mm39_genes
## [2] ENSMUSG00000004054.10 ENSMUSG00000004054.10 mm39_genes
## [3] ENSMUSG00000005986.17 ENSMUSG00000005986.17 mm39_genes
## [4] ENSMUSG00000006456.11 ENSMUSG00000006456.11 mm39_genes
## [5] ENSMUSG00000006457.5 ENSMUSG00000006457.5 mm39_genes
## ... ... ... ...
## [263] ENSMUSG00002076406.1 ENSMUSG00002076406.1 mm39_genes
## [264] ENSMUSG00002076495.1 ENSMUSG00002076495.1 mm39_genes
## [265] ENSMUSG00002076626.1 ENSMUSG00002076626.1 mm39_genes
## [266] ENSMUSG00002076763.1 ENSMUSG00002076763.1 mm39_genes
## [267] ENSMUSG00002076852.1 ENSMUSG00002076852.1 mm39_genes
## -------
## seqinfo: 26 sequences from mm39 genome8 Detecting DMR, type : regions
According to the bioconductor drmseq vignette, dmrseq-vignette the default smoothing parameters are designed to focus on local DMRs.
In some applications, it is of interest to effectively ‘zoom out’ in order to detect larger (lower-resolution) methylation blocks on the order of hundreds of thousands to millions of bases.
To do so, you can set the block argument to true, which will only include candidate regions with at least blockSize basepairs (default = 5000) and redefine the default smoothing parameters.
register(MulticoreParam(32))
#register(SerialParam)
if(DMR_TYPE == "regions"){
dmr_obj <- try(dmrseq(bs=bs,
cutoff = CUTOFF,
testCovariate="group",
smooth = FALSE,
bpSpan = 1000,
maxGap = MAX_GAP,
minInSpan = 10,
maxGapSmooth = 5000,
maxPerms = 20,
minNumRegion = MIN_CPG))
}
if(DMR_TYPE == "blocks"){
dmr_obj <- try(dmrseq(bs= bs,
cutoff = CUTOFF,
testCovariate= "group",
block = TRUE,
minInSpan = 500,
bpSpan = 5e4,
maxGapSmooth = 1e6,
maxGap = 5e3,))
}if (typeof(dmr_obj) == "character"){
print("No candidate regions, try to modifie parameters")
notempty.dmr = FALSE
} else{
notempty.dmr = TRUE
head(dmr_obj)
}## GRanges object with 6 ranges and 7 metadata columns:
## seqnames ranges strand | L area beta stat
## <Rle> <IRanges> <Rle> | <integer> <numeric> <numeric> <numeric>
## [1] chr19 4136450-4137295 * | 10 2.59330 -0.898511 -17.3584
## [2] chr19 3188578-3190912 * | 19 13.43793 1.800119 14.9216
## [3] chr19 4788497-4791513 * | 23 5.91157 -0.878239 -14.4476
## [4] chr19 4775860-4780378 * | 25 7.38874 -0.915836 -14.1526
## [5] chr19 4155424-4158167 * | 34 9.42607 -0.808288 -12.8922
## [6] chr19 4619651-4620185 * | 10 3.12804 -0.983919 -12.5583
## pval qval index
## <numeric> <numeric> <IRanges>
## [1] 0.00274725 0.00813874 3988-3997
## [2] 0.00274725 0.00813874 67-85
## [3] 0.00274725 0.00813874 7030-7052
## [4] 0.00274725 0.00813874 6914-6938
## [5] 0.00274725 0.00813874 4121-4154
## [6] 0.00274725 0.00813874 6177-6186
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengthsif( (notempty.dmr == TRUE) && (length(dmr_obj) == 0)){
notempty.dmr = FALSE
}9 Significant DMRs (regions)
Select DMRs with a q-value (FDR) < 0.05
if(notempty.dmr){
sigdmr <- dmr_obj[dmr_obj$qval < FDR,]
if(length(sigdmr) > 0){
nb_sig <- length(sigdmr)
nb_nosig <- length(dmr_obj)-nb_sig
df_sig <- data.frame(length(dmr_obj), nb_sig, nb_nosig)
percentage_sig <- c(100, round(((nb_sig/length(dmr_obj))*100),2), round(((nb_nosig/length(dmr_obj))*100),2))
colnames(df_sig) <- c("all", 'Significant', "Non-significant")
df_sig <- tidyr::gather(df_sig, key = "Status", value = "count")
sig_bar <- ggplot(df_sig, aes(x = Status, y = count, fill = Status)) +
geom_bar(position = "dodge" , stat = "identity", alpha=0.5, colour = "black") +
labs(title = paste0("Number of DMRs significants, FDR: ", FDR), y = "Number of DMRs") +
scale_color_brewer(palette="Dark2") +
geom_text(aes(label = paste0(percentage_sig, "%" )), position = position_stack(vjust = 0.5), size = 5)
theme_minimal()+theme_classic()
}else{
print("No significant found")
sigdmr <- data.frame()
sig_bar <- data.frame()
}
}else{
print("No candidate, try to modifie parameters")
sigdmr <- data.frame()
sig_bar <- data.frame()
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## - attr(*, "class")= chr [1:2] "theme" "gg"
## - attr(*, "complete")= logi TRUE
## - attr(*, "validate")= logi TRUEsig_bar
suppressWarnings(rm(df_sig))
suppressWarnings(rm(precentage_sig))
suppressWarnings(rm(nb_sig, nb_nosig))10 Statistics about DMRs
10.1 Boxplot of number of CpG in DMRs and DMRs width
if(notempty.dmr){
df_dmr <- as.data.frame(dmr_obj)
par(mfrow = c(1,2))
boxplot(df_dmr$L)
title("Number of CpGs by DMRs")
boxplot(df_dmr$width)
title("DMRs width")
}else{
print("No candidate, try to modifie parameters")
}
10.2 Boxplot of number of CpG in DMRs and DMRs width for significant DMRs only
if(length(sigdmr) > 0){
df_dmr_sig <- as.data.frame(sigdmr)
par(mfrow = c(1,2))
boxplot(df_dmr_sig$L)
title("Number of CpGs by DMRs")
boxplot(df_dmr_sig$width)
title("DMRs width")
}else{
print("No significants candidate, try to modifie parameters")
}
10.3 Plot number of hypo and hyper-methylated DMRs
if(notempty.dmr){
dmr_hyper <- sum(dmr_obj$stat > 0)
dmr_hypo <- sum(dmr_obj$stat < 0)
df_meth <- data.frame(dmr_hyper, dmr_hypo)
colnames(df_meth) <- c("hyper","hypo")
df_meth <- tidyr::gather(df_meth, key = "Methylation_status", value = "count")
perc_meth <- c(round(((dmr_hyper/length(dmr_obj))*100),2), round(((dmr_hypo/length(dmr_obj))*100),2))
plot_meth <- ggplot(df_meth, aes(x = Methylation_status, y = count, fill = Methylation_status)) +
geom_bar(position = "dodge" , stat = "identity", alpha=0.5, colour = "black") +
labs(title = "Number of hypo and hyper methylated DMRs ", y = "Number of DMRs") +
scale_color_brewer(palette="Dark2") +
geom_text(aes(label = paste0(perc_meth, "%" )), position = position_stack(vjust = 0.5), size = 5) +
theme_minimal() + theme_classic()
}else{
print("No candidate, try to modifie parameters")
plot_meth <- data.frame()
}
plot_meth
rm(df_meth)
rm(perc_meth)
rm(dmr_hyper, dmr_hypo)10.4 Plot number of hypo and hypermethylated DMRs for significant DMRs only
if(length(sigdmr) > 0){
sigdmr_hyper <- sum(sigdmr$stat > 0)
sigdmr_hypo <- sum(sigdmr$stat < 0)
df_sigmeth <- data.frame(sigdmr_hyper, sigdmr_hypo)
colnames(df_sigmeth) <- c("hyper","hypo")
df_sigmeth <- tidyr::gather(df_sigmeth, key = "Methylation_status", value = "count")
perc_sigmeth <- c(round(((sigdmr_hyper/length(sigdmr))*100),2), round(((sigdmr_hypo/length(sigdmr))*100),2))
sig_meth <- ggplot(df_sigmeth, aes(x = Methylation_status, y = count, fill = Methylation_status)) +
geom_bar(position = "dodge" , stat = "identity", colour = "black") +
labs(title = "Number of hypo and hyper methylated significants DMRs ", y = "Number of DMRs") +
theme_bw(base_size = 14) +
scale_fill_manual("", values=c('Hyper' = hyper.color,
'Hypo' = hypo.color,
'None' = not.color)) +
geom_text(aes(label = paste0(perc_sigmeth, "%" )), position = position_stack(vjust = 0.5), size = 5)
}else{
sig_meth <- data.frame()
print("No significant candidate, try to modifie parameters")
}
sig_meth
suppressWarnings(rm(df_sigmeth))
suppressWarnings(rm(perc_sigmeth))
suppressWarnings(rm(sigdmr_hyper, sigdmr_hypo))10.5 Plot number of DMRs by chromosome
for each chromosome plot the number of DMRs hypo and hyper methylated
if(notempty.dmr){
df_dmr <- as.data.frame(dmr_obj)
df_dmr$seqnames <- as.vector(df_dmr$seqnames)
df_dmr$status <- ifelse(df_dmr$stat > 0, "hyper", "hypo")
df <- data.frame(
chr = df_dmr$seqnames,
status = df_dmr$status)
count_per_chromosome <- table(df$chr, df$status)
count_df <- as.data.frame.matrix(count_per_chromosome)
count_df$chr <- rownames(count_df)
# test si absence de hypo ou hyper
if(dim(count_df)[2] == 2){
if(colnames(count_df[1]) == "hypo"){
count_df$hyper <- rep(0, dim(count_df)[1])
}else{
count_df$hypo <- rep(0, dim(count_df)[1])
}
}
chr_others <- count_df[1,]
rownames(chr_others) <- "others"
chr_others$hypo <- chr_others$hyper <- 0
chr_others$chr <- "others"
delete_row <- 0
for(i in 1:length(count_df$chr)){
if((nchar(count_df$chr[i]) > 5) == TRUE){
chr_others$hypo <- (chr_others$hypo + count_df[i,]$hypo)
chr_others$hyper <- (chr_others$hyper + count_df[i,]$hyper)
delete_row <- c(delete_row, i)
}
}
if(is.na(delete_row[2]) == FALSE){ # test si il n'y a aucune colonne à remove (delete_row = 0), car sinon génère des bugs
count_df <- count_df[-delete_row,]
}
count_df<- rbind(count_df, chr_others)
count_df$chr <- str_sort(count_df$chr, numeric = TRUE)
count_df_long <- tidyr::gather(count_df, key = "status", value = "count", hypo, hyper)
hist_dmr_chr <- ggplot(count_df_long, aes(x = chr, y = count, fill = status)) +
geom_bar(position = "dodge" , stat = "identity", colour = "black") +
labs(title = "Number of DMR hypo and hyper methylated by chromosome",
x = "Chromosome",
y = "DMRs") +
theme_bw(base_size = 14) +
scale_fill_manual("", values=c('Hyper' = hyper.color,
'Hypo' = hypo.color,
'None' = not.color)) +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
rm(df_dmr, df)
rm(delete_row)
rm(count_df, count_df_long)
rm(chr_others)
}else{
hist_dmr_chr <- data.frame()
print("No candidate, try to modifie parameters")
}
hist_dmr_chr
11 Plot top ten significant DMRs
By default, DMRs are sorted by their q-value
if(length(sigdmr) > 0){
if(length(sigdmr) >= 10){
plotDMRs(bs, regions=sigdmr[1:10,], testCovariate="group", extend = 2000, annoTrack = annoTrack, horizLegend = TRUE)
}else{
plotDMRs(bs, regions=sigdmr[1:length(dmr_obj),], testCovariate="group", extend = 2000, annoTrack = annoTrack, horizLegend = TRUE)
}
}else{
print("Impossible to plot top ten significant DMRs, no candidate, try to modifie parameters")
}
12 Annot significants DMRs object with genes and promoters only
Annotates significant DMRSs that intersect with genes. Use the Annotatr package. Warning: if several genes or promoters are associated with the same DMRS, create several lines in the CSV file, with the DMR annotated with the different genes. The CSV file is likely to be larger, so DMRs that are not associated with genes are not displayed.
if(length(sigdmr) > 0){
sigdmr_annot <- annotate_regions(regions = sigdmr, annotations = annotations, ignore.strand = TRUE, quiet = FALSE)
sigdmr_annot_genes <- sigdmr_annot[sigdmr_annot$annot$type == paste0(ORG, "_genes") | sigdmr_annot$annot$type == paste0(ORG, "_promoters") , ]
head(sigdmr_annot_genes)
}else{
print("No candidate, try to modifie parameters")
sigdmr_annot <- NULL
sigdmr_annot_genes <- NULL
}## GRanges object with 6 ranges and 8 metadata columns:
## seqnames ranges strand | L area beta stat
## <Rle> <IRanges> <Rle> | <integer> <numeric> <numeric> <numeric>
## [1] chr19 4136450-4137295 * | 10 2.59330 -0.898511 -17.3584
## [2] chr19 3188578-3190912 * | 19 13.43793 1.800119 14.9216
## [3] chr19 3188578-3190912 * | 19 13.43793 1.800119 14.9216
## [4] chr19 3188578-3190912 * | 19 13.43793 1.800119 14.9216
## [5] chr19 4788497-4791513 * | 23 5.91157 -0.878239 -14.4476
## [6] chr19 4788497-4791513 * | 23 5.91157 -0.878239 -14.4476
## pval qval index annot
## <numeric> <numeric> <IRanges> <GRanges>
## [1] 0.00274725 0.00813874 3988-3997 chr19:4131578-4137340:+
## [2] 0.00274725 0.00813874 67-85 chr19:3190760-3194760:*
## [3] 0.00274725 0.00813874 67-85 chr19:3184984-3188984:*
## [4] 0.00274725 0.00813874 67-85 chr19:3103071-3247732:-
## [5] 0.00274725 0.00813874 7030-7052 chr19:4790941-4794941:*
## [6] 0.00274725 0.00813874 7030-7052 chr19:4761195-4802388:+
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengths12.1 Plot number of hypo and hypermethylated DMRs for significant DMRs associate with annotations type
if(length(sigdmr) > 0){
list_annot_type <- NULL
list_nb_dmr_hypo <- NULL
list_nb_dmr_hyper <- NULL
for(type in names(table(sigdmr_annot$annot$type))){
dmr_type <- sigdmr_annot[sigdmr_annot$annot$type == type, ]
nb_dmr_hypo <- length(table(dmr_type[dmr_type$stat < 0,]))
nb_dmr_hyper <- length(table(dmr_type[dmr_type$stat > 0,]))
list_annot_type <- c(list_annot_type, type)
list_nb_dmr_hypo <- c(list_nb_dmr_hypo, nb_dmr_hypo)
list_nb_dmr_hyper <- c(list_nb_dmr_hyper, nb_dmr_hyper)
}
df_dmr_annot <- data.frame(list_annot_type, list_nb_dmr_hyper, list_nb_dmr_hypo)
colnames(df_dmr_annot) <- c("Annot_type", "Hyper", "Hypo")
df_dmr_annot_long <- pivot_longer(df_dmr_annot, cols = c(Hyper, Hypo), names_to = "Meth_status", values_to = "Nb_DMRs")
hist_dmr_annot <- ggplot(df_dmr_annot_long, aes(x = Annot_type, y = Nb_DMRs, fill = Meth_status)) +
geom_bar(position = "dodge" , stat = "identity", colour = "black") +
labs(title = "Number of DMR hypo et hyper methylated by annotations types",
x = "Annotations type",
y = "DMRs") +
guides(fill=guide_legend(title="Methylation Status")) +
theme_bw(base_size = 14) +
scale_fill_manual("", values=c('Hyper' = hyper.color,
'Hypo' = hypo.color,
'None' = not.color)) +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
}else{
print("No candidate, try to modifie parameters")
hist_dmr_annot <- data.frame()
}
hist_dmr_annot
suppressWarnings(rm(list_annot_type))
suppressWarnings(rm(list_nb_dmr_hypo))
suppressWarnings(rm(list_nb_dmr_hyper))
suppressWarnings(rm(df_dmr_annot, df_dmr_annot_long))13 Exporting results to CSV files
Generate 3 CSV files, one for all DMRs, one for significant DMRs and one for significant DMRS with genes annotations
if(notempty.dmr){
write.csv(as.data.frame(dmr_obj), file= paste0(results_path, "DMR/", CONDI, "_DMRs.csv"))
}else{
print("Impossible to export results, no candidate DMR, try to modifie parameters")
}
if(length(sigdmr) > 0){
write.csv(as.data.frame(sigdmr), file= paste0(results_path, "DMR/", CONDI, "_significant_DMRs.csv"))
}else{
print("Impossible to export results, no significant DMR, try to modifie parameters")
}
if(length(sigdmr_annot_genes) > 0){
write.csv(as.data.frame(sigdmr_annot_genes), file= paste0(results_path, "DMR/", CONDI, "_significant_annot_DMRs.csv"))
}else{
print("Impossible to export results, no significant DMR with genes annotations, try to modifie parameters")
}14 Save RData
Saves the whole R session of the script (variables, objects …)
at the end of its execution. The RData file produced can then be opened in a local environment (if you have enough space)
to make modifications. For example to retouch figures.
save.image(file = rdata_out)15 R session information
# R Session
sessionInfo()## R version 4.1.3 (2022-03-10)
## Platform: x86_64-conda-linux-gnu (64-bit)
## Running under: Debian GNU/Linux 11 (bullseye)
##
## Matrix products: default
## BLAS/LAPACK: /opt/conda/envs/wgbsflow/lib/libopenblasp-r0.3.21.so
##
## locale:
## [1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8
## [4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8
## [7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C
## [10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] methylKit_1.20.0 stringr_1.5.0
## [3] plyranges_1.14.0 dmrseq_1.14.0
## [5] yaml_2.3.5 bsseq_1.30.0
## [7] SummarizedExperiment_1.24.0 MatrixGenerics_1.6.0
## [9] matrixStats_0.63.0 factoextra_1.0.7
## [11] FactoMineR_2.6 annotatr_1.20.0
## [13] GenomicFeatures_1.46.1 AnnotationDbi_1.56.2
## [15] Biobase_2.54.0 GenomicRanges_1.46.1
## [17] GenomeInfoDb_1.30.1 IRanges_2.28.0
## [19] S4Vectors_0.32.4 BiocGenerics_0.40.0
## [21] tidyr_1.3.0 dplyr_1.0.10
## [23] ggplot2_3.3.6 BiocParallel_1.28.3
##
## loaded via a namespace (and not attached):
## [1] AnnotationHub_3.2.0 BiocFileCache_2.2.0
## [3] plyr_1.8.8 splines_4.1.3
## [5] digest_0.6.31 foreach_1.5.2
## [7] htmltools_0.5.4 fansi_1.0.4
## [9] magrittr_2.0.3 memoise_2.0.1
## [11] BSgenome_1.62.0 cluster_2.1.4
## [13] tzdb_0.3.0 limma_3.50.3
## [15] Biostrings_2.62.0 readr_2.1.2
## [17] R.utils_2.12.2 bdsmatrix_1.3-6
## [19] prettyunits_1.1.1 colorspace_2.1-0
## [21] blob_1.2.3 rappdirs_0.3.3
## [23] ggrepel_0.9.3 xfun_0.37
## [25] crayon_1.5.2 RCurl_1.98-1.10
## [27] jsonlite_1.8.4 iterators_1.0.14
## [29] glue_1.6.2 gtable_0.3.1
## [31] zlibbioc_1.40.0 emmeans_1.8.4-1
## [33] XVector_0.34.0 DelayedArray_0.20.0
## [35] Rhdf5lib_1.16.0 HDF5Array_1.22.1
## [37] scales_1.2.1 mvtnorm_1.1-3
## [39] rngtools_1.5.2 DBI_1.1.3
## [41] Rcpp_1.0.10 emdbook_1.3.12
## [43] xtable_1.8-4 progress_1.2.2
## [45] bumphunter_1.36.0 mclust_6.0.0
## [47] flashClust_1.01-2 bit_4.0.5
## [49] DT_0.27 htmlwidgets_1.6.1
## [51] httr_1.4.4 RColorBrewer_1.1-3
## [53] ellipsis_0.3.2 farver_2.1.1
## [55] R.methodsS3_1.8.2 pkgconfig_2.0.3
## [57] XML_3.99-0.11 multcompView_0.1-8
## [59] sass_0.4.5 dbplyr_2.3.0
## [61] locfit_1.5-9.7 utf8_1.2.3
## [63] labeling_0.4.2 tidyselect_1.2.0
## [65] rlang_1.0.6 reshape2_1.4.4
## [67] later_1.3.0 munsell_0.5.0
## [69] BiocVersion_3.14.0 tools_4.1.3
## [71] cachem_1.0.6 cli_3.6.0
## [73] generics_0.1.3 RSQLite_2.2.20
## [75] fastseg_1.40.0 evaluate_0.20
## [77] fastmap_1.1.0 outliers_0.15
## [79] knitr_1.42 bit64_4.0.5
## [81] purrr_1.0.1 KEGGREST_1.34.0
## [83] doRNG_1.8.6 nlme_3.1-162
## [85] sparseMatrixStats_1.6.0 mime_0.12
## [87] R.oo_1.25.0 leaps_3.1
## [89] xml2_1.3.3 biomaRt_2.50.0
## [91] compiler_4.1.3 filelock_1.0.2
## [93] curl_4.3.3 png_0.1-8
## [95] interactiveDisplayBase_1.32.0 tibble_3.1.8
## [97] bslib_0.4.2 stringi_1.7.12
## [99] highr_0.10 lattice_0.20-45
## [101] Matrix_1.5-3 permute_0.9-7
## [103] vctrs_0.5.2 pillar_1.8.1
## [105] lifecycle_1.0.3 rhdf5filters_1.6.0
## [107] BiocManager_1.30.19 jquerylib_0.1.4
## [109] estimability_1.4.1 data.table_1.14.2
## [111] bitops_1.0-7 qvalue_2.26.0
## [113] httpuv_1.6.8 rtracklayer_1.54.0
## [115] R6_2.5.1 BiocIO_1.4.0
## [117] promises_1.2.0.1 codetools_0.2-19
## [119] MASS_7.3-58.2 gtools_3.9.4
## [121] assertthat_0.2.1 rhdf5_2.38.1
## [123] rjson_0.2.21 withr_2.5.0
## [125] regioneR_1.26.0 GenomicAlignments_1.30.0
## [127] Rsamtools_2.10.0 GenomeInfoDbData_1.2.7
## [129] parallel_4.1.3 hms_1.1.2
## [131] grid_4.1.3 coda_0.19-4
## [133] rmarkdown_2.20 DelayedMatrixStats_1.16.0
## [135] bbmle_1.0.25 numDeriv_2016.8-1.1
## [137] scatterplot3d_0.3-42 shiny_1.7.4
## [139] restfulr_0.0.15