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Plot_SMF_single_loci_CA_ES_plus_TKOs.r
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Plot_SMF_single_loci_CA_ES_plus_TKOs.r
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#TITLE: Plotting SMF single locus plots - for CA analysis - ES and TKOs
#AUTHOR: Elisa Kreibich
#DATE: 15-11-2022
#AIM: Create single locus SMF plots with average and single molecules plots.
# For ES and for DNMT and TET TKOs.
#Set environment -------------
WD <- '/g/krebs/kreibich/Kreibich_2023_5mC_at_enhancers/'
#Load libraries --------------
library(tidyverse)
library(GenomicRanges)
library(plyranges)
library(QuasR)
library(ggpubr)
library(patchwork)
library(RColorBrewer)
library(SingleMoleculeFootprinting)
source(paste0(WD, 'scripts/utilities/RFunctions_for_SingleMoleculeFootprinting_plotting.r'))
source(paste0(WD, 'scripts/utilities/COLORS.R'))
# Import Arguments --------------------------------------------------------
SAMPLES <- c("ES_NO", "TKO_DE", "TETTKO_NO")
REPOI <- 1
TKO_TREAT <- "DE"
TET_TREAT <- "NO"
#Define file path of QuasR input file with all SMF data sets.
Qinput <- '/g/krebs/kreibich/HTS/SMF/MM/QuasR/QuasR_rmdup/QuasR_aligned_files_ALL_rmdup.txt'
MySamples_1 <- c("ES_NO_R1", "ES_NO_R2", "ES_NO_R5a6")
print(MySamples_1)
if(TKO_TREAT == "DE"){
MySamples_2 <- c("TKO_DE_R1a2", "TKO_DE_R3a4", "TKO_DE_R5a6")
} else if (TKO_TREAT == "NO"){
MySamples_2 <- c("TKO_NO_R3", "TKO_NO_R4", "TKO_NO_R3")
}
print(MySamples_2)
MySamples_3 <- c("TETTKO_NO_R1", "TETTKO_NO_R2", "TETTKO_NO_R1")
canidateSamples <- c(MySamples_1[REPOI], MySamples_2[REPOI], MySamples_3[REPOI])
print(canidateSamples)
# Load Data ---------------------------------------------------------------
## BAMs
QuasRprj <- qAlign(sampleFile= Qinput,
genome="BSgenome.Mmusculus.UCSC.mm10",
paired= "fr",
bisulfite="undir")
QuasRprj@aligner <- "Rbowtie"
Samples <- QuasR::alignments(QuasRprj)[[1]]$SampleName
### ES
Samples_1 <- Samples[grepl(canidateSamples[1], Samples)]
print(Samples_1)
### TKO
Samples_2 <- Samples[grepl(canidateSamples[2], Samples)]
print(Samples_2)
### TETTKO
Samples_3 <- Samples[grepl(canidateSamples[3], Samples)]
print(Samples_3)
## TFBS
MotDBf <- readRDS(paste0(WD, 'data/mapped_jaspar2018_ChIP_score10_inBaits_BANP.rds'))
names(MotDBf) <- paste('TFBS_', seq_along(MotDBf), sep = '')
## Candidate sites
#Load in GRange object including states annotation made in scripts/Make_Final_CA_CMH_data_tibbles_cell_line.Rmd
INPUT_FILE <- '/g/krebs/kreibich/analysis/SMF/revision/Final_data_tibble_CA_2022-10-21_SMF_MM_ES_NO_R1_R2_R5a6_curatedICR_cObin10_cOCMH30_states_gr.rds'
candidates <- readRDS(INPUT_FILE)
names(candidates) <- paste0(candidates$bin)
#If a file is loaded in without GRange annotation, run the following code to annotate the genomic information per bin.
# CpGs in bins
# bin_gr <- readRDS('paste0(WD, 'data/CGs_over_baits_101bp.rds'))
# bin_gr$bin <- names(bin_gr)
# bin_gr <- as_tibble(bin_gr)
# candidates <- candidates %>% left_join(., bin_gr) %>% as_granges()
# names(candidates) <- paste0(candidates$bin)
# Choose regions of interest (ROIs) ----------------------------------------
ROIs <- candidates[grepl("antagonist", candidates$state2)]
# ROIs <- candidates[grepl("bin_34655$",candidates$bin)]
#Resize ROI for plotting (401 is the usual plotting width for SMF data)
ROIs_rsz <- resize(ROIs, 401, fix = "center")
# Plotting settings ------------------------------------------------------
#Define which plots you want to create
WITH_WT <- TRUE #Include wildtype (WT) data
WITH_DNMT_TKO <- TRUE #Include DNMT TKO data
WITH_TET_TKO <- TRUE #Include TET TKO data
WITH_TFBS <- FALSE #Sort for TFBS of interest
PLOT_MOTIFS <- FALSE #Plot TF motifs within the genomic window (MotDBf has to be loaded)
MOTIFS_OI <- "" #If PLOT_MOTIFS==TRUE, do you want to plot only a specific TF of interest (e.g. "CTCF")
PLOT_AVG_ONLY <- TRUE #Plot only average plots
PLOT_INDIVIDU <- TRUE #Plot individual plots
PLOT_COMBI <- TRUE #Plot WT plus the one TKO
PLOT_COMBI_ALL <- TRUE #Plot WT plus both TKOs
OUTDIR <- paste0(WD, 'data_results/plots_single_loci/')
OUTDIR <- paste0(WD, 'test/')
dir.create(OUTDIR)
COV <- 10 #Coverage cutoff for plotting (10x is normally used, as in the single molecule methylation analysis)
WDW_SIZE <- 101 #Genomic window of chromatin accessibility (CA) analysis.
START <- "SM_plot" #Start of final filename of all the plots
ADD <- "" #Additional info added to the end of the file name (FILENAME, ADD, ".png")
WHAT <- "CA" #Type of analysis "CA" for chromatin accessibility analysis | "TF" for TFBS analysis
# SM plotting -------------------------------------------------------------
# Before you start:
# - define which features you want to include in the plotname (PLOT.OUT)
# - define which features you want to include in the plot title (TITLE)
# - define which features you want to include in the plot caption (CAPTION)
# i = 1
for(i in seq_along(ROIs)){
print(i)
ICR_name <- ROIs[i]$names %>% str_replace("/", "-")
if(WHAT == "CA"){
PLOT.OUT <- paste0(OUTDIR, paste(START, ROIs[i]$state2, ROIs[i]$bin, sep = "_"))
} else if (WHAT == "TF"){
PLOT.OUT <- paste0(OUTDIR, paste(START, ROIs[i]$state2, ROIs[i]$representative_motif, ROIs[i]$TFBS, sep = "_"))
}
TITLE <- paste(ROIs[i]$bin, ROIs[i]$state2, ROIs[i]$chromHMM, sep = " | ")
CAPTION <- paste("COR =", round(ROIs[i]$COR, 2), "| -log10(p) = ", round(-log10(ROIs[i]$pval)))
TFBS <- ROIs[i]
if(WHAT == "CA"){
TFBS_center <- start(TFBS) + (end(TFBS)-start(TFBS))/2
BinsCoordinates <- IRanges(start = c(TFBS_center-(WDW_SIZE-1)/2),
end = c(TFBS_center+(WDW_SIZE-1)/2))
range_rs <- resize(ROIs_rsz[i], width = 3, fix = "center")
}
#TFBS window
if(WITH_TFBS == TRUE){
TFBS_MOTIF.OI <- str_replace(ROIs_rsz[i]$TFBS_motif, "_", "::")
TFBSs <- subsetByOverlaps(MotDBf, ROIs_rsz[i])
TFBSs <- subsetByOverlaps(TFBSs, resize(ROIs_rsz[i], width = 30, fix = "center"))
TFBS <- TFBSs[grepl(paste0("^", TFBS_MOTIF.OI, "$"), TFBSs$name)]
if(length(TFBS) != 1){
TFBS <- TFBSs[1]
}
bins <- list(c(-35,-25), c(-15,+15), c(+25,+35))
TFBS_center <- start(TFBS) + (end(TFBS)-start(TFBS))/2
BinsCoordinates_TFBS <- IRanges(start = c(TFBS_center+bins[[1]][1], TFBS_center+bins[[2]][1], TFBS_center+bins[[3]][1]),
end = c(TFBS_center+bins[[1]][2], TFBS_center+bins[[2]][2], TFBS_center+bins[[3]][2]))
} else {
if(PLOT_MOTIFS == TRUE){
TFBSs <- subsetByOverlaps(MotDBf, ROIs_rsz[i])
TFBSs <- TFBSs[grepl(paste(MOTIFS_OI, collapse = "|"), TFBSs$name)]
} else {
TFBSs <- NULL
}
}
## ES ----------
MethGR_1 <- try(CallContextMethylation(sampleSheet = Qinput, sample = Samples_1, genome = BSgenome.Mmusculus.UCSC.mm10, coverage = COV, RegionOfInterest = ROIs_rsz[i]))
if("try-error" %in% class(MethGR_1)) {next}
if(length(subsetByOverlaps(MethGR_1[[1]][[2]], range_rs, ignore.strand = TRUE)) == 0) {next}
Sample_name <- str_remove(Samples_1, "SMF_MM_")
Sort <- SortReads.CA.EK(MethSM = MethGR_1, BinsCoordinates, range = ROIs_rsz[i], DE = FALSE, WHAT = WHAT) #Simple sorting for CA
Sort_me <- SortReads.CA.CGme.EK(MethSM = MethGR_1, BinsCoordinates, range = ROIs_rsz[i], DE = FALSE, WHAT = WHAT) #Double sorting for CA and then for 5mC -- THIS IS THE PREFERED SORTING!
SM_count <- length(Sort[[1]]) + length(Sort[[2]])
TFBSs_range <- GRanges() #empty GRange object for CA anlaysis
### Plot ES only
pAvg_1 <- PlotAvgSMF.EK(MethGR = MethGR_1[[1]], TFBSs = TFBSs_range, range = ROIs_rsz[i]) +
scale_y_continuous(limits = c(-0.05, 1.05), expand = c(0, 0), breaks = seq(0, 1, 0.25), name = "SMF", sec.axis = sec_axis(~ ., name = "CG methylation", breaks = seq(0, 1, 0.25))) +
labs(title = TITLE, caption = CAPTION) +
theme(panel.grid.major = element_blank())
#Additional plot to see CA window
range_CpG <- resize(ROIs_rsz[i], width = 3, fix = "center")
CpG_loc <- subsetByOverlaps(MethGR_1[[1]][[2]], range_CpG)
pAvg_1_window <- pAvg_1 +
geom_vline(xintercept = c(start(CpG_loc) + ((WDW_SIZE-1)/2), start(CpG_loc) - ((WDW_SIZE-1)/2)), linetype = 3)
### Plot sorting according to CA binding + 5mC
pSM_CG_1.CA <- PlotSM.CpG.EK(MethSM = MethGR_1, range = ROIs_rsz[i], SortedReads = Sort_me, accessibility = TRUE, CGme_sort = TRUE, WHAT = WHAT, title = Sample_name)
pSM_GC_1.CA <- PlotSM.GpC.EK(MethSM = MethGR_1, range = ROIs_rsz[i], SortedReads = Sort_me, accessibility = TRUE, CGme_sort = TRUE, WHAT = WHAT, title = Sample_name)
pStates_1.CA <- StateQuantificationPlot.GpC.EK(SortedReads = Sort_me, accessibility = TRUE) + labs(subtitle = "CA")
pStatesMeth_1.CA <- StateQuantificationPlot.CpG.EK(MethSM = MethGR_1, SortedReads = Sort_me, range = ROIs_rsz[i], range_width = 101, accessibility = TRUE, WHAT = "TF")
### Combine and plot - with double sorting (accessibility & 5mC)
pAvg_1.2 <- pAvg_1 + plot_spacer() + plot_layout(ncol = 2, widths = c(1, 0.75))
pSMs_1.CA <- (pSM_GC_1.CA + pStates_1.CA + pSM_CG_1.CA + pStatesMeth_1.CA & theme(plot.background = element_blank())) + plot_layout(ncol = 4, widths = c(1,0.2,1,0.2))
pSMs_1.CA.l <- (pSM_GC_1.CA + pStates_1.CA + pSM_CG_1.CA + pStatesMeth_1.CA & theme(plot.background = element_blank(), legend.position = "none")) + plot_layout(ncol = 4, widths = c(1,0.2,1,0.2)) #Version without legend when combined with TKO plots
final_plot_1.CA <- pAvg_1.2 / pSMs_1.CA + plot_layout(nrow = 2, heights = c(1, 0.75))
# final_plot_1.CA
### Save plots
PLOTNAME <- paste0(PLOT.OUT, "_", Samples_1, ADD)
ggsave(plot = final_plot_1.CA, filename = paste0(PLOTNAME, ".pdf"), height = 8, width = 8)
## TKO -----------------------------
#For DNMT TKOs we will not plot the 5mC (and also not sort for it), because it is 0 for "NO" and shows accessibility in "DE" datasets.
if(WITH_DNMT_TKO == TRUE){
MethGR_2 <- CallContextMethylation(sampleSheet = Qinput, sample = Samples_2, genome = BSgenome.Mmusculus.UCSC.mm10, coverage = COV, RegionOfInterest = ROIs_rsz[i])
if (TKO_TREAT == "DE"){DE_INPUT <- TRUE} else if (TKO_TREAT == "NO"){DE_INPUT <- FALSE}
Sample_name2 <- str_remove(Samples_2, "SMF_MM_")
### Sorting SM
Sort_2 <- SortReads.CA.EK(MethSM = MethGR_2, BinsCoordinates, range = ROIs_rsz[i], DE = DE_INPUT, WHAT = WHAT)
SM_count_2 <- length(Sort_2[[1]]) + length(Sort_2[[2]])
### Plot TKO - only TKO
pAvg_2 <- PlotAvgSMF.EK(MethGR = MethGR_1[[1]], TFBSs = TFBSs_range, range = ROIs_rsz[i],
TKO = TRUE, DE = DE_INPUT, MethGR_TKO = MethGR_2[[1]], TKOonly = TRUE) +
scale_y_continuous(limits = c(-0.05, 1.05), expand = c(0, 0), breaks = seq(0, 1, 0.25), name = "SMF", sec.axis = sec_axis(~ ., name = "CG methylation", breaks = seq(0, 1, 0.25))) +
labs(title = TITLE, caption = CAPTION) +
theme(panel.grid.major = element_blank())
### Plot TKO - ES + TKO
pAvg_2.2 <- PlotAvgSMF.EK(MethGR = MethGR_1[[1]], TFBSs = TFBSs_range, range = ROIs_rsz[i],
TKO = TRUE, DE = DE_INPUT, MethGR_TKO = MethGR_2[[1]]) +
scale_y_continuous(limits = c(-0.05, 1.05), expand = c(0, 0), breaks = seq(0, 1, 0.25), name = "SMF", sec.axis = sec_axis(~ ., name = "CG methylation", breaks = seq(0, 1, 0.25))) +
labs(title = TITLE, caption = CAPTION) +
theme(panel.grid.major = element_blank())
### Plot TKO SM
pSM_GC_2.CA <- PlotSM.GpC.EK(MethSM = MethGR_2, range = ROIs_rsz[i], SortedReads = Sort_2, DE = DE_INPUT, accessibility = TRUE, CGme_sort = FALSE, WHAT = WHAT, title = Sample_name2)
pStates_2.CA <- StateQuantificationPlot.GpC.EK(Sort_2, accessibility = TRUE) + labs(subtitle = "CA")
### Plot TKO 5mC Box
pBox_2 <- Plot.5mC.Box.EK(MethGR1 = MethGR_1[[1]], n_samples = 2, range = ROIs_rsz[i], MethGR2 = MethGR_2[[1]], DE2 = DE_INPUT) +
labs(title = TITLE)
### Combine and plot - AVERAGE PLOT
pAvg_2.1 <- pAvg_2 + plot_spacer() + plot_layout(ncol = 2, widths = c(1,0.75))
pAvg_2.3 <- pAvg_2.2 + plot_spacer() + plot_layout(ncol = 2, widths = c(1,0.75))
pAvg_box_2 <- pBox_2 + (pAvg_2.2 + theme(title = element_blank())) + plot_layout(ncol = 1, height = c(0.1, 1))
### Combine and plot
### SMF PLOT withOUT double sorting & withOUT 5mC
pSMs_2.CA <- (pSM_GC_2.CA + pStates_2.CA + plot_spacer() + plot_spacer() & theme(plot.background = element_blank())) + plot_layout(ncol = 4, widths = c(1,0.2,1,0.2))
pSMs_2.CA.l <- pSMs_2.CA & theme(legend.position = "none")
final_plot_2. <- pAvg_2.1 / pSMs_2.CA + plot_layout(nrow = 2, heights = c(1,0.75))
### Save plots
PLOTNAME <- paste0(PLOT.OUT, "_", Samples_2, ADD)
if(PLOT_AVG_ONLY == TRUE){
ggsave(plot = pAvg_box_2, filename = paste0(PLOTNAME, "_avBox.pdf"), height = 5, width = 6)
# ggsave(plot = pAvg_2, filename = paste0(PLOTNAME, "_av.pdf"), height = 3, width = 4)
message("DONE: Plot PLOT_AVG_ONLY - ", Samples_2)
}
if(PLOT_INDIVIDU == TRUE){
ggsave(plot = final_plot_2., filename = paste0(PLOTNAME, ".pdf"), height = 8, width = 8)
# ggsave(plot = final_plot_2., filename = paste0(PLOTNAME, ".png"), height = 8, width = 8)
message("DONE: Plot PLOT_INDIVIDU - ", Samples_2)
}
if(PLOT_COMBI == TRUE){
PLOTNAME <- paste0(PLOT.OUT, "_", Samples_1, Samples_2, ADD)
### Combo plot SMF
pSMs_2.CA <- (pSM_GC_2.CA + pStates_2.CA + plot_spacer() + plot_spacer() & theme(plot.background = element_blank())) + plot_layout(ncol = 4, widths = c(1,0.2,1,0.2))
pSMs_2.CA.l <- pSMs_2.CA & theme(legend.position = "none")
final_plot_2.combi <- pAvg_2.3 / pSMs_1.CA.l / pSMs_2.CA + plot_layout(nrow = 3, heights = c(1, 0.75, 0.75))
ggsave(plot = final_plot_2., filename = paste0(PLOTNAME, "_combi.pdf"), height = 8, width = 8)
# ggsave(plot = final_plot_2., filename = paste0(PLOTNAME, ".png"), height = 8, width = 8)
message("DONE: Plot PLOT_COMBI - ", Samples_1, Samples_2)
}
}
## TET TKO -------
if(WITH_TET_TKO == TRUE){
MethGR_3 <- CallContextMethylation(sampleSheet = Qinput, sample = Samples_3, genome = BSgenome.Mmusculus.UCSC.mm10, coverage = COV, RegionOfInterest = ROIs_rsz[i])
if (TET_TREAT == "DE"){DE_INPUT_3 <- TRUE} else if (TET_TREAT == "NO"){DE_INPUT_3 <- FALSE}
Sample_name3 <- str_remove(Samples_3, "SMF_MM_")
### Sorting SM
Sort_3 <- SortReads.CA.EK(MethSM = MethGR_3, BinsCoordinates, range = ROIs_rsz[i], DE = DE_INPUT_3, WHAT = WHAT)
SM_count_3 <- length(Sort_3[[1]]) + length(Sort_3[[2]])
if (TET_TREAT != "DE"){
Sort_me_3 <- SortReads.CA.CGme.EK(MethSM = MethGR_3, BinsCoordinates, range = ROIs_rsz[i], DE = DE_INPUT_3, WHAT = WHAT)
}
### Plot TET - only TET
pAvg_3 <- PlotAvgSMF.EK(MethGR = MethGR_1[[1]], TFBSs = TFBSs_range, range = ROIs_rsz[i],
TETTKO = TRUE, DE = DE_INPUT_3, MethGR_TETTKO = MethGR_3[[1]], TETonly = TRUE) +
scale_y_continuous(limits = c(-0.05, 1.05), expand = c(0, 0), breaks = seq(0, 1, 0.25), name = "SMF", sec.axis = sec_axis(~ ., name = "CG methylation", breaks = seq(0, 1, 0.25))) +
labs(title = TITLE, caption = CAPTION) +
theme(panel.grid.major = element_blank())
### Plot TET - ES + TET
pAvg_3.2 <- PlotAvgSMF.EK(MethGR = MethGR_1[[1]], TFBSs = TFBSs_range, range = ROIs_rsz[i],
TETTKO = TRUE, DE = DE_INPUT_3, MethGR_TETTKO = MethGR_3[[1]]) +
scale_y_continuous(limits = c(-0.05, 1.05), expand = c(0, 0), breaks = seq(0, 1, 0.25), name = "SMF", sec.axis = sec_axis(~ ., name = "CG methylation", breaks = seq(0, 1, 0.25))) +
labs(title = TITLE, caption = CAPTION) +
theme(panel.grid.major = element_blank())
### Plot TET SM
if (TET_TREAT != "DE"){
### Plot TET SM - with 5mC
pSM_CG_3.CA <- PlotSM.CpG.EK(MethSM = MethGR_3, range = ROIs_rsz[i], SortedReads = Sort_me_3, accessibility = TRUE, WHAT = WHAT, title = Sample_name3)
pStatesMeth_3.CA <- StateQuantificationPlot.CpG.EK(MethSM = MethGR_3, SortedReads = Sort_me_3, range = ROIs_rsz[i], range_width = 201, accessibility = TRUE, WHAT = WHAT)
pSM_GC_3.CA <- PlotSM.GpC.EK(MethSM = MethGR_3, range = ROIs_rsz[i], SortedReads = Sort_me_3, accessibility = TRUE, WHAT = WHAT, title = Sample_name3)
pStates_3.CA <- StateQuantificationPlot.GpC.EK(Sort_me_3, accessibility = TRUE) + labs(subtitle = "CA")
} else if (TET_TREAT == "DE"){
### Plot TET SM - withOUT 5mC
pSM_GC_3.CA <- PlotSM.GpC.EK(MethSM = MethGR_3, range = ROIs_rsz[i], SortedReads = Sort_3, DE = DE_INPUT_3, accessibility = TRUE, title = Sample_name3)
pStates_3.CA <- StateQuantificationPlot.GpC.EK(Sort_3, accessibility = TRUE) + labs(subtitle = "CA")
}
### Plot TET 5mC Box
pBox_3 <- Plot.5mC.Box.EK(MethGR1 = MethGR_1[[1]], n_samples = 2, range = ROIs_rsz[i], MethGR2 = MethGR_3[[1]], DE2 = DE_INPUT_3) +
labs(title = TITLE)
### Combine and plot - AVERAGE PLOT
pAvg_3.1 <- pAvg_3 + plot_spacer() + plot_layout(ncol = 2, widths = c(1,0.75))
pAvg_3.3 <- pAvg_3.2 + plot_spacer() + plot_layout(ncol = 2, widths = c(1,0.75))
pAvg_box_3 <- pBox_3 + (pAvg_3.2 + theme(title = element_blank())) + plot_layout(ncol = 1, height = c(0.1, 1))
### Combine and plot
if (TET_TREAT != "DE"){
### SMF PLOT with double sorting
pSMs_3.CA <- (pSM_GC_3.CA + pStates_3.CA + pSM_CG_3.CA + pStatesMeth_3.CA & theme(plot.background = element_blank())) + plot_layout(ncol = 4, widths = c(1,0.2,1,0.2))
pSMs_3.CA.l <- (pSM_GC_3.CA + pStates_3.CA + pSM_CG_3.CA + pStatesMeth_3.CA & theme(plot.background = element_blank(), legend.position = "none")) + plot_layout(ncol = 4, widths = c(1,0.2,1,0.2))
final_plot_3. <- pAvg_3.1 / pSMs_3.CA + plot_layout(nrow = 2, heights = c(1,0.75))
} else if (TET_TREAT == "DE"){
### SMF PLOT withOUT double sorting & withOUT 5mC
pSMs_3.CA <- (pSM_GC_3.CA + pStates_3.CA + plot_spacer() + plot_spacer() & theme(plot.background = element_blank())) + plot_layout(ncol = 4, widths = c(1,0.2,1,0.2))
pSMs_3.CA.l <- (pSM_GC_3.CA + pStates_3.CA + plot_spacer() + plot_spacer() & theme(plot.background = element_blank(), legend.position = "none")) + plot_layout(ncol = 4, widths = c(1,0.2,1,0.2))
final_plot_3. <- pAvg_3.1 / pSMs_3.CA + plot_layout(nrow = 2, heights = c(1,0.75))
}
### Save plots
PLOTNAME <- paste0(PLOT.OUT, "_", Samples_3, ADD)
if(PLOT_AVG_ONLY == TRUE){
ggsave(plot = pAvg_box_3, filename = paste0(PLOTNAME, "_avBox.pdf"), height = 5, width = 6)
# ggsave(plot = pAvg_3, filename = paste0(PLOTNAME, "_av.pdf"), height = 3, width = 4)
message("DONE: Plot PLOT_AVG_ONLY - ", Samples_3)
}
if(PLOT_INDIVIDU == TRUE){
ggsave(plot = final_plot_3., filename = paste0(PLOTNAME, ".pdf"), height = 8, width = 8)
# ggsave(plot = final_plot_3., filename = paste0(PLOTNAME, ".png"), height = 8, width = 8)
message("DONE: Plot PLOT_INDIVIDU - ", Samples_3)
}
if(PLOT_COMBI == TRUE){
PLOTNAME <- paste0(PLOT.OUT, "_", Samples_1, Samples_3, ADD)
### Combo plot SMF
final_plot_3.combi <- pAvg_3.3 / pSMs_1.CA.l / pSMs_3.CA + plot_layout(nrow = 3, heights = c(1, 0.75, 0.75))
ggsave(plot = final_plot_3., filename = paste0(PLOTNAME, "_combi.pdf"), height = 8, width = 8)
# ggsave(plot = final_plot_3., filename = paste0(PLOTNAME, ".png"), height = 8, width = 8)
message("DONE: Plot PLOT_COMBI - ", Samples_1, Samples_3)
}
}
## BOTH TKOs ----------------------------
if(WITH_DNMT_TKO == TRUE & WITH_TET_TKO == TRUE){
### Plot ES + TKO + TET
pAvg_ALL <- PlotAvgSMF.EK(MethGR = MethGR_1[[1]], TFBSs = TFBSs_range, range = ROIs_rsz[i],
TKO = TRUE, DE = DE_INPUT, MethGR_TKO = MethGR_2[[1]],
TETTKO = TRUE, MethGR_TETTKO = MethGR_3[[1]]) +
scale_y_continuous(limits = c(-0.05, 1.05), expand = c(0, 0), breaks = seq(0, 1, 0.25), name = "SMF", sec.axis = sec_axis(~ ., name = "CG methylation", breaks = seq(0, 1, 0.25))) +
theme(panel.grid.major = element_blank()) +
labs(title = TITLE, caption = CAPTION)
pBox_ALL <- Plot.5mC.Box.EK(MethGR1 = MethGR_1[[1]], n_samples = 3, range = ROIs_rsz[i],
MethGR2 = MethGR_2[[1]], DE2 = DE_INPUT, MethGR3 = MethGR_3[[1]], DE3 = DE_INPUT_3) +
labs(title = TITLE)
### Combine and plot ALL
final_plot_ALL <- pAvg_ALL + pSMs_1.CA.l + pSMs_2.CA.l + pSMs_3.CA + plot_layout(nrow = 4, heights = c(1.5,1,1,1.3))
pAvg_box_ALL <- pBox_ALL + (pAvg_ALL + theme(title = element_blank())) + plot_layout(ncol = 1, height = c(0.2, 1))
### Save plots
PLOTNAME <- paste0(PLOT.OUT, "_", Samples_1, "_", Samples_2, "_", Samples_3, ADD)
if(PLOT_COMBI_ALL == TRUE){
ggsave(plot = pAvg_box_ALL, filename = paste0(PLOTNAME, "_avBox.pdf"), height = 5, width = 6)
ggsave(plot = final_plot_ALL, filename = paste0(PLOTNAME, ".pdf"), height = 16, width = 8)
message("DONE: Plot PLOT_COMBI_ALL - ", Samples_1, Samples_2, Samples_3)
}
}
}