R/checkIntensityFile.R
In smgogarten/GWASTools: Tools for Genome Wide Association Studies
Defines functions
checkIntensityFile
Documented in
checkIntensityFile
checkIntensityFile <- function(path=".",
filename,
file.type=c("gds", "ncdf"),
snp.annotation,
scan.annotation,
sep.type,
skip.num,
col.total,
col.nums,
scan.name.in.file,
check.scan.index,
n.scans.loaded,
affy.inten = FALSE,
diagnostics.filename = "checkIntensityFile.diagnostics.RData",
verbose = TRUE) {
## sx is vector of sample indices to check
## N is the number of samples loaded so far
if(!all(is.element(check.scan.index,1:n.scans.loaded))) stop("check.scan.index must be included in 1:n.scans.loaded")
### get file type
file.type <- match.arg(file.type)
if (file.type == "gds") {
quantfile <- GdsIntensityReader(filename)
} else if (file.type == "ncdf") {
quantfile <- NcdfIntensityReader(filename)
}
## get sample ids
quant.sampid <- getScanID(quantfile, index=1:n.scans.loaded)
## get snp ids from the netCDF file
nc.snpid <- getSnpID(quantfile)
## get sample info and file names
stopifnot(all(c("scanID", "scanName", "file") %in% names(scan.annotation)))
if(any(!is.element(quant.sampid, scan.annotation$scanID))) stop("some sample id(s) in data file not found in sample annotation dataframe")
scan.annotation <- scan.annotation[match(quant.sampid, scan.annotation$scanID),]
files <- file.path(path, scan.annotation$file)
## check col.nums vector
col.nums <- col.nums[!is.na(col.nums)]
intensity.vars <- c("quality", "X", "Y", "rawX", "rawY", "R", "Theta", "BAlleleFreq","LogRRatio")
if(!all(names(col.nums) %in% c("snp", "sample", "geno", "a1", "a2", intensity.vars))) stop("problem with col.nums vector names")
if(!is.integer(col.nums)) stop("col.nums vector class is not integer")
if(!("snp" %in% names(col.nums))) stop("snp id missing in col.nums")
if( max(col.nums) > col.total) stop("some element of col.nums is greater than total number of columns")
## compare with ncdf
varnames <- getVariableNames(quantfile)
chk.vars <- intersect(names(col.nums), intensity.vars)
if (length(chk.vars) > 0) {
if(!all(is.element(chk.vars, varnames))) stop("variables designated in col.nums are not all defined in the data file")
}
## check snp.annotation
stopifnot(all(c("snpID", "snpName") %in% names(snp.annotation)))
if(any(snp.annotation$snpID != sort(snp.annotation$snpID))) stop("snp annotation ids not in order")
if(any(snp.annotation$snpID != nc.snpid)) stop("snp annotation ids not the same as in data file")
n <- nrow(snp.annotation)
## generate colClasses vector for read.table
cc <- rep("NULL",col.total)
## don't need to check genotype variables
## cc[col.nums[names(col.nums) %in% c("snp","sample","geno","a1","a2")]] <- "character"
cc[col.nums[names(col.nums) %in% c("snp","sample")]] <- "character"
cc[col.nums[names(col.nums) %in% intensity.vars]] <- "double"
## generate names for the genotype data.frame
df.names <- names(sort(col.nums))
## set up objects to keep track of things for each file
## refresh diagnostics from when the ncdf was created
fn <- length(files)
read.file <- rep(NA, fn) ## keeps track of whether the file was readable or not
row.num <- rep(NA, fn) ## number of rows read
sample.names <- vector("list",fn) ## list of vectors of unique sample names in each file
sample.match <- rep(NA, fn) ## indicator whether sample name inside file matches sample names in sample annotation data.frame
## missg <- vector("list",fn) ## vector of character string(s) used for missing genotypes (i.e. not AA, AB or BB)
snp.chk <- rep(NA,fn)
chk <- rep(NA,fn) ## ## final check on data ready to load into ncdf
## ## new diagnostics
## snp.order <- rep(NA,fn)
## geno.chk <- rep(NA,fn)
qs.chk <- rep(NA,fn)
z <- rep(NA,fn)
inten.chk <- list(z,z,z,z,z,z,z,z)
names(inten.chk) <- intensity.vars[-1]
## diagnostics for Affy intensity files
read.file.inten <- rep(NA,fn)
sample.match.inten <- rep(NA,fn)
rows.equal <- rep(NA,fn)
snp.chk.inten <- rep(NA,fn)
## Set tolerance for intensity comparisons
tol <- 1e-4 ## - used for difference
rtol <- 1e-6 ## used for ratio of difference to raw data file value
## specify intensity variables to check
qvars <- c("X", "Y", "rawX", "rawY", "R", "Theta", "BAlleleFreq", "LogRRatio")
varin <- names(col.nums)
if(affy.inten) varin <- c(varin, "X","Y")
qvars <- qvars[is.element(qvars, varin)]
m <- length(qvars)
inten.chk <- inten.chk[is.element(names(inten.chk), qvars)]
nsx <- length(check.scan.index)
if (verbose) start <- Sys.time()
for(i in check.scan.index){
## save diagnostics at each step in case of crash
diagnostics <- list(read.file, row.num, sample.names, sample.match, snp.chk, chk, qs.chk,
rows.equal, inten.chk)
names(diagnostics) <- c("read.file", "row.num", "sample.names", "sample.match", "snp.chk", "chk",
"qs.chk",
"rows.equal", "inten.chk")
save(diagnostics, file=diagnostics.filename)
## non-affy - one line per snp
if (!affy.inten) {
##read in the file for one sample and keep columns of interest; skip to next file if there is a read error (using function "try")
if(scan.name.in.file==-1) {skip.num <- skip.num-1; head<-TRUE} else {head<-FALSE}
dat <- try(fread(files[i], header=head, sep=sep.type, skip=skip.num, colClasses=cc, data.table=FALSE))
if (inherits(dat, "try-error")) { read.file[i] <- 0; message(paste("error reading file",i)); next; k <- k+1 }
read.file[i] <- 1
## get sample name from column heading for Affy
if(scan.name.in.file==-1) {tmp.names <- names(dat)}
names(dat) <- df.names
##check and save row number
row.num[i] <- dim(dat)[1]
if(row.num[i]!=n) {rm(dat); next; k <- k+1} ## each file should have the same number of rows (one per snp)
## Sample names for Illumina
if(is.element("sample", names(dat))){
sample.names[[i]] <- unique(dat$sample)
if(length(sample.names[[i]])>1) {rm(dat);next; k <- k+1} ## there should only be one sample per file
if(sample.names[[i]]!=scan.annotation$scanName[i]) {sample.match[i] <- 0; rm(dat); next; k <- k+1} else {sample.match[i] <- 1}
## sample name inside file should match sample.name vector
}
## Sample names for Affy
if(scan.name.in.file==-1) {
tmp <- paste(scan.annotation$scanName[i], c("_Call", "_Confidence",".cel"),sep="")
if(!any(is.element(tmp, tmp.names))) {sample.match[i] <- 0; rm(dat); next} else {sample.match[i] <- 1}
} ## sample names embedded in file and column names should match
##check for duplicate snp names
if(any(duplicated(dat$snp))) {snp.chk[i] <- 0; rm(dat); next; k <- k+1}
##check that all expected snps are present
if(!setequal(dat$snp,snp.annotation$snpName)) {snp.chk[i] <- 0; rm(dat); next; k <- k+1} else snp.chk[i] <- 1
##Using the first raw data file to make it this far, put the int.ids in same order as in raw data
## (expecting all to be in this order)
dat <- dat[match(snp.annotation$snpName, dat$snp),]
## if(!exists("snp2")) {
## row.names(snp.annotation) <- snp.annotation$snpName
## snp2 <- snp.annotation[dat$snp, ]
## }
## check to be sure snp ids are in the same order in each file
## if(!all(snp2$snpName==dat$snp)) { rm(dat); snp.order[i] <- 0; next; k <- k+1} else {snp.order[i] <- 1}
## set non-finite values to missing
for (v in intersect(names(dat), intensity.vars)) dat[[v]][!is.finite(dat[[v]])]<- NA
## load quality score from ncdf and check
if(is.element("quality", names(dat))) {
qs <- getQuality(quantfile, start=c(1,i), count=c(n,1))
## qs <- qs[match(snp2$snpID, nc.snpid)]
if(!all(is.na(qs)==is.na(dat$quality))) {
rm(dat); rm(qs); qs.chk[i] <- 0; next
} else {
qs <- qs[!is.na(qs)]
dqs <- dat$quality[!is.na(dat$quality)]
dif <- abs(qs-dqs)
ratio <- dif/dqs
chkr <- ratio<rtol
chkd <- dif < tol
if(all(chkr | chkd)) {qs.chk[i] <- 1; rm(qs); rm(dqs)} else {rm(dat); rm(qs); rm(dqs); qs.chk[i] <- 0; next}
}
}
## For Affy, intensity files have two rows per snp
} else {
## Get the intensity data
## must use read.table instead of fread to filter comment lines with "#"
dat <- try(read.table(files[i], sep="\t", header=TRUE, colClasses=c("character","double")))
if (inherits(dat, "try-error")) { read.file[i] <- 0; message(paste("error reading intensity file",i)); next }
read.file[i] <- 1
## check sample names
tmp.names <- names(dat)
tmp <- paste(scan.annotation$scanName[i], c("_Call", "_Confidence",".cel"),sep="")
if(!any(is.element(tmp, tmp.names))) {sample.match[i] <- 0; rm(dat); next} else {sample.match[i] <- 1}
names(dat) <- c("sub.probe.id", "inten")
##check and save row number
row.num[i] <- dim(dat)[1]
if(row.num[i]!=(n*2)) {rm(dat); next; k <- k+1} ## each file should have the same number of rows (two per snp)
## rearrange to get one row per snp with X and Y for each row
dat$nchar <- nchar(dat$sub.probe.id)
dat$sub <- substr(dat$sub.probe.id, dat$nchar, dat$nchar)
dat$probe.id <- substr(dat$sub.probe.id, 1, dat$nchar-2)
dat.a <- dat[is.element(dat$sub,"A"),][,c("inten", "probe.id")]; names(dat.a) <- c("X", "probe.id")
dat.b <- dat[is.element(dat$sub,"B"),][,c("inten", "probe.id")]; names(dat.b) <- c("Y", "probe.id")
if(nrow(dat.a)!=nrow(dat.b) && any(dat.a$probe.id!=dat.b$probe.id)) { rows.equal[i] <- 0; rm(dat); next }
rows.equal[i] <- 1
dat <- cbind(dat.a,dat.b[,"Y"]); names(dat)[3] <- "Y"
rm(list=(c("dat.a","dat.b")))
## remove "AFFX snps, check for duplicate snp names, check that all expected snps are present
## dat <- dat[is.element(dat$probe.id,snp.annotation$snpName),]
dat <- dat[match(snp.annotation$snpName, dat$probe.id),]
if(nrow(dat)!=n) {snp.chk[i] <- 0; rm(dat); next }
if(any(duplicated(dat$probe.id))) {snp.chk[i] <- 0; rm(dat); next}
if(any(!is.element(snp.annotation$snpName,dat$probe.id))) {snp.chk[i] <- 0; rm(dat); next} else snp.chk[i] <- 1
}
## load other intensity variable(s) from ncdf and check against what's in the data file
if(m>0) {
for(j in 1:m) {
z <- getVariable(quantfile, varname=qvars[j], snp=c(1,n), scan=c(i,1))
## z <- z[match(snp2$snpID, nc.snpid)] ## put in the same order as dat
dz <- dat[,qvars[j]]
if(!all(is.na(z)==is.na(dz))) {
rm(dat); rm(z); inten.chk[[j]][i] <- 0; break
} else {
z <- z[!is.na(z)]
dz <- dz[!is.na(dz)]
dif <- abs(z-dz)
ratio <- dif/dz
chkr <- ratio<rtol
chkd <- dif < tol
if(all(chkr | chkd)) {
inten.chk[[j]][i] <- 1; rm(z); rm(dz)
} else {
rm(dat); rm(z); rm(dz); inten.chk[[j]][i] <- 0; break
}
}
}
}
chk[i] <- 1 ## made it this far
if (exists("dat")) rm(dat)
## to monitor progress
if(verbose & i%%10==0) {
rate <- (Sys.time()-start)/10
percent <- 100*i/nsx
message(paste("file", i, "-", format(percent,digits=3), "percent completed - rate =", format(rate,digits=4)))
start <- Sys.time()
}
} ## end of loop
close(quantfile)
diagnostics <- list(read.file, row.num, sample.names, sample.match, snp.chk, chk, qs.chk,
rows.equal, inten.chk)
names(diagnostics) <- c("read.file", "row.num", "sample.names", "sample.match", "snp.chk", "chk",
"qs.chk",
"rows.equal", "inten.chk")
save(diagnostics, file=diagnostics.filename)
return(diagnostics)
}
smgogarten/GWASTools documentation built on May 18, 2024, 1:19 a.m.
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Defines functions checkIntensityFile
Documented in checkIntensityFile
checkIntensityFile <- function(path=".",
filename,
file.type=c("gds", "ncdf"),
snp.annotation,
scan.annotation,
sep.type,
skip.num,
col.total,
col.nums,
scan.name.in.file,
check.scan.index,
n.scans.loaded,
affy.inten = FALSE,
diagnostics.filename = "checkIntensityFile.diagnostics.RData",
verbose = TRUE) {
## sx is vector of sample indices to check
## N is the number of samples loaded so far
if(!all(is.element(check.scan.index,1:n.scans.loaded))) stop("check.scan.index must be included in 1:n.scans.loaded")
### get file type
file.type <- match.arg(file.type)
if (file.type == "gds") {
quantfile <- GdsIntensityReader(filename)
} else if (file.type == "ncdf") {
quantfile <- NcdfIntensityReader(filename)
}
## get sample ids
quant.sampid <- getScanID(quantfile, index=1:n.scans.loaded)
## get snp ids from the netCDF file
nc.snpid <- getSnpID(quantfile)
## get sample info and file names
stopifnot(all(c("scanID", "scanName", "file") %in% names(scan.annotation)))
if(any(!is.element(quant.sampid, scan.annotation$scanID))) stop("some sample id(s) in data file not found in sample annotation dataframe")
scan.annotation <- scan.annotation[match(quant.sampid, scan.annotation$scanID),]
files <- file.path(path, scan.annotation$file)
## check col.nums vector
col.nums <- col.nums[!is.na(col.nums)]
intensity.vars <- c("quality", "X", "Y", "rawX", "rawY", "R", "Theta", "BAlleleFreq","LogRRatio")
if(!all(names(col.nums) %in% c("snp", "sample", "geno", "a1", "a2", intensity.vars))) stop("problem with col.nums vector names")
if(!is.integer(col.nums)) stop("col.nums vector class is not integer")
if(!("snp" %in% names(col.nums))) stop("snp id missing in col.nums")
if( max(col.nums) > col.total) stop("some element of col.nums is greater than total number of columns")
## compare with ncdf
varnames <- getVariableNames(quantfile)
chk.vars <- intersect(names(col.nums), intensity.vars)
if (length(chk.vars) > 0) {
if(!all(is.element(chk.vars, varnames))) stop("variables designated in col.nums are not all defined in the data file")
}
## check snp.annotation
stopifnot(all(c("snpID", "snpName") %in% names(snp.annotation)))
if(any(snp.annotation$snpID != sort(snp.annotation$snpID))) stop("snp annotation ids not in order")
if(any(snp.annotation$snpID != nc.snpid)) stop("snp annotation ids not the same as in data file")
n <- nrow(snp.annotation)
## generate colClasses vector for read.table
cc <- rep("NULL",col.total)
## don't need to check genotype variables
## cc[col.nums[names(col.nums) %in% c("snp","sample","geno","a1","a2")]] <- "character"
cc[col.nums[names(col.nums) %in% c("snp","sample")]] <- "character"
cc[col.nums[names(col.nums) %in% intensity.vars]] <- "double"
## generate names for the genotype data.frame
df.names <- names(sort(col.nums))
## set up objects to keep track of things for each file
## refresh diagnostics from when the ncdf was created
fn <- length(files)
read.file <- rep(NA, fn) ## keeps track of whether the file was readable or not
row.num <- rep(NA, fn) ## number of rows read
sample.names <- vector("list",fn) ## list of vectors of unique sample names in each file
sample.match <- rep(NA, fn) ## indicator whether sample name inside file matches sample names in sample annotation data.frame
## missg <- vector("list",fn) ## vector of character string(s) used for missing genotypes (i.e. not AA, AB or BB)
snp.chk <- rep(NA,fn)
chk <- rep(NA,fn) ## ## final check on data ready to load into ncdf
## ## new diagnostics
## snp.order <- rep(NA,fn)
## geno.chk <- rep(NA,fn)
qs.chk <- rep(NA,fn)
z <- rep(NA,fn)
inten.chk <- list(z,z,z,z,z,z,z,z)
names(inten.chk) <- intensity.vars[-1]
## diagnostics for Affy intensity files
read.file.inten <- rep(NA,fn)
sample.match.inten <- rep(NA,fn)
rows.equal <- rep(NA,fn)
snp.chk.inten <- rep(NA,fn)
## Set tolerance for intensity comparisons
tol <- 1e-4 ## - used for difference
rtol <- 1e-6 ## used for ratio of difference to raw data file value
## specify intensity variables to check
qvars <- c("X", "Y", "rawX", "rawY", "R", "Theta", "BAlleleFreq", "LogRRatio")
varin <- names(col.nums)
if(affy.inten) varin <- c(varin, "X","Y")
qvars <- qvars[is.element(qvars, varin)]
m <- length(qvars)
inten.chk <- inten.chk[is.element(names(inten.chk), qvars)]
nsx <- length(check.scan.index)
if (verbose) start <- Sys.time()
for(i in check.scan.index){
## save diagnostics at each step in case of crash
diagnostics <- list(read.file, row.num, sample.names, sample.match, snp.chk, chk, qs.chk,
rows.equal, inten.chk)
names(diagnostics) <- c("read.file", "row.num", "sample.names", "sample.match", "snp.chk", "chk",
"qs.chk",
"rows.equal", "inten.chk")
save(diagnostics, file=diagnostics.filename)
## non-affy - one line per snp
if (!affy.inten) {
##read in the file for one sample and keep columns of interest; skip to next file if there is a read error (using function "try")
if(scan.name.in.file==-1) {skip.num <- skip.num-1; head<-TRUE} else {head<-FALSE}
dat <- try(fread(files[i], header=head, sep=sep.type, skip=skip.num, colClasses=cc, data.table=FALSE))
if (inherits(dat, "try-error")) { read.file[i] <- 0; message(paste("error reading file",i)); next; k <- k+1 }
read.file[i] <- 1
## get sample name from column heading for Affy
if(scan.name.in.file==-1) {tmp.names <- names(dat)}
names(dat) <- df.names
##check and save row number
row.num[i] <- dim(dat)[1]
if(row.num[i]!=n) {rm(dat); next; k <- k+1} ## each file should have the same number of rows (one per snp)
## Sample names for Illumina
if(is.element("sample", names(dat))){
sample.names[[i]] <- unique(dat$sample)
if(length(sample.names[[i]])>1) {rm(dat);next; k <- k+1} ## there should only be one sample per file
if(sample.names[[i]]!=scan.annotation$scanName[i]) {sample.match[i] <- 0; rm(dat); next; k <- k+1} else {sample.match[i] <- 1}
## sample name inside file should match sample.name vector
}
## Sample names for Affy
if(scan.name.in.file==-1) {
tmp <- paste(scan.annotation$scanName[i], c("_Call", "_Confidence",".cel"),sep="")
if(!any(is.element(tmp, tmp.names))) {sample.match[i] <- 0; rm(dat); next} else {sample.match[i] <- 1}
} ## sample names embedded in file and column names should match
##check for duplicate snp names
if(any(duplicated(dat$snp))) {snp.chk[i] <- 0; rm(dat); next; k <- k+1}
##check that all expected snps are present
if(!setequal(dat$snp,snp.annotation$snpName)) {snp.chk[i] <- 0; rm(dat); next; k <- k+1} else snp.chk[i] <- 1
##Using the first raw data file to make it this far, put the int.ids in same order as in raw data
## (expecting all to be in this order)
dat <- dat[match(snp.annotation$snpName, dat$snp),]
## if(!exists("snp2")) {
## row.names(snp.annotation) <- snp.annotation$snpName
## snp2 <- snp.annotation[dat$snp, ]
## }
## check to be sure snp ids are in the same order in each file
## if(!all(snp2$snpName==dat$snp)) { rm(dat); snp.order[i] <- 0; next; k <- k+1} else {snp.order[i] <- 1}
## set non-finite values to missing
for (v in intersect(names(dat), intensity.vars)) dat[[v]][!is.finite(dat[[v]])]<- NA
## load quality score from ncdf and check
if(is.element("quality", names(dat))) {
qs <- getQuality(quantfile, start=c(1,i), count=c(n,1))
## qs <- qs[match(snp2$snpID, nc.snpid)]
if(!all(is.na(qs)==is.na(dat$quality))) {
rm(dat); rm(qs); qs.chk[i] <- 0; next
} else {
qs <- qs[!is.na(qs)]
dqs <- dat$quality[!is.na(dat$quality)]
dif <- abs(qs-dqs)
ratio <- dif/dqs
chkr <- ratio<rtol
chkd <- dif < tol
if(all(chkr | chkd)) {qs.chk[i] <- 1; rm(qs); rm(dqs)} else {rm(dat); rm(qs); rm(dqs); qs.chk[i] <- 0; next}
}
}
## For Affy, intensity files have two rows per snp
} else {
## Get the intensity data
## must use read.table instead of fread to filter comment lines with "#"
dat <- try(read.table(files[i], sep="\t", header=TRUE, colClasses=c("character","double")))
if (inherits(dat, "try-error")) { read.file[i] <- 0; message(paste("error reading intensity file",i)); next }
read.file[i] <- 1
## check sample names
tmp.names <- names(dat)
tmp <- paste(scan.annotation$scanName[i], c("_Call", "_Confidence",".cel"),sep="")
if(!any(is.element(tmp, tmp.names))) {sample.match[i] <- 0; rm(dat); next} else {sample.match[i] <- 1}
names(dat) <- c("sub.probe.id", "inten")
##check and save row number
row.num[i] <- dim(dat)[1]
if(row.num[i]!=(n*2)) {rm(dat); next; k <- k+1} ## each file should have the same number of rows (two per snp)
## rearrange to get one row per snp with X and Y for each row
dat$nchar <- nchar(dat$sub.probe.id)
dat$sub <- substr(dat$sub.probe.id, dat$nchar, dat$nchar)
dat$probe.id <- substr(dat$sub.probe.id, 1, dat$nchar-2)
dat.a <- dat[is.element(dat$sub,"A"),][,c("inten", "probe.id")]; names(dat.a) <- c("X", "probe.id")
dat.b <- dat[is.element(dat$sub,"B"),][,c("inten", "probe.id")]; names(dat.b) <- c("Y", "probe.id")
if(nrow(dat.a)!=nrow(dat.b) && any(dat.a$probe.id!=dat.b$probe.id)) { rows.equal[i] <- 0; rm(dat); next }
rows.equal[i] <- 1
dat <- cbind(dat.a,dat.b[,"Y"]); names(dat)[3] <- "Y"
rm(list=(c("dat.a","dat.b")))
## remove "AFFX snps, check for duplicate snp names, check that all expected snps are present
## dat <- dat[is.element(dat$probe.id,snp.annotation$snpName),]
dat <- dat[match(snp.annotation$snpName, dat$probe.id),]
if(nrow(dat)!=n) {snp.chk[i] <- 0; rm(dat); next }
if(any(duplicated(dat$probe.id))) {snp.chk[i] <- 0; rm(dat); next}
if(any(!is.element(snp.annotation$snpName,dat$probe.id))) {snp.chk[i] <- 0; rm(dat); next} else snp.chk[i] <- 1
}
## load other intensity variable(s) from ncdf and check against what's in the data file
if(m>0) {
for(j in 1:m) {
z <- getVariable(quantfile, varname=qvars[j], snp=c(1,n), scan=c(i,1))
## z <- z[match(snp2$snpID, nc.snpid)] ## put in the same order as dat
dz <- dat[,qvars[j]]
if(!all(is.na(z)==is.na(dz))) {
rm(dat); rm(z); inten.chk[[j]][i] <- 0; break
} else {
z <- z[!is.na(z)]
dz <- dz[!is.na(dz)]
dif <- abs(z-dz)
ratio <- dif/dz
chkr <- ratio<rtol
chkd <- dif < tol
if(all(chkr | chkd)) {
inten.chk[[j]][i] <- 1; rm(z); rm(dz)
} else {
rm(dat); rm(z); rm(dz); inten.chk[[j]][i] <- 0; break
}
}
}
}
chk[i] <- 1 ## made it this far
if (exists("dat")) rm(dat)
## to monitor progress
if(verbose & i%%10==0) {
rate <- (Sys.time()-start)/10
percent <- 100*i/nsx
message(paste("file", i, "-", format(percent,digits=3), "percent completed - rate =", format(rate,digits=4)))
start <- Sys.time()
}
} ## end of loop
close(quantfile)
diagnostics <- list(read.file, row.num, sample.names, sample.match, snp.chk, chk, qs.chk,
rows.equal, inten.chk)
names(diagnostics) <- c("read.file", "row.num", "sample.names", "sample.match", "snp.chk", "chk",
"qs.chk",
"rows.equal", "inten.chk")
save(diagnostics, file=diagnostics.filename)
return(diagnostics)
}
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