library(readr)
library(data.table)
setwd("~/Documents/RobertsLab/VirgCGOut")## Read the firs two samples in
ATCACG_CG <- read_delim("~/Documents/RobertsLab/VirgCGOut/2112_lane1_ATCACG_CG.output.txt", "\t", escape_double = FALSE, na = ".", trim_ws = TRUE)Parsed with column specification:
cols(
`#CHROM` = col_character(),
POS = col_integer(),
CONTEXT = col_character(),
`Watson METH` = col_integer(),
`Watson COVERAGE` = col_integer(),
`Watson QUAL` = col_integer(),
`Crick METH` = col_integer(),
`Crick COVERAGE` = col_integer(),
`Crick QUAL` = col_integer()
)ACAGTG_CG <- read_delim("~/Documents/RobertsLab/VirgCGOut/2112_lane1_ACAGTG_CG.output.txt", "\t", escape_double = FALSE, na = ".", trim_ws = TRUE)Parsed with column specification:
cols(
`#CHROM` = col_character(),
POS = col_integer(),
CONTEXT = col_character(),
`Watson METH` = col_integer(),
`Watson COVERAGE` = col_integer(),
`Watson QUAL` = col_integer(),
`Crick METH` = col_integer(),
`Crick COVERAGE` = col_integer(),
`Crick QUAL` = col_integer()
)
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|=====================================================================================================| 100% 40 MB## Sums Watson and Crick strand methylation and total coverage counts
ACAGTG_CG[,10] <- rowSums(cbind(ACAGTG_CG$`Watson METH`, ACAGTG_CG$`Crick METH`), na.rm = T)
colnames(ACAGTG_CG)[10] <- "ACAGTG_MethSum"
ACAGTG_CG[,11] <- rowSums(cbind(ACAGTG_CG$`Watson COVERAGE`, ACAGTG_CG$`Crick COVERAGE`), na.rm = T)
colnames(ACAGTG_CG)[11] <- "ACAGTG_TotSum"
ATCACG_CG[,10] <- rowSums(cbind(ATCACG_CG$`Watson METH`, ATCACG_CG$`Crick METH`), na.rm = T)
colnames(ATCACG_CG)[10] <- "ATCACG_MethSum"
ATCACG_CG[,11] <- rowSums(cbind(ATCACG_CG$`Watson COVERAGE`, ATCACG_CG$`Crick COVERAGE`), na.rm = T)
colnames(ATCACG_CG)[11] <- "ATCACG_TotSum"
## Subset for only CpG methylation.
ATCACG_CG <- ATCACG_CG[ATCACG_CG$CONTEXT == "CG",]
ACAGTG_CG <- ACAGTG_CG[ACAGTG_CG$CONTEXT == "CG",]
## Combines Scaffold and Position for a single orienting vector for merging.
ATCACG_CG[,12] <- paste0(ATCACG_CG$`#CHROM`, "-", ATCACG_CG$POS)
colnames(ATCACG_CG)[12] <- "Loc"
ACAGTG_CG[,12] <- paste0(ACAGTG_CG$`#CHROM`, "-", ACAGTG_CG$POS)
colnames(ACAGTG_CG)[12] <- "Loc"## Subsetting sample 1
ACAGTG_CG_1 <- ACAGTG_CG[(ACAGTG_CG$`#CHROM` %in% unique(ACAGTG_CG$`#CHROM`)[1:100]) , ]
ACAGTG_CG_2 <- ACAGTG_CG[(ACAGTG_CG$`#CHROM` %in% unique(ACAGTG_CG$`#CHROM`)[101:200]) , ]
ACAGTG_CG_3 <- ACAGTG_CG[(ACAGTG_CG$`#CHROM` %in% unique(ACAGTG_CG$`#CHROM`)[201:length(unique(ACAGTG_CG$`#CHROM`))]) , ]
## Check to make sure that all observations are in the subsetted groups.
print(nrow(ACAGTG_CG) - sum(nrow(ACAGTG_CG_1), nrow(ACAGTG_CG_2), nrow(ACAGTG_CG_3)))[1] 0## subsetting Sample 2
ATCACG_CG_1 <- ATCACG_CG[(ATCACG_CG$`#CHROM` %in% unique(ATCACG_CG$`#CHROM`)[1:100]) , ]
ATCACG_CG_2 <- ATCACG_CG[(ATCACG_CG$`#CHROM` %in% unique(ATCACG_CG$`#CHROM`)[101:200]) , ]
ATCACG_CG_3 <- ATCACG_CG[(ATCACG_CG$`#CHROM` %in% unique(ATCACG_CG$`#CHROM`)[201:length(unique(ATCACG_CG$`#CHROM`))]) , ]ACA_ATC_1 <- merge(ACAGTG_CG_1[,c(10,11,12)], ATCACG_CG_1[,c(10,11,12)], by = "Loc", all = TRUE)
ACA_ATC_2 <- merge(ACAGTG_CG_2[,c(10,11,12)], ATCACG_CG_2[,c(10,11,12)], by = "Loc", all = TRUE)
ACA_ATC_3 <- merge(ACAGTG_CG_3[,c(10,11,12)], ATCACG_CG_3[,c(10,11,12)], by = "Loc", all = TRUE)## Sample 3.
CAGATC_CG <- read_delim("~/Documents/RobertsLab/VirgCGOut/2112_lane1_CAGATC_CG.output.txt", "\t", escape_double = FALSE, na = ".", trim_ws = TRUE)Parsed with column specification:
cols(
`#CHROM` = col_character(),
POS = col_integer(),
CONTEXT = col_character(),
`Watson METH` = col_integer(),
`Watson COVERAGE` = col_integer(),
`Watson QUAL` = col_integer(),
`Crick METH` = col_integer(),
`Crick COVERAGE` = col_integer(),
`Crick QUAL` = col_integer()
)CAGATC_CG <- CAGATC_CG[CAGATC_CG$CONTEXT == "CG",]
CAGATC_CG[,10] <- rowSums(cbind(CAGATC_CG$`Watson METH`, CAGATC_CG$`Crick METH`), na.rm = T)
colnames(CAGATC_CG)[10] <- "CAGATC_MethSum"
CAGATC_CG[,11] <- rowSums(cbind(CAGATC_CG$`Watson COVERAGE`, CAGATC_CG$`Crick COVERAGE`), na.rm = T)
colnames(CAGATC_CG)[11] <- "CAGATC_TotSum"
CAGATC_CG[,12] <- paste0(CAGATC_CG$`#CHROM`, "-", CAGATC_CG$POS)
colnames(CAGATC_CG)[12] <- "Loc"
length(unique(CAGATC_CG$`#CHROM`))[1] 358CAGATC_CG_1 <- CAGATC_CG[(CAGATC_CG$`#CHROM` %in% unique(CAGATC_CG$`#CHROM`)[1:100]) , ]
CAGATC_CG_2 <- CAGATC_CG[(CAGATC_CG$`#CHROM` %in% unique(CAGATC_CG$`#CHROM`)[101:200]) , ]
CAGATC_CG_3 <- CAGATC_CG[(CAGATC_CG$`#CHROM` %in% unique(CAGATC_CG$`#CHROM`)[201:length(unique(CAGATC_CG$`#CHROM`))]) , ]
print(nrow(CAGATC_CG) - sum(nrow(CAGATC_CG_1), nrow(CAGATC_CG_2), nrow(CAGATC_CG_3)))[1] 0ACA_ATC_1 <- merge(ACA_ATC_1, CAGATC_CG_1[,c(10,11,12)], by = "Loc", all = TRUE)
ACA_ATC_2 <- merge(ACA_ATC_2, CAGATC_CG_2[,c(10,11,12)], by = "Loc", all = TRUE)
ACA_ATC_3 <- merge(ACA_ATC_3, CAGATC_CG_3[,c(10,11,12)], by = "Loc", all = TRUE)## Sample 4
GCCAAT_CG <- read_delim("~/Documents/RobertsLab/VirgCGOut/2112_lane1_GCCAAT_CG.output.txt", "\t", escape_double = FALSE, na = ".", trim_ws = TRUE)Parsed with column specification:
cols(
`#CHROM` = col_character(),
POS = col_integer(),
CONTEXT = col_character(),
`Watson METH` = col_integer(),
`Watson COVERAGE` = col_integer(),
`Watson QUAL` = col_integer(),
`Crick METH` = col_integer(),
`Crick COVERAGE` = col_integer(),
`Crick QUAL` = col_integer()
)GCCAAT_CG <- GCCAAT_CG[GCCAAT_CG$CONTEXT == "CG",]
GCCAAT_CG[,10] <- rowSums(cbind(GCCAAT_CG$`Watson METH`, GCCAAT_CG$`Crick METH`), na.rm = T)
colnames(GCCAAT_CG)[10] <- "GCCAAT_MethSum"
GCCAAT_CG[,11] <- rowSums(cbind(GCCAAT_CG$`Watson COVERAGE`, GCCAAT_CG$`Crick COVERAGE`), na.rm = T)
colnames(GCCAAT_CG)[11] <- "GCCAAT_TotSum"
GCCAAT_CG[,12] <- paste0(GCCAAT_CG$`#CHROM`, "-", GCCAAT_CG$POS)
colnames(GCCAAT_CG)[12] <- "Loc"
length(unique(GCCAAT_CG$`#CHROM`))[1] 358GCCAAT_CG_1 <- GCCAAT_CG[(GCCAAT_CG$`#CHROM` %in% unique(GCCAAT_CG$`#CHROM`)[1:100]) , ]
GCCAAT_CG_2 <- GCCAAT_CG[(GCCAAT_CG$`#CHROM` %in% unique(GCCAAT_CG$`#CHROM`)[101:200]) , ]
GCCAAT_CG_3 <- GCCAAT_CG[(GCCAAT_CG$`#CHROM` %in% unique(GCCAAT_CG$`#CHROM`)[201:length(unique(GCCAAT_CG$`#CHROM`))]) , ]
print(nrow(GCCAAT_CG) - sum(nrow(GCCAAT_CG_1), nrow(GCCAAT_CG_2), nrow(GCCAAT_CG_3)))[1] 0ACA_ATC_1 <- merge(ACA_ATC_1, GCCAAT_CG_1[,c(10,11,12)], by = "Loc", all = TRUE)
ACA_ATC_2 <- merge(ACA_ATC_2, GCCAAT_CG_2[,c(10,11,12)], by = "Loc", all = TRUE)
ACA_ATC_3 <- merge(ACA_ATC_3, GCCAAT_CG_3[,c(10,11,12)], by = "Loc", all = TRUE)## Sample 5
TGACCA_CG <- read_delim("~/Documents/RobertsLab/VirgCGOut/2112_lane1_TGACCA_CG.output.txt", "\t", escape_double = FALSE, na = ".", trim_ws = TRUE)Parsed with column specification:
cols(
`#CHROM` = col_character(),
POS = col_integer(),
CONTEXT = col_character(),
`Watson METH` = col_integer(),
`Watson COVERAGE` = col_integer(),
`Watson QUAL` = col_integer(),
`Crick METH` = col_integer(),
`Crick COVERAGE` = col_integer(),
`Crick QUAL` = col_integer()
)TGACCA_CG <- TGACCA_CG[TGACCA_CG$CONTEXT == "CG",]
TGACCA_CG[,10] <- rowSums(cbind(TGACCA_CG$`Watson METH`, TGACCA_CG$`Crick METH`), na.rm = T)
colnames(TGACCA_CG)[10] <- "TGACCA_MethSum"
TGACCA_CG[,11] <- rowSums(cbind(TGACCA_CG$`Watson COVERAGE`, TGACCA_CG$`Crick COVERAGE`), na.rm = T)
colnames(TGACCA_CG)[11] <- "TGACCA_TotSum"
TGACCA_CG[,12] <- paste0(TGACCA_CG$`#CHROM`, "-", TGACCA_CG$POS)
colnames(TGACCA_CG)[12] <- "Loc"
length(unique(TGACCA_CG$`#CHROM`))[1] 357TGACCA_CG_1 <- TGACCA_CG[(TGACCA_CG$`#CHROM` %in% unique(TGACCA_CG$`#CHROM`)[1:100]) , ]
TGACCA_CG_2 <- TGACCA_CG[(TGACCA_CG$`#CHROM` %in% unique(TGACCA_CG$`#CHROM`)[101:200]) , ]
TGACCA_CG_3 <- TGACCA_CG[(TGACCA_CG$`#CHROM` %in% unique(TGACCA_CG$`#CHROM`)[201:length(unique(TGACCA_CG$`#CHROM`))]) , ]
print(nrow(TGACCA_CG) - sum(nrow(TGACCA_CG_1), nrow(TGACCA_CG_2), nrow(TGACCA_CG_3)))[1] 0ACA_ATC_1 <- merge(ACA_ATC_1, TGACCA_CG_1[,c(10,11,12)], by = "Loc", all = TRUE)
ACA_ATC_2 <- merge(ACA_ATC_2, TGACCA_CG_2[,c(10,11,12)], by = "Loc", all = TRUE)
ACA_ATC_3 <- merge(ACA_ATC_3, TGACCA_CG_3[,c(10,11,12)], by = "Loc", all = TRUE)## Sample 6
TTAGGC_CG <- read_delim("~/Documents/RobertsLab/VirgCGOut/2112_lane1_TTAGGC_CG.output.txt", "\t", escape_double = FALSE, na = ".", trim_ws = TRUE)Parsed with column specification:
cols(
`#CHROM` = col_character(),
POS = col_integer(),
CONTEXT = col_character(),
`Watson METH` = col_integer(),
`Watson COVERAGE` = col_integer(),
`Watson QUAL` = col_integer(),
`Crick METH` = col_integer(),
`Crick COVERAGE` = col_integer(),
`Crick QUAL` = col_integer()
)TTAGGC_CG <- TTAGGC_CG[TTAGGC_CG$CONTEXT == "CG",]
TTAGGC_CG[,10] <- rowSums(cbind(TTAGGC_CG$`Watson METH`, TTAGGC_CG$`Crick METH`), na.rm = T)
colnames(TTAGGC_CG)[10] <- "TTAGGC_MethSum"
TTAGGC_CG[,11] <- rowSums(cbind(TTAGGC_CG$`Watson COVERAGE`, TTAGGC_CG$`Crick COVERAGE`), na.rm = T)
colnames(TTAGGC_CG)[11] <- "TTAGGC_TotSum"
TTAGGC_CG[,12] <- paste0(TTAGGC_CG$`#CHROM`, "-", TTAGGC_CG$POS)
colnames(TTAGGC_CG)[12] <- "Loc"
length(unique(TTAGGC_CG$`#CHROM`))[1] 358TTAGGC_CG_1 <- TTAGGC_CG[(TTAGGC_CG$`#CHROM` %in% unique(TTAGGC_CG$`#CHROM`)[1:100]) , ]
TTAGGC_CG_2 <- TTAGGC_CG[(TTAGGC_CG$`#CHROM` %in% unique(TTAGGC_CG$`#CHROM`)[101:200]) , ]
TTAGGC_CG_3 <- TTAGGC_CG[(TTAGGC_CG$`#CHROM` %in% unique(TTAGGC_CG$`#CHROM`)[201:length(unique(TTAGGC_CG$`#CHROM`))]) , ]
print(nrow(TTAGGC_CG) - sum(nrow(TTAGGC_CG_1), nrow(TTAGGC_CG_2), nrow(TTAGGC_CG_3)))[1] 0ACA_ATC_1 <- merge(ACA_ATC_1, TTAGGC_CG_1[,c(10,11,12)], by = "Loc", all = TRUE)
ACA_ATC_2 <- merge(ACA_ATC_2, TTAGGC_CG_2[,c(10,11,12)], by = "Loc", all = TRUE)
ACA_ATC_3 <- merge(ACA_ATC_3, TTAGGC_CG_3[,c(10,11,12)], by = "Loc", all = TRUE)Combines the subsets in to single data tables, then subsets them for 10x coverage. There’s an additional set of data tables where I kick out the TGACCA tagged sample, as it seems to be of poor quality, or somehow malformed.
## Combining subsets and splitting out in to Methylation Counts and Total Counts
TotalMethCounts <- as.data.table(rbind(ACA_ATC_1[,c(1,2,4,6,8,10,12)], ACA_ATC_2[,c(1,2,4,6,8,10,12)], ACA_ATC_3[,c(1,2,4,6,8,10,12)]))
TotalTotalCounts <- as.data.table(rbind(ACA_ATC_1[,c(1,3,5,7,9,11,13)], ACA_ATC_2[,c(1,3,5,7,9,11,13)], ACA_ATC_3[,c(1,3,5,7,9,11,13)]))
CompleteMethCounts <- TotalMethCounts[which(complete.cases(TotalMethCounts) == TRUE),]
CompleteTotalCounts <- TotalTotalCounts[which(complete.cases(TotalTotalCounts) == TRUE),]
Cov10xTotalCounts <- CompleteTotalCounts[apply(CompleteTotalCounts[, -1], MARGIN = 1, function(x) all(x > 10)),]
Cov10xTotal5Samps <- CompleteTotalCounts[apply(CompleteTotalCounts[, c(2,3,4,5,7)], MARGIN = 1, function(x) all(x > 10)),c(1,2,3,4,5,7)]
Cov10XMeth <- CompleteMethCounts[which(CompleteMethCounts$Loc %in% Cov10xTotalCounts$Loc),]
Cov10XMeth5Samps <- CompleteMethCounts[which(CompleteMethCounts$Loc %in% Cov10xTotal5Samps$Loc),c(1,2,3,4,5,7)]
colnames(Cov10XMeth) <- c("id", "ACAGTG", "ATCACG", "CAGATC", "GCCAAT", "TGACCA", "TTAGGC")
colnames(Cov10XMeth5Samps) <- c("id", "ACAGTG", "ATCACG", "CAGATC", "GCCAAT", "TTAGGC")
colnames(Cov10xTotalCounts) <- c("id", "ACAGTG", "ATCACG", "CAGATC", "GCCAAT", "TGACCA", "TTAGGC")
colnames(Cov10xTotal5Samps) <- c("id", "ACAGTG", "ATCACG", "CAGATC", "GCCAAT", "TTAGGC")Writes out the final count files as .csv files
write.csv(Cov10XMeth, file = "10XCoverageMethylationCounts.txt", row.names = FALSE)
write.csv(Cov10XMeth5Samps, file = "10XCoverageMethylationCounts5Samples.txt", row.names = FALSE)
write.csv(Cov10xTotalCounts, file = "10XCoverageTotalCounts.txt", row.names = FALSE)
write.csv(Cov10xTotal5Samps, file = "10XCoverageTotalCounts5Samples.txt", row.names = FALSE)system("head 10XCoverageMethylationCounts.txt")"id","ACAGTG","ATCACG","CAGATC","GCCAAT","TGACCA","TTAGGC"
"KV918244.1-1079055",491,128,337,40,52,238
"KV918244.1-1079059",516,147,360,22,52,263
"KV918244.1-1079093",569,145,392,45,57,282
"KV918244.1-1079105",526,126,294,34,34,264
"KV918244.1-1079109",536,146,335,41,31,286
"KV918244.1-1081497",253,92,193,63,18,217
"KV918244.1-1081500",260,97,192,67,20,227
"KV918244.1-1081534",276,139,303,63,49,340
"KV918244.1-1081546",233,130,279,61,49,329system("head 10XCoverageMethylationCounts5Samples.txt")"id","ACAGTG","ATCACG","CAGATC","GCCAAT","TTAGGC"
"KV918244.1-1073185",220,67,93,28,131
"KV918244.1-1073197",134,44,116,45,176
"KV918244.1-1073210",2,63,131,26,74
"KV918244.1-1076017",30,25,54,11,33
"KV918244.1-1076418",118,34,112,14,88
"KV918244.1-1076420",191,80,180,26,128
"KV918244.1-1076426",203,40,185,26,132
"KV918244.1-1076431",254,80,233,35,159
"KV918244.1-1076442",324,89,300,44,235system("head 10XCoverageTotalCounts.txt")"id","ACAGTG","ATCACG","CAGATC","GCCAAT","TGACCA","TTAGGC"
"KV918244.1-1079055",503,132,354,43,55,249
"KV918244.1-1079059",528,151,374,26,54,267
"KV918244.1-1079093",579,157,402,46,57,304
"KV918244.1-1079105",566,152,387,43,40,291
"KV918244.1-1079109",556,149,389,42,34,291
"KV918244.1-1081497",263,96,202,72,19,227
"KV918244.1-1081500",269,98,203,72,20,238
"KV918244.1-1081534",288,142,322,70,50,368
"KV918244.1-1081546",247,139,306,66,49,357system("head 10XCoverageTotalCounts5Samples.txt")"id","ACAGTG","ATCACG","CAGATC","GCCAAT","TTAGGC"
"KV918244.1-1073185",225,81,212,29,137
"KV918244.1-1073197",251,107,247,46,188
"KV918244.1-1073210",261,134,261,57,219
"KV918244.1-1076017",30,29,62,23,35
"KV918244.1-1076418",123,70,118,15,93
"KV918244.1-1076420",197,83,184,26,130
"KV918244.1-1076426",208,84,193,27,138
"KV918244.1-1076431",255,83,235,35,161
"KV918244.1-1076442",335,95,316,49,242