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Commit 38a09ebb authored by Matteo Barcella's avatar Matteo Barcella
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Adding scripts for Allele Fractions variants

parent 8d31118e
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WES/AF_comparison.R 0 → 100755
View file @ 38a09ebb
# Script for analyzing AF
library(openxlsx)
source(file = "Filter_variants_v3.R")
inputdata <- list()
for (mysample in c("12_27_1","12_27_2", "10_395_3", "10_395_4")) {
inputdata[["rawcall"]][[mysample]] <- filter_variants_v3(infile = paste0(mysample, "_dbsnp_cc_cnc_dbnsfp_refseq_light_raw_filtered.fields.txt"), sampleid = mysample, dp = 50)
inputdata[["passonly"]][[mysample]] <- filter_variants_v3(infile = paste0(mysample, "_dbsnp_cc_cnc_dbnsfp_refseq_light.fields.txt"), sampleid = mysample, dp = 50)
}
source(file = "/home/mbarcella/scripts/bitbucket/exometools/Compare_variants_AF_v3.R")
Comparison_12_27 <- list()
Comparison_10_395 <- list()
for(i in names(inputdata)){
Comparison_12_27[[i]] <- Compare_variants_AF_v3(dblist = inputdata[[i]], sampleid_1 = "12_27_1", sampleid_2 = "12_27_2",
my.width = 8, my.height = 4, my.res = 120,
tag = i, outfolder = "12_27/")
Comparison_10_395[[i]] <- Compare_variants_AF_v3(dblist = inputdata[[i]], sampleid_1 = "10_395_3", sampleid_2 = "10_395_4",
my.width = 8, my.height = 4, my.res = 120,
tag = i, outfolder = "10_395/")
}
# plot AF density in H and L (full list)
AF_density_raw <- list()
library(cowplot)
tmpdf_1_raw <- subset.data.frame(x = Comparison_12_27$rawcall$unionannot, select = c("AF_GFP_H"), subset = AF_GFP_H > 0)
tmpdf_2_raw <- subset.data.frame(x = Comparison_12_27$rawcall$unionannot, select = c("AF_GFP_L"), subset = AF_GFP_L > 0)
tmpdf_3_raw <- subset.data.frame(x = Comparison_10_395$rawcall$unionannot, select = c("AF_GFP_H"), subset = AF_GFP_H > 0)
tmpdf_4_raw <- subset.data.frame(x = Comparison_10_395$rawcall$unionannot, select = c("AF_GFP_L"), subset = AF_GFP_L > 0)
AF_density_raw[["12-27_miRNA_L"]] <- ggplot(data = tmpdf_1_raw,
mapping = aes(AF_GFP_H)) + ylim(0,600) +
geom_histogram(bins = 50, fill = alpha("#003366",0.8)) + xlab("miRNA_low") +
theme(axis.text = element_text(size = 14), legend.title = element_blank()) +
ggtitle("12-27_miRNA_L")
AF_density_raw[["12-27_miRNA_H"]] <- ggplot(data = tmpdf_2_raw,
mapping = aes(AF_GFP_L)) + ylim(0,600) +
geom_histogram(bins = 50, fill = alpha("#fc2803",0.8))+ xlab("miRNA_high") +
theme(axis.text = element_text(size = 14), legend.title = element_blank()) +
ggtitle("12-27_miRNA_H")
AF_density_raw[["10-395_miRNA_L"]] <- ggplot(data = tmpdf_3_raw,
mapping = aes(AF_GFP_H)) + ylim(0,600) +
geom_histogram(bins = 50, fill = alpha("#003366",0.8)) + xlab("miRNA_low") +
theme(axis.text = element_text(size = 14), legend.title = element_blank()) +
ggtitle("10-395_miRNA_L")
AF_density_raw[["10-395_miRNA_H"]] <- ggplot(data = tmpdf_4_raw,
mapping = aes(AF_GFP_L)) + ylim(0,600) +
geom_histogram(bins = 50, fill = alpha("#fc2803",0.8)) + xlab("miRNA_high") +
theme(axis.text = element_text(size = 14), legend.title = element_blank()) +
ggtitle("10-395_miRNA_H")
png(filename = "AF_density_raw_variants.png", width = 8, height = 6, units = "in", res = 300)
plot_grid(AF_density_raw[["12-27_miRNA_L"]], AF_density_raw[["12-27_miRNA_H"]],
AF_density_raw[["10-395_miRNA_L"]] , AF_density_raw[["10-395_miRNA_H"]],
nrow = 2)
dev.off()
## PLOTTING PASS-ONLY
tmpdf_1_pass <- subset.data.frame(x = Comparison_12_27$passonly$unionannot, select = c("AF_GFP_H"), subset = AF_GFP_H > 0)
tmpdf_2_pass <- subset.data.frame(x = Comparison_12_27$passonly$unionannot, select = c("AF_GFP_L"), subset = AF_GFP_L > 0)
tmpdf_3_pass <- subset.data.frame(x = Comparison_10_395$passonly$unionannot, select = c("AF_GFP_H"), subset = AF_GFP_H > 0)
tmpdf_4_pass <- subset.data.frame(x = Comparison_10_395$passonly$unionannot, select = c("AF_GFP_L"), subset = AF_GFP_L > 0)
AF_density_pass <- list()
AF_density_pass[["12-27_miRNA_L"]] <- ggplot(data = tmpdf_1_pass,
mapping = aes(AF_GFP_H)) + ylim(0,300) +
geom_histogram(bins = 50, fill = alpha("#003366",0.8)) + xlab("miRNA_low") +
theme(axis.text = element_text(size = 14), legend.title = element_blank()) +
ggtitle("12-27_miRNA_L")
AF_density_pass[["12-27_miRNA_H"]] <- ggplot(data = tmpdf_2_pass,
mapping = aes(AF_GFP_L)) + ylim(0,300) +
geom_histogram(bins = 50, fill = alpha("#fc2803",0.8))+ xlab("miRNA_high") +
theme(axis.text = element_text(size = 14), legend.title = element_blank()) +
ggtitle("12-27_miRNA_H")
AF_density_pass[["10-395_miRNA_L"]] <- ggplot(data = tmpdf_3_pass,
mapping = aes(AF_GFP_H)) + ylim(0,300) +
geom_histogram(bins = 50, fill = alpha("#003366",0.8)) + xlab("miRNA_low") +
theme(axis.text = element_text(size = 14), legend.title = element_blank()) +
ggtitle("10-395_miRNA_L")
AF_density_pass[["10-395_miRNA_H"]] <- ggplot(data = tmpdf_4_pass,
mapping = aes(AF_GFP_L)) + ylim(0,300) +
geom_histogram(bins = 50, fill = alpha("#fc2803",0.8)) + xlab("miRNA_high") +
theme(axis.text = element_text(size = 14), legend.title = element_blank()) +
ggtitle("10-395_miRNA_H")
png(filename = "AF_density_pass_variants.png", width = 8, height = 6, units = "in", res = 300)
plot_grid(AF_density_pass[["12-27_miRNA_L"]], AF_density_pass[["12-27_miRNA_H"]],
AF_density_pass[["10-395_miRNA_L"]] , AF_density_pass[["10-395_miRNA_H"]],
nrow = 2)
dev.off()
# Histograms with PASS or clustered_events
AF_density_mildfilt <- list()
Mild_vars_12_27 <- Comparison_12_27$rawcall$unionannot
# Recoding filter variable, PASS / no PASS
Mild_vars_12_27$FILTER_GFP_H_simple <- ifelse(test = Mild_vars_12_27$FILTER_GFP_H == "PASS", "PASS","NOPASS")
Mild_vars_12_27$FILTER_GFP_L_simple <- ifelse(test = Mild_vars_12_27$FILTER_GFP_L == "PASS", "PASS","NOPASS")
tmpdf_1_mild <- subset.data.frame(x = Mild_vars_12_27, select = c("AF_GFP_H"),
subset = AF_GFP_H > 0 & (FILTER_GFP_H_simple == "PASS" | FILTER_GFP_L_simple == "PASS") & !HGVS_P == "")
tmpdf_2_mild <- subset.data.frame(x = Mild_vars_12_27, select = c("AF_GFP_L"),
subset = AF_GFP_L > 0 & (FILTER_GFP_H_simple == "PASS" | FILTER_GFP_L_simple == "PASS") & !HGVS_P == "")
Mild_vars_10_395 <- Comparison_10_395$rawcall$unionannot
Mild_vars_10_395$FILTER_GFP_H_simple <- ifelse(test = Mild_vars_10_395$FILTER_GFP_H == "PASS", "PASS","NOPASS")
Mild_vars_10_395$FILTER_GFP_L_simple <- ifelse(test = Mild_vars_10_395$FILTER_GFP_L == "PASS", "PASS","NOPASS")
tmpdf_3_mild <- subset.data.frame(x = Mild_vars_10_395, select = c("AF_GFP_H"),
subset = AF_GFP_H > 0 & (FILTER_GFP_H_simple == "PASS" | FILTER_GFP_L_simple == "PASS") & !HGVS_P == "")
tmpdf_4_mild <- subset.data.frame(x = Mild_vars_10_395, select = c("AF_GFP_L"),
subset = AF_GFP_L > 0 & (FILTER_GFP_H_simple == "PASS" | FILTER_GFP_L_simple == "PASS") & !HGVS_P == "")
AF_density_mildfilt[["12-27_miRNA_L"]] <- ggplot(data = tmpdf_1_mild,
mapping = aes(AF_GFP_H)) + ylim(0,300) +
geom_histogram(bins = 50, fill = alpha("#003366",0.8)) + xlab("miRNA_low") +
theme(axis.text = element_text(size = 14), legend.title = element_blank()) +
ggtitle("12-27_miRNA_L")
AF_density_mildfilt[["12-27_miRNA_H"]] <- ggplot(data = tmpdf_2_mild,
mapping = aes(AF_GFP_L)) + ylim(0,300) +
geom_histogram(bins = 50, fill = alpha("#fc2803",0.8))+ xlab("miRNA_high") +
theme(axis.text = element_text(size = 14), legend.title = element_blank()) +
ggtitle("12-27_miRNA_H")
AF_density_mildfilt[["10-395_miRNA_L"]] <- ggplot(data = tmpdf_3_mild,
mapping = aes(AF_GFP_H)) + ylim(0,300) +
geom_histogram(bins = 50, fill = alpha("#003366",0.8)) + xlab("miRNA_low") +
theme(axis.text = element_text(size = 14), legend.title = element_blank()) +
ggtitle("10-395_miRNA_L")
AF_density_mildfilt[["10-395_miRNA_H"]] <- ggplot(data = tmpdf_4_mild,
mapping = aes(AF_GFP_L)) + ylim(0,300) +
geom_histogram(bins = 50, fill = alpha("#fc2803",0.8)) + xlab("miRNA_high") +
theme(axis.text = element_text(size = 14), legend.title = element_blank()) +
ggtitle("10-395_miRNA_H")
png(filename = "AF_density_mildfilt_variants.png", width = 8, height = 6, units = "in", res = 300)
plot_grid(AF_density_mildfilt[["12-27_miRNA_L"]], AF_density_mildfilt[["12-27_miRNA_H"]],
AF_density_mildfilt[["10-395_miRNA_L"]] , AF_density_mildfilt[["10-395_miRNA_H"]],
nrow = 2)
dev.off()
# removing not exonic by using & !HGVS_P == ""
mild_12_27 <- subset.data.frame(x = Mild_vars_12_27, subset = (FILTER_GFP_H_simple == "PASS" | FILTER_GFP_L_simple == "PASS") & !HGVS_P == "")
mild_12_27$FILTER <- paste0(mild_12_27$FILTER_GFP_H_simple, "_", mild_12_27$FILTER_GFP_L_simple)
png(filename = "12_27_variants_PASS_H_or_L_scatterplot_mild.png", width = 12, height = 9, units = "in", res = 900)
ggplot(data = mild_12_27, mapping = aes(x = AF_GFP_H, y = AF_GFP_L, col=FILTER)) + geom_point() +
theme(legend.text = element_text(size = 7), legend.title = element_blank(), legend.position = "top",
legend.key.height = unit(1, units = "mm"), legend.key.width = unit(1, units = "mm"),
plot.title = element_text(hjust = 0.5), plot.subtitle = element_text(hjust = 0.5),
legend.justification = 0.5) + ggtitle("12-27 variants", subtitle = "PASS in at least one the 2 populations")
dev.off()
mild_10_395 <- subset.data.frame(x = Mild_vars_10_395, subset = (FILTER_GFP_H_simple == "PASS" | FILTER_GFP_L_simple == "PASS") & !HGVS_P == "")
mild_10_395$FILTER <- paste0(mild_10_395$FILTER_GFP_H_simple, "_", mild_10_395$FILTER_GFP_L_simple)
png(filename = "10_395_variants_PASS_H_or_L_scatterplot_mild.png", width = 12, height = 9, units = "in", res = 900)
ggplot(data = mild_10_395, mapping = aes(x = AF_GFP_H, y = AF_GFP_L, col=FILTER)) + geom_point() +
theme(legend.text = element_text(size = 7), legend.title = element_blank(), legend.position = "top",
legend.key.height = unit(1, units = "mm"), legend.key.width = unit(1, units = "mm"),
plot.title = element_text(hjust = 0.5), plot.subtitle = element_text(hjust = 0.5),
legend.justification = 0.5) + ggtitle("10_395 variants", subtitle = "PASS in at least one the 2 populations")
dev.off()
# Variants NA in miRNAlow 12-27 and PASS in miRNA-H and viceversa
library(openxlsx)
write.xlsx(x = mild_12_27[is.na(mild_12_27$FILTER_GFP_H) & mild_12_27$FILTER_GFP_L == "PASS",],
file = "mild_12_27_PASS_GFP_L_NA_GFP_H.xlsx", asTable = T)
write.xlsx(x = mild_12_27[is.na(mild_12_27$FILTER_GFP_L) & mild_12_27$FILTER_GFP_H == "PASS",],
file = "mild_12_27_PASS_GFP_H_NA_GFP_L.xlsx", asTable = T)
write.xlsx(x = mild_10_395[is.na(mild_10_395$FILTER_GFP_H) & mild_10_395$FILTER_GFP_L == "PASS",],
file = "mild_10_395_PASS_GFP_L_NA_GFP_H.xlsx", asTable = T)
write.xlsx(x = mild_10_395[is.na(mild_10_395$FILTER_GFP_L) & mild_10_395$FILTER_GFP_H == "PASS",],
file = "mild_10_395_PASS_GFP_H_NA_GFP_L.xlsx", asTable = T)
# saving also only annotated (COSMIC or dbSNP)
mild_12_27_pass_GFP_L_NA_GFP_H <- subset.data.frame(x = mild_12_27, subset = !ID == "" & is.na(FILTER_GFP_H) & FILTER_GFP_L == "PASS")
writeLines(text = mild_12_27_pass_GFP_L_NA_GFP_H$variantkey, con = "mild_12_27_pass_GFP_L_NA_GFP_H.txt")
mild_12_27_pass_GFP_H_NA_GFP_L <- subset.data.frame(x = mild_12_27, subset = !ID == "" & is.na(FILTER_GFP_L) & FILTER_GFP_H == "PASS")
writeLines(text = mild_12_27_pass_GFP_H_NA_GFP_L$variantkey, con = "mild_12_27_pass_GFP_H_NA_GFP_L.txt")
write.xlsx(x = mild_12_27_pass_GFP_L_NA_GFP_H,
file = "mild_12_27_PASS_GFP_L_NA_GFP_H_cosmic_dbsnp.xlsx", asTable = T)
write.xlsx(x = mild_12_27_pass_GFP_H_NA_GFP_L,
file = "mild_12_27_PASS_GFP_H_NA_GFP_L_cosmic_dbsnp.xlsx", asTable = T)
mild_10_395_pass_GFP_L_NA_GFP_H <- subset.data.frame(x = mild_10_395, subset = !ID == "" & is.na(FILTER_GFP_H) & FILTER_GFP_L == "PASS")
writeLines(text = mild_10_395_pass_GFP_L_NA_GFP_H$variantkey, con = "mild_10_395_pass_GFP_L_NA_GFP_H.txt")
mild_10_395_pass_GFP_H_NA_GFP_L <- subset.data.frame(x = mild_10_395, subset = !ID == "" & is.na(FILTER_GFP_L) & FILTER_GFP_H == "PASS")
writeLines(text = mild_10_395_pass_GFP_H_NA_GFP_L$variantkey, con = "mild_10_395_pass_GFP_H_NA_GFP_L.txt")
write.xlsx(x = mild_10_395_pass_GFP_L_NA_GFP_H,
file = "mild_10_395_PASS_GFP_L_NA_GFP_H_cosmic_dbsnp.xlsx", asTable = T)
write.xlsx(x = mild_10_395_pass_GFP_H_NA_GFP_L,
file = "mild_10_395_PASS_GFP_H_NA_GFP_L_cosmic_dbsnp.xlsx", asTable = T)
# plotting CR vs DGN variants
# importing variants
dgn_vs_cr_vars <- read.xlsx(xlsxFile = "12_27_Somatic_DGN_only_variants.xlsx")
mykeys <- dgn_vs_cr_vars$variantkey
raw_vars_12_27_1 <- read.table(file = "12_27_1_dbsnp_cc_cnc_dbnsfp_refseq_light_raw_filtered.fields.txt",
sep = "\t" ,header = T, stringsAsFactors = F)
raw_vars_12_27_2 <- read.table(file = "12_27_2_dbsnp_cc_cnc_dbnsfp_refseq_light_raw_filtered.fields.txt",
sep = "\t" ,header = T, stringsAsFactors = F)
colnames(raw_vars_12_27_1) <- c("Chromosome","Position","Reference","Alternative","AF","DP","AD.R","AD.A","GT","ID","FILTER","HGVS_P","Gene","Biotype",
"Rank","Effect","Impact","Common","G5",colnames(raw_vars_12_27_1)[20:35])
raw_vars_12_27_1$sampleid <- "12_27_1"
raw_vars_12_27_1$variantkey <- paste(raw_vars_12_27_1$Chromosome,
raw_vars_12_27_1$Position,
raw_vars_12_27_1$Reference,
raw_vars_12_27_1$Alternative,sep = "_")
colnames(raw_vars_12_27_2) <- c("Chromosome","Position","Reference","Alternative","AF","DP","AD.R","AD.A","GT","ID","FILTER","HGVS_P","Gene","Biotype",
"Rank","Effect","Impact","Common","G5",colnames(raw_vars_12_27_2)[20:35])
raw_vars_12_27_2$sampleid <- "12_27_2"
raw_vars_12_27_2$variantkey <- paste(raw_vars_12_27_2$Chromosome,
raw_vars_12_27_2$Position,
raw_vars_12_27_2$Reference,
raw_vars_12_27_2$Alternative,sep = "_")
# subsetting dfs
df_1 <- subset.data.frame(x = raw_vars_12_27_1, subset = variantkey %in% mykeys, select = c("variantkey","AF","DP","Gene","Effect","FILTER"))
colnames(df_1) <- c("variantkey","AF_GFP_H","DP_GFP_H","Gene", "Effect","FILTER")
df_2 <- subset.data.frame(x = raw_vars_12_27_2, subset = variantkey %in% mykeys, select = c("variantkey","AF","DP","Gene","Effect","FILTER"))
colnames(df_2) <- c("variantkey","AF_GFP_L","DP_GFP_L","Gene", "Effect","FILTER")
union_df <- merge.data.frame(x = df_1, y=df_2, by = c("variantkey","Gene","Effect"), all = T)
union_df$FILTER.x[is.na(union_df$FILTER.x)] <- "NOPASS"
union_df$FILTER.y[is.na(union_df$FILTER.y)] <- "NOPASS"
union_df$AF_1[is.na(union_df$AF_GFP_H)] <- 0
union_df$AF_2[is.na(union_df$AF_GFP_L)] <- 0
union_df$DP_1[is.na(union_df$DP_GFP_H)] <- 0
union_df$DP_2[is.na(union_df$DP_GFP_L)] <- 0
union_df$FILTER <- ifelse(test = (union_df$FILTER.x == "" | union_df$FILTER.y == ""),
yes = paste0(union_df$FILTER.x, "_", union_df$FILTER.y),
no = union_df$FILTER.y)
union_df$FILTER.x <- NULL
union_df$FILTER.y <- NULL
# subsetting only variants in exons
union_df_exonic <- union_df[union_df$Effect %in% c("frameshift_variant","missense_variant","synonymous_variant","splice_region_variant&intron_variant"),]
write.xlsx(x = union_df_exonic, file = "DGN_vs_CR_variants_exonic.xlsx")
library(ggrepel)
png(filename = paste0("AF_Comparison_12_27_1_vs_12_27_2_DGN_vs_CR_variants.png"), width = 8, height = 6, units = "in", res = 200)
print(ggplot(data = union_df_exonic, mapping = aes(x = AF_GFP_H, y = AF_GFP_L, label = Gene)) + xlim(0,1) + ylim(0,1) +
geom_point(mapping = aes(col = "black"), size = 2) + #geom_abline(intercept = 0, slope = 1) +
xlab(label = "GFP_HIGH") + ylab(label = "GFP_LOW") + ggtitle(label = "12_27 Somatic DGN only variants") +
#geom_point(data=union_df[union_df$AF_GFP_H == 0, ], aes(x=AF_GFP_H, y=AF_GFP_L), colour="red",size = 2) +
geom_text_repel(size=3, show.legend = FALSE, segment.alpha = 0.4) +
theme(plot.title = element_text(hjust = 0.5), legend.title = element_blank(), legend.position = "none"))
dev.off()
WES/Filter_variants_v3.R 0 → 100644
View file @ 38a09ebb
filter_variants_v3 <- function(infile, sampleid = "Sample01", dp = 50){
library(openxlsx)
df <- read.table(file = infile, sep = "\t" ,header = T, stringsAsFactors = F)
write.xlsx(x = df, file = paste0(sampleid,"_full_variants_annotated.xlsx"), asTable = T)
colnames(df) <- c("Chromosome","Position","Reference","Alternative","AF","DP","AD.R","AD.A","GT","ID","FILTER","HGVS_P","Gene","Biotype",
"Rank","Effect","Impact","Common","G5",colnames(df)[20:35])
df$Common[is.na(df$Common)] <- 0
df <- subset.data.frame(x = df, subset = DP > dp & Common == 0 &
Biotype == "protein_coding" & !Impact %in% c("LOW", "MODIFIER")
)
df$sampleid <- sampleid
df$variantkey <- paste(df$Chromosome, df$Position, df$Reference, df$Alternative,sep = "_")
write.xlsx(x = df, file = paste0(sampleid,"_rawvariants_dp_",dp,"_coding_lowimpact_notcommon",".xlsx"), asTable = T)
return(df)
}
WES/MethylAnalysis_bulk_v3.R 0 → 100644
View file @ 38a09ebb
MethylAnalysis_bulk_v3 <- function(methcall.folder = NULL, # input folder with .cov files (bismark)
outfolder = NULL, # output folder (if not exist it will be created)
proj.id = "myproject", # project id to prefix in putput files
samplesheet = NULL,
sheetnum = 1,
design.var = "Disease", # variable to subset samplesheet
case.var = "Case", # 1 in treatment vector
control.var = "Control", # 0 in treatment vector
idcol = "SampleID",
mincoverage = 10,
lowperc = 5,
lowcount = 10,
assem="hg38",
do.subset = T,
chr.subset = "chr9",
start.subset = 136668000,
end.subset = 136671000,
pipeline.meth = "bismarkCoverage",
plot.covariates = c("Condition","Batch","MRD",
"Torelapse","Chemorefractory",
"Sex","CytoA","Tissue"),
idstoremove = NULL,
delta.meth = 10,
plot.categorical.vars = c("Condition","Batch","MRD","Torelapse","Chemorefractory","Sex","CytoA"),
plot.continuous.vars = c("miR-126","egfl7"),
plot.height = 9,
plot.width = 12,
cellw = 15,
cellh = 15,
fontnumsize = 5,
fontsize = 8
){
# load libraries
require(methylKit)
require(ggplot2)
require(reshape2)
require(plyr)
require(ggrepel)
library(GenomicRanges)
library(openxlsx)
library(pheatmap)
# check vars
if(is.null(methcall.folder)){
stop("Please set methcall.folder")
}
if(is.null(outfolder)){
stop("Please set outfolder")
}
if(is.null(samplesheet)){
stop("Please set samplesheet")
}
# Setup variables
infolder = paste0(methcall.folder, "/")
dir.create(infolder, showWarnings = F)
outfolder = paste0(outfolder, "/")
dir.create(outfolder, showWarnings = F)
qcfolder <- paste0(outfolder, "QC/")
dir.create(qcfolder, showWarnings = F)
# reading sample sheet with metadata
ssheet <- read.xlsx(samplesheet, sheet = sheetnum)
if(!is.null(idstoremove)){
ssheet <- subset.data.frame(x = ssheet, subset = !ssheet[[idcol]] %in% idstoremove)
}
# defining design vector according to variable
ssheet <- subset.data.frame(x = ssheet, subset = ssheet[[design.var]] %in% c(case.var, control.var))
d.vec <- ssheet[[design.var]]
d.vector <- ifelse(d.vec == case.var, 1, 0)
# initialzing coverage .cov files list
covs <- list()
sampleids <- as.list(ssheet[[idcol]])
names(sampleids) <- ssheet[[idcol]]
for (i in ssheet[[idcol]]) {
covs[[i]] <- paste0(infolder, "/", i, ".bismark.cov")
}
saveRDS(object = covs, file = "covs_object.rds")
# creating object
myobj <- methRead(location = covs,
sample.id = sampleids,
assembly = assem,
pipeline = pipeline.meth,
treatment = d.vector,
context="CpG",
mincov = mincoverage)
saveRDS(myobj, file = "Myobject.rds")
names(myobj) <- ssheet[[idcol]]
saveRDS(myobj, file = "Myobject_with_names.rds")
# subsetting if declared
if(isTRUE(do.subset)){
my.win = GRanges(seqnames = chr.subset, ranges = IRanges(start = start.subset, end = end.subset))
myobj <- selectByOverlap(myobj,my.win)
}
saveRDS(myobj, file = "Myobject_with_names_aftersubset.rds")
# filtering on minimum coverage
myobj <- filterByCoverage(methylObj = myobj, lo.count=lowcount, lo.perc = lowperc)
# Normalization
myobj <- normalizeCoverage(obj = myobj)
# saveRDS(object = myobj, file = "Initial_object.rds")
# Calculate basic stats and PCs
metricsfolder <- paste0(qcfolder, "Metrics/")
dir.create(path = metricsfolder, showWarnings = F)
for (id in ssheet[[idcol]]) {
png(filename = paste0(metricsfolder,proj.id,"_CpG_pct_methylation_sample_", id, ".png"),
width = 9, height = 6, units = "in", res = 96)
print(getMethylationStats(myobj[[id]],plot=TRUE,both.strands=FALSE))
dev.off()
png(filename = paste0(metricsfolder, proj.id,"_Coverage_stats_sample_", id, ".png"),
width = 9, height = 6, units = "in", res = 96)
print(getCoverageStats(myobj[[id]],plot=TRUE,both.strands=FALSE))
dev.off()
}
# create meth obj
#meth <- unite(object = myobj, destrand=FALSE)
meth <- unite(object = myobj, destrand=FALSE) # for debugging
saveRDS(meth, "savemeth.tmp.rds")
# Perform correlation
sink(paste0(qcfolder, proj.id, "_Correlations.txt"))
getCorrelation(meth,plot=FALSE)
sink()
if(length(ssheet[[idcol]]) < 15){
png(filename = paste0(qcfolder,proj.id, "_Correlations_pearson_pairwise.png"),
width = 9, height = 6, units = "in", res = 96)
print(getCorrelation(meth,plot=TRUE))
dev.off()
}
png(filename = paste0(qcfolder, proj.id, "_Clustering.png"),
width = 9, height = 6, units = "in", res = 96)
clusterSamples(meth, dist="euclidean", plot=TRUE, method = "ward.D2")
dev.off()
# Re-plotting PCs (custom chart)
# compute PCs and store in object
pca_compt <- PCASamples(meth, obj.return = T, screeplot = F)
# extract PCs components
pcafolder <- paste0(qcfolder, "PCA/")
dir.create(path = pcafolder, showWarnings = F)
pca_pc1_2 <- as.data.frame(x = pca_compt$x[,1:2])
for(myvars in plot.covariates){
pca_pc1_2$condition <- as.factor(ssheet[[myvars]])
png(filename = paste0(pcafolder, proj.id, "_PCA_",myvars,".png"),
width = 9, height = 6, units = "in",res=96)
print(ggplot(data = pca_pc1_2,
mapping = aes(x = PC1, y=PC2, col=condition, label=rownames(pca_pc1_2))) +
geom_point(size=3) + geom_text_repel(size=3) + ggtitle(label = "Principal component analysis", subtitle = myvars) +
theme(plot.title = element_text(size = 16, face = "bold", hjust = 0.5)) +
theme(plot.subtitle=element_text(size=12, hjust=0.5, face="italic", color="black")) +
theme(axis.title = element_text(size=12, hjust=0.5, face="bold", color="black")) +
theme(legend.text = element_text(size=8, hjust=0.5)) +
theme(legend.title = element_blank()) +
theme(axis.text = element_text(size=12, hjust=0.5, color="black")))
dev.off()
}
# retrieve and store % of methylation
perc.meth <- percMethylation(meth)
saveRDS(perc.meth,"pctmethly.rds")
base::rownames(perc.meth) <- paste0(meth$chr, "_", meth$start)
# Perform diff methylation
myDiff=calculateDiffMeth(meth)
write.table(myDiff,paste0(outfolder,proj.id,"_DiffMeth_single_CpG.txt"), row.names = F)
difftest <- read.table(paste0(outfolder,proj.id,"_DiffMeth_single_CpG.txt"), header = T)
difftest$comparison <- proj.id
difftest$qvalue_r <- as.character(cut(x = difftest$qvalue,
breaks = c(-1, 1e-100, 1e-10, 1e-02, 1),
labels = c("***","**","*","ns")))
myindex <- abs(difftest$meth.diff) < delta.meth
difftest$meth.diff <- abs(difftest$meth.diff)
difftest$qvalue_r[myindex] <- "ns"
write.table(difftest,paste0(outfolder,proj.id,"_DiffMeth_single_CpG.txt"), row.names = F)
# Adding color list and annotations
library(RColorBrewer)
color_list <- list()
annrows <- NULL
anncols <- subset.data.frame(difftest, select = c("qvalue_r","meth.diff"))
base::rownames(anncols) <- difftest$start
rows.annot.vars.cat = plot.categorical.vars
rows.annot.vars.con = plot.continuous.vars
# if(!is.null(rows.annot.vars.cat) | !is.null(rows.annot.vars.con)){
# rownames(ssheet) <- ssheet$SampleID
# annrows <- subset.data.frame(x = ssheet, select = c(rows.annot.vars.cat, rows.annot.vars.con))
# concolors <- RColorBrewer::brewer.pal(n = 9, name = "Set1")
# catcolors <- NULL
# for (varcon in 1:length(rows.annot.vars.con)) {
# color_list[[rows.annot.vars.con[varcon]]] <- colorRampPalette(c("lightgrey", concolors[varcon]))(10)
# }
# for (varcat in 1:length(rows.annot.vars.cat)) {
# ncolor <- 1
# for (val in unique(ssheet[[rows.annot.vars.cat[varcat]]])[order(unique(ssheet[[rows.annot.vars.cat[varcat]]]))]){
# if(length(unique(ssheet[[rows.annot.vars.cat[varcat]]])) > 9){
# catcolors <- colorRampPalette(RColorBrewer::brewer.pal(n = 9, name = "Set3"))(length(unique(ssheet[[rows.annot.vars.cat[varcat]]])))
# }
# else{
# catcolors <- RColorBrewer::brewer.pal(n = 9, name = "Set3")
# }
# color_list[[rows.annot.vars.cat[varcat]]][[val]] <- catcolors[ncolor]
# ncolor <- ncolor + 1
# }
# }
# }
color_list[["qvalue_r"]] = c("*" = "#6497b1", "**" = "#03396c", "***" = "#011f4b", ns= "#c4cacf")
color_list[["meth.diff"]] = colorRampPalette(brewer.pal(n = 11, "Reds"))(100)
color_list[["miR-126"]] <- brewer.pal(n = 9, "PuRd")
# Heatmap
pctmeth_matrix <- t(perc.meth)
base::colnames(pctmeth_matrix) <- gsub(x = base::colnames(pctmeth_matrix), pattern = "chr[0-9]+_", replacement = "")
pheatmap(mat = pctmeth_matrix, main = gsub(x = proj.id, pattern = "_", replacement = " "),
filename = paste0(outfolder, proj.id,"_CpG_percent_methylation_matrix_pheatmap.pdf"), width = plot.width, height = plot.height,
na_col = "black",
cluster_cols = FALSE,
cluster_rows = TRUE,
annotation_row = annrows,
cellwidth = cellw,
cellheight = cellh,
display_numbers = T,
fontsize = fontsize,
fontsize_number = fontnumsize,
number_format = "%.0f",
#border_color = "#CBBEB5",
annotation_col = anncols,
#labels_row = lrow,
#labels_col = lcol,
#gaps_row = gaps.row,
gaps_col = c(8),
annotation_colors = color_list,
color = c("#F5F5F5","#EEEEEE","#CCCCCC","#999999", "#666666","#333333","#000000"), breaks = c(0,10,20,30,50,70,90,100)
)
saveRDS(object = pctmeth_matrix, paste0(outfolder, proj.id,"_CpG_percent_methylation_matrix_pheatmap.rds"))
saveRDS(object = list(anncol = anncols, annrow = annrows, anncolors = color_list), paste0(outfolder, proj.id,"_annotations_matrix_pheatmap.rds"))
saveRDS(object = difftest, paste0(outfolder, proj.id,"_CpG_differential_methylation.rds"))
saveRDS(object = perc.meth, paste0(outfolder, proj.id,"_CpG_percent_methylation.rds"))
write.table(x = perc.meth, file = paste0(outfolder, proj.id,"_CpG_percent_methylation.txt"))
write.table(x = difftest, file = paste0(outfolder, proj.id,"_CpG_differential_methylation.txt"))
saveRDS(myobj, file = paste0(outfolder,proj.id,"_methylkit.rds"))
saveRDS(myobj, file = paste0(outfolder,proj.id,"_methylkit_meth.rds"))
}
\ No newline at end of file
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