Nothing
R/DGV_extraction.r
In nanotatoR: nanotatoR: next generation structural variant annotation and classification
Defines functions
DGV_extraction
Documented in
DGV_extraction
#' Frequency calculation of variants compared to DGV.
#'
#' @param hgpath character. Path to Database of Genomic Variants (DGV)
#' Text file.
#' @param smappath character. Path for smap textfile.
#' @param smap character. File name for smap textfile.
#' @param input_fmt_DGV character. Choice between text or data frame as
#' an input to the DGV frequency calculator.
#' @param smap_data dataframe. Dataset containing smap data.
#' @param win_indel_DGV Numeric. Insertion and deletion error window.Default
#' 10000
#' bases.
#' @param win_inv_trans_DGV Numeric. Inversion and translocation error window.
#' Default 50000 bases.
#' @param perc_similarity_DGV Numeric. ThresholdPercentage similarity
#' of the query SV and reference SV. Default 0.5.
#' @param returnMethod character. Choice between text or data frame as the
#' output.
#' @param outpath character. Path where gene lists are saved.
#' @param usample Numeric. Number of unique samples.
#' @return Text and character vector containg gene list and terms associated
#' with them
#' are stored as text files.
#' @examples
#' \dontrun{
#' smap <- "F1.1_TestSample1_solo_hg19.smap"
#' smappath <- system.file("extdata", package = "nanotatoR")
#' hgpath <- system.file("extdata", "GRCh37_hg19_variants_2016-05-15.txt",
#' package = "nanotatoR")
#' win_indel_DGV=10000;win_inv_trans_DGV=50000;perc_similarity_DGV=0.5;
#' usample = 54946
#' dgv <- DGV_extraction (hgpath, input_fmt_DGV = "Text",smap=smap,
#' smappath, win_indel_DGV = 10000, win_inv_trans_DGV = 50000,
#' perc_similarity_DGV = 0.5,returnMethod="dataFrame",usample = 54946)
#' }
#' @import utils
#' @export
DGV_extraction <- function(hgpath, smappath, smap, smap_data,
input_fmt_DGV = c("Text", "dataFrame"), win_indel_DGV = 10000,
win_inv_trans_DGV = 50000, perc_similarity_DGV = 0.5,
returnMethod = c("Text", "dataFrame"), outpath, usample = 54946) {
# S='F' Change the window for Inversion/translocation 50000
## Reading the DGV file and the smap file
r <- read.table(paste(hgpath), header = TRUE, sep = "\t")
## Unique variant length extraction for frquency Calculation
samp <- as.character(unique(r$samples))
#usamp <- UniqueSample(samp)
usamp <- usample
# varaccl<-length(unique(varacc)) close(con)
##Checking if the input format is dataframe or Text
if (input_fmt_DGV == "Text") {
##Pattern matching needs to be done to remove #
con <- file(file.path(smappath, smap), "r")
r10 <- readLines(con, n = -1)
close(con)
datfinal <- data.frame()
g1 <- grep("RawConfidence", r10)
g2 <- grep("RefStartPos", r10)
if (g1 == g2){
dat <- gsub("# ", "", r10)
# dat<-gsub('\t',' ',r1)
dat4 <- textConnection(dat[g1:length(dat)])
r1 <- read.table(dat4, sep = "\t", header = TRUE)
close(dat4)
}
else{
stop("column names doesnot Match")
}
}
else if (input_fmt_DGV == "dataFrame") {
r1 <- smap_data
}
else{
stop("Incorrect format")
}
## Checking Sex male/female and assigning chromosome number accordingly
chro1 <- (unique(r1$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(chro1)){
print(paste("Chromosome:", chro1[ii]))
## Extracting data from DGV dataset
if (ii == 23){
kk <- "X"
}
else if (ii == 24){
kk <- "Y"
}
else{
kk <- chro1[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 == chro1[ii]),]
## Adding the windows to the breakpoints
rf <- dat1$RefStartPos
rf_wb_ind <- rf - win_indel_DGV
rf_fb_ind <- rf + win_indel_DGV
rf_wb_int <- rf - win_inv_trans_DGV
rf_fb_int <- rf + win_inv_trans_DGV
re <- dat1$RefEndPos
re_wf_ind <- re + win_indel_DGV
re_wb_ind <- re - win_indel_DGV
re_wb_int <- re - win_inv_trans_DGV
re_fb_int <- re + win_inv_trans_DGV
## Calculating size
size_bn <- dat1$Size
variantType2 <- as.character(dat1$Type)
# countfre<-0 percn<-c()
datf <- c()
for (nn in seq_len(length(rf))){
#print(nn)
## Comparing the conditions
if (variantType2[nn] == "deletion" |
variantType2[nn] == "insertion") {
dat2 <- dat[which((dat$start <= rf[nn] &
dat$start >= rf_wb_ind[nn] | dat$start >= rf[nn] &
dat$start <= rf_fb_ind[nn]) &
( dat$end >= re[nn] & dat$end <= re_wf_ind[nn] |
dat$end <= re[nn] & dat$end >= re_wb_ind[nn])),]
size1 <- size_bn[nn]
#print(dim(dat2))
## Writing if the dgv_match is TRUE or not
if (nrow(dat2) > 1){
countfre <- 0
countfreunfilt <- 0
type <- dat2$variantsubtype
for (ll in seq_len(nrow(dat2))){
size_dgv <- dat2$end[ll] - dat2$start[ll]
perc <- (size1 / size_dgv)
if (perc >= perc_similarity_DGV &
(identical(type[ll],variantType2[nn]))){
fre <- as.character(dat2$samples[ll])
if (length(fre) >= 1){
g <- grep(",", fre)
if (length(g) > 0) {
fre1 <- as.character(
strsplit(as.character(fre),
split = ",")[[1]])
}
else{
fre1 <- fre
}
}
else{
fre1 <- NULL
}
countfre <- countfre + length(fre1)
}
else{
countfre <- countfre + 0
}
# ctr=ctr+1
}
data1 <- data.frame(dat1[nn,],
DGV_Freq_Perc = format(((countfre / usamp) * 100),
scientific = FALSE), stringsAsFactors = FALSE)
datf <- rbind(datf, data1)
}
else if (nrow(dat2) == 1){
# dgv_match=TRUE Calculating percentage similarity
type <- dat2$variantsubtype
size_dgv <- dat2$end - dat2$start
perc <- (size1 / size_dgv)
if (perc >= perc_similarity_DGV &
(identical(type, variantType2[nn]))){
fre <- as.character(dat2$samples)
if (length(fre) >= 1){
g <- grep(",", fre)
countfre <- 0
if (length(g) > 0){
countfre <-
length(as.character(
strsplit(as.character(fre),
split = ",")[[1]]))
}
else{
countfre <- countfre + length(fre1)
}
}
else{
countfre <- NULL
}
}
else{
countfre <- 0
}
data1 <- data.frame( dat1[nn,],
DGV_Freq_Perc = format(((countfre / usamp) * 100),
scientific = FALSE), stringsAsFactors = FALSE)
datf <- rbind(datf, data1)
}
else{
# dgv_match=FALSE
data1 <- data.frame(dat1[nn,], DGV_Freq_Perc = 0,
stringsAsFactors = FALSE)
datf <- rbind(datf, data1)
# next;
}
}
else if ((variantType2[nn] == "duplication" |
variantType2[nn] == "duplication_split"|
variantType2[nn] == "duplication_inverted")){
dat2 <- dat[which((dat$start <= rf[nn] &
dat$start >= rf_wb_ind[nn] |
dat$start >= rf[nn] &
dat$start <= rf_fb_ind[nn]) &
(dat$end >= re[nn] & dat$end <= re_wf_ind[nn] |
dat$end <= re[nn] & dat$end >= re_wb_ind[nn])),]
size1 <- size_bn[nn]
#print(dim(dat2))
## Writing if the dgv_match is TRUE or not
if (nrow(dat2) > 1){
countfre <- 0
countfreunfilt <- 0
type <- dat2$variantsubtype
for (ll in seq_len(nrow(dat2))){
size_dgv <- dat2$end[ll] - dat2$start[ll]
perc <- (size1 / size_dgv)
if (perc >= perc_similarity_DGV &
(identical(type[ll], variantType2[nn]))){
fre <- as.character(dat2$samples[ll])
if (length(fre) >= 1){
g <- grep(",", fre)
if (length(g) > 0){
fre1 <- as.character(
strsplit(
as.character(fre),
split = ",")[[1]])
}
else {
fre1 <- fre
}
}
else{
fre1 <- NULL
}
countfre <- countfre + length(fre1)
}
else{
countfre <- countfre + 0
}
# ctr=ctr+1
}
data1 <- data.frame(dat1[nn,],DGV_Freq_Perc =
format(((countfre / usamp) * 100),
scientific = FALSE), stringsAsFactors = FALSE)
datf <- rbind(datf, data1)
}
else if (nrow(dat2) == 1){
# dgv_match=TRUE Calculating percentage similarity
type <- dat2$variantsubtype
size_dgv <- dat2$end - dat2$start
perc <- (size1 / size_dgv)
if (perc >= perc_similarity_DGV &
(identical(type, variantType2[nn]))){
fre <- as.character(dat2$samples)
if (length(fre) >= 1){
g <- grep(",", fre)
countfre <- 0
if (length(g) > 0){
countfre <- length(
as.character(
strsplit(as.character(fre),
split = ",")[[1]]))
}
else{
countfre <- countfre + length(fre1)
}
}
else{
countfre <- NULL
}
}
else {
countfre <- 0
}
# ctr=ctr+1
data1 <- data.frame(dat1[nn,],
DGV_Freq_Perc = format(((countfre / usamp) * 100),
scientific = FALSE), stringsAsFactors = FALSE)
datf <- rbind(datf, data1)
}
else{
# dgv_match=FALSE
data1 <- data.frame(dat1[nn,],DGV_Freq_Perc = 0,
stringsAsFactors = FALSE)
datf <- rbind(datf, data1)
# next;
}
}
else if (length(grep("inversion", variantType2[nn])) >= 1 |
length(grep("translocation", variantType2[nn])) >= 1){
dat2 <- dat[which(( dat$start <= rf[nn] &
dat$start >= rf_wb_int[nn] | dat$start >= rf[nn] &
dat$start <= rf_fb_int[nn]) &
(dat$end >= re[nn] & dat$end <= re_fb_int[nn] |
dat$end <= re[nn] & dat$end >= re_wb_int[nn])),]
size1 <- size_bn[nn]
#print(dim(dat2))
## Writing if the dgv_match is TRUE or not
countfre <- c()
countfreunfilt <- 0
if (nrow(dat2) > 1){
countfre <- 0; countfreunfilt <- 0
type <- dat2$variantsubtype
for (ll in seq_len(nrow(dat2))) {
#print(ll)
if ((identical(type[ll],variantType2[nn]))){
#print("1")
fre <- as.character(dat2$samples[ll])
if (length(fre) >= 1){
g <- grep(",", fre)
if (length(g) > 0){
fre1 <- as.character(
strsplit(as.character(fre),
split = ",")[[1]])
}
else{
fre1 <- fre
}
}
else{
fre1 <- NULL
}
countfre <- countfre + length(fre1)
}
else{
#print("2")
countfre <- countfre + 0
}
# ctr=ctr+1
}
data1 <- data.frame(dat1[nn,],
DGV_Freq_Perc = format(((countfre / usamp) * 100),
scientific = FALSE), stringsAsFactors = FALSE)
datf <- rbind(datf, data1)
}
else if (nrow(dat2) == 1){
# dgv_match=TRUE Calculating percentage similarity
type <- dat2$variantsubtype
size_dgv <- dat2$end - dat2$start
perc <- (size1 / size_dgv)
if ((identical(type, variantType2[nn]))){
fre <- as.character(dat2$samples)
if (length(fre) >= 1){
g <- grep(",", fre)
countfre <- 0
if (length(g) > 0){
countfre <- length(as.character(
strsplit(as.character(fre), split = ","
)[[1]]))
}
else{
countfre <- length(fre)
}
}
else{
countfre <- NULL
}
}
else{
countfre <- 0
}
data1 <- data.frame(dat1[nn,],
DGV_Freq_Perc = format(((countfre / usamp) * 100),
scientific = FALSE), stringsAsFactors = FALSE)
datf <- rbind(datf, data1)
}
else{
# dgv_match=FALSE
data1 <- data.frame(dat1[nn,],
DGV_Freq_Perc = 0, stringsAsFactors = FALSE)
datf <- rbind(datf, data1)
# next;
}
}
else{
data1 <- data.frame(dat1[nn,], DGV_Freq_Perc = 0,
stringsAsFactors = FALSE)
datf <- rbind(datf, data1)
}
}
dataFinal <- rbind(dataFinal, datf)
}
##Return Mode Dataframe or Text
if (returnMethod == "Text") {
st1 <- strsplit(smap, ".txt")
fname <- st1[[1]][1]
row.names(dataFinal) <- c()
write.table(dataFinal, paste(outpath, fname, "_DGV.txt", sep = ""),
sep = "\t", row.names = FALSE)
}
else if (returnMethod == "dataFrame") {
return (dataFinal)
}
else{
stop ("ReturnMethod Incorrect")
}
}
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Defines functions DGV_extraction
Documented in DGV_extraction
#' Frequency calculation of variants compared to DGV.
#'
#' @param hgpath character. Path to Database of Genomic Variants (DGV)
#' Text file.
#' @param smappath character. Path for smap textfile.
#' @param smap character. File name for smap textfile.
#' @param input_fmt_DGV character. Choice between text or data frame as
#' an input to the DGV frequency calculator.
#' @param smap_data dataframe. Dataset containing smap data.
#' @param win_indel_DGV Numeric. Insertion and deletion error window.Default
#' 10000
#' bases.
#' @param win_inv_trans_DGV Numeric. Inversion and translocation error window.
#' Default 50000 bases.
#' @param perc_similarity_DGV Numeric. ThresholdPercentage similarity
#' of the query SV and reference SV. Default 0.5.
#' @param returnMethod character. Choice between text or data frame as the
#' output.
#' @param outpath character. Path where gene lists are saved.
#' @param usample Numeric. Number of unique samples.
#' @return Text and character vector containg gene list and terms associated
#' with them
#' are stored as text files.
#' @examples
#' \dontrun{
#' smap <- "F1.1_TestSample1_solo_hg19.smap"
#' smappath <- system.file("extdata", package = "nanotatoR")
#' hgpath <- system.file("extdata", "GRCh37_hg19_variants_2016-05-15.txt",
#' package = "nanotatoR")
#' win_indel_DGV=10000;win_inv_trans_DGV=50000;perc_similarity_DGV=0.5;
#' usample = 54946
#' dgv <- DGV_extraction (hgpath, input_fmt_DGV = "Text",smap=smap,
#' smappath, win_indel_DGV = 10000, win_inv_trans_DGV = 50000,
#' perc_similarity_DGV = 0.5,returnMethod="dataFrame",usample = 54946)
#' }
#' @import utils
#' @export
DGV_extraction <- function(hgpath, smappath, smap, smap_data,
input_fmt_DGV = c("Text", "dataFrame"), win_indel_DGV = 10000,
win_inv_trans_DGV = 50000, perc_similarity_DGV = 0.5,
returnMethod = c("Text", "dataFrame"), outpath, usample = 54946) {
# S='F' Change the window for Inversion/translocation 50000
## Reading the DGV file and the smap file
r <- read.table(paste(hgpath), header = TRUE, sep = "\t")
## Unique variant length extraction for frquency Calculation
samp <- as.character(unique(r$samples))
#usamp <- UniqueSample(samp)
usamp <- usample
# varaccl<-length(unique(varacc)) close(con)
##Checking if the input format is dataframe or Text
if (input_fmt_DGV == "Text") {
##Pattern matching needs to be done to remove #
con <- file(file.path(smappath, smap), "r")
r10 <- readLines(con, n = -1)
close(con)
datfinal <- data.frame()
g1 <- grep("RawConfidence", r10)
g2 <- grep("RefStartPos", r10)
if (g1 == g2){
dat <- gsub("# ", "", r10)
# dat<-gsub('\t',' ',r1)
dat4 <- textConnection(dat[g1:length(dat)])
r1 <- read.table(dat4, sep = "\t", header = TRUE)
close(dat4)
}
else{
stop("column names doesnot Match")
}
}
else if (input_fmt_DGV == "dataFrame") {
r1 <- smap_data
}
else{
stop("Incorrect format")
}
## Checking Sex male/female and assigning chromosome number accordingly
chro1 <- (unique(r1$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(chro1)){
print(paste("Chromosome:", chro1[ii]))
## Extracting data from DGV dataset
if (ii == 23){
kk <- "X"
}
else if (ii == 24){
kk <- "Y"
}
else{
kk <- chro1[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 == chro1[ii]),]
## Adding the windows to the breakpoints
rf <- dat1$RefStartPos
rf_wb_ind <- rf - win_indel_DGV
rf_fb_ind <- rf + win_indel_DGV
rf_wb_int <- rf - win_inv_trans_DGV
rf_fb_int <- rf + win_inv_trans_DGV
re <- dat1$RefEndPos
re_wf_ind <- re + win_indel_DGV
re_wb_ind <- re - win_indel_DGV
re_wb_int <- re - win_inv_trans_DGV
re_fb_int <- re + win_inv_trans_DGV
## Calculating size
size_bn <- dat1$Size
variantType2 <- as.character(dat1$Type)
# countfre<-0 percn<-c()
datf <- c()
for (nn in seq_len(length(rf))){
#print(nn)
## Comparing the conditions
if (variantType2[nn] == "deletion" |
variantType2[nn] == "insertion") {
dat2 <- dat[which((dat$start <= rf[nn] &
dat$start >= rf_wb_ind[nn] | dat$start >= rf[nn] &
dat$start <= rf_fb_ind[nn]) &
( dat$end >= re[nn] & dat$end <= re_wf_ind[nn] |
dat$end <= re[nn] & dat$end >= re_wb_ind[nn])),]
size1 <- size_bn[nn]
#print(dim(dat2))
## Writing if the dgv_match is TRUE or not
if (nrow(dat2) > 1){
countfre <- 0
countfreunfilt <- 0
type <- dat2$variantsubtype
for (ll in seq_len(nrow(dat2))){
size_dgv <- dat2$end[ll] - dat2$start[ll]
perc <- (size1 / size_dgv)
if (perc >= perc_similarity_DGV &
(identical(type[ll],variantType2[nn]))){
fre <- as.character(dat2$samples[ll])
if (length(fre) >= 1){
g <- grep(",", fre)
if (length(g) > 0) {
fre1 <- as.character(
strsplit(as.character(fre),
split = ",")[[1]])
}
else{
fre1 <- fre
}
}
else{
fre1 <- NULL
}
countfre <- countfre + length(fre1)
}
else{
countfre <- countfre + 0
}
# ctr=ctr+1
}
data1 <- data.frame(dat1[nn,],
DGV_Freq_Perc = format(((countfre / usamp) * 100),
scientific = FALSE), stringsAsFactors = FALSE)
datf <- rbind(datf, data1)
}
else if (nrow(dat2) == 1){
# dgv_match=TRUE Calculating percentage similarity
type <- dat2$variantsubtype
size_dgv <- dat2$end - dat2$start
perc <- (size1 / size_dgv)
if (perc >= perc_similarity_DGV &
(identical(type, variantType2[nn]))){
fre <- as.character(dat2$samples)
if (length(fre) >= 1){
g <- grep(",", fre)
countfre <- 0
if (length(g) > 0){
countfre <-
length(as.character(
strsplit(as.character(fre),
split = ",")[[1]]))
}
else{
countfre <- countfre + length(fre1)
}
}
else{
countfre <- NULL
}
}
else{
countfre <- 0
}
data1 <- data.frame( dat1[nn,],
DGV_Freq_Perc = format(((countfre / usamp) * 100),
scientific = FALSE), stringsAsFactors = FALSE)
datf <- rbind(datf, data1)
}
else{
# dgv_match=FALSE
data1 <- data.frame(dat1[nn,], DGV_Freq_Perc = 0,
stringsAsFactors = FALSE)
datf <- rbind(datf, data1)
# next;
}
}
else if ((variantType2[nn] == "duplication" |
variantType2[nn] == "duplication_split"|
variantType2[nn] == "duplication_inverted")){
dat2 <- dat[which((dat$start <= rf[nn] &
dat$start >= rf_wb_ind[nn] |
dat$start >= rf[nn] &
dat$start <= rf_fb_ind[nn]) &
(dat$end >= re[nn] & dat$end <= re_wf_ind[nn] |
dat$end <= re[nn] & dat$end >= re_wb_ind[nn])),]
size1 <- size_bn[nn]
#print(dim(dat2))
## Writing if the dgv_match is TRUE or not
if (nrow(dat2) > 1){
countfre <- 0
countfreunfilt <- 0
type <- dat2$variantsubtype
for (ll in seq_len(nrow(dat2))){
size_dgv <- dat2$end[ll] - dat2$start[ll]
perc <- (size1 / size_dgv)
if (perc >= perc_similarity_DGV &
(identical(type[ll], variantType2[nn]))){
fre <- as.character(dat2$samples[ll])
if (length(fre) >= 1){
g <- grep(",", fre)
if (length(g) > 0){
fre1 <- as.character(
strsplit(
as.character(fre),
split = ",")[[1]])
}
else {
fre1 <- fre
}
}
else{
fre1 <- NULL
}
countfre <- countfre + length(fre1)
}
else{
countfre <- countfre + 0
}
# ctr=ctr+1
}
data1 <- data.frame(dat1[nn,],DGV_Freq_Perc =
format(((countfre / usamp) * 100),
scientific = FALSE), stringsAsFactors = FALSE)
datf <- rbind(datf, data1)
}
else if (nrow(dat2) == 1){
# dgv_match=TRUE Calculating percentage similarity
type <- dat2$variantsubtype
size_dgv <- dat2$end - dat2$start
perc <- (size1 / size_dgv)
if (perc >= perc_similarity_DGV &
(identical(type, variantType2[nn]))){
fre <- as.character(dat2$samples)
if (length(fre) >= 1){
g <- grep(",", fre)
countfre <- 0
if (length(g) > 0){
countfre <- length(
as.character(
strsplit(as.character(fre),
split = ",")[[1]]))
}
else{
countfre <- countfre + length(fre1)
}
}
else{
countfre <- NULL
}
}
else {
countfre <- 0
}
# ctr=ctr+1
data1 <- data.frame(dat1[nn,],
DGV_Freq_Perc = format(((countfre / usamp) * 100),
scientific = FALSE), stringsAsFactors = FALSE)
datf <- rbind(datf, data1)
}
else{
# dgv_match=FALSE
data1 <- data.frame(dat1[nn,],DGV_Freq_Perc = 0,
stringsAsFactors = FALSE)
datf <- rbind(datf, data1)
# next;
}
}
else if (length(grep("inversion", variantType2[nn])) >= 1 |
length(grep("translocation", variantType2[nn])) >= 1){
dat2 <- dat[which(( dat$start <= rf[nn] &
dat$start >= rf_wb_int[nn] | dat$start >= rf[nn] &
dat$start <= rf_fb_int[nn]) &
(dat$end >= re[nn] & dat$end <= re_fb_int[nn] |
dat$end <= re[nn] & dat$end >= re_wb_int[nn])),]
size1 <- size_bn[nn]
#print(dim(dat2))
## Writing if the dgv_match is TRUE or not
countfre <- c()
countfreunfilt <- 0
if (nrow(dat2) > 1){
countfre <- 0; countfreunfilt <- 0
type <- dat2$variantsubtype
for (ll in seq_len(nrow(dat2))) {
#print(ll)
if ((identical(type[ll],variantType2[nn]))){
#print("1")
fre <- as.character(dat2$samples[ll])
if (length(fre) >= 1){
g <- grep(",", fre)
if (length(g) > 0){
fre1 <- as.character(
strsplit(as.character(fre),
split = ",")[[1]])
}
else{
fre1 <- fre
}
}
else{
fre1 <- NULL
}
countfre <- countfre + length(fre1)
}
else{
#print("2")
countfre <- countfre + 0
}
# ctr=ctr+1
}
data1 <- data.frame(dat1[nn,],
DGV_Freq_Perc = format(((countfre / usamp) * 100),
scientific = FALSE), stringsAsFactors = FALSE)
datf <- rbind(datf, data1)
}
else if (nrow(dat2) == 1){
# dgv_match=TRUE Calculating percentage similarity
type <- dat2$variantsubtype
size_dgv <- dat2$end - dat2$start
perc <- (size1 / size_dgv)
if ((identical(type, variantType2[nn]))){
fre <- as.character(dat2$samples)
if (length(fre) >= 1){
g <- grep(",", fre)
countfre <- 0
if (length(g) > 0){
countfre <- length(as.character(
strsplit(as.character(fre), split = ","
)[[1]]))
}
else{
countfre <- length(fre)
}
}
else{
countfre <- NULL
}
}
else{
countfre <- 0
}
data1 <- data.frame(dat1[nn,],
DGV_Freq_Perc = format(((countfre / usamp) * 100),
scientific = FALSE), stringsAsFactors = FALSE)
datf <- rbind(datf, data1)
}
else{
# dgv_match=FALSE
data1 <- data.frame(dat1[nn,],
DGV_Freq_Perc = 0, stringsAsFactors = FALSE)
datf <- rbind(datf, data1)
# next;
}
}
else{
data1 <- data.frame(dat1[nn,], DGV_Freq_Perc = 0,
stringsAsFactors = FALSE)
datf <- rbind(datf, data1)
}
}
dataFinal <- rbind(dataFinal, datf)
}
##Return Mode Dataframe or Text
if (returnMethod == "Text") {
st1 <- strsplit(smap, ".txt")
fname <- st1[[1]][1]
row.names(dataFinal) <- c()
write.table(dataFinal, paste(outpath, fname, "_DGV.txt", sep = ""),
sep = "\t", row.names = FALSE)
}
else if (returnMethod == "dataFrame") {
return (dataFinal)
}
else{
stop ("ReturnMethod Incorrect")
}
}
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