Nothing
R/cohortDB_comp.r
In nanotatoR: nanotatoR: next generation structural variant annotation and classification
Defines functions
cohortFrequency
makeMergedSmapData
Documented in
cohortFrequency
makeMergedSmapData
#' Merges Bionano SV files to one common SV file
#'
#' @param path character. Path to the solo files.
#' @param pattern character. file name pattern for solo files.
#' @param dbOutput character. Output type. Default text and dataframe.
#' @param fname character. filename if dbOutput = text.
#' @param outpath character. path for output file if dbOutput = text.
#' @return Text file containing all the solo SMAP files.
#' @examples
#' path <- system.file("extdata", "Bionano_config/", package = "nanotatoR")
#' pattern <- "_hg19.txt"
#' mergedSmap <- makeMergedSmapData(path, pattern, dbOutput = "dataframe")
#' @importFrom stats na.omit
#' @export
makeMergedSmapData <- function(path, pattern, outpath,fname,
dbOutput=c("dataframe","text")) {
#setwd(path)
allfiles <- list.files(path, pattern)
nam <- c()
datfinal <- data.frame()
for (files in seq_along(allfiles)) {
print(files)
##### print(dim(dat4))
r1 <- read.table(file.path(path,allfiles[files]), header = TRUE)
print(dim(r1))
samp<-as.character(r1$sample)
typ<-as.character(r1$type)
zygo<-as.character(r1$zygosity)
if(typ[1] == "trans"){
chro1<-as.numeric(r1$chrA)
chro2<-as.numeric(r1$chrB)
bpstrt<-as.numeric(r1$bkptA)
bpend<-as.numeric(r1$bkptB)
siz<-as.numeric(rep(-1,length(bpend)))
}
else{
chro1<-as.numeric(r1$chr)
chro2<-as.numeric(r1$chr)
bpstrt<-as.numeric(r1$start)
bpend<-as.numeric(r1$end)
siz<-as.numeric(r1$size)
}
score<-as.numeric(r1$score)
smapID<-as.numeric(r1$smapId)
typ<-gsub("^ins$","insertion",typ)
typ<-gsub("^del$","deletion",typ)
typ<-gsub("^inv$","inversion",typ)
typ<-gsub("^trans$","translocation",typ)
dattemp<-data.frame(SmapID=smapID,Sample=samp,Type=typ,
Chromosome1=chro1,
Chromosome2=chro2,BreakPntStrt=bpstrt,BreakPntEnd=bpend,
Size=siz,Zygosity=zygo,Score=score)
datfinal<-rbind(datfinal,dattemp)
}
if(dbOutput == "text"){
filename <- paste(fname, "_merged.txt", sep = "")
write.table(datfinal, file.path(outpath, filename), sep = "\t")
}else if(dbOutput == "dataframe"){
return(datfinal)
}else{stop("method not found")}
}
#' Calculates the internal frequencies of SV in bionano cohorts
#'
#' @param internalBNDB character. Path to the merged BN files.
#' @param smappath character. path to the query smap file.
#' @param smap character. File name for the smap
#' @param smapdata character. SV data in dataframe.
#' @param dbOutput character. Output of merged bionano data.
#' @param fname character. Filename in case dbOutput = Text.
#' @param buildBNInternalDB boolean. Checking whether the merged bionano
#' file database exist.
#' @param input_fmt character. Choice between Text and DataFrame.
#' @param outpath character. Path to merged SV solo datasets.
#' @param win_indel Numeric. Insertion and deletion error window.
#' @param win_inv_trans Numeric. Inversion and translocation error window.
#' @param perc_similarity Numeric . ThresholdPercentage similarity
#' of the query SV and reference SV.
#' @param limsize Numeric . Minimum size to consider for a SV.
#' @param BNDBPath Path of Bionano database files.
#' @param BNDBPattern Pattern of Bionano database files.
#' @param indelconf Numeric. Threshold for insertion and deletion Score.
#' @param invconf Numeric. Threshold for inversion Score.
#' @param transconf Numeric. Threshold for translocation Score.
#' @param returnMethod character. Choice between Text and DataFrame.
#' @return Text file or data frames containing internalFrequency data.
#' @examples
#' mergedFiles <- system.file("extdata", "BNSOLO2_merged.txt",
#' package = "nanotatoR")
#' smapName <- "F1.1_TestSample1_solo_hg19.smap"
#' smappath <- system.file("extdata", package = "nanotatoR")
#' win_indel = 10000; win_inv_trans = 50000; perc_similarity = 0.5;
#' indelconf = 0.5; invconf = 0.01;transconf = 0.1
#' cohortFreq<-cohortFrequency(internalBNDB = mergedFiles , smappath ,
#' smap=smapName, input_fmt ="Text",
#' buildBNInternalDB=FALSE, win_indel, win_inv_trans,
#' perc_similarity , indelconf, invconf,
#' transconf,returnMethod="dataFrame")
#' @importFrom stats na.omit
#' @import hash
#' @export
cohortFrequency <- function(internalBNDB, smappath, smap,
buildBNInternalDB=FALSE, smapdata,
input_fmt=c("Text","dataFrame"),
dbOutput=c("dataframe","text"), BNDBPath,
BNDBPattern, fname,
outpath, win_indel = 10000, win_inv_trans = 50000,
perc_similarity = 0.5, indelconf = 0.5, invconf = 0.01,
limsize=1000,
transconf = 0.1,returnMethod=c("Text","dataFrame")) {
#library(hash)
print("###Cohort Frequency Calculation###")
if(buildBNInternalDB == TRUE){
r<-makeMergedSmapData(path=BNDBPath, pattern=BNDBPattern,
dbOutput=dbOutput)
} else{
r <- read.table(internalBNDB, sep="\t", header=TRUE)
}
##Calculating the sample size
usampt<-as.character(unique(r$Sample))
strst<-strsplit(usampt,split="_")
usampp<-c()
for(o in seq_len(length(strst))){
usampp<-c(usampp,as.character(strst[[o]][1]))
}
usamp<- length(unique(usampp))
##Extracting the smap data
if(input_fmt == "Text"){
con <- file(file.path(smappath, smap), "r")
r10 <- readLines(con, n=-1)
close(con)
# datfinal<-data.frame()
g1 <- grep("RawConfidence", r10)
g2 <- grep("RefStartPos", r10)
if (g1 == g2){
dat <- gsub("# ", "", as.character(r10))
# dat<-gsub('\t',' ',r10)
dat4 <- textConnection(dat[g1:length(dat)])
r1 <- read.table(dat4, sep = "\t", header = TRUE)
close(dat4)
} else{
stop("column names doesnot Match")
}
}else if(input_fmt == "dataFrame"){
r1<-smapdata
}else{
stop("Input Format incorrect")
}
## Checking Sex male/female and assigning chromosome number accordingly
chro <-unique(r1$RefcontigID1)
dataFinal <- c()
for (ii in seq_along(chro))
{
print(paste('Chrom:',ii,sep=''))
dat <- r[which(r$Chromosome1 == ii), ]
# variantType1<-dat$variantsubtype Changing the variant terms in DGV to
# match svmap
variantType1 <- as.character(dat$Type)
#BSPQI_status_DB <- as.character(dat$Found_in_self_BSPQI_molecules)
#BSSSI_status_DB <- as.character(dat$Found_in_self_BSSSI_molecules)
## Extracting data from SVmap
dat1 <- r1[which(r1$RefcontigID1 == ii), ]
chromo2<-dat1$RefcontigID2
## Adding the windows to the breakpoints
rf <- dat1$RefStartPos
rf_wb_ind <- rf - win_indel
rf_fb_ind <- rf + win_indel
rf_wb_int <- rf - win_inv_trans
rf_fb_int <- rf + win_inv_trans
re <- dat1$RefEndPos
re_wf_ind <- re + win_indel
re_wb_ind <- re - win_indel
re_wb_int <- re - win_inv_trans
re_fb_int <- re + win_inv_trans
## Calculating size
size_bn <- dat1$Size
conf1 <- dat1$Confidence
variantType2 <- as.character(dat1$Type)
# conf<-dat$Confidence countfre<-0 percn<-c()
datf <- c()
for (nn in seq_len(length(rf)))
{
#print(paste("nn:",nn))
if ((variantType2[nn] == "deletion" |
variantType2[nn] == "insertion")){
dat2 <- dat[which((dat$BreakPntStrt <= rf[nn] &
dat$BreakPntStrt >= rf_wb_ind[nn] |
dat$BreakPntStrt >= rf[nn] & dat$BreakPntStrt <= rf_fb_ind[nn])
& (dat$BreakPntEnd >= re[nn] & dat$BreakPntEnd <= re_wf_ind[nn]
| dat$BreakPntEnd <= re[nn] & dat$BreakPntEnd >= re_wb_ind[nn]
)), ]
size1 <- size_bn[nn]
## Calculating Internal Frequency
if (nrow(dat2) > 1)
{
countfre <- c();countfreunfilt<-c()
svStrt<-as.numeric(dat2$BreakPntStrt)
svEnd<-as.numeric(dat2$BreakPntEnd)
size_internal <- as.numeric(dat2$Size)
type <- as.character(dat2$Type)
conf <- as.numeric(dat2$Score)
zygo <- as.character(dat2$Zygosity)
samp <- as.character(dat2$Sample)
sampid<-c();sampid_unfiltered<-c();zyg<-c();
zyg_unfiltered<-c()
homozygo<-c(); unmapped<-c()
for (ll in seq_len(nrow(dat2))) {
perc <- (size1/size_internal[ll])
#### print(perc) print(type[ll])
#print(perc)
#### print(svfamid1)
if (perc >= perc_similarity & identical(type[ll],
variantType2[nn]) & (conf[ll] >= indelconf) &
size_internal[ll] >=limsize){
countfre <- c(countfre,1)
sampid<-c(sampid,samp[ll])
zyg<-c(zyg,zygo[ll])
if(as.character(zygo[ll]) == "homozygous"){
homozygo<-c(homozygo, as.character(samp[ll]))
}
else{
homozygo<-c(homozygo, NULL)
}
} else {
unmapped<-c(unmapped, ll)
}
##Unfiltered
if (perc >= perc_similarity & identical(type[ll],
variantType2[nn]))
{
countfreunfilt <- c(countfreunfilt, 1)
sampid_unfiltered<-c(sampid_unfiltered,
as.character(samp[ll]))
zyg_unfiltered<-c(zyg_unfiltered,
as.character(zygo[ll]))
}
}
##Calculating filtered Frequency
##Filtration
if ((length(sampid) >= 1) & length(countfre) >= 1)
{
dat_temp <- data.frame(sampid, countfre,zyg)
countfre1 <- 0
if (nrow(dat_temp) > 0)
{
#### print(paste('dat_temp',dim(dat_temp)))
usampid <- as.character(unique(sampid))
for (u in seq_len(length(usampid)))
{
dat_temp1 <- dat_temp[which(dat_temp$sampid %in%
usampid[u]), ]
zygo<-as.character(dat_temp1$zyg)
ct <- sum(dat_temp1$countfre)
if (ct >= 1 & nrow (dat_temp1)>= 1)
{
if(length(unique(as.character(zygo)))==1){
if(unique(as.character(zygo)) ==
"homozygous"){
countfre1 <- countfre1 + 2
}
else if(
unique(as.character(zygo)) == "Unknown"
){
countfre1 <- countfre1 + 2
}
else {
countfre1 <- countfre1 + 1
}
}
else{
g1<-grep("homozygous",as.character(zygo))
g2<-grep("heterozygous",as.character(zygo))
g3<-grep("unknown",as.character(zygo))
if(length(g1)>=1 & length(g2)>=1 &
length(g3)>=1){
countfre1 <- countfre1 + 2
}
else if(length(g1) == 0 & length(g2)>=1 &
length(g3)>=1){
countfre1 <- countfre1 + 2
((1*length(g2)) + (2*length(g3)))
}else if(length(g1)>=1 & length(g2)>=1 &
length(g3) == 0){
countfre1 <- countfre1 + 2
}
else if(length(g1)>=1 & length(g2) == 0 &
length(g3)>=1){
countfre1 <- countfre1 + 2
}
else{
countfre1 <- countfre1 + 0
}
}
}else{
countfre1 <- countfre1 + 0
}
}
}else
{
countfre1 <- 0
}
}else{
countfre1 <- 0
}
#### Unfiltered
if ((length(sampid_unfiltered) >= 1) &
length(countfreunfilt) >= 1)
{
dat_temp <- data.frame(sampid_unfiltered,
countfreunfilt,zyg_unfiltered)
countfreunfilt1 <- 0
if (nrow(dat_temp) > 0)
{
#### print(paste('dat_temp',dim(dat_temp)))
usampid <- as.character(unique(sampid_unfiltered))
for (u in seq_len(length(usampid))) {
dat_temp1 <- dat_temp[which(
dat_temp$sampid_unfiltered %in% usampid[u]),
]
zygo<-as.character(dat_temp1$zyg_unfiltered)
ct <- sum(dat_temp1$countfreunfilt)
if (ct >= 1 & nrow (dat_temp1)>= 1)
{
if(length(unique(as.character(zygo))) == 1){
if(unique(as.character(zygo)) ==
"homozygous"){
countfreunfilt1 <- countfreunfilt1 + 2
}
else if(unique(as.character(zygo)) ==
"Unknown"){
countfreunfilt1 <- countfreunfilt1 + 2
}
else {
countfreunfilt1 <- countfreunfilt1 + 1
}
}
else{
g1<-grep("homozygous",as.character(zygo))
g2<-grep("heterozygous",as.character(zygo))
g3<-grep("unknown",as.character(zygo))
if(length(g1)>=1 & length(g2)>=1 &
length(g3)>=1){
countfreunfilt1 <- countfreunfilt1 + 2
}
else if(length(g1) == 0 & length(g2)>=1 &
length(g3)>=1){
countfreunfilt1 <- countfreunfilt1 + 2
}
else if(length(g1)>=1 & length(g2)>=1 &
length(g3) == 0){
countfreunfilt1 <- countfreunfilt1 + 2
}
else if(length(g1)>=1 & length(g2) == 0 &
length(g3)>=1){
countfreunfilt1 <- countfreunfilt1 + 2
}
else{
countfreunfilt1 <- countfreunfilt1 + 0
}
}
}else{
countfreunfilt1 <- countfreunfilt1 + 0
}
}
}else
{
countfreunfilt1 <- 0
}
}else{
countfreunfilt1 <- 0
}
##Extracting the number of Homozygotes
if(length(homozygo)>=1){
homozygo <- length(unique(homozygo))
}
else if(length(homozygo) == 1){
homozygo <- length(homozygo)
}
else{
homozygo <- 0
}
data1 <- data.frame(dat1[nn, ],
BNG_Freq_Perc_Filtered = as.numeric(format(round
((countfre1/(2*(usamp))) * 100,digits=3),scientific = FALSE)),
BNG_Freq_Perc_UnFiltered = as.numeric(format(round(
countfreunfilt1/(2*(usamp))) * 100,digits=3),
scientific = FALSE),
BNG_Homozygotes=as.numeric(homozygo), stringsAsFactors = FALSE)
#### print(dim(data1)) print(dim(data1))
datf <- rbind(datf, data1)
}
else if (nrow(dat2) == 1) {
# dgv_match=TRUE Calculating percentage similarity
countfre <- 0;countfreunfilt<-0
conf <- dat2$Score
size_internal <- dat2$Size
perc <- (size1/size_internal)
zygo <- as.character(dat2$Zygosity)
type <- as.character(dat2$Type)
samp <- as.character(dat2$Sample)
homozygo<-c()
if (perc >= perc_similarity & identical(type,
variantType2[nn]) &
conf >= indelconf & size_internal>=limsize){
if(zygo =="homozygous"){
countfre <- countfre+2
homozygo <- as.character(samp)
}else if(zygo =="unknown"){
countfre <- countfre+2
}else{
countfre <- countfre+1
}
}else{
countfre <- 0
}
if (identical(type, variantType2[nn]) & identical
(type, variantType2[nn])){
if(zygo =="homozygous"){
countfreunfilt <- countfreunfilt + 2
}else if(zygo =="unknown"){
countfreunfilt <- countfreunfilt + 2
}else{
countfreunfilt <- countfreunfilt + 1
}
}
##Calculating the length
if(length(homozygo)>0){
homozygo <- length(homozygo)
}else{
homozygo=0
}
data1 <- data.frame(dat1[nn, ], BNG_Freq_Perc_Filtered
= as.numeric(format(round((countfre/(2*(usamp)))
* 100,digits=3), scientific = FALSE)),
BNG_Freq_Perc_UnFiltered = as.numeric
(format(round((countfreunfilt/(2*(usamp))) * 100,digits=3),
scientific = FALSE)), BNG_Homozygotes=as.numeric(homozygo),
stringsAsFactors = FALSE)
datf <- rbind(datf, data1)
} else{
# dgv_match=FALSE
data1 <- data.frame(dat1[nn, ], BNG_Freq_Perc_Filtered = 0,
BNG_Freq_Perc_UnFiltered = 0, BNG_Homozygotes = 0,
stringsAsFactors = FALSE)
#### print(dim(data1)) print(names(data1)[56:58])
#### print(names(datf)[56:58])
#### print(identical((names(data1)[56]),(names(datf)[56])))
#### print(identical((names(data1)[57]),(names(datf)[57])))
#### print(identical((names(data1)[58]),(names(datf)[58])))
datf <- rbind(datf, data1)
# next;
}
}else if ((variantType2[nn] == "duplication" |
variantType2[nn] == "duplication_split"
|variantType2[nn] == "duplication_inverted"|
variantType2[nn] == "insertion")){
dat2 <- dat[which((dat$BreakPntStrt <= rf[nn] &
dat$BreakPntStrt >= rf_wb_ind[nn] |
dat$BreakPntStrt >= rf[nn] & dat$BreakPntStrt <= rf_fb_ind[nn])
& (dat$BreakPntEnd >= re[nn] & dat$BreakPntEnd <= re_wf_ind[nn]
| dat$BreakPntEnd <= re[nn] & dat$BreakPntEnd >= re_wb_ind[nn]
)), ]
size1 <- size_bn[nn]
## Calculating Internal Frequency
if (nrow(dat2) > 1){
countfre <- c();countfreunfilt<- c()
# svind1<-dat2$SVIndex
size_internal <- dat2$Size
type <- as.character(dat2$Type)
conf <- dat2$Score
zygo <- as.character(dat2$Zygosity)
samp <- as.character(dat2$Sample)
sampid<-c();sampid_unfiltered<-c();zyg<-c();
zyg_unfiltered<-c()
unmapped<-c()
homozygo<-c()
for (ll in seq_len(nrow(dat2))){
#print (ll)
#print(type[ll])
#perc <- (size1/size_internal[ll])
#### print(perc)
#### print(svfamid1)
if ((identical(type[ll], "duplication") |
identical(type[ll], "duplication_split")|
identical(type[ll], "duplication_inverted"))){
countfre <- c(countfre,1)
sampid<-c(sampid,samp[ll])
zyg<-c(zyg,zygo[ll])
if(as.character(zygo[ll])=="homozygous"){
homozygo<-c(homozygo, as.character(samp[ll]))
}
else{
homozygo<-c(homozygo, NULL)
}
}
else if ((identical(type[ll], "insertion"))
& (size_internal[ll]>=limsize)
& (conf[ll] >= indelconf)){
countfre <- c(countfre,1)
sampid<-c(sampid,samp[ll])
zyg<-c(zyg,zygo[ll])
if(as.character(zygo[ll])=="homozygous"){
homozygo<-c(homozygo, as.character(samp[ll]))
}
else{
homozygo<-c(homozygo, NULL)
}
}
else {
unmapped<-c(unmapped, ll)
}
##Unfiltered
if ((identical(type[ll], "duplication") |
identical(type[ll], "duplication_split")|
identical(type[ll], "duplication_inverted")|
identical(type[ll], "insertion"))) {
countfreunfilt <- c(countfreunfilt, 1)
sampid_unfiltered<-c(sampid_unfiltered,
as.character(samp[ll]))
zyg_unfiltered<-c(zyg_unfiltered,
as.character(zygo[ll]))
}
}
##Calculating filtered Frequency
##Filtration
##Filtration
if ((length(sampid) >= 1) & length(countfre) >= 1){
dat_temp <- data.frame(sampid, countfre,zyg)
countfre1 <- 0
if (nrow(dat_temp) > 0){
#### print(paste('dat_temp',dim(dat_temp)))
usampid <- as.character(unique(sampid))
for (u in seq_len(length(usampid))){
dat_temp1 <- dat_temp[which(dat_temp$sampid
%in% usampid[u]), ]
zygo<-as.character(dat_temp1$zyg)
ct <- sum(dat_temp1$countfre)
if (ct >= 1 & nrow (dat_temp1)>= 1){
if(length(unique(as.character(zygo)))==1){
if(unique(as.character(zygo)) ==
"homozygous"){
countfre1 <- countfre1 + 2
}
else if(unique(as.character(zygo))==
"Unknown"){
countfre1 <- countfre1 + 2
}
else {
countfre1 <- countfre1 + 1
}
}
else{
g1<-grep("homozygous",as.character(zygo
))
g2<-grep("heterozygous",
as.character(zygo))
g3<-grep("unknown",as.character(zygo))
if(length(g1)>=1 & length(g2)>=1 &
length(g3)>=1){
countfre1 <- countfre1 + 2
}
else if(length(g1)==0 & length(g2)>=1
& length(g3)>=1){
countfre1 <- countfre1 + 2
}
else if(length(g1)>=1 &
length(g2)>=1 & length(g3)==0){
countfre1 <- countfre1 + 2
}
else if(length(g1)>=1 & length(g2)==0 &
length(g3)>=1){
countfre1 <- countfre1 + 2
}
else{
countfre1 <- countfre1 + 0
}
}
}else{
countfre1 <- countfre1 + 0
}
}
}else{
countfre1 <- 0
}
}else{
countfre1 <- 0
}
#### Unfiltered
if ((length(sampid_unfiltered) >= 1) &
length(countfreunfilt) >= 1){
dat_temp <- data.frame(sampid_unfiltered,
countfreunfilt,zyg_unfiltered)
countfreunfilt1 <- 0
if (nrow(dat_temp) > 0){
#### print(paste('dat_temp',dim(dat_temp)))
usampid <- as.character(unique(sampid_unfiltered))
for (u in seq_len(length(usampid))) {
dat_temp1 <- dat_temp[which(
dat_temp$sampid_unfiltered %in% usampid[u]), ]
zygo<-as.character(dat_temp1$zyg_unfiltered)
ct <- sum(dat_temp1$countfreunfilt)
if (ct >= 1 & nrow (dat_temp1)>= 1)
{
if(length(unique(as.character(zygo)))==1){
if(unique(as.character(zygo))==
"homozygous"){
countfreunfilt1 <- countfreunfilt1 + 2
}
else if(unique(as.character(zygo)) ==
"Unknown"){
countfreunfilt1 <- countfreunfilt1 + 2
}
else {
countfreunfilt1 <- countfreunfilt1 + 1
}
}
else{
g1<-grep("homozygous",as.character(zygo))
g2<-grep("heterozygous",as.character(zygo))
g3<-grep("unknown",as.character(zygo))
if(length(g1)>=1 & length(g2)>=1 &
length(g3)>=1){
countfreunfilt1 <- countfreunfilt1 + 2
}
else if(length(g1)==0 & length(g2)>=1 &
length(g3)>=1){
countfreunfilt1 <- countfreunfilt1 + 2
}
else if(length(g1)>=1 & length(g2)>=1 &
length(g3)==0){
countfreunfilt1 <- countfreunfilt1 + 2
}
else if(length(g1)>=1 & length(g2)==0
& length(g3)>=1){
countfreunfilt1 <- countfreunfilt1 + 2
}
else{
countfreunfilt1 <- countfreunfilt1 + 0
}
}
}
else{
countfreunfilt1 <- countfreunfilt1 + 0
}
}
}
else{
countfreunfilt1 <- 0
}
}
else{
countfreunfilt1 <- 0
}
##Extracting the number of Homozygotes
if(length(homozygo)>=1){
homozygo <- length(unique(homozygo))
}
else if(length(homozygo)==1){
homozygo <- length(homozygo)
}
else{
homozygo <- 0
}
data1 <- data.frame(dat1[nn, ], BNG_Freq_Perc_Filtered
= as.numeric(format(round((countfre1/(2*(usamp))) *
100,digits=3),
scientific = FALSE)),BNG_Freq_Perc_UnFiltered
= as.numeric(format(round((countfreunfilt1/(2*(usamp))) *
100,digits=3),
scientific = FALSE)), BNG_Homozygotes=as.numeric(homozygo),
stringsAsFactors = FALSE)
datf <- rbind(datf, data1)
}
else if (nrow(dat2) == 1)
{
# dgv_match=TRUE Calculating percentage similarity
countfre <- 0;countfreunfilt<-0
conf <- dat2$Score
size_internal <- dat2$Size
samp <- as.character(dat2$Sample)
type <- as.character(dat2$Type)
zygo <- as.character(dat2$Zygosity)
homozygo<-c()
if ((identical(type, "duplication") |
identical(type, "duplication_split")|
identical(type, "duplication_inverted")))
{
if(zygo =="homozygous"){
countfre <- countfre+2
homozygo<-as.character(samp)
}
else if(zygo =="unknown"){
countfre <- countfre+2
}
else{
countfre <- countfre+1
}
}
else if ((identical(type, "insertion")) &
(size_internal >=limsize)
& (conf >= indelconf))
{
if(zygo =="homozygous"){
countfre <- countfre+2
homozygo<-as.character(samp)
}else if(zygo =="unknown"){
countfre <- countfre+2
}else{
countfre <- countfre+1
}
}
else {
countfre <- 0
}
##Unfiltered
if ((identical(type, "duplication") |
identical(type, "duplication_split")|
identical(type, "duplication_inverted")|
identical(type, "insertion")))
{
if(zygo =="homozygous"){
countfreunfilt <- countfreunfilt + 2
}
else if(zygo =="unknown"){
countfreunfilt <- countfreunfilt + 2
}
else{
countfreunfilt <- countfreunfilt + 1
}
}
else{
countfreunfilt <- 0
}
'if ((identical(type, "duplication") |
identical(type, "duplication_split")|
identical(type, "duplication_inverted")|
identical(type, "insertion")))
{ ##Filtered
if(zygo =="homozygous"){
countfre <- countfre+2
homozygo<-as.character(samp)
}else if(zygo =="unknown"){
countfre <- countfre+2
}else{
countfre <- countfre+1
}
##Un-Filtered
if(zygo =="homozygous"){
countfreunfilt <- countfreunfilt + 2
}else if(zygo =="unknown"){
countfreunfilt <- countfreunfilt + 2
}else{
countfreunfilt <- countfreunfilt + 1
}
} else
{
}'
##Calculating the length
if(length(homozygo)>0){
homozygo <- length(homozygo)
}else{
homozygo=0
}
data1 <- data.frame(dat1[nn, ], BNG_Freq_Perc_Filtered
= as.numeric(format(round((countfre/(2*(usamp))) *
100,digits=3),
scientific = FALSE)),BNG_Freq_Perc_UnFiltered
= as.numeric(format(round((countfreunfilt/(2*(usamp))) *
100,digits=3),
scientific = FALSE)), BNG_Homozygotes=as.numeric(homozygo),
stringsAsFactors = FALSE)
datf <- rbind(datf, data1)
} else
{
# dgv_match=FALSE
data1 <- data.frame(dat1[nn, ], BNG_Freq_Perc_Filtered = 0,
BNG_Freq_Perc_UnFiltered = 0,BNG_Homozygotes= 0,
stringsAsFactors = FALSE)
#### print(dim(data1)) print(names(data1)[56:58])
#### print(names(datf)[56:58])
#### print(identical((names(data1)[56]),(names(datf)[56])))
#### print(identical((names(data1)[57]),(names(datf)[57])))
#### print(identical((names(data1)[58]),(names(datf)[58])))
datf <- rbind(datf, data1)
# next;
}
}
else if ((length(grep("inversion", variantType2[nn])) >= 1) |
(length(grep("translocation", variantType2[nn])) >= 1))
{
#print(nn)
dat2 <- dat[which((dat$BreakPntStrt <= rf[nn] &
dat$BreakPntStrt >= rf_wb_int[nn] |
dat$BreakPntStrt >= rf[nn] &
dat$BreakPntStrt <= rf_fb_int[nn]) &
(dat$BreakPntEnd >= re[nn] &
dat$BreakPntEnd <= re_fb_int[nn] |
dat$BreakPntEnd <= re[nn] &
dat$BreakPntEnd >= re_wb_int[nn])), ]
size1 <- size_bn[nn]
## Writing if the dgv_match is TRUE or not
countfre <- c()
if (nrow(dat2) > 1)
{
countfre <- c();countfreunfilt<-c()
#size_internal <- dat2$Size
type <- as.character(dat2$Type)
conf <- as.numeric(dat2$Score)
zygo <- as.character(dat2$Zygosity)
samp <- as.character(dat2$Sample)
chrom2<-dat2$Chromosome2
sampid<-c();sampid_unfiltered<-c();zyg<-c();
zyg_unfiltered<-c()
unmapped<-c()
homozygo<-c()
for (ll in seq_len(nrow(dat2))) {
#perc <- (size1/size_internal[ll])
if ((chrom2[ll]==chromo2[nn]) & identical(type[ll],
variantType2[nn]) & ((identical(type[ll], "inversion") &
conf[ll] >= invconf) |
(identical(type[ll], "translocation") &
conf[ll] >= transconf)))
{ countfre <- c(countfre,1)
sampid<-c(sampid,samp[ll])
zyg<-c(zyg,zygo[ll])
if(as.character(zygo[ll])=="homozygous"){
homozygo<-c(homozygo, as.character(samp[ll]))
}
else{
homozygo<-c(homozygo, NULL)
}
}
else{ unmapped<-c(unmapped,ll)}
###Un filtered
if (identical(type[ll], variantType2[nn]) &
(chrom2[ll]==chromo2[nn]))
{
countfreunfilt <- c(countfreunfilt,1)
sampid_unfiltered<-c(sampid_unfiltered,
as.character(samp[ll]))
zyg_unfiltered<-c(zyg_unfiltered,
as.character(zygo[ll]))
}
}
##Filtration
if ((length(sampid) >= 1) & length(countfre) >= 1)
{
dat_temp <- data.frame(sampid, countfre,zyg)
countfre1 <- 0
if (nrow(dat_temp) > 0)
{
#### print(paste('dat_temp',dim(dat_temp)))
usampid <- as.character(unique(sampid))
for (u in seq_len(length(usampid))) {
dat_temp1 <- dat_temp[which(dat_temp$sampid %in%
usampid[u]), ]
zygo<-as.character(dat_temp1$zyg)
ct <- sum(dat_temp1$countfre)
if (ct >= 1 & nrow (dat_temp1)>= 1)
{
if(length(unique(as.character(zygo)))==1){
if(unique(as.character(zygo))=="homozygous")
{
countfre1 <- countfre1 + 2
}
else if(unique(as.character(zygo))==
"Unknown"){
countfre1 <- countfre1 + 2
}
else {
countfre1 <- countfre1 + 1
}
}
else{
g1<-grep("homozygous",as.character(zygo))
g2<-grep("heterozygous",as.character(zygo))
g3<-grep("unknown",as.character(zygo))
if(length(g1)>=1 & length(g2)>=1 &
length(g3)>=1){
countfre1 <- countfre1 + 2
}
else if(length(g1)==0 & length(g2)>=1 &
length(g3)>=1){
countfre1 <- countfre1 + 2
}
else if(length(g1)>=1 & length(g2)>=1 &
length(g3)==0){
countfre1 <- countfre1 + 2
}
else if(length(g1)>=1 & length(g2)==0 &
length(g3)>=1){
countfre1 <- countfre1 + 2
}
else{
countfre1 <- countfre1 + 0
}
}
}
else{
countfre1 <- countfre1 + 0
}
}
}
else
{
countfre1 <- 0
}
}
else{
countfre1 <- 0
}
#### Unfiltered
if ((length(sampid_unfiltered) >= 1) &
length(countfreunfilt) >= 1)
{
dat_temp <- data.frame(sampid_unfiltered,
countfreunfilt,zyg_unfiltered)
countfreunfilt1 <- 0
if (nrow(dat_temp) > 0)
{
#### print(paste('dat_temp',dim(dat_temp)))
usampid <- as.character(unique(sampid_unfiltered))
for (u in seq_len(length(usampid))) {
dat_temp1 <- dat_temp[which(
dat_temp$sampid_unfiltered %in% usampid[u]), ]
zygo<-as.character(dat_temp1$zyg_unfiltered)
ct <- sum(dat_temp1$countfreunfilt)
if (ct >= 1 & nrow (dat_temp1)>= 1)
{
if(length(unique(as.character(zygo)))==1){
if(unique(as.character(zygo))=="homozygous"
)
{
countfreunfilt1 <- countfreunfilt1 +2
}
else if(unique(as.character(zygo))==
"Unknown"){
countfreunfilt1 <- countfreunfilt1 + 2
}
else {
countfreunfilt1 <- countfreunfilt1 + 1
}
}
else{
g1<-grep("homozygous",as.character(zygo))
g2<-grep("heterozygous",as.character(zygo))
g3<-grep("unknown",as.character(zygo))
if(length(g1)>=1 & length(g2)>=1 &
length(g3)>=1){
countfreunfilt1 <- countfreunfilt1 + 2
}
else if(length(g1)==0 & length(g2)>=1 &
length(g3)>=1){
countfreunfilt1 <- countfreunfilt1 + 2
}
else if(length(g1)>=1 & length(g2)>=1 &
length(g3)==0){
countfreunfilt1 <- countfreunfilt1 + 2
}
else if(length(g1)>=1 & length(g2)==0 &
length(g3)>=1){
countfreunfilt1 <- countfreunfilt1 + 2
}
else{
countfreunfilt1 <- countfreunfilt1 + 0
}
}
}else{
countfreunfilt1 <- countfreunfilt1 + 0
}
}
}
else
{
countfreunfilt1 <- 0
}
}
else{
countfreunfilt1 <- 0
}
##### print(paste('INVTRANS:',countfre,sep=''))
if(length(homozygo)>=1){
homozygo <- length(unique(homozygo))
}
else if(length(homozygo)==1){
homozygo <- length(homozygo)
}
else{
homozygo <- 0
}
data1 <- data.frame(dat1[nn, ], BNG_Freq_Perc_Filtered
= as.numeric(format(round((countfre1/(2*(usamp))) *
100,digits=3),
scientific = FALSE)),BNG_Freq_Perc_UnFiltered
= as.numeric(format(round((countfreunfilt1/(2*(usamp)) *
100),digits=3),
scientific = FALSE)), BNG_Homozygotes=as.numeric(homozygo),
stringsAsFactors = FALSE)
#### print(dim(data1))
datf <- rbind(datf, data1)
}
else if (nrow(dat2) == 1)
{
## dgv_match=TRUE Calculating percentage similarity
countfre<-0;countfreunfilt<-0
chrom2<-dat2$Chromosome2
type <- as.character(dat2$Type)
conf <- dat2$Score
samp <- as.character(dat2$Sample)
zygo <- as.character(dat2$Zygosity)
homozygo<-c()
if ((chrom2 == chromo2[nn]) &
identical(type,variantType2[nn]) &
((identical(type, "inversion") & conf >= invconf) |
(identical(type, "translocation") & conf >= transconf)))
{
if(zygo == "homozygous"){
countfre <- countfre+2
homozygo<-as.character(samp)
}
else if(zygo == "unknown"){
countfre <- countfre+2
}else{
countfre <- countfre+1
}
} else
{
countfre <- 0
}
##Unfiletered Frequency
if (identical(type, variantType2[nn]) &
(chrom2==chromo2[nn])){
if(zygo == "homozygous"){
countfreunfilt <- countfreunfilt + 2
}
else if(zygo == "unknown"){
countfreunfilt <- countfreunfilt + 2
}
else{
countfreunfilt <- countfreunfilt + 1
}
}
##### print(paste('INVTRANS:',countfre,sep=''))
##Calculating the length
if(length(homozygo)>0){
homozygo <- length(homozygo)
}
else{
homozygo = 0
}
data1 <- data.frame(dat1[nn, ], BNG_Freq_Perc_Filtered
= as.numeric(format(round((countfre/(2*(usamp)))
* 100,digits=3),
scientific = FALSE)),BNG_Freq_Perc_UnFiltered
= as.numeric(format(round((countfreunfilt/(2*(usamp))) *
100,digits=3),
scientific = FALSE)), BNG_Homozygotes=as.numeric(homozygo),
stringsAsFactors = FALSE)
#### print(dim(data1))
datf <- rbind(datf, data1)
}
else
{
data1 <- data.frame(dat1[nn, ], BNG_Freq_Perc_Filtered= 0,
BNG_Freq_Perc_UnFiltered =0, BNG_Homozygotes= 0,
stringsAsFactors = FALSE)
#### print(dim(data1))
datf <- rbind(datf, data1)
# next;
}
}
else
{
#### print(paste('QueryData:',dim(dat1[nn,]),sep=''))
data1 <- data.frame(dat1[nn, ], BNG_Freq_Perc_Filtered= 0,
BNG_Freq_Perc_UnFiltered =0, BNG_Homozygotes= 0,
stringsAsFactors = FALSE)
#### print(dim(data1))
datf <- rbind(datf, data1)
}
}
dataFinal <- rbind(dataFinal, datf)
}
if(returnMethod == "Text"){
st1 <- strsplit(smap, ".txt")
fname <- st1[[1]][1]
row.names(dataFinal) <- c()
filenam<-paste(fname,"_Int.txt",sep="")
write.table(dataFinal, file.path(smappath, fname), sep = "\t",
row.names = FALSE)
}
else if (returnMethod=="dataFrame"){
return(dataFinal)
}
else{
stop("returnMethod Incorrect")
}
}
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Defines functions cohortFrequency makeMergedSmapData
Documented in cohortFrequency makeMergedSmapData
#' Merges Bionano SV files to one common SV file
#'
#' @param path character. Path to the solo files.
#' @param pattern character. file name pattern for solo files.
#' @param dbOutput character. Output type. Default text and dataframe.
#' @param fname character. filename if dbOutput = text.
#' @param outpath character. path for output file if dbOutput = text.
#' @return Text file containing all the solo SMAP files.
#' @examples
#' path <- system.file("extdata", "Bionano_config/", package = "nanotatoR")
#' pattern <- "_hg19.txt"
#' mergedSmap <- makeMergedSmapData(path, pattern, dbOutput = "dataframe")
#' @importFrom stats na.omit
#' @export
makeMergedSmapData <- function(path, pattern, outpath,fname,
dbOutput=c("dataframe","text")) {
#setwd(path)
allfiles <- list.files(path, pattern)
nam <- c()
datfinal <- data.frame()
for (files in seq_along(allfiles)) {
print(files)
##### print(dim(dat4))
r1 <- read.table(file.path(path,allfiles[files]), header = TRUE)
print(dim(r1))
samp<-as.character(r1$sample)
typ<-as.character(r1$type)
zygo<-as.character(r1$zygosity)
if(typ[1] == "trans"){
chro1<-as.numeric(r1$chrA)
chro2<-as.numeric(r1$chrB)
bpstrt<-as.numeric(r1$bkptA)
bpend<-as.numeric(r1$bkptB)
siz<-as.numeric(rep(-1,length(bpend)))
}
else{
chro1<-as.numeric(r1$chr)
chro2<-as.numeric(r1$chr)
bpstrt<-as.numeric(r1$start)
bpend<-as.numeric(r1$end)
siz<-as.numeric(r1$size)
}
score<-as.numeric(r1$score)
smapID<-as.numeric(r1$smapId)
typ<-gsub("^ins$","insertion",typ)
typ<-gsub("^del$","deletion",typ)
typ<-gsub("^inv$","inversion",typ)
typ<-gsub("^trans$","translocation",typ)
dattemp<-data.frame(SmapID=smapID,Sample=samp,Type=typ,
Chromosome1=chro1,
Chromosome2=chro2,BreakPntStrt=bpstrt,BreakPntEnd=bpend,
Size=siz,Zygosity=zygo,Score=score)
datfinal<-rbind(datfinal,dattemp)
}
if(dbOutput == "text"){
filename <- paste(fname, "_merged.txt", sep = "")
write.table(datfinal, file.path(outpath, filename), sep = "\t")
}else if(dbOutput == "dataframe"){
return(datfinal)
}else{stop("method not found")}
}
#' Calculates the internal frequencies of SV in bionano cohorts
#'
#' @param internalBNDB character. Path to the merged BN files.
#' @param smappath character. path to the query smap file.
#' @param smap character. File name for the smap
#' @param smapdata character. SV data in dataframe.
#' @param dbOutput character. Output of merged bionano data.
#' @param fname character. Filename in case dbOutput = Text.
#' @param buildBNInternalDB boolean. Checking whether the merged bionano
#' file database exist.
#' @param input_fmt character. Choice between Text and DataFrame.
#' @param outpath character. Path to merged SV solo datasets.
#' @param win_indel Numeric. Insertion and deletion error window.
#' @param win_inv_trans Numeric. Inversion and translocation error window.
#' @param perc_similarity Numeric . ThresholdPercentage similarity
#' of the query SV and reference SV.
#' @param limsize Numeric . Minimum size to consider for a SV.
#' @param BNDBPath Path of Bionano database files.
#' @param BNDBPattern Pattern of Bionano database files.
#' @param indelconf Numeric. Threshold for insertion and deletion Score.
#' @param invconf Numeric. Threshold for inversion Score.
#' @param transconf Numeric. Threshold for translocation Score.
#' @param returnMethod character. Choice between Text and DataFrame.
#' @return Text file or data frames containing internalFrequency data.
#' @examples
#' mergedFiles <- system.file("extdata", "BNSOLO2_merged.txt",
#' package = "nanotatoR")
#' smapName <- "F1.1_TestSample1_solo_hg19.smap"
#' smappath <- system.file("extdata", package = "nanotatoR")
#' win_indel = 10000; win_inv_trans = 50000; perc_similarity = 0.5;
#' indelconf = 0.5; invconf = 0.01;transconf = 0.1
#' cohortFreq<-cohortFrequency(internalBNDB = mergedFiles , smappath ,
#' smap=smapName, input_fmt ="Text",
#' buildBNInternalDB=FALSE, win_indel, win_inv_trans,
#' perc_similarity , indelconf, invconf,
#' transconf,returnMethod="dataFrame")
#' @importFrom stats na.omit
#' @import hash
#' @export
cohortFrequency <- function(internalBNDB, smappath, smap,
buildBNInternalDB=FALSE, smapdata,
input_fmt=c("Text","dataFrame"),
dbOutput=c("dataframe","text"), BNDBPath,
BNDBPattern, fname,
outpath, win_indel = 10000, win_inv_trans = 50000,
perc_similarity = 0.5, indelconf = 0.5, invconf = 0.01,
limsize=1000,
transconf = 0.1,returnMethod=c("Text","dataFrame")) {
#library(hash)
print("###Cohort Frequency Calculation###")
if(buildBNInternalDB == TRUE){
r<-makeMergedSmapData(path=BNDBPath, pattern=BNDBPattern,
dbOutput=dbOutput)
} else{
r <- read.table(internalBNDB, sep="\t", header=TRUE)
}
##Calculating the sample size
usampt<-as.character(unique(r$Sample))
strst<-strsplit(usampt,split="_")
usampp<-c()
for(o in seq_len(length(strst))){
usampp<-c(usampp,as.character(strst[[o]][1]))
}
usamp<- length(unique(usampp))
##Extracting the smap data
if(input_fmt == "Text"){
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("# ", "", as.character(r10))
# dat<-gsub('\t',' ',r10)
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 == "dataFrame"){
r1<-smapdata
}else{
stop("Input Format incorrect")
}
## Checking Sex male/female and assigning chromosome number accordingly
chro <-unique(r1$RefcontigID1)
dataFinal <- c()
for (ii in seq_along(chro))
{
print(paste('Chrom:',ii,sep=''))
dat <- r[which(r$Chromosome1 == ii), ]
# variantType1<-dat$variantsubtype Changing the variant terms in DGV to
# match svmap
variantType1 <- as.character(dat$Type)
#BSPQI_status_DB <- as.character(dat$Found_in_self_BSPQI_molecules)
#BSSSI_status_DB <- as.character(dat$Found_in_self_BSSSI_molecules)
## Extracting data from SVmap
dat1 <- r1[which(r1$RefcontigID1 == ii), ]
chromo2<-dat1$RefcontigID2
## Adding the windows to the breakpoints
rf <- dat1$RefStartPos
rf_wb_ind <- rf - win_indel
rf_fb_ind <- rf + win_indel
rf_wb_int <- rf - win_inv_trans
rf_fb_int <- rf + win_inv_trans
re <- dat1$RefEndPos
re_wf_ind <- re + win_indel
re_wb_ind <- re - win_indel
re_wb_int <- re - win_inv_trans
re_fb_int <- re + win_inv_trans
## Calculating size
size_bn <- dat1$Size
conf1 <- dat1$Confidence
variantType2 <- as.character(dat1$Type)
# conf<-dat$Confidence countfre<-0 percn<-c()
datf <- c()
for (nn in seq_len(length(rf)))
{
#print(paste("nn:",nn))
if ((variantType2[nn] == "deletion" |
variantType2[nn] == "insertion")){
dat2 <- dat[which((dat$BreakPntStrt <= rf[nn] &
dat$BreakPntStrt >= rf_wb_ind[nn] |
dat$BreakPntStrt >= rf[nn] & dat$BreakPntStrt <= rf_fb_ind[nn])
& (dat$BreakPntEnd >= re[nn] & dat$BreakPntEnd <= re_wf_ind[nn]
| dat$BreakPntEnd <= re[nn] & dat$BreakPntEnd >= re_wb_ind[nn]
)), ]
size1 <- size_bn[nn]
## Calculating Internal Frequency
if (nrow(dat2) > 1)
{
countfre <- c();countfreunfilt<-c()
svStrt<-as.numeric(dat2$BreakPntStrt)
svEnd<-as.numeric(dat2$BreakPntEnd)
size_internal <- as.numeric(dat2$Size)
type <- as.character(dat2$Type)
conf <- as.numeric(dat2$Score)
zygo <- as.character(dat2$Zygosity)
samp <- as.character(dat2$Sample)
sampid<-c();sampid_unfiltered<-c();zyg<-c();
zyg_unfiltered<-c()
homozygo<-c(); unmapped<-c()
for (ll in seq_len(nrow(dat2))) {
perc <- (size1/size_internal[ll])
#### print(perc) print(type[ll])
#print(perc)
#### print(svfamid1)
if (perc >= perc_similarity & identical(type[ll],
variantType2[nn]) & (conf[ll] >= indelconf) &
size_internal[ll] >=limsize){
countfre <- c(countfre,1)
sampid<-c(sampid,samp[ll])
zyg<-c(zyg,zygo[ll])
if(as.character(zygo[ll]) == "homozygous"){
homozygo<-c(homozygo, as.character(samp[ll]))
}
else{
homozygo<-c(homozygo, NULL)
}
} else {
unmapped<-c(unmapped, ll)
}
##Unfiltered
if (perc >= perc_similarity & identical(type[ll],
variantType2[nn]))
{
countfreunfilt <- c(countfreunfilt, 1)
sampid_unfiltered<-c(sampid_unfiltered,
as.character(samp[ll]))
zyg_unfiltered<-c(zyg_unfiltered,
as.character(zygo[ll]))
}
}
##Calculating filtered Frequency
##Filtration
if ((length(sampid) >= 1) & length(countfre) >= 1)
{
dat_temp <- data.frame(sampid, countfre,zyg)
countfre1 <- 0
if (nrow(dat_temp) > 0)
{
#### print(paste('dat_temp',dim(dat_temp)))
usampid <- as.character(unique(sampid))
for (u in seq_len(length(usampid)))
{
dat_temp1 <- dat_temp[which(dat_temp$sampid %in%
usampid[u]), ]
zygo<-as.character(dat_temp1$zyg)
ct <- sum(dat_temp1$countfre)
if (ct >= 1 & nrow (dat_temp1)>= 1)
{
if(length(unique(as.character(zygo)))==1){
if(unique(as.character(zygo)) ==
"homozygous"){
countfre1 <- countfre1 + 2
}
else if(
unique(as.character(zygo)) == "Unknown"
){
countfre1 <- countfre1 + 2
}
else {
countfre1 <- countfre1 + 1
}
}
else{
g1<-grep("homozygous",as.character(zygo))
g2<-grep("heterozygous",as.character(zygo))
g3<-grep("unknown",as.character(zygo))
if(length(g1)>=1 & length(g2)>=1 &
length(g3)>=1){
countfre1 <- countfre1 + 2
}
else if(length(g1) == 0 & length(g2)>=1 &
length(g3)>=1){
countfre1 <- countfre1 + 2
((1*length(g2)) + (2*length(g3)))
}else if(length(g1)>=1 & length(g2)>=1 &
length(g3) == 0){
countfre1 <- countfre1 + 2
}
else if(length(g1)>=1 & length(g2) == 0 &
length(g3)>=1){
countfre1 <- countfre1 + 2
}
else{
countfre1 <- countfre1 + 0
}
}
}else{
countfre1 <- countfre1 + 0
}
}
}else
{
countfre1 <- 0
}
}else{
countfre1 <- 0
}
#### Unfiltered
if ((length(sampid_unfiltered) >= 1) &
length(countfreunfilt) >= 1)
{
dat_temp <- data.frame(sampid_unfiltered,
countfreunfilt,zyg_unfiltered)
countfreunfilt1 <- 0
if (nrow(dat_temp) > 0)
{
#### print(paste('dat_temp',dim(dat_temp)))
usampid <- as.character(unique(sampid_unfiltered))
for (u in seq_len(length(usampid))) {
dat_temp1 <- dat_temp[which(
dat_temp$sampid_unfiltered %in% usampid[u]),
]
zygo<-as.character(dat_temp1$zyg_unfiltered)
ct <- sum(dat_temp1$countfreunfilt)
if (ct >= 1 & nrow (dat_temp1)>= 1)
{
if(length(unique(as.character(zygo))) == 1){
if(unique(as.character(zygo)) ==
"homozygous"){
countfreunfilt1 <- countfreunfilt1 + 2
}
else if(unique(as.character(zygo)) ==
"Unknown"){
countfreunfilt1 <- countfreunfilt1 + 2
}
else {
countfreunfilt1 <- countfreunfilt1 + 1
}
}
else{
g1<-grep("homozygous",as.character(zygo))
g2<-grep("heterozygous",as.character(zygo))
g3<-grep("unknown",as.character(zygo))
if(length(g1)>=1 & length(g2)>=1 &
length(g3)>=1){
countfreunfilt1 <- countfreunfilt1 + 2
}
else if(length(g1) == 0 & length(g2)>=1 &
length(g3)>=1){
countfreunfilt1 <- countfreunfilt1 + 2
}
else if(length(g1)>=1 & length(g2)>=1 &
length(g3) == 0){
countfreunfilt1 <- countfreunfilt1 + 2
}
else if(length(g1)>=1 & length(g2) == 0 &
length(g3)>=1){
countfreunfilt1 <- countfreunfilt1 + 2
}
else{
countfreunfilt1 <- countfreunfilt1 + 0
}
}
}else{
countfreunfilt1 <- countfreunfilt1 + 0
}
}
}else
{
countfreunfilt1 <- 0
}
}else{
countfreunfilt1 <- 0
}
##Extracting the number of Homozygotes
if(length(homozygo)>=1){
homozygo <- length(unique(homozygo))
}
else if(length(homozygo) == 1){
homozygo <- length(homozygo)
}
else{
homozygo <- 0
}
data1 <- data.frame(dat1[nn, ],
BNG_Freq_Perc_Filtered = as.numeric(format(round
((countfre1/(2*(usamp))) * 100,digits=3),scientific = FALSE)),
BNG_Freq_Perc_UnFiltered = as.numeric(format(round(
countfreunfilt1/(2*(usamp))) * 100,digits=3),
scientific = FALSE),
BNG_Homozygotes=as.numeric(homozygo), stringsAsFactors = FALSE)
#### print(dim(data1)) print(dim(data1))
datf <- rbind(datf, data1)
}
else if (nrow(dat2) == 1) {
# dgv_match=TRUE Calculating percentage similarity
countfre <- 0;countfreunfilt<-0
conf <- dat2$Score
size_internal <- dat2$Size
perc <- (size1/size_internal)
zygo <- as.character(dat2$Zygosity)
type <- as.character(dat2$Type)
samp <- as.character(dat2$Sample)
homozygo<-c()
if (perc >= perc_similarity & identical(type,
variantType2[nn]) &
conf >= indelconf & size_internal>=limsize){
if(zygo =="homozygous"){
countfre <- countfre+2
homozygo <- as.character(samp)
}else if(zygo =="unknown"){
countfre <- countfre+2
}else{
countfre <- countfre+1
}
}else{
countfre <- 0
}
if (identical(type, variantType2[nn]) & identical
(type, variantType2[nn])){
if(zygo =="homozygous"){
countfreunfilt <- countfreunfilt + 2
}else if(zygo =="unknown"){
countfreunfilt <- countfreunfilt + 2
}else{
countfreunfilt <- countfreunfilt + 1
}
}
##Calculating the length
if(length(homozygo)>0){
homozygo <- length(homozygo)
}else{
homozygo=0
}
data1 <- data.frame(dat1[nn, ], BNG_Freq_Perc_Filtered
= as.numeric(format(round((countfre/(2*(usamp)))
* 100,digits=3), scientific = FALSE)),
BNG_Freq_Perc_UnFiltered = as.numeric
(format(round((countfreunfilt/(2*(usamp))) * 100,digits=3),
scientific = FALSE)), BNG_Homozygotes=as.numeric(homozygo),
stringsAsFactors = FALSE)
datf <- rbind(datf, data1)
} else{
# dgv_match=FALSE
data1 <- data.frame(dat1[nn, ], BNG_Freq_Perc_Filtered = 0,
BNG_Freq_Perc_UnFiltered = 0, BNG_Homozygotes = 0,
stringsAsFactors = FALSE)
#### print(dim(data1)) print(names(data1)[56:58])
#### print(names(datf)[56:58])
#### print(identical((names(data1)[56]),(names(datf)[56])))
#### print(identical((names(data1)[57]),(names(datf)[57])))
#### print(identical((names(data1)[58]),(names(datf)[58])))
datf <- rbind(datf, data1)
# next;
}
}else if ((variantType2[nn] == "duplication" |
variantType2[nn] == "duplication_split"
|variantType2[nn] == "duplication_inverted"|
variantType2[nn] == "insertion")){
dat2 <- dat[which((dat$BreakPntStrt <= rf[nn] &
dat$BreakPntStrt >= rf_wb_ind[nn] |
dat$BreakPntStrt >= rf[nn] & dat$BreakPntStrt <= rf_fb_ind[nn])
& (dat$BreakPntEnd >= re[nn] & dat$BreakPntEnd <= re_wf_ind[nn]
| dat$BreakPntEnd <= re[nn] & dat$BreakPntEnd >= re_wb_ind[nn]
)), ]
size1 <- size_bn[nn]
## Calculating Internal Frequency
if (nrow(dat2) > 1){
countfre <- c();countfreunfilt<- c()
# svind1<-dat2$SVIndex
size_internal <- dat2$Size
type <- as.character(dat2$Type)
conf <- dat2$Score
zygo <- as.character(dat2$Zygosity)
samp <- as.character(dat2$Sample)
sampid<-c();sampid_unfiltered<-c();zyg<-c();
zyg_unfiltered<-c()
unmapped<-c()
homozygo<-c()
for (ll in seq_len(nrow(dat2))){
#print (ll)
#print(type[ll])
#perc <- (size1/size_internal[ll])
#### print(perc)
#### print(svfamid1)
if ((identical(type[ll], "duplication") |
identical(type[ll], "duplication_split")|
identical(type[ll], "duplication_inverted"))){
countfre <- c(countfre,1)
sampid<-c(sampid,samp[ll])
zyg<-c(zyg,zygo[ll])
if(as.character(zygo[ll])=="homozygous"){
homozygo<-c(homozygo, as.character(samp[ll]))
}
else{
homozygo<-c(homozygo, NULL)
}
}
else if ((identical(type[ll], "insertion"))
& (size_internal[ll]>=limsize)
& (conf[ll] >= indelconf)){
countfre <- c(countfre,1)
sampid<-c(sampid,samp[ll])
zyg<-c(zyg,zygo[ll])
if(as.character(zygo[ll])=="homozygous"){
homozygo<-c(homozygo, as.character(samp[ll]))
}
else{
homozygo<-c(homozygo, NULL)
}
}
else {
unmapped<-c(unmapped, ll)
}
##Unfiltered
if ((identical(type[ll], "duplication") |
identical(type[ll], "duplication_split")|
identical(type[ll], "duplication_inverted")|
identical(type[ll], "insertion"))) {
countfreunfilt <- c(countfreunfilt, 1)
sampid_unfiltered<-c(sampid_unfiltered,
as.character(samp[ll]))
zyg_unfiltered<-c(zyg_unfiltered,
as.character(zygo[ll]))
}
}
##Calculating filtered Frequency
##Filtration
##Filtration
if ((length(sampid) >= 1) & length(countfre) >= 1){
dat_temp <- data.frame(sampid, countfre,zyg)
countfre1 <- 0
if (nrow(dat_temp) > 0){
#### print(paste('dat_temp',dim(dat_temp)))
usampid <- as.character(unique(sampid))
for (u in seq_len(length(usampid))){
dat_temp1 <- dat_temp[which(dat_temp$sampid
%in% usampid[u]), ]
zygo<-as.character(dat_temp1$zyg)
ct <- sum(dat_temp1$countfre)
if (ct >= 1 & nrow (dat_temp1)>= 1){
if(length(unique(as.character(zygo)))==1){
if(unique(as.character(zygo)) ==
"homozygous"){
countfre1 <- countfre1 + 2
}
else if(unique(as.character(zygo))==
"Unknown"){
countfre1 <- countfre1 + 2
}
else {
countfre1 <- countfre1 + 1
}
}
else{
g1<-grep("homozygous",as.character(zygo
))
g2<-grep("heterozygous",
as.character(zygo))
g3<-grep("unknown",as.character(zygo))
if(length(g1)>=1 & length(g2)>=1 &
length(g3)>=1){
countfre1 <- countfre1 + 2
}
else if(length(g1)==0 & length(g2)>=1
& length(g3)>=1){
countfre1 <- countfre1 + 2
}
else if(length(g1)>=1 &
length(g2)>=1 & length(g3)==0){
countfre1 <- countfre1 + 2
}
else if(length(g1)>=1 & length(g2)==0 &
length(g3)>=1){
countfre1 <- countfre1 + 2
}
else{
countfre1 <- countfre1 + 0
}
}
}else{
countfre1 <- countfre1 + 0
}
}
}else{
countfre1 <- 0
}
}else{
countfre1 <- 0
}
#### Unfiltered
if ((length(sampid_unfiltered) >= 1) &
length(countfreunfilt) >= 1){
dat_temp <- data.frame(sampid_unfiltered,
countfreunfilt,zyg_unfiltered)
countfreunfilt1 <- 0
if (nrow(dat_temp) > 0){
#### print(paste('dat_temp',dim(dat_temp)))
usampid <- as.character(unique(sampid_unfiltered))
for (u in seq_len(length(usampid))) {
dat_temp1 <- dat_temp[which(
dat_temp$sampid_unfiltered %in% usampid[u]), ]
zygo<-as.character(dat_temp1$zyg_unfiltered)
ct <- sum(dat_temp1$countfreunfilt)
if (ct >= 1 & nrow (dat_temp1)>= 1)
{
if(length(unique(as.character(zygo)))==1){
if(unique(as.character(zygo))==
"homozygous"){
countfreunfilt1 <- countfreunfilt1 + 2
}
else if(unique(as.character(zygo)) ==
"Unknown"){
countfreunfilt1 <- countfreunfilt1 + 2
}
else {
countfreunfilt1 <- countfreunfilt1 + 1
}
}
else{
g1<-grep("homozygous",as.character(zygo))
g2<-grep("heterozygous",as.character(zygo))
g3<-grep("unknown",as.character(zygo))
if(length(g1)>=1 & length(g2)>=1 &
length(g3)>=1){
countfreunfilt1 <- countfreunfilt1 + 2
}
else if(length(g1)==0 & length(g2)>=1 &
length(g3)>=1){
countfreunfilt1 <- countfreunfilt1 + 2
}
else if(length(g1)>=1 & length(g2)>=1 &
length(g3)==0){
countfreunfilt1 <- countfreunfilt1 + 2
}
else if(length(g1)>=1 & length(g2)==0
& length(g3)>=1){
countfreunfilt1 <- countfreunfilt1 + 2
}
else{
countfreunfilt1 <- countfreunfilt1 + 0
}
}
}
else{
countfreunfilt1 <- countfreunfilt1 + 0
}
}
}
else{
countfreunfilt1 <- 0
}
}
else{
countfreunfilt1 <- 0
}
##Extracting the number of Homozygotes
if(length(homozygo)>=1){
homozygo <- length(unique(homozygo))
}
else if(length(homozygo)==1){
homozygo <- length(homozygo)
}
else{
homozygo <- 0
}
data1 <- data.frame(dat1[nn, ], BNG_Freq_Perc_Filtered
= as.numeric(format(round((countfre1/(2*(usamp))) *
100,digits=3),
scientific = FALSE)),BNG_Freq_Perc_UnFiltered
= as.numeric(format(round((countfreunfilt1/(2*(usamp))) *
100,digits=3),
scientific = FALSE)), BNG_Homozygotes=as.numeric(homozygo),
stringsAsFactors = FALSE)
datf <- rbind(datf, data1)
}
else if (nrow(dat2) == 1)
{
# dgv_match=TRUE Calculating percentage similarity
countfre <- 0;countfreunfilt<-0
conf <- dat2$Score
size_internal <- dat2$Size
samp <- as.character(dat2$Sample)
type <- as.character(dat2$Type)
zygo <- as.character(dat2$Zygosity)
homozygo<-c()
if ((identical(type, "duplication") |
identical(type, "duplication_split")|
identical(type, "duplication_inverted")))
{
if(zygo =="homozygous"){
countfre <- countfre+2
homozygo<-as.character(samp)
}
else if(zygo =="unknown"){
countfre <- countfre+2
}
else{
countfre <- countfre+1
}
}
else if ((identical(type, "insertion")) &
(size_internal >=limsize)
& (conf >= indelconf))
{
if(zygo =="homozygous"){
countfre <- countfre+2
homozygo<-as.character(samp)
}else if(zygo =="unknown"){
countfre <- countfre+2
}else{
countfre <- countfre+1
}
}
else {
countfre <- 0
}
##Unfiltered
if ((identical(type, "duplication") |
identical(type, "duplication_split")|
identical(type, "duplication_inverted")|
identical(type, "insertion")))
{
if(zygo =="homozygous"){
countfreunfilt <- countfreunfilt + 2
}
else if(zygo =="unknown"){
countfreunfilt <- countfreunfilt + 2
}
else{
countfreunfilt <- countfreunfilt + 1
}
}
else{
countfreunfilt <- 0
}
'if ((identical(type, "duplication") |
identical(type, "duplication_split")|
identical(type, "duplication_inverted")|
identical(type, "insertion")))
{ ##Filtered
if(zygo =="homozygous"){
countfre <- countfre+2
homozygo<-as.character(samp)
}else if(zygo =="unknown"){
countfre <- countfre+2
}else{
countfre <- countfre+1
}
##Un-Filtered
if(zygo =="homozygous"){
countfreunfilt <- countfreunfilt + 2
}else if(zygo =="unknown"){
countfreunfilt <- countfreunfilt + 2
}else{
countfreunfilt <- countfreunfilt + 1
}
} else
{
}'
##Calculating the length
if(length(homozygo)>0){
homozygo <- length(homozygo)
}else{
homozygo=0
}
data1 <- data.frame(dat1[nn, ], BNG_Freq_Perc_Filtered
= as.numeric(format(round((countfre/(2*(usamp))) *
100,digits=3),
scientific = FALSE)),BNG_Freq_Perc_UnFiltered
= as.numeric(format(round((countfreunfilt/(2*(usamp))) *
100,digits=3),
scientific = FALSE)), BNG_Homozygotes=as.numeric(homozygo),
stringsAsFactors = FALSE)
datf <- rbind(datf, data1)
} else
{
# dgv_match=FALSE
data1 <- data.frame(dat1[nn, ], BNG_Freq_Perc_Filtered = 0,
BNG_Freq_Perc_UnFiltered = 0,BNG_Homozygotes= 0,
stringsAsFactors = FALSE)
#### print(dim(data1)) print(names(data1)[56:58])
#### print(names(datf)[56:58])
#### print(identical((names(data1)[56]),(names(datf)[56])))
#### print(identical((names(data1)[57]),(names(datf)[57])))
#### print(identical((names(data1)[58]),(names(datf)[58])))
datf <- rbind(datf, data1)
# next;
}
}
else if ((length(grep("inversion", variantType2[nn])) >= 1) |
(length(grep("translocation", variantType2[nn])) >= 1))
{
#print(nn)
dat2 <- dat[which((dat$BreakPntStrt <= rf[nn] &
dat$BreakPntStrt >= rf_wb_int[nn] |
dat$BreakPntStrt >= rf[nn] &
dat$BreakPntStrt <= rf_fb_int[nn]) &
(dat$BreakPntEnd >= re[nn] &
dat$BreakPntEnd <= re_fb_int[nn] |
dat$BreakPntEnd <= re[nn] &
dat$BreakPntEnd >= re_wb_int[nn])), ]
size1 <- size_bn[nn]
## Writing if the dgv_match is TRUE or not
countfre <- c()
if (nrow(dat2) > 1)
{
countfre <- c();countfreunfilt<-c()
#size_internal <- dat2$Size
type <- as.character(dat2$Type)
conf <- as.numeric(dat2$Score)
zygo <- as.character(dat2$Zygosity)
samp <- as.character(dat2$Sample)
chrom2<-dat2$Chromosome2
sampid<-c();sampid_unfiltered<-c();zyg<-c();
zyg_unfiltered<-c()
unmapped<-c()
homozygo<-c()
for (ll in seq_len(nrow(dat2))) {
#perc <- (size1/size_internal[ll])
if ((chrom2[ll]==chromo2[nn]) & identical(type[ll],
variantType2[nn]) & ((identical(type[ll], "inversion") &
conf[ll] >= invconf) |
(identical(type[ll], "translocation") &
conf[ll] >= transconf)))
{ countfre <- c(countfre,1)
sampid<-c(sampid,samp[ll])
zyg<-c(zyg,zygo[ll])
if(as.character(zygo[ll])=="homozygous"){
homozygo<-c(homozygo, as.character(samp[ll]))
}
else{
homozygo<-c(homozygo, NULL)
}
}
else{ unmapped<-c(unmapped,ll)}
###Un filtered
if (identical(type[ll], variantType2[nn]) &
(chrom2[ll]==chromo2[nn]))
{
countfreunfilt <- c(countfreunfilt,1)
sampid_unfiltered<-c(sampid_unfiltered,
as.character(samp[ll]))
zyg_unfiltered<-c(zyg_unfiltered,
as.character(zygo[ll]))
}
}
##Filtration
if ((length(sampid) >= 1) & length(countfre) >= 1)
{
dat_temp <- data.frame(sampid, countfre,zyg)
countfre1 <- 0
if (nrow(dat_temp) > 0)
{
#### print(paste('dat_temp',dim(dat_temp)))
usampid <- as.character(unique(sampid))
for (u in seq_len(length(usampid))) {
dat_temp1 <- dat_temp[which(dat_temp$sampid %in%
usampid[u]), ]
zygo<-as.character(dat_temp1$zyg)
ct <- sum(dat_temp1$countfre)
if (ct >= 1 & nrow (dat_temp1)>= 1)
{
if(length(unique(as.character(zygo)))==1){
if(unique(as.character(zygo))=="homozygous")
{
countfre1 <- countfre1 + 2
}
else if(unique(as.character(zygo))==
"Unknown"){
countfre1 <- countfre1 + 2
}
else {
countfre1 <- countfre1 + 1
}
}
else{
g1<-grep("homozygous",as.character(zygo))
g2<-grep("heterozygous",as.character(zygo))
g3<-grep("unknown",as.character(zygo))
if(length(g1)>=1 & length(g2)>=1 &
length(g3)>=1){
countfre1 <- countfre1 + 2
}
else if(length(g1)==0 & length(g2)>=1 &
length(g3)>=1){
countfre1 <- countfre1 + 2
}
else if(length(g1)>=1 & length(g2)>=1 &
length(g3)==0){
countfre1 <- countfre1 + 2
}
else if(length(g1)>=1 & length(g2)==0 &
length(g3)>=1){
countfre1 <- countfre1 + 2
}
else{
countfre1 <- countfre1 + 0
}
}
}
else{
countfre1 <- countfre1 + 0
}
}
}
else
{
countfre1 <- 0
}
}
else{
countfre1 <- 0
}
#### Unfiltered
if ((length(sampid_unfiltered) >= 1) &
length(countfreunfilt) >= 1)
{
dat_temp <- data.frame(sampid_unfiltered,
countfreunfilt,zyg_unfiltered)
countfreunfilt1 <- 0
if (nrow(dat_temp) > 0)
{
#### print(paste('dat_temp',dim(dat_temp)))
usampid <- as.character(unique(sampid_unfiltered))
for (u in seq_len(length(usampid))) {
dat_temp1 <- dat_temp[which(
dat_temp$sampid_unfiltered %in% usampid[u]), ]
zygo<-as.character(dat_temp1$zyg_unfiltered)
ct <- sum(dat_temp1$countfreunfilt)
if (ct >= 1 & nrow (dat_temp1)>= 1)
{
if(length(unique(as.character(zygo)))==1){
if(unique(as.character(zygo))=="homozygous"
)
{
countfreunfilt1 <- countfreunfilt1 +2
}
else if(unique(as.character(zygo))==
"Unknown"){
countfreunfilt1 <- countfreunfilt1 + 2
}
else {
countfreunfilt1 <- countfreunfilt1 + 1
}
}
else{
g1<-grep("homozygous",as.character(zygo))
g2<-grep("heterozygous",as.character(zygo))
g3<-grep("unknown",as.character(zygo))
if(length(g1)>=1 & length(g2)>=1 &
length(g3)>=1){
countfreunfilt1 <- countfreunfilt1 + 2
}
else if(length(g1)==0 & length(g2)>=1 &
length(g3)>=1){
countfreunfilt1 <- countfreunfilt1 + 2
}
else if(length(g1)>=1 & length(g2)>=1 &
length(g3)==0){
countfreunfilt1 <- countfreunfilt1 + 2
}
else if(length(g1)>=1 & length(g2)==0 &
length(g3)>=1){
countfreunfilt1 <- countfreunfilt1 + 2
}
else{
countfreunfilt1 <- countfreunfilt1 + 0
}
}
}else{
countfreunfilt1 <- countfreunfilt1 + 0
}
}
}
else
{
countfreunfilt1 <- 0
}
}
else{
countfreunfilt1 <- 0
}
##### print(paste('INVTRANS:',countfre,sep=''))
if(length(homozygo)>=1){
homozygo <- length(unique(homozygo))
}
else if(length(homozygo)==1){
homozygo <- length(homozygo)
}
else{
homozygo <- 0
}
data1 <- data.frame(dat1[nn, ], BNG_Freq_Perc_Filtered
= as.numeric(format(round((countfre1/(2*(usamp))) *
100,digits=3),
scientific = FALSE)),BNG_Freq_Perc_UnFiltered
= as.numeric(format(round((countfreunfilt1/(2*(usamp)) *
100),digits=3),
scientific = FALSE)), BNG_Homozygotes=as.numeric(homozygo),
stringsAsFactors = FALSE)
#### print(dim(data1))
datf <- rbind(datf, data1)
}
else if (nrow(dat2) == 1)
{
## dgv_match=TRUE Calculating percentage similarity
countfre<-0;countfreunfilt<-0
chrom2<-dat2$Chromosome2
type <- as.character(dat2$Type)
conf <- dat2$Score
samp <- as.character(dat2$Sample)
zygo <- as.character(dat2$Zygosity)
homozygo<-c()
if ((chrom2 == chromo2[nn]) &
identical(type,variantType2[nn]) &
((identical(type, "inversion") & conf >= invconf) |
(identical(type, "translocation") & conf >= transconf)))
{
if(zygo == "homozygous"){
countfre <- countfre+2
homozygo<-as.character(samp)
}
else if(zygo == "unknown"){
countfre <- countfre+2
}else{
countfre <- countfre+1
}
} else
{
countfre <- 0
}
##Unfiletered Frequency
if (identical(type, variantType2[nn]) &
(chrom2==chromo2[nn])){
if(zygo == "homozygous"){
countfreunfilt <- countfreunfilt + 2
}
else if(zygo == "unknown"){
countfreunfilt <- countfreunfilt + 2
}
else{
countfreunfilt <- countfreunfilt + 1
}
}
##### print(paste('INVTRANS:',countfre,sep=''))
##Calculating the length
if(length(homozygo)>0){
homozygo <- length(homozygo)
}
else{
homozygo = 0
}
data1 <- data.frame(dat1[nn, ], BNG_Freq_Perc_Filtered
= as.numeric(format(round((countfre/(2*(usamp)))
* 100,digits=3),
scientific = FALSE)),BNG_Freq_Perc_UnFiltered
= as.numeric(format(round((countfreunfilt/(2*(usamp))) *
100,digits=3),
scientific = FALSE)), BNG_Homozygotes=as.numeric(homozygo),
stringsAsFactors = FALSE)
#### print(dim(data1))
datf <- rbind(datf, data1)
}
else
{
data1 <- data.frame(dat1[nn, ], BNG_Freq_Perc_Filtered= 0,
BNG_Freq_Perc_UnFiltered =0, BNG_Homozygotes= 0,
stringsAsFactors = FALSE)
#### print(dim(data1))
datf <- rbind(datf, data1)
# next;
}
}
else
{
#### print(paste('QueryData:',dim(dat1[nn,]),sep=''))
data1 <- data.frame(dat1[nn, ], BNG_Freq_Perc_Filtered= 0,
BNG_Freq_Perc_UnFiltered =0, BNG_Homozygotes= 0,
stringsAsFactors = FALSE)
#### print(dim(data1))
datf <- rbind(datf, data1)
}
}
dataFinal <- rbind(dataFinal, datf)
}
if(returnMethod == "Text"){
st1 <- strsplit(smap, ".txt")
fname <- st1[[1]][1]
row.names(dataFinal) <- c()
filenam<-paste(fname,"_Int.txt",sep="")
write.table(dataFinal, file.path(smappath, fname), sep = "\t",
row.names = FALSE)
}
else if (returnMethod=="dataFrame"){
return(dataFinal)
}
else{
stop("returnMethod Incorrect")
}
}
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