R/DecipherExtraction.r
In VilainLab/Nanotator: Next generation structural variant annotation and classification
Defines functions
Decipherfrequency
Documented in
Decipherfrequency
#' Frequency calculation of variants compared to Decipher.
#'
#' @param decipherpath character. Decipher Text file.
#' @param outpath character. Path where gene lists are saved.
#' @param smap_data Dataset containing smap data.
#' @param smap character Filepath for smap.
#' @param win_indel character indel window. Default 10000.
#' @param perc_similarity Numeric . ThresholdPercentage similarity
#' of the query SV and reference SV.
#' @param EnzymeType boolean . Options SE and SVMerge.
#' @param input_fmt_SV boolean . Options SE and SVMerge.
#' @param returnMethod character. Choice between
#' text or data frame as the output.
#' @return dataframe containing decipher data.
#' are stored as text files.
#' @examples
#' decipherpath = system.file("extdata", "population_cnv.txt",
#' package="nanotatoR")
#' smappath=system.file("extdata", "GM24385_Ason_DLE1_VAP_trio5.smap",
#' package="nanotatoR")
#' datdecipher <- Decipherfrequency (decipherpath = decipherpath,
#' smap = smappath, win_indel = 10000,
#' EnzymeType= "SE",
#' perc_similarity = 0.5,returnMethod="dataFrame",
#' input_fmt_SV = "Text")
#' datdecipher[1,]
#' @export
Decipherfrequency <- function(decipherpath, smap,
smap_data,
win_indel = 10000, perc_similarity = 0.5,
returnMethod = c("Text", "dataFrame"),
input_fmt_SV = c("Text", "dataFrame"),
EnzymeType = c("SVMerge", "SE"),
outpath)
{
# S='F' Change the window for Inversion/translocation 50000
print("###Calculating Decipher Frequency###")
## Reading the DGV file and the smap file
r <- read.table(decipherpath, header = TRUE, sep = "\t")
## Unique variant length extraction for frquency Calculation samp <-
## as.character(unique(r$samples)) usamp <- UniqueSample(samp)
#usamp <- 7965
datfinal <- data.frame()
# varaccl<-length(unique(varacc)) close(con) Checking if the input
# format is dataframe or Text
if(input_fmt_SV=="dataFrame"){
smapdata = smap_data
if(EnzymeType == "SVMerge"){
#smapdata <- readSMap(smap, input_fmt_smap = "Text")
SVID<-smapdata$SVIndex
}
else{
#smapdata <- readSMap_DLE(smap, input_fmt_smap)
SVID<-smapdata$SmapEntryID
}
}
else if(input_fmt_SV=="Text"){
if(EnzymeType == "SVMerge"){
smapdata <- readSMap(smap, input_fmt_smap = "Text")
SVID<-smapdata$SVIndex
}
else{
smapdata <- readSMap_DLE(smap, input_fmt_smap = "Text")
SVID<-smapdata$SmapEntryID
}
}
else{
stop("Input format for SMAP Incorrect")
}
r1 <- smapdata
## Checking Sex male/female and assigning chromosome number accordingly
chro <- (unique(r1$RefcontigID1))
#chro1 <- c(1:chro)
dataFinal <- c()
## Extracting Data for 1 chromosome at a time and comparing Make change
## in XY
for (ii in seq_along(chro)) # for (ii in 1:20) for (ii in 1)
{
# print(paste('Chromosome:', chro[ii])) Extracting data from DGV
# dataset print(paste('ii:',ii))
if (ii == 23)
{
kk <- "X"
} else if (ii == 24)
{
kk <- "Y"
} else
{
kk <- chro[ii]
}
dat <- r[which(r$chr == kk), ]
# variantType1<-dat$variantsubtype Changing the variant terms in DGV to
# match svmap dat$variantsubtype <- gsub('loss', 'deletion',
# dat$variantsubtype) dat$variantsubtype <- gsub('gain', 'insertion',
# dat$variantsubtype) dat$variantsubtype <- gsub('gain+loss',
# 'insertion+deletion', dat$variantsubtype) variantType1 <-
# dat$variantsubtype Extracting data from SVmap
dat1 <- r1[which(r1$RefcontigID1 == chro[ii]), ]
## Adding the windows to the breakpoints
rf <- dat1$RefStartPos
rf_wb_ind <- rf - win_indel
rf_fb_ind <- rf + win_indel
re <- dat1$RefEndPos
re_wf_ind <- re + win_indel
re_wb_ind <- re - win_indel
## Calculating size
size_bn <- dat1$Size
variantType2 <- as.character(dat1$Type)
# countfre<-0 percn<-c()
datf <- c()
for (nn in 1:length(rf)) # for (nn in 1:20)
{
# print(paste('nn:',nn)) Comparing the conditions
if (variantType2[nn] == "deletion")
{
dat2 <- dat[which((dat$start >= rf_wb_ind[nn]
& dat$end <= re_wf_ind[nn])), ]
size1 <- size_bn[nn]
# print(dim(dat2)) Writing if the dgv_match is TRUE or not
if (nrow(dat2) > 1)
{
countfre <- 0
# type <- dat2$variantsubtype
del_freq <- as.numeric(dat2$deletion_frequency)
del_samp <- as.numeric(dat2$sample_size)
dat2$size_dec <- dat2$end - dat2$start
dat2$perc_ref_query <- as.numeric(dat2$size_dec)/size1
dat2$perc_query_ref <- size1/as.numeric(dat2$size_dec)
type = c()
freq = c()
samp_size = c()
# print(paste('nrow(dat2):',nrow(dat2),sep=''))
for (ll in 1:nrow(dat2))
{
size_dec <- dat2$end[ll] - dat2$start[ll]
#perc <- (size1/size_dec)
if ((dat2$perc_ref_query[ll] >= perc_similarity & dat2$perc_query_ref[ll] >= perc_similarity) & (del_freq[ll] > 0))
{
# print(paste('del_freq:',del_freq[ll],sep=''))
freq <- c(freq, as.numeric(del_freq[ll]))
# ctr=ctr+1
} else
{
freq <- c(freq, 0)
}
}
# freq=paste(freq,collapse=',')
if (mean(freq) == 0)
{
freq = 0
} else
{
freq = round(mean(freq), 2)
}
# samp_size=paste(samp_size,collapse=',') type=paste(type,collapse=',')
data1 <- data.frame(dat1[nn, ], DECIPHER_Frequency = freq,
stringsAsFactors = FALSE)
datf <- rbind(datf, data1)
} else if (nrow(dat2) == 1)
{
# dgv_match=TRUE Calculating percentage similarity
del_freq <- as.numeric(dat2$deletion_frequency)
del_samp <- as.numeric(dat2$sample_size)
size_dec <- dat2$end - dat2$start
perc_ref_query <- as.numeric(size_dec)/size1
perc_query_ref <- size1/as.numeric(size_dec)
# ctr=ctr+1
if ((perc_ref_query >= perc_similarity
& perc_query_ref >= perc_similarity)
& (del_freq > 0))
{
# print(paste('del_freq:',del_freq,sep=''))
freq <- round(del_freq, 2)
# ctr=ctr+1
} else
{
freq <- 0
}
# freq=paste(as.numeric(freq),collapse=',')
# samp_size=paste(as.numeric(samp_size),collapse=',')
data1 <- data.frame(dat1[nn, ], DECIPHER_Frequency = freq,
stringsAsFactors = FALSE)
datf <- rbind(datf, data1)
} else
{
# dgv_match=FALSE
data1 <- data.frame(dat1[nn, ], DECIPHER_Frequency = 0,
stringsAsFactors = FALSE)
datf <- rbind(datf, data1)
# next;
}
} else if (variantType2[nn] == "insertion"
| variantType2[nn] == "duplication")
{
dat2 <- dat[which((dat$start >= rf_wb_ind[nn]
& dat$end <= re_wf_ind[nn])), ]
size1 <- size_bn[nn]
# print(dim(dat2)) Writing if the dgv_match is TRUE or not
if (nrow(dat2) > 1)
{
# print(paste('nrow(dat2):',nrow(dat2),sep=''))
countfre <- 0
# type <- dat2$variantsubtype
ins_freq <- as.numeric(dat2$duplication_frequency)
ins_samp <- as.numeric(dat2$sample_size)
dat2$size_dec <- dat2$end - dat2$start
dat2$perc_ref_query <- as.numeric(dat2$size_dec)/size1
dat2$perc_query_ref <- size1/as.numeric(dat2$size_dec)
type = c()
freq = c()
samp_size = c()
for (ll in 1:nrow(dat2))
{
'size_dec <- dat2$end[ll] - dat2$start[ll]
perc <- (size1/size_dec)'
if ((dat2$perc_ref_query[ll] >= perc_similarity
& dat2$perc_query_ref[ll] >= perc_similarity)
& (ins_freq[ll] > 0))
{
# print(paste('ins_freq:',ins_freq[ll],sep=''))
freq <- c(freq, as.numeric(ins_freq[ll]))
# type<-c(type,'Insertion')
# samp_size<-c(samp_size,as.numeric(ins_samp[ll])) ctr=ctr+1
} else
{
freq <- c(freq, 0)
# type<-c(type,'-') samp_size<-c(samp_size,'-')
}
}
if (mean(freq) == 0)
{
freq = 0
} else
{
freq = round(mean(freq), 2)
}
# samp_size=paste(samp_size,collapse=',') type=paste(type,collapse=',')
data1 <- data.frame(dat1[nn, ], DECIPHER_Frequency = freq,
stringsAsFactors = FALSE)
datf <- rbind(datf, data1)
} else if (nrow(dat2) == 1)
{
# dgv_match=TRUE Calculating percentage similarity
ins_freq <- as.numeric(dat2$duplication_frequency)
ins_samp <- as.numeric(dat2$sample_size)
size_dec <- dat2$end - dat2$start
perc_ref_query <- as.numeric(size_dec)/size1
perc_query_ref <- size1/as.numeric(size_dec)
# ctr=ctr+1
if ((perc_ref_query >= perc_similarity & perc_query_ref >= perc_similarity)
& (ins_freq > 0))
{
# print(paste('ins_freq:',ins_freq,sep=''))
freq <- round(ins_freq, 2)
# ctr=ctr+1
} else
{
freq <- 0
}
data1 <- data.frame(dat1[nn, ], DECIPHER_Frequency = freq,
stringsAsFactors = FALSE)
datf <- rbind(datf, data1)
} else
{
# dgv_match=FALSE
data1 <- data.frame(dat1[nn, ], DECIPHER_Frequency = 0,
stringsAsFactors = FALSE)
datf <- rbind(datf, data1)
# next;
}
} else
{
# dgv_match=FALSE
data1 <- data.frame(dat1[nn, ], DECIPHER_Frequency = 0,
stringsAsFactors = FALSE)
datf <- rbind(datf, data1)
# next;
}
}
dataFinal <- rbind(dataFinal, datf)
}
## Return Mode Dataframe or Text
if (returnMethod == "Text")
{
st1 <- strsplit(smap, ".txt")
fname <- st1[[1]][1]
row.names(dataFinal) <- c()
filename <- paste(fname, "_Decipher.txt", sep = "")
write.table(dataFinal, file.path(outpath, filename),
sep = "\t", row.names = FALSE)
} else if (returnMethod == "dataFrame")
{
return(dataFinal)
} else
{
stop("ReturnMethod Incorrect")
}
}
VilainLab/Nanotator documentation built on Oct. 9, 2021, 8:59 p.m.
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Defines functions Decipherfrequency
Documented in Decipherfrequency
#' Frequency calculation of variants compared to Decipher.
#'
#' @param decipherpath character. Decipher Text file.
#' @param outpath character. Path where gene lists are saved.
#' @param smap_data Dataset containing smap data.
#' @param smap character Filepath for smap.
#' @param win_indel character indel window. Default 10000.
#' @param perc_similarity Numeric . ThresholdPercentage similarity
#' of the query SV and reference SV.
#' @param EnzymeType boolean . Options SE and SVMerge.
#' @param input_fmt_SV boolean . Options SE and SVMerge.
#' @param returnMethod character. Choice between
#' text or data frame as the output.
#' @return dataframe containing decipher data.
#' are stored as text files.
#' @examples
#' decipherpath = system.file("extdata", "population_cnv.txt",
#' package="nanotatoR")
#' smappath=system.file("extdata", "GM24385_Ason_DLE1_VAP_trio5.smap",
#' package="nanotatoR")
#' datdecipher <- Decipherfrequency (decipherpath = decipherpath,
#' smap = smappath, win_indel = 10000,
#' EnzymeType= "SE",
#' perc_similarity = 0.5,returnMethod="dataFrame",
#' input_fmt_SV = "Text")
#' datdecipher[1,]
#' @export
Decipherfrequency <- function(decipherpath, smap,
smap_data,
win_indel = 10000, perc_similarity = 0.5,
returnMethod = c("Text", "dataFrame"),
input_fmt_SV = c("Text", "dataFrame"),
EnzymeType = c("SVMerge", "SE"),
outpath)
{
# S='F' Change the window for Inversion/translocation 50000
print("###Calculating Decipher Frequency###")
## Reading the DGV file and the smap file
r <- read.table(decipherpath, header = TRUE, sep = "\t")
## Unique variant length extraction for frquency Calculation samp <-
## as.character(unique(r$samples)) usamp <- UniqueSample(samp)
#usamp <- 7965
datfinal <- data.frame()
# varaccl<-length(unique(varacc)) close(con) Checking if the input
# format is dataframe or Text
if(input_fmt_SV=="dataFrame"){
smapdata = smap_data
if(EnzymeType == "SVMerge"){
#smapdata <- readSMap(smap, input_fmt_smap = "Text")
SVID<-smapdata$SVIndex
}
else{
#smapdata <- readSMap_DLE(smap, input_fmt_smap)
SVID<-smapdata$SmapEntryID
}
}
else if(input_fmt_SV=="Text"){
if(EnzymeType == "SVMerge"){
smapdata <- readSMap(smap, input_fmt_smap = "Text")
SVID<-smapdata$SVIndex
}
else{
smapdata <- readSMap_DLE(smap, input_fmt_smap = "Text")
SVID<-smapdata$SmapEntryID
}
}
else{
stop("Input format for SMAP Incorrect")
}
r1 <- smapdata
## Checking Sex male/female and assigning chromosome number accordingly
chro <- (unique(r1$RefcontigID1))
#chro1 <- c(1:chro)
dataFinal <- c()
## Extracting Data for 1 chromosome at a time and comparing Make change
## in XY
for (ii in seq_along(chro)) # for (ii in 1:20) for (ii in 1)
{
# print(paste('Chromosome:', chro[ii])) Extracting data from DGV
# dataset print(paste('ii:',ii))
if (ii == 23)
{
kk <- "X"
} else if (ii == 24)
{
kk <- "Y"
} else
{
kk <- chro[ii]
}
dat <- r[which(r$chr == kk), ]
# variantType1<-dat$variantsubtype Changing the variant terms in DGV to
# match svmap dat$variantsubtype <- gsub('loss', 'deletion',
# dat$variantsubtype) dat$variantsubtype <- gsub('gain', 'insertion',
# dat$variantsubtype) dat$variantsubtype <- gsub('gain+loss',
# 'insertion+deletion', dat$variantsubtype) variantType1 <-
# dat$variantsubtype Extracting data from SVmap
dat1 <- r1[which(r1$RefcontigID1 == chro[ii]), ]
## Adding the windows to the breakpoints
rf <- dat1$RefStartPos
rf_wb_ind <- rf - win_indel
rf_fb_ind <- rf + win_indel
re <- dat1$RefEndPos
re_wf_ind <- re + win_indel
re_wb_ind <- re - win_indel
## Calculating size
size_bn <- dat1$Size
variantType2 <- as.character(dat1$Type)
# countfre<-0 percn<-c()
datf <- c()
for (nn in 1:length(rf)) # for (nn in 1:20)
{
# print(paste('nn:',nn)) Comparing the conditions
if (variantType2[nn] == "deletion")
{
dat2 <- dat[which((dat$start >= rf_wb_ind[nn]
& dat$end <= re_wf_ind[nn])), ]
size1 <- size_bn[nn]
# print(dim(dat2)) Writing if the dgv_match is TRUE or not
if (nrow(dat2) > 1)
{
countfre <- 0
# type <- dat2$variantsubtype
del_freq <- as.numeric(dat2$deletion_frequency)
del_samp <- as.numeric(dat2$sample_size)
dat2$size_dec <- dat2$end - dat2$start
dat2$perc_ref_query <- as.numeric(dat2$size_dec)/size1
dat2$perc_query_ref <- size1/as.numeric(dat2$size_dec)
type = c()
freq = c()
samp_size = c()
# print(paste('nrow(dat2):',nrow(dat2),sep=''))
for (ll in 1:nrow(dat2))
{
size_dec <- dat2$end[ll] - dat2$start[ll]
#perc <- (size1/size_dec)
if ((dat2$perc_ref_query[ll] >= perc_similarity & dat2$perc_query_ref[ll] >= perc_similarity) & (del_freq[ll] > 0))
{
# print(paste('del_freq:',del_freq[ll],sep=''))
freq <- c(freq, as.numeric(del_freq[ll]))
# ctr=ctr+1
} else
{
freq <- c(freq, 0)
}
}
# freq=paste(freq,collapse=',')
if (mean(freq) == 0)
{
freq = 0
} else
{
freq = round(mean(freq), 2)
}
# samp_size=paste(samp_size,collapse=',') type=paste(type,collapse=',')
data1 <- data.frame(dat1[nn, ], DECIPHER_Frequency = freq,
stringsAsFactors = FALSE)
datf <- rbind(datf, data1)
} else if (nrow(dat2) == 1)
{
# dgv_match=TRUE Calculating percentage similarity
del_freq <- as.numeric(dat2$deletion_frequency)
del_samp <- as.numeric(dat2$sample_size)
size_dec <- dat2$end - dat2$start
perc_ref_query <- as.numeric(size_dec)/size1
perc_query_ref <- size1/as.numeric(size_dec)
# ctr=ctr+1
if ((perc_ref_query >= perc_similarity
& perc_query_ref >= perc_similarity)
& (del_freq > 0))
{
# print(paste('del_freq:',del_freq,sep=''))
freq <- round(del_freq, 2)
# ctr=ctr+1
} else
{
freq <- 0
}
# freq=paste(as.numeric(freq),collapse=',')
# samp_size=paste(as.numeric(samp_size),collapse=',')
data1 <- data.frame(dat1[nn, ], DECIPHER_Frequency = freq,
stringsAsFactors = FALSE)
datf <- rbind(datf, data1)
} else
{
# dgv_match=FALSE
data1 <- data.frame(dat1[nn, ], DECIPHER_Frequency = 0,
stringsAsFactors = FALSE)
datf <- rbind(datf, data1)
# next;
}
} else if (variantType2[nn] == "insertion"
| variantType2[nn] == "duplication")
{
dat2 <- dat[which((dat$start >= rf_wb_ind[nn]
& dat$end <= re_wf_ind[nn])), ]
size1 <- size_bn[nn]
# print(dim(dat2)) Writing if the dgv_match is TRUE or not
if (nrow(dat2) > 1)
{
# print(paste('nrow(dat2):',nrow(dat2),sep=''))
countfre <- 0
# type <- dat2$variantsubtype
ins_freq <- as.numeric(dat2$duplication_frequency)
ins_samp <- as.numeric(dat2$sample_size)
dat2$size_dec <- dat2$end - dat2$start
dat2$perc_ref_query <- as.numeric(dat2$size_dec)/size1
dat2$perc_query_ref <- size1/as.numeric(dat2$size_dec)
type = c()
freq = c()
samp_size = c()
for (ll in 1:nrow(dat2))
{
'size_dec <- dat2$end[ll] - dat2$start[ll]
perc <- (size1/size_dec)'
if ((dat2$perc_ref_query[ll] >= perc_similarity
& dat2$perc_query_ref[ll] >= perc_similarity)
& (ins_freq[ll] > 0))
{
# print(paste('ins_freq:',ins_freq[ll],sep=''))
freq <- c(freq, as.numeric(ins_freq[ll]))
# type<-c(type,'Insertion')
# samp_size<-c(samp_size,as.numeric(ins_samp[ll])) ctr=ctr+1
} else
{
freq <- c(freq, 0)
# type<-c(type,'-') samp_size<-c(samp_size,'-')
}
}
if (mean(freq) == 0)
{
freq = 0
} else
{
freq = round(mean(freq), 2)
}
# samp_size=paste(samp_size,collapse=',') type=paste(type,collapse=',')
data1 <- data.frame(dat1[nn, ], DECIPHER_Frequency = freq,
stringsAsFactors = FALSE)
datf <- rbind(datf, data1)
} else if (nrow(dat2) == 1)
{
# dgv_match=TRUE Calculating percentage similarity
ins_freq <- as.numeric(dat2$duplication_frequency)
ins_samp <- as.numeric(dat2$sample_size)
size_dec <- dat2$end - dat2$start
perc_ref_query <- as.numeric(size_dec)/size1
perc_query_ref <- size1/as.numeric(size_dec)
# ctr=ctr+1
if ((perc_ref_query >= perc_similarity & perc_query_ref >= perc_similarity)
& (ins_freq > 0))
{
# print(paste('ins_freq:',ins_freq,sep=''))
freq <- round(ins_freq, 2)
# ctr=ctr+1
} else
{
freq <- 0
}
data1 <- data.frame(dat1[nn, ], DECIPHER_Frequency = freq,
stringsAsFactors = FALSE)
datf <- rbind(datf, data1)
} else
{
# dgv_match=FALSE
data1 <- data.frame(dat1[nn, ], DECIPHER_Frequency = 0,
stringsAsFactors = FALSE)
datf <- rbind(datf, data1)
# next;
}
} else
{
# dgv_match=FALSE
data1 <- data.frame(dat1[nn, ], DECIPHER_Frequency = 0,
stringsAsFactors = FALSE)
datf <- rbind(datf, data1)
# next;
}
}
dataFinal <- rbind(dataFinal, datf)
}
## Return Mode Dataframe or Text
if (returnMethod == "Text")
{
st1 <- strsplit(smap, ".txt")
fname <- st1[[1]][1]
row.names(dataFinal) <- c()
filename <- paste(fname, "_Decipher.txt", sep = "")
write.table(dataFinal, file.path(outpath, filename),
sep = "\t", row.names = FALSE)
} else if (returnMethod == "dataFrame")
{
return(dataFinal)
} else
{
stop("ReturnMethod Incorrect")
}
}
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