R/filterread_SE_Trio.r
In VilainLab/nanotatoR: Next generation structural variant annotation and classification
Defines functions
run_bionano_filter_SE_Trio
Documented in
run_bionano_filter_SE_Trio
#' Getting the data from annotated smaps to extract SV
#' information based on type of variants.
#'
#' @param input_fmt_geneList character. Choice of gene list input
#' Text or Dataframe.
#' @param input_fmt_SV character. Choice of gene list input
#' Text or Dataframe.
#' @param smap character. SV file name.
#' @param svData Dataframe Input data containing SV data.
#' @param dat_geneList Dataframe Input data containing geneList data.
#' @param fileName Character Name of file containing Gene List data.
#' @param outpath Character Directory to the output file.
#' @param outputFilename Character Output filename.
#' @param RZIPpath Character Path for the Rtools Zip package.
#' @param outputType Character. Variants in excel tabs or in different csv files.
#' Options Excel or csv.
#' @param EnzymeType Character. Enzyme type used. Options SVmerge or SE.
#' @param fileprefix Character. fileprefix to use for each of the files in the directory.
#' @param directoryName Character. Directory name where individual SV files will be stored.
#' @param primaryGenesPresent boolean Checks whether the primary gene List is present or not.
#' @return Excel file containing the annotated SV map, tabs divided based on
#' type of SVs.
#' @examples
#' smapName <- "GM24385_Ason_DLE1_VAP_trio5.smap"
#' outputFilename <- "GM24385_Ason_DLE1_VAP_trio5_out"
#' smappath <- system.file("extdata", smapName, package = "nanotatoR")
#' outpath <- system.file("extdata", smapName, package = "nanotatoR")
#' RZIPpath <- system.file("extdata", "zip.exe", package = "nanotatoR")
#' smap = system.file("extdata", smapName, package="nanotatoR")
#' bedFile <- system.file("extdata", "HomoSapienGRCH19_lift37.bed", package="nanotatoR")
#' outpath <- system.file("extdata", package="nanotatoR")
#' directoryName <- system.file("extdata", package="nanotatoR")
#' datcomp<-overlapnearestgeneSearch(smap = smap,
#' bed=bedFile, inputfmtBed = "bed", outpath,
#' n = 3, returnMethod_bedcomp = c("dataFrame"),
#' input_fmt_SV = "Text",
#' EnzymeType = "SE",
#' bperrorindel = 3000, bperrorinvtrans = 50000)
#' hgpath=system.file("extdata", "GRCh37_hg19_variants_2016-05-15.txt", package="nanotatoR")
#' datDGV <- DGVfrequency (hgpath = hgpath,
#' smap_data = datcomp,
#' win_indel_DGV = 10000,
#' input_fmt_SV = "dataFrame",EnzymeType = "SE",
#' perc_similarity_DGV = 0.5,returnMethod="dataFrame")
#' indelconf = 0.5; invconf = 0.01;transconf = 0.1;
#' datInf <- internalFrequencyTrio_Duo(smapdata = datDGV,
#' buildSVInternalDB=TRUE, win_indel=10000,
#' win_inv_trans=50000,
#' labelType = c("SE"), EnzymeType = "SE",
#' SE_path = system.file("extdata", "SoloFile/", package="nanotatoR"),
#' SE_pattern = "*_DLE1_*", perc_similarity_parents =0.9,
#' Samplecodes = system.file("extdata", "nanotatoR_sample_codes.csv", package="nanotatoR"),
#' mergeKey = system.file("extdata", "nanotatoR_control_sample_codes.csv", package="nanotatoR"),
#' mergedKeyoutpath = system.file("extdata", package="nanotatoR"),
#' mergedKeyFname = "Sample_index.csv",
#' indexfile = system.file("extdata", mergedKeyFname, package="nanotatoR"),
#' perc_similarity = 0.5, indelconf = 0.5, invconf = 0.01,
#' transconf = 0.1, limsize = 1000, win_indel_parents = 5000,
#' win_inv_trans_parents=40000,
#' returnMethod="dataFrame", input_fmt_SV = "dataFrame")
#' path <- system.file("extdata", "Bionano_config/", package = "nanotatoR")
#' pattern <- "*_hg19_*"
#' datBNDB <- BNDBfrequency(smapdata = datInf,
#' buildBNInternalDB=TRUE,
#' input_fmt_SV = "dataFrame",
#' dbOutput="dataframe",
#' BNDBpath = path,
#' BNDBpattern = pattern,
#' 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("dataFrame"),
#' EnzymeType = c("SE"))
#' decipherpath = system.file("extdata", "population_cnv.txt", package="nanotatoR")
#' datdecipher <- Decipherfrequency (decipherpath = decipherpath,
#' smap_data = datBNDB, win_indel = 10000,
#' perc_similarity = 0.5,returnMethod="dataFrame",
#' input_fmt_SV = "dataFrame", EnzymeType = c("SE"))
#' run_bionano_filter_SE_Trio (input_fmt_geneList = c("Text"),
#' input_fmt_SV = c("dataFrame"),
#' svData = datdecipher,
#' dat_geneList = dat_geneList,
#' RZIPpath = RZIPpath, EnzymeType = c("SE"),
#' outputType = c("csv"),
#' primaryGenesPresent = FALSE,
#' directoryName = directoryName,
#' fileprefix = "AnnotatedSamplesGM24385")
#' @import openxlsx
#' @import hash
#' @importFrom stats na.omit
#' @export
run_bionano_filter_SE_Trio <- function(primaryGenesPresent = TRUE,
input_fmt_geneList = c("Text", "dataFrame"),
input_fmt_SV = c("Text", "dataFrame"),
smap = NULL, svData, dat_geneList, fileName, outpath,
outputFilename = "", RZIPpath,
outputType = c("Excel", "csv"),
directoryName, fileprefix,
EnzymeType = c("SVMerge", "SE" )) {
# library(openxlsx)
# library(hash)
# setwd(path)##change the directory to your working directory
Sys.setenv("R_ZIPCMD" = RZIPpath)
## GeneList Input Format
' if (input_fmt_geneList == "Text") {
rr <- read.table(fileName, header = TRUE)
}
else if (input_fmt_geneList == "dataFrame") {
rr <- dat_geneList
}
else {
stop("Dataframe Incorrect!!")
}
ha <- hash()
.set(ha, keys = rr$Genes, values = rr$Terms)
pg <- as.character(rr$Genes)
gen <- paste("^", pg, "$", sep = "")'
# ll<-list.files(pattern="txt")
if(input_fmt_SV=="dataFrame"){
smapdata = svData
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")
}
r <- smapdata
primaryGenesPresent = primaryGenesPresent
if (primaryGenesPresent == TRUE){
if (input_fmt_geneList == "Text") {
rr <- read.table(fileName, header = TRUE)
}
else if (input_fmt_geneList == "dataFrame") {
rr <- dat_geneList
}
else {
stop("input_fmt_geneList Incorrect!!")
}
ogene <- as.character(r$OverlapGenes_strand_perc)
upgene <- as.character(r$Upstream_nonOverlapGenes_dist_kb)
dngene <- as.character(r$Downstream_nonOverlapGenes_dist_kb)
# nogene<-c(upgene,dngene)
### OverlapGene
dataPGOV <- overlappingGenes (rr, ogene)
### Non-Overlap Up-stream Gene
dataPGUP <- nonOverlappingUPGenes (rr, upgene)
### Non-Overlap Down-stream Gene
dataPGDN <- nonOverlappingDNGenes (rr, dngene)
### Non-OverlapDnGene
# len<-length(pagene)-length(pg)
# genesPG<-c(as.character(pg),rep("-",len))
data <- data.frame(cbind(
r, Overlap_PG = as.character(dataPGOV$pagene),
Overlap_Terms = as.character(dataPGOV$pagene_term),
Overlap_ClinicalSig = as.character(dataPGOV$pagene_clinSig),
Non_Overlap_UP_PG = as.character(dataPGUP$nopageneup),
Non_Overlap_UP_Terms = as.character(dataPGUP$nopageneup_term),
Non_Overlap_UP_ClinicalSig = as.character(dataPGUP$nopageneup_clinSig),
Non_Overlap_DN_PG = as.character(dataPGDN$nopagenedn),
Non_Overlap_DN_Terms = as.character(dataPGDN$nopagenedn_term),
Non_Overlap_DN_ClinicalSig = as.character(dataPGDN$nopagenedn_clinSig)))
}else if (primaryGenesPresent == FALSE){
data <- smapdata
}else {stop("primaryGenesPresent Incorrect!!")}
data$BNG_Freq_Perc_Filtered<-gsub("-",0,as.character(
data$BNG_Freq_Perc_Filtered))
data$BNG_Freq_Perc_UnFiltered<-gsub("-",0,as.character(
data$BNG_Freq_Perc_UnFiltered))
data$DGV_Freq_Perc<-as.numeric(data$DGV_Freq_Perc)
data$Internal_Freq_Perc_Filtered<-as.numeric(
data$Internal_Freq_Perc_Filtered)
data$Internal_Freq_Perc_Unfiltered<-as.numeric(
data$Internal_Freq_Perc_Unfiltered)
data$BNG_Freq_Perc_Filtered<-as.numeric(
data$BNG_Freq_Perc_Filtered)
data$BNG_Freq_Perc_UnFiltered<-as.numeric(
data$BNG_Freq_Perc_UnFiltered)
data$DECIPHER_Frequency<-as.numeric(data$DECIPHER_Frequency)
dat <- data[which(data$Type %in% "insertion"), ]
dat1 <- data[which(data$Type %in% "deletion"), ]
dat44 <- data[which(data$Type %in% "duplication"), ]
dat45 <- data[which(data$Type %in% "duplication_split"), ]
dat46 <- data[which(data$Type %in% "duplication_inverted"), ]
dat_dup_rem<-rbind(dat44,dat45,dat46)
dat_dup_rem_FINAL<-dat_dup_rem[which(((dat_dup_rem$Type %in% "duplication") |
(dat_dup_rem$Type %in% "duplication_split") | (dat_dup_rem$Type %in% "duplication_inverted"))&
(dat_dup_rem$Fail_assembly_chimeric_score == "pass")), ]
dat3 <- rbind(dat1, dat, dat_dup_rem_FINAL)
dat10 <- dat3[which((dat3$Found_in_self_molecules == "yes") &
((dat3$Found_in_parents_molecules == "none"
| dat3$Found_in_parents_molecules == "-"))), ]
dat11 <- dat3[which((dat3$Found_in_self_molecules == "yes") &
((dat3$Found_in_parents_molecules == "both"))),]
dat12 <- dat3[which((dat3$Found_in_self_molecules == "yes") &
((dat3$Found_in_parents_molecules == "mother"))),]
dat13 <- dat3[which((dat3$Found_in_self_molecules == "yes") &
((dat3$Found_in_parents_molecules == "father"))),]
dat14 <- rbind(dat12, dat13)
'gg <- grep("inversion", as.character(data$Type))
dat8 <- data[gg, ]'
dat8 <- data[which(data$Type %in% "inversion"
| data$Type %in% "inversion_partial"
| data$Type %in% "inversion_paired"
| data$Type %in% "inversion_repeat"), ]
dat8 <-dat8[which((dat8$Found_in_self_molecules == "yes")
& (dat8$Fail_assembly_chimeric_score == "pass")), ]
gg1 <- grep("translocation", as.character(data$Type))
dat7 <- data[gg1, ]
dat7 <-dat7[which((dat7$Found_in_self_molecules == "yes")
& (dat7$Fail_assembly_chimeric_score == "pass")), ]
dat6 <- data[which(data$Type %in% "MisMatch"), ]
ovrlapPG<-which(!((as.character(data$Overlap_PG)=="-")))
datovrlapPG<-data[ovrlapPG,]
nonovrlapupPG<-which(!((as.character(data$Non_Overlap_UP_PG)=="-")))
datnonovrlapUPPG<-data[nonovrlapupPG,]
nonovrlapdnPG<-which(!((as.character(data$Non_Overlap_DN_PG)=="-")))
datnonovrlapDNPG<-data[nonovrlapdnPG,]
datOvrLap <- rbind(datovrlapPG,datnonovrlapUPPG,datnonovrlapDNPG)
'list_of_datasets <- list(
"all" = data, "indel_dup_denovo" = dat10,
"indel_dup_both" = dat11,
"indel_dup_mother" = dat12, "indel_dup_father" = dat13,
"indel_dup_cmpdHET" = dat14, "inv" = dat8, "trans" = dat7, "mismatch" = dat6,
"all_PG_OV" = datovrlapPG, "all_PG_Non_OV_UP" = datnonovrlapUPPG,"all_PG_Non_OV_DN" = datnonovrlapDNPG
)'
if (outputType == "Excel"){
list_of_datasets <- list(
"indel_dup_denovo" = dat10,
"indel_dup_both" = dat11, "indel_dup_cmpdHET" = dat14,
"inv" = dat8, "trans" = dat7,
"indel_dup_mother" = dat12, "indel_dup_father" = dat13,
"all_PG_OV" = datOvrLap, "all" = data)
fname <- paste(outputFilename, ".xlsx", sep = "")
write.xlsx(list_of_datasets, file = file.path(outpath, fname), keepNA = TRUE)
} else if (outputType == "csv"){
write.csv(dat10, file.path(directoryName, paste(
fileprefix,"_indel_dup.csv",sep = "")), row.names = FALSE)
write.csv(dat11, file.path(directoryName, paste(
fileprefix,"_indel_dup_both.csv",sep = "")), row.names = FALSE)
write.csv(dat14, file.path(directoryName, paste(
fileprefix,"_indel_dup_cmpdHET.csv",sep = "")), row.names = FALSE)
write.csv(dat8, file.path(directoryName, paste(
fileprefix,"_inv.csv",sep = "")), row.names = FALSE)
write.csv(dat7, file.path(directoryName, paste(
fileprefix,"_trans.csv",sep = "")), row.names = FALSE)
write.csv(dat12, file.path(directoryName, paste(
fileprefix,"_indel_dup_mother.csv",sep = "")), row.names = FALSE)
write.csv(dat13, file.path(directoryName, paste(
fileprefix,"_indel_dup_father.csv",sep = "")), row.names = FALSE)
write.csv(datOvrLap, file.path(directoryName, paste(
fileprefix,"_all_PG_OV.csv",sep = "")), row.names = FALSE)
write.csv(data, file.path(directoryName, paste(
fileprefix,"_all.csv",sep = "")), row.names = FALSE)
} else {stop(" outputType incorrect !!")}
# gg<-grep("inversion",as.character(data$Type))
# dat8<-data[gg,]
# gg1<-grep("translocation",as.character(data$Type))
# dat7<-data[gg1,]
# dat6<-data[which(data$Type %in% "MisMatch"),]
# list_of_datasets <- list("all" = data, "indel" = dat3, "indel_denovo"= dat10, "indel_both"= dat11,
# "indel_mother"= dat12, "indel father"= dat13, "indel_cmpdHET"=dat14, "inv"= dat8, "trans"=dat7, "mismatch"=dat6,
# "all_PG_OV"=data, "all_PG_Non_OV"= data)
# st1<-strsplit(smap,".txt")[[1]][1]
'fname <- paste(outputFilename, "_DLE.xlsx", sep = "")
write.xlsx(list_of_datasets, file = file.path(outpath, fname),keepNA=TRUE)'
}
### Running the code
### Copy this 1 line at a time without the hash (#)
### Warning1: Don't remove the # from before the command from filterread.r
### Warning2: give the fullpath with filename for the R code
# source("Z:\\bionano\\Test_R_Codes\\filterread.r")##path of where the Rcode is
#
# run_bionano_filter(path,smap,fileName)
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Defines functions run_bionano_filter_SE_Trio
Documented in run_bionano_filter_SE_Trio
#' Getting the data from annotated smaps to extract SV
#' information based on type of variants.
#'
#' @param input_fmt_geneList character. Choice of gene list input
#' Text or Dataframe.
#' @param input_fmt_SV character. Choice of gene list input
#' Text or Dataframe.
#' @param smap character. SV file name.
#' @param svData Dataframe Input data containing SV data.
#' @param dat_geneList Dataframe Input data containing geneList data.
#' @param fileName Character Name of file containing Gene List data.
#' @param outpath Character Directory to the output file.
#' @param outputFilename Character Output filename.
#' @param RZIPpath Character Path for the Rtools Zip package.
#' @param outputType Character. Variants in excel tabs or in different csv files.
#' Options Excel or csv.
#' @param EnzymeType Character. Enzyme type used. Options SVmerge or SE.
#' @param fileprefix Character. fileprefix to use for each of the files in the directory.
#' @param directoryName Character. Directory name where individual SV files will be stored.
#' @param primaryGenesPresent boolean Checks whether the primary gene List is present or not.
#' @return Excel file containing the annotated SV map, tabs divided based on
#' type of SVs.
#' @examples
#' smapName <- "GM24385_Ason_DLE1_VAP_trio5.smap"
#' outputFilename <- "GM24385_Ason_DLE1_VAP_trio5_out"
#' smappath <- system.file("extdata", smapName, package = "nanotatoR")
#' outpath <- system.file("extdata", smapName, package = "nanotatoR")
#' RZIPpath <- system.file("extdata", "zip.exe", package = "nanotatoR")
#' smap = system.file("extdata", smapName, package="nanotatoR")
#' bedFile <- system.file("extdata", "HomoSapienGRCH19_lift37.bed", package="nanotatoR")
#' outpath <- system.file("extdata", package="nanotatoR")
#' directoryName <- system.file("extdata", package="nanotatoR")
#' datcomp<-overlapnearestgeneSearch(smap = smap,
#' bed=bedFile, inputfmtBed = "bed", outpath,
#' n = 3, returnMethod_bedcomp = c("dataFrame"),
#' input_fmt_SV = "Text",
#' EnzymeType = "SE",
#' bperrorindel = 3000, bperrorinvtrans = 50000)
#' hgpath=system.file("extdata", "GRCh37_hg19_variants_2016-05-15.txt", package="nanotatoR")
#' datDGV <- DGVfrequency (hgpath = hgpath,
#' smap_data = datcomp,
#' win_indel_DGV = 10000,
#' input_fmt_SV = "dataFrame",EnzymeType = "SE",
#' perc_similarity_DGV = 0.5,returnMethod="dataFrame")
#' indelconf = 0.5; invconf = 0.01;transconf = 0.1;
#' datInf <- internalFrequencyTrio_Duo(smapdata = datDGV,
#' buildSVInternalDB=TRUE, win_indel=10000,
#' win_inv_trans=50000,
#' labelType = c("SE"), EnzymeType = "SE",
#' SE_path = system.file("extdata", "SoloFile/", package="nanotatoR"),
#' SE_pattern = "*_DLE1_*", perc_similarity_parents =0.9,
#' Samplecodes = system.file("extdata", "nanotatoR_sample_codes.csv", package="nanotatoR"),
#' mergeKey = system.file("extdata", "nanotatoR_control_sample_codes.csv", package="nanotatoR"),
#' mergedKeyoutpath = system.file("extdata", package="nanotatoR"),
#' mergedKeyFname = "Sample_index.csv",
#' indexfile = system.file("extdata", mergedKeyFname, package="nanotatoR"),
#' perc_similarity = 0.5, indelconf = 0.5, invconf = 0.01,
#' transconf = 0.1, limsize = 1000, win_indel_parents = 5000,
#' win_inv_trans_parents=40000,
#' returnMethod="dataFrame", input_fmt_SV = "dataFrame")
#' path <- system.file("extdata", "Bionano_config/", package = "nanotatoR")
#' pattern <- "*_hg19_*"
#' datBNDB <- BNDBfrequency(smapdata = datInf,
#' buildBNInternalDB=TRUE,
#' input_fmt_SV = "dataFrame",
#' dbOutput="dataframe",
#' BNDBpath = path,
#' BNDBpattern = pattern,
#' 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("dataFrame"),
#' EnzymeType = c("SE"))
#' decipherpath = system.file("extdata", "population_cnv.txt", package="nanotatoR")
#' datdecipher <- Decipherfrequency (decipherpath = decipherpath,
#' smap_data = datBNDB, win_indel = 10000,
#' perc_similarity = 0.5,returnMethod="dataFrame",
#' input_fmt_SV = "dataFrame", EnzymeType = c("SE"))
#' run_bionano_filter_SE_Trio (input_fmt_geneList = c("Text"),
#' input_fmt_SV = c("dataFrame"),
#' svData = datdecipher,
#' dat_geneList = dat_geneList,
#' RZIPpath = RZIPpath, EnzymeType = c("SE"),
#' outputType = c("csv"),
#' primaryGenesPresent = FALSE,
#' directoryName = directoryName,
#' fileprefix = "AnnotatedSamplesGM24385")
#' @import openxlsx
#' @import hash
#' @importFrom stats na.omit
#' @export
run_bionano_filter_SE_Trio <- function(primaryGenesPresent = TRUE,
input_fmt_geneList = c("Text", "dataFrame"),
input_fmt_SV = c("Text", "dataFrame"),
smap = NULL, svData, dat_geneList, fileName, outpath,
outputFilename = "", RZIPpath,
outputType = c("Excel", "csv"),
directoryName, fileprefix,
EnzymeType = c("SVMerge", "SE" )) {
# library(openxlsx)
# library(hash)
# setwd(path)##change the directory to your working directory
Sys.setenv("R_ZIPCMD" = RZIPpath)
## GeneList Input Format
' if (input_fmt_geneList == "Text") {
rr <- read.table(fileName, header = TRUE)
}
else if (input_fmt_geneList == "dataFrame") {
rr <- dat_geneList
}
else {
stop("Dataframe Incorrect!!")
}
ha <- hash()
.set(ha, keys = rr$Genes, values = rr$Terms)
pg <- as.character(rr$Genes)
gen <- paste("^", pg, "$", sep = "")'
# ll<-list.files(pattern="txt")
if(input_fmt_SV=="dataFrame"){
smapdata = svData
if(EnzymeType == "SVMerge"){
#smapdata <- readSMap(smap, input_fmt_smap = "Text")
SVID<-smapdata$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$SmapEntryID
}
}
else{
stop("Input format for SMAP Incorrect")
}
r <- smapdata
primaryGenesPresent = primaryGenesPresent
if (primaryGenesPresent == TRUE){
if (input_fmt_geneList == "Text") {
rr <- read.table(fileName, header = TRUE)
}
else if (input_fmt_geneList == "dataFrame") {
rr <- dat_geneList
}
else {
stop("input_fmt_geneList Incorrect!!")
}
ogene <- as.character(r$OverlapGenes_strand_perc)
upgene <- as.character(r$Upstream_nonOverlapGenes_dist_kb)
dngene <- as.character(r$Downstream_nonOverlapGenes_dist_kb)
# nogene<-c(upgene,dngene)
### OverlapGene
dataPGOV <- overlappingGenes (rr, ogene)
### Non-Overlap Up-stream Gene
dataPGUP <- nonOverlappingUPGenes (rr, upgene)
### Non-Overlap Down-stream Gene
dataPGDN <- nonOverlappingDNGenes (rr, dngene)
### Non-OverlapDnGene
# len<-length(pagene)-length(pg)
# genesPG<-c(as.character(pg),rep("-",len))
data <- data.frame(cbind(
r, Overlap_PG = as.character(dataPGOV$pagene),
Overlap_Terms = as.character(dataPGOV$pagene_term),
Overlap_ClinicalSig = as.character(dataPGOV$pagene_clinSig),
Non_Overlap_UP_PG = as.character(dataPGUP$nopageneup),
Non_Overlap_UP_Terms = as.character(dataPGUP$nopageneup_term),
Non_Overlap_UP_ClinicalSig = as.character(dataPGUP$nopageneup_clinSig),
Non_Overlap_DN_PG = as.character(dataPGDN$nopagenedn),
Non_Overlap_DN_Terms = as.character(dataPGDN$nopagenedn_term),
Non_Overlap_DN_ClinicalSig = as.character(dataPGDN$nopagenedn_clinSig)))
}else if (primaryGenesPresent == FALSE){
data <- smapdata
}else {stop("primaryGenesPresent Incorrect!!")}
data$BNG_Freq_Perc_Filtered<-gsub("-",0,as.character(
data$BNG_Freq_Perc_Filtered))
data$BNG_Freq_Perc_UnFiltered<-gsub("-",0,as.character(
data$BNG_Freq_Perc_UnFiltered))
data$DGV_Freq_Perc<-as.numeric(data$DGV_Freq_Perc)
data$Internal_Freq_Perc_Filtered<-as.numeric(
data$Internal_Freq_Perc_Filtered)
data$Internal_Freq_Perc_Unfiltered<-as.numeric(
data$Internal_Freq_Perc_Unfiltered)
data$BNG_Freq_Perc_Filtered<-as.numeric(
data$BNG_Freq_Perc_Filtered)
data$BNG_Freq_Perc_UnFiltered<-as.numeric(
data$BNG_Freq_Perc_UnFiltered)
data$DECIPHER_Frequency<-as.numeric(data$DECIPHER_Frequency)
dat <- data[which(data$Type %in% "insertion"), ]
dat1 <- data[which(data$Type %in% "deletion"), ]
dat44 <- data[which(data$Type %in% "duplication"), ]
dat45 <- data[which(data$Type %in% "duplication_split"), ]
dat46 <- data[which(data$Type %in% "duplication_inverted"), ]
dat_dup_rem<-rbind(dat44,dat45,dat46)
dat_dup_rem_FINAL<-dat_dup_rem[which(((dat_dup_rem$Type %in% "duplication") |
(dat_dup_rem$Type %in% "duplication_split") | (dat_dup_rem$Type %in% "duplication_inverted"))&
(dat_dup_rem$Fail_assembly_chimeric_score == "pass")), ]
dat3 <- rbind(dat1, dat, dat_dup_rem_FINAL)
dat10 <- dat3[which((dat3$Found_in_self_molecules == "yes") &
((dat3$Found_in_parents_molecules == "none"
| dat3$Found_in_parents_molecules == "-"))), ]
dat11 <- dat3[which((dat3$Found_in_self_molecules == "yes") &
((dat3$Found_in_parents_molecules == "both"))),]
dat12 <- dat3[which((dat3$Found_in_self_molecules == "yes") &
((dat3$Found_in_parents_molecules == "mother"))),]
dat13 <- dat3[which((dat3$Found_in_self_molecules == "yes") &
((dat3$Found_in_parents_molecules == "father"))),]
dat14 <- rbind(dat12, dat13)
'gg <- grep("inversion", as.character(data$Type))
dat8 <- data[gg, ]'
dat8 <- data[which(data$Type %in% "inversion"
| data$Type %in% "inversion_partial"
| data$Type %in% "inversion_paired"
| data$Type %in% "inversion_repeat"), ]
dat8 <-dat8[which((dat8$Found_in_self_molecules == "yes")
& (dat8$Fail_assembly_chimeric_score == "pass")), ]
gg1 <- grep("translocation", as.character(data$Type))
dat7 <- data[gg1, ]
dat7 <-dat7[which((dat7$Found_in_self_molecules == "yes")
& (dat7$Fail_assembly_chimeric_score == "pass")), ]
dat6 <- data[which(data$Type %in% "MisMatch"), ]
ovrlapPG<-which(!((as.character(data$Overlap_PG)=="-")))
datovrlapPG<-data[ovrlapPG,]
nonovrlapupPG<-which(!((as.character(data$Non_Overlap_UP_PG)=="-")))
datnonovrlapUPPG<-data[nonovrlapupPG,]
nonovrlapdnPG<-which(!((as.character(data$Non_Overlap_DN_PG)=="-")))
datnonovrlapDNPG<-data[nonovrlapdnPG,]
datOvrLap <- rbind(datovrlapPG,datnonovrlapUPPG,datnonovrlapDNPG)
'list_of_datasets <- list(
"all" = data, "indel_dup_denovo" = dat10,
"indel_dup_both" = dat11,
"indel_dup_mother" = dat12, "indel_dup_father" = dat13,
"indel_dup_cmpdHET" = dat14, "inv" = dat8, "trans" = dat7, "mismatch" = dat6,
"all_PG_OV" = datovrlapPG, "all_PG_Non_OV_UP" = datnonovrlapUPPG,"all_PG_Non_OV_DN" = datnonovrlapDNPG
)'
if (outputType == "Excel"){
list_of_datasets <- list(
"indel_dup_denovo" = dat10,
"indel_dup_both" = dat11, "indel_dup_cmpdHET" = dat14,
"inv" = dat8, "trans" = dat7,
"indel_dup_mother" = dat12, "indel_dup_father" = dat13,
"all_PG_OV" = datOvrLap, "all" = data)
fname <- paste(outputFilename, ".xlsx", sep = "")
write.xlsx(list_of_datasets, file = file.path(outpath, fname), keepNA = TRUE)
} else if (outputType == "csv"){
write.csv(dat10, file.path(directoryName, paste(
fileprefix,"_indel_dup.csv",sep = "")), row.names = FALSE)
write.csv(dat11, file.path(directoryName, paste(
fileprefix,"_indel_dup_both.csv",sep = "")), row.names = FALSE)
write.csv(dat14, file.path(directoryName, paste(
fileprefix,"_indel_dup_cmpdHET.csv",sep = "")), row.names = FALSE)
write.csv(dat8, file.path(directoryName, paste(
fileprefix,"_inv.csv",sep = "")), row.names = FALSE)
write.csv(dat7, file.path(directoryName, paste(
fileprefix,"_trans.csv",sep = "")), row.names = FALSE)
write.csv(dat12, file.path(directoryName, paste(
fileprefix,"_indel_dup_mother.csv",sep = "")), row.names = FALSE)
write.csv(dat13, file.path(directoryName, paste(
fileprefix,"_indel_dup_father.csv",sep = "")), row.names = FALSE)
write.csv(datOvrLap, file.path(directoryName, paste(
fileprefix,"_all_PG_OV.csv",sep = "")), row.names = FALSE)
write.csv(data, file.path(directoryName, paste(
fileprefix,"_all.csv",sep = "")), row.names = FALSE)
} else {stop(" outputType incorrect !!")}
# gg<-grep("inversion",as.character(data$Type))
# dat8<-data[gg,]
# gg1<-grep("translocation",as.character(data$Type))
# dat7<-data[gg1,]
# dat6<-data[which(data$Type %in% "MisMatch"),]
# list_of_datasets <- list("all" = data, "indel" = dat3, "indel_denovo"= dat10, "indel_both"= dat11,
# "indel_mother"= dat12, "indel father"= dat13, "indel_cmpdHET"=dat14, "inv"= dat8, "trans"=dat7, "mismatch"=dat6,
# "all_PG_OV"=data, "all_PG_Non_OV"= data)
# st1<-strsplit(smap,".txt")[[1]][1]
'fname <- paste(outputFilename, "_DLE.xlsx", sep = "")
write.xlsx(list_of_datasets, file = file.path(outpath, fname),keepNA=TRUE)'
}
### Running the code
### Copy this 1 line at a time without the hash (#)
### Warning1: Don't remove the # from before the command from filterread.r
### Warning2: give the fullpath with filename for the R code
# source("Z:\\bionano\\Test_R_Codes\\filterread.r")##path of where the Rcode is
#
# run_bionano_filter(path,smap,fileName)
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