scripts/5_CpGSBE_SNP_classification.R
In BenjaminPlanterose/UMtools: An R-package for analysing Illumina DNA Methylation arrays at the fluorescence intensity level
library(data.table)
extract.maf <- function(metadata)
{
partB = unlist(lapply(strsplit(metadata, split= "CAF="), function(x) x[2]))
CAF = unlist(lapply(strsplit(partB, split= ";"), function(x) x[1]))
MAF = unlist(lapply(strsplit(CAF, split = ","), function(x) min(suppressWarnings(sort(as.numeric(x), decreasing = T)[1:2]))))
return(MAF)
}
extract.caf <- function(metadata)
{
partB = unlist(lapply(strsplit(metadata, split= "CAF="), function(x) x[2]))
CAF = unlist(lapply(strsplit(partB, split= ";"), function(x) x[1]))
return(CAF)
}
prep_alternate_allele <- function(CpG_SNP, Thr)
{
# Extract metadata from sites with multiple minor alleles
minor = CpG_SNP$m
l_m = sapply(strsplit(minor, split = ","), length)
where = which(l_m >= 2)
metadata = as.vector(CpG_SNP$metadata)[where]
CAF = extract.caf(metadata)
# Eliminate alleles if below a threshold
eliminate = lapply(strsplit(CAF, split = ","), function(x) which(x == "." | as.numeric(x) < Thr)-1)
length0 = which(sapply(eliminate, length) == 0)
splitted = strsplit(minor[where], split = ",")
minor_cor = minor
minor_cor[where] = unlist(lapply(1:length(splitted), function(x) paste((splitted[[x]])[-eliminate[[x]]], collapse = ",")))
minor_cor[where][length0] = minor[where][length0]
CpG_SNP$m = minor_cor
return(CpG_SNP)
}
#### 450K
library(IlluminaHumanMethylation450kanno.ilmn12.hg19)
data(Manifest); head(Manifest)
data(Locations); head(Locations)
annotation <- cbind(Locations, Manifest); rm(Manifest, Locations); gc()
sum(!(rownames(annotation) == annotation$Name)) # 0
dim(annotation) # 485512 11
setwd("/home/ben/Documents/Git/UMtools_dat/dat_450K/vcf/proc/")
####
#### EPIC
library(IlluminaHumanMethylationEPICanno.ilm10b4.hg19)
data(Manifest); head(Manifest)
data(Locations); head(Locations)
annotation <- cbind(Locations, Manifest); rm(Manifest, Locations); gc()
sum(!(rownames(annotation) == annotation$Name)) # 0
dim(annotation) # 865859 11
setwd("/home/ben/Documents/Git/UMtools_dat/dat_EPIC/vcf/proc/")
####
# CpG-SNP
CpG_SNP = fread("CpG_SNP.vcf")
CpG_SNP = as.data.frame(CpG_SNP)
colnames(CpG_SNP) = c("chr", "start", "rs", "M", "m", "v6", "v7", "metadata", "chr2", "start2", "end2", "cg")
CpG_SNP$type = annotation[CpG_SNP$cg,"Type"]
CpG_SNP$strand = annotation[CpG_SNP$cg,"strand"]
CpG_SNP$real_pos = annotation[CpG_SNP$cg, "pos"]
metadata = as.vector(CpG_SNP$metadata); MAF = extract.maf(metadata)
CpG_SNP = CpG_SNP[MAF > 0.05,] # Filter > 5 % variants
# SBE-SNP
SBE_SNP = fread("SBE_SNP.vcf")
SBE_SNP = as.data.frame(SBE_SNP)
colnames(SBE_SNP) = c("chr", "start", "rs", "M", "m", "v6", "v7", "metadata", "chr2", "start2", "end2", "cg")
SBE_SNP$strand = annotation[SBE_SNP$cg,"strand"]
SBE_SNP$real_pos = annotation[SBE_SNP$cg, "pos"]
SBE_SNP$type = annotation[SBE_SNP$cg,"Type"]
metadata = as.vector(SBE_SNP$metadata); MAF = extract.maf(metadata)
SBE_SNP = SBE_SNP[MAF > 0.05,] # Filter > 5 % variants
###################################### Biallelic Vs triallelic CpG/SNP ####################################
CpG_SNP <- prep_alternate_allele(CpG_SNP, 0.05) # Eliminates alternate alleles < Thr
where = sapply(strsplit(CpG_SNP$m, split = ","), length) > 1
CpG_SNP_Tri = CpG_SNP[where, ]
CpG_SNP_Bi = CpG_SNP[!where, ]
rownames(CpG_SNP_Bi) = rownames(CpG_SNP_Tri) = NULL
table(CpG_SNP_Tri$M, CpG_SNP_Tri$m) # 450K
# A,C A,T C,T G,T
# C 0 4 0 4
# G 6 4 2 0
table(CpG_SNP_Bi$M, CpG_SNP_Bi$m) # 450K
# A C G T
# C 316 0 329 2570
# G 3366 437 0 407
setwd("/home/ben/Documents/Git/UMtools/data/")
save(CpG_SNP_Tri, file = "triallelic_CpG_SNP_450K.RData")
save(CpG_SNP_Tri, file = "triallelic_CpG_SNP_EPIC.RData")
###################################### SBE ####################################
SBE_SNP <- prep_alternate_allele(SBE_SNP, 0.05) # Eliminates alternate alleles < Thr
where = sapply(strsplit(SBE_SNP$m, split = ","), length) > 1; sum(where) # 0
table(SBE_SNP$M, SBE_SNP$m)
# A C G T
# A 0 8 35 9
# C 16 0 23 57
# G 56 28 0 20
# T 8 28 7 0
###################################### CpG/SBE-SNPs ####################################
# II
# (+)
# SNP1
# C -> T/A SNP is interpreted as unmethylated allele
# C -> G SNP is interpreted as methylated allele
# SNP2
# G -> A/C/T 3'-overhang: probe failure
# (-)
# SNP1
# C -> A/G/T 3'-overhang: probe failure
# SNP2
# G -> T/A SNP is interpreted as unmethylated allele
# G -> C SNP is interpreted as methylated allele
# I
# (+)
# SNP0
# T <-> C <-> A U/M Informative detection in the same channel
# A/T/C <-> G U/M Signal goes to the wrong channel (oob)
# SNP1
# C -> T SNP is interpreted as unmethylated allele
# C -> A/G 3’-overhang: probe failure for either U or M
# SNP2
# G -> A/C/T 3'-overhang: probe failure for either U or M
# (-)
# SNP1
# C -> A/G/T 3'-overhang: probe failure for either U or M
# SNP2
# G -> A SNP is interpreted as unmethylated allele
# G -> C/T 3'-overhang: probe failure for either U or M
# SNP3
# T <-> C <-> A U/M Informative detection in the same channel
# A/T/C <-> G U/M Signal goes to the wrong channel (oob)
#
names = c("II_plus_SNP1_C_TA", "II_plus_SNP1_C_G", "II_plus_SNP2_G_ACT",
"II_minus_SNP1_C_AGT", "II_minus_SNP2_G_TA", "II_minus_SNP2_G_C",
"I_plus_SNP0_A_C_T", "I_plus_SNP0_ACT_G", "I_plus_SNP1_C_T", "I_plus_SNP1_C_AG", "I_plus_SNP2_G_ACT",
"I_minus_SNP1_C_AGT", "I_minus_SNP2_G_A", "I_minus_SNP2_G_CT", "I_minus_SNP3_A_G_T", "I_minus_SNP3_AGT_C")
classification = as.list(rep(NA, length(names)))
names(classification) = names
classification[["II_plus_SNP1_C_TA"]] = CpG_SNP_Bi[CpG_SNP_Bi$type == "II" &
CpG_SNP_Bi$strand == "+" &
CpG_SNP_Bi$M == "C" &
CpG_SNP_Bi$m %in% c("T", "A"),]
classification[["II_plus_SNP1_C_G"]] = CpG_SNP_Bi[CpG_SNP_Bi$type == "II" &
CpG_SNP_Bi$strand == "+" &
CpG_SNP_Bi$M == "C" &
CpG_SNP_Bi$m %in% c("G"),]
classification[["II_plus_SNP2_G_ACT"]] = CpG_SNP_Bi[CpG_SNP_Bi$type == "II" &
CpG_SNP_Bi$strand == "+" &
CpG_SNP_Bi$M == "G" &
CpG_SNP_Bi$m %in% c("A", "C", "T"),]
classification[["II_minus_SNP1_C_AGT"]] = CpG_SNP_Bi[CpG_SNP_Bi$type == "II" &
CpG_SNP_Bi$strand == "-" &
CpG_SNP_Bi$M == "C" &
CpG_SNP_Bi$m %in% c("A", "G", "T"),]
classification[["II_minus_SNP2_G_TA"]] = CpG_SNP_Bi[CpG_SNP_Bi$type == "II" &
CpG_SNP_Bi$strand == "-" &
CpG_SNP_Bi$M == "G" &
CpG_SNP_Bi$m %in% c("A", "T"),]
classification[["II_minus_SNP2_G_C"]] = CpG_SNP_Bi[CpG_SNP_Bi$type == "II" &
CpG_SNP_Bi$strand == "-" &
CpG_SNP_Bi$M == "G" &
CpG_SNP_Bi$m %in% c("C"),]
classification[["I_plus_SNP1_C_T"]] = CpG_SNP_Bi[CpG_SNP_Bi$type == "I" &
CpG_SNP_Bi$strand == "+" &
CpG_SNP_Bi$M == "C" &
CpG_SNP_Bi$m %in% c("T"),]
classification[["I_plus_SNP1_C_AG"]] = CpG_SNP_Bi[CpG_SNP_Bi$type == "I" &
CpG_SNP_Bi$strand == "+" &
CpG_SNP_Bi$M == "C" &
CpG_SNP_Bi$m %in% c("A", "G"),]
classification[["I_plus_SNP2_G_ACT"]] = CpG_SNP_Bi[CpG_SNP_Bi$type == "I" &
CpG_SNP_Bi$strand == "+" &
CpG_SNP_Bi$M == "G" &
CpG_SNP_Bi$m %in% c("A", "C", "T"),]
classification[["I_minus_SNP1_C_AGT"]] = CpG_SNP_Bi[CpG_SNP_Bi$type == "I" &
CpG_SNP_Bi$strand == "-" &
CpG_SNP_Bi$M == "C" &
CpG_SNP_Bi$m %in% c("A", "G", "T"),]
classification[["I_minus_SNP2_G_A"]] = CpG_SNP_Bi[CpG_SNP_Bi$type == "I" &
CpG_SNP_Bi$strand == "-" &
CpG_SNP_Bi$M == "G" &
CpG_SNP_Bi$m %in% c("A"),]
classification[["I_minus_SNP2_G_CT"]] = CpG_SNP_Bi[CpG_SNP_Bi$type == "I" &
CpG_SNP_Bi$strand == "-" &
CpG_SNP_Bi$M == "G" &
CpG_SNP_Bi$m %in% c("C", "T"),]
classification[["I_plus_SNP0_A_C_T"]] = SBE_SNP[SBE_SNP$type == "I" &
SBE_SNP$strand == "+" &
SBE_SNP$M %in% c("C", "T", "A") & SBE_SNP$m %in% c("C", "T", "A"),]
classification[["I_plus_SNP0_ACT_G"]] = SBE_SNP[SBE_SNP$type == "I" &
SBE_SNP$strand == "+" &
(SBE_SNP$M %in% c("C", "T", "A", "G") & SBE_SNP$m %in% c("G") |
SBE_SNP$M %in% c("G") & SBE_SNP$m %in% c("C", "T", "A", "G")),]
classification[["I_minus_SNP3_A_G_T"]] = SBE_SNP[SBE_SNP$type == "I" &
SBE_SNP$strand == "-" &
SBE_SNP$M %in% c("G", "T", "A") & SBE_SNP$m %in% c("G", "T", "A"),]
classification[["I_minus_SNP3_AGT_C"]] = SBE_SNP[SBE_SNP$type == "I" &
SBE_SNP$strand == "-" &
(SBE_SNP$M %in% c("G", "T", "A") & SBE_SNP$m %in% c("C") |
SBE_SNP$M %in% c("C") & SBE_SNP$m %in% c("G", "T", "A")),]
sapply(classification, nrow) # 450K
# II_plus_SNP1_C_TA II_plus_SNP1_C_G II_plus_SNP2_G_ACT II_minus_SNP1_C_AGT II_minus_SNP2_G_TA
# 1131 111 1266 1221 1916
# II_minus_SNP2_G_C I_plus_SNP0_A_C_T I_plus_SNP0_ACT_G I_plus_SNP1_C_T I_plus_SNP1_C_AG
# 188 49 97 283 98
# I_plus_SNP2_G_ACT I_minus_SNP1_C_AGT I_minus_SNP2_G_A I_minus_SNP2_G_CT I_minus_SNP3_A_G_T
# 364 371 355 121 62
# I_minus_SNP3_AGT_C
# 89
sum(sapply(classification, nrow)) # 7722; 450K
nrow(SBE_SNP) + nrow(CpG_SNP_Bi) # 7722; 450K
setwd("/home/ben/Documents/Git/UMtools/data/")
save(classification, file = "classification_CpG_SNP_450K.RData")
save(classification, file = "classification_CpG_SNP_EPIC.RData")
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library(data.table)
extract.maf <- function(metadata)
{
partB = unlist(lapply(strsplit(metadata, split= "CAF="), function(x) x[2]))
CAF = unlist(lapply(strsplit(partB, split= ";"), function(x) x[1]))
MAF = unlist(lapply(strsplit(CAF, split = ","), function(x) min(suppressWarnings(sort(as.numeric(x), decreasing = T)[1:2]))))
return(MAF)
}
extract.caf <- function(metadata)
{
partB = unlist(lapply(strsplit(metadata, split= "CAF="), function(x) x[2]))
CAF = unlist(lapply(strsplit(partB, split= ";"), function(x) x[1]))
return(CAF)
}
prep_alternate_allele <- function(CpG_SNP, Thr)
{
# Extract metadata from sites with multiple minor alleles
minor = CpG_SNP$m
l_m = sapply(strsplit(minor, split = ","), length)
where = which(l_m >= 2)
metadata = as.vector(CpG_SNP$metadata)[where]
CAF = extract.caf(metadata)
# Eliminate alleles if below a threshold
eliminate = lapply(strsplit(CAF, split = ","), function(x) which(x == "." | as.numeric(x) < Thr)-1)
length0 = which(sapply(eliminate, length) == 0)
splitted = strsplit(minor[where], split = ",")
minor_cor = minor
minor_cor[where] = unlist(lapply(1:length(splitted), function(x) paste((splitted[[x]])[-eliminate[[x]]], collapse = ",")))
minor_cor[where][length0] = minor[where][length0]
CpG_SNP$m = minor_cor
return(CpG_SNP)
}
#### 450K
library(IlluminaHumanMethylation450kanno.ilmn12.hg19)
data(Manifest); head(Manifest)
data(Locations); head(Locations)
annotation <- cbind(Locations, Manifest); rm(Manifest, Locations); gc()
sum(!(rownames(annotation) == annotation$Name)) # 0
dim(annotation) # 485512 11
setwd("/home/ben/Documents/Git/UMtools_dat/dat_450K/vcf/proc/")
####
#### EPIC
library(IlluminaHumanMethylationEPICanno.ilm10b4.hg19)
data(Manifest); head(Manifest)
data(Locations); head(Locations)
annotation <- cbind(Locations, Manifest); rm(Manifest, Locations); gc()
sum(!(rownames(annotation) == annotation$Name)) # 0
dim(annotation) # 865859 11
setwd("/home/ben/Documents/Git/UMtools_dat/dat_EPIC/vcf/proc/")
####
# CpG-SNP
CpG_SNP = fread("CpG_SNP.vcf")
CpG_SNP = as.data.frame(CpG_SNP)
colnames(CpG_SNP) = c("chr", "start", "rs", "M", "m", "v6", "v7", "metadata", "chr2", "start2", "end2", "cg")
CpG_SNP$type = annotation[CpG_SNP$cg,"Type"]
CpG_SNP$strand = annotation[CpG_SNP$cg,"strand"]
CpG_SNP$real_pos = annotation[CpG_SNP$cg, "pos"]
metadata = as.vector(CpG_SNP$metadata); MAF = extract.maf(metadata)
CpG_SNP = CpG_SNP[MAF > 0.05,] # Filter > 5 % variants
# SBE-SNP
SBE_SNP = fread("SBE_SNP.vcf")
SBE_SNP = as.data.frame(SBE_SNP)
colnames(SBE_SNP) = c("chr", "start", "rs", "M", "m", "v6", "v7", "metadata", "chr2", "start2", "end2", "cg")
SBE_SNP$strand = annotation[SBE_SNP$cg,"strand"]
SBE_SNP$real_pos = annotation[SBE_SNP$cg, "pos"]
SBE_SNP$type = annotation[SBE_SNP$cg,"Type"]
metadata = as.vector(SBE_SNP$metadata); MAF = extract.maf(metadata)
SBE_SNP = SBE_SNP[MAF > 0.05,] # Filter > 5 % variants
###################################### Biallelic Vs triallelic CpG/SNP ####################################
CpG_SNP <- prep_alternate_allele(CpG_SNP, 0.05) # Eliminates alternate alleles < Thr
where = sapply(strsplit(CpG_SNP$m, split = ","), length) > 1
CpG_SNP_Tri = CpG_SNP[where, ]
CpG_SNP_Bi = CpG_SNP[!where, ]
rownames(CpG_SNP_Bi) = rownames(CpG_SNP_Tri) = NULL
table(CpG_SNP_Tri$M, CpG_SNP_Tri$m) # 450K
# A,C A,T C,T G,T
# C 0 4 0 4
# G 6 4 2 0
table(CpG_SNP_Bi$M, CpG_SNP_Bi$m) # 450K
# A C G T
# C 316 0 329 2570
# G 3366 437 0 407
setwd("/home/ben/Documents/Git/UMtools/data/")
save(CpG_SNP_Tri, file = "triallelic_CpG_SNP_450K.RData")
save(CpG_SNP_Tri, file = "triallelic_CpG_SNP_EPIC.RData")
###################################### SBE ####################################
SBE_SNP <- prep_alternate_allele(SBE_SNP, 0.05) # Eliminates alternate alleles < Thr
where = sapply(strsplit(SBE_SNP$m, split = ","), length) > 1; sum(where) # 0
table(SBE_SNP$M, SBE_SNP$m)
# A C G T
# A 0 8 35 9
# C 16 0 23 57
# G 56 28 0 20
# T 8 28 7 0
###################################### CpG/SBE-SNPs ####################################
# II
# (+)
# SNP1
# C -> T/A SNP is interpreted as unmethylated allele
# C -> G SNP is interpreted as methylated allele
# SNP2
# G -> A/C/T 3'-overhang: probe failure
# (-)
# SNP1
# C -> A/G/T 3'-overhang: probe failure
# SNP2
# G -> T/A SNP is interpreted as unmethylated allele
# G -> C SNP is interpreted as methylated allele
# I
# (+)
# SNP0
# T <-> C <-> A U/M Informative detection in the same channel
# A/T/C <-> G U/M Signal goes to the wrong channel (oob)
# SNP1
# C -> T SNP is interpreted as unmethylated allele
# C -> A/G 3’-overhang: probe failure for either U or M
# SNP2
# G -> A/C/T 3'-overhang: probe failure for either U or M
# (-)
# SNP1
# C -> A/G/T 3'-overhang: probe failure for either U or M
# SNP2
# G -> A SNP is interpreted as unmethylated allele
# G -> C/T 3'-overhang: probe failure for either U or M
# SNP3
# T <-> C <-> A U/M Informative detection in the same channel
# A/T/C <-> G U/M Signal goes to the wrong channel (oob)
#
names = c("II_plus_SNP1_C_TA", "II_plus_SNP1_C_G", "II_plus_SNP2_G_ACT",
"II_minus_SNP1_C_AGT", "II_minus_SNP2_G_TA", "II_minus_SNP2_G_C",
"I_plus_SNP0_A_C_T", "I_plus_SNP0_ACT_G", "I_plus_SNP1_C_T", "I_plus_SNP1_C_AG", "I_plus_SNP2_G_ACT",
"I_minus_SNP1_C_AGT", "I_minus_SNP2_G_A", "I_minus_SNP2_G_CT", "I_minus_SNP3_A_G_T", "I_minus_SNP3_AGT_C")
classification = as.list(rep(NA, length(names)))
names(classification) = names
classification[["II_plus_SNP1_C_TA"]] = CpG_SNP_Bi[CpG_SNP_Bi$type == "II" &
CpG_SNP_Bi$strand == "+" &
CpG_SNP_Bi$M == "C" &
CpG_SNP_Bi$m %in% c("T", "A"),]
classification[["II_plus_SNP1_C_G"]] = CpG_SNP_Bi[CpG_SNP_Bi$type == "II" &
CpG_SNP_Bi$strand == "+" &
CpG_SNP_Bi$M == "C" &
CpG_SNP_Bi$m %in% c("G"),]
classification[["II_plus_SNP2_G_ACT"]] = CpG_SNP_Bi[CpG_SNP_Bi$type == "II" &
CpG_SNP_Bi$strand == "+" &
CpG_SNP_Bi$M == "G" &
CpG_SNP_Bi$m %in% c("A", "C", "T"),]
classification[["II_minus_SNP1_C_AGT"]] = CpG_SNP_Bi[CpG_SNP_Bi$type == "II" &
CpG_SNP_Bi$strand == "-" &
CpG_SNP_Bi$M == "C" &
CpG_SNP_Bi$m %in% c("A", "G", "T"),]
classification[["II_minus_SNP2_G_TA"]] = CpG_SNP_Bi[CpG_SNP_Bi$type == "II" &
CpG_SNP_Bi$strand == "-" &
CpG_SNP_Bi$M == "G" &
CpG_SNP_Bi$m %in% c("A", "T"),]
classification[["II_minus_SNP2_G_C"]] = CpG_SNP_Bi[CpG_SNP_Bi$type == "II" &
CpG_SNP_Bi$strand == "-" &
CpG_SNP_Bi$M == "G" &
CpG_SNP_Bi$m %in% c("C"),]
classification[["I_plus_SNP1_C_T"]] = CpG_SNP_Bi[CpG_SNP_Bi$type == "I" &
CpG_SNP_Bi$strand == "+" &
CpG_SNP_Bi$M == "C" &
CpG_SNP_Bi$m %in% c("T"),]
classification[["I_plus_SNP1_C_AG"]] = CpG_SNP_Bi[CpG_SNP_Bi$type == "I" &
CpG_SNP_Bi$strand == "+" &
CpG_SNP_Bi$M == "C" &
CpG_SNP_Bi$m %in% c("A", "G"),]
classification[["I_plus_SNP2_G_ACT"]] = CpG_SNP_Bi[CpG_SNP_Bi$type == "I" &
CpG_SNP_Bi$strand == "+" &
CpG_SNP_Bi$M == "G" &
CpG_SNP_Bi$m %in% c("A", "C", "T"),]
classification[["I_minus_SNP1_C_AGT"]] = CpG_SNP_Bi[CpG_SNP_Bi$type == "I" &
CpG_SNP_Bi$strand == "-" &
CpG_SNP_Bi$M == "C" &
CpG_SNP_Bi$m %in% c("A", "G", "T"),]
classification[["I_minus_SNP2_G_A"]] = CpG_SNP_Bi[CpG_SNP_Bi$type == "I" &
CpG_SNP_Bi$strand == "-" &
CpG_SNP_Bi$M == "G" &
CpG_SNP_Bi$m %in% c("A"),]
classification[["I_minus_SNP2_G_CT"]] = CpG_SNP_Bi[CpG_SNP_Bi$type == "I" &
CpG_SNP_Bi$strand == "-" &
CpG_SNP_Bi$M == "G" &
CpG_SNP_Bi$m %in% c("C", "T"),]
classification[["I_plus_SNP0_A_C_T"]] = SBE_SNP[SBE_SNP$type == "I" &
SBE_SNP$strand == "+" &
SBE_SNP$M %in% c("C", "T", "A") & SBE_SNP$m %in% c("C", "T", "A"),]
classification[["I_plus_SNP0_ACT_G"]] = SBE_SNP[SBE_SNP$type == "I" &
SBE_SNP$strand == "+" &
(SBE_SNP$M %in% c("C", "T", "A", "G") & SBE_SNP$m %in% c("G") |
SBE_SNP$M %in% c("G") & SBE_SNP$m %in% c("C", "T", "A", "G")),]
classification[["I_minus_SNP3_A_G_T"]] = SBE_SNP[SBE_SNP$type == "I" &
SBE_SNP$strand == "-" &
SBE_SNP$M %in% c("G", "T", "A") & SBE_SNP$m %in% c("G", "T", "A"),]
classification[["I_minus_SNP3_AGT_C"]] = SBE_SNP[SBE_SNP$type == "I" &
SBE_SNP$strand == "-" &
(SBE_SNP$M %in% c("G", "T", "A") & SBE_SNP$m %in% c("C") |
SBE_SNP$M %in% c("C") & SBE_SNP$m %in% c("G", "T", "A")),]
sapply(classification, nrow) # 450K
# II_plus_SNP1_C_TA II_plus_SNP1_C_G II_plus_SNP2_G_ACT II_minus_SNP1_C_AGT II_minus_SNP2_G_TA
# 1131 111 1266 1221 1916
# II_minus_SNP2_G_C I_plus_SNP0_A_C_T I_plus_SNP0_ACT_G I_plus_SNP1_C_T I_plus_SNP1_C_AG
# 188 49 97 283 98
# I_plus_SNP2_G_ACT I_minus_SNP1_C_AGT I_minus_SNP2_G_A I_minus_SNP2_G_CT I_minus_SNP3_A_G_T
# 364 371 355 121 62
# I_minus_SNP3_AGT_C
# 89
sum(sapply(classification, nrow)) # 7722; 450K
nrow(SBE_SNP) + nrow(CpG_SNP_Bi) # 7722; 450K
setwd("/home/ben/Documents/Git/UMtools/data/")
save(classification, file = "classification_CpG_SNP_450K.RData")
save(classification, file = "classification_CpG_SNP_EPIC.RData")
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