R/StdMatchR.R
In Beirnaert/MetaboMeeseeks: Helper functions for metabolomics analysis
#' Standards Locater
#'
#' This function searches for XCMS objects (peaks, groups) that match with the provided standards (optionally with isotopic profiles).
#'
#'
#' @param Standards.mz A vector (single standards) or matrix (isotopic profiles) with the standards.
#' @param Standards.RT A vector with the RT values
#' @param Data This can be either a) a matrix with 2 or 3* columns (mz, RT, Intensity*) without missing values, b) an xcmsSet object post grouping (the groups are features, intensity ratio's can be checked) or c) an XCMS@groups matrix object (if only this is supplied there will be no intensity ratio check). Note that an XCMS@peaks object is also supported but the function only returns 1 match per standard (the closest one, or the first of a number of equally close ones)
#' @param Standards.ratios Optional matrix. Matrix with the intensity ratio's
#' @param max.ppm maximal ppm distance
#' @param max.mz maximal mz distance (not necessary if ppm supplied)
#' @param max.RT.narrow preferred maximal RT distance
#' @param max.RT.wide extended RT search area. A peak is only accepted here when the improvement in ppm difference is at least accept.ppm.diff
#' @param accept.ppm.diff The improvement in ppm difference a peak in the wider RT area has to have for it to be accepted
#'
#' @return
#' A list with 3 vector/matrix entries (the same size as the supplied vector/matrix with standards).
#' The first are the matched peaks/groups. The second the ppm differences. The third the RT differences.
#'
#'
#' @author Charlie Beirnaert, \email{charlie.beirnaert@@uantwerpen.be}
#'
#'
#' @export
StdMatchR = function(Standards.mz, Standards.RT, Data, Standards.ratios = NULL, max.ppm = NULL, max.mz = NULL, max.RT.narrow = NULL, max.RT.wide = NULL, accept.ppm.diff = NULL){
ratioCheck = FALSE
if( !is.null(Standards.ratios) ){
if( ! "matrix" %in% class(Standards.ratios) ){
stop("Standards.ratios is not a matrix. Should be of format (Standard X Intensitie ratio)")
}
if(!"xcmsSet" %in% class(Data) ){
if(ncol(Data) != 3){
stop("Data is not an xcmsSet object, nor is it a matrix with 3 columns (mz, RT, Intensity). One of these is necassary for the ratio check")
}
}
if( !all( dim(Standards.ratios) == dim(Standards.mz)) ) {
stop("The dimensions of Standards.ratios is not equal to those of Standards.mz")
}
ratioCheck = TRUE
}
if("matrix" %in% class(Standards.mz)){
n.stds = dim(Standards.mz)[1]
n.isotopes = dim(Standards.mz)[2]
} else if("numeric" %in% class(Standards.mz)){
n.stds = length(Standards.mz)
n.isotopes = 1
Standards.mz = matrix(Standards.mz, nrow = n.stds, ncol = n.isotopes)
}
xcmsRatio = FALSE
if("xcmsSet" %in% class(Data)){
xcmsData = Data
Data = xcmsData@groups[,c(1,4)]
xcmsRatio = TRUE
} else if(ncol(Data) > 3){
Data = Data[,c(1,4)]
ratioCheck = FALSE
} else if(ncol(Data) == 2){
ratioCheck = FALSE
}
if( is.null(max.ppm) & is.null(max.mz) ){
warning("No maximal values submitted for the mz or ppm difference. max.ppm set to 50 and max.mz to 0.2")
max.ppm = 50
max.mz = 0.2
}
if(is.null(max.mz)){
max.mz = max(Data[,1])*max.ppm/(10^6) + 0.1
}
if(is.null(max.ppm)){
max.ppm = 10^6 * max.mz/min(Data[,1])
}
if(is.null(max.RT.narrow)){
max.RT.narrow = 300 # 5 minutes
}
if(is.null(max.RT.wide)){
max.RT.wide = max.RT.narrow
}
if(is.null(accept.ppm.diff)){
accept.ppm.diff = 5
}
standards_groups = matrix(NA, nrow = n.stds, ncol = n.isotopes)
standards_groups.onlynarrow = matrix(NA, nrow = n.stds, ncol = n.isotopes)
standards_groups.ppmDiff = matrix(NA, nrow = n.stds, ncol = n.isotopes)
standards_groups.RTDiff = matrix(NA, nrow = n.stds, ncol = n.isotopes)
for(mt in 1:n.stds){
for(is in 1:n.isotopes){
if( !is.na(Standards.mz[mt,is])){
mz.match = abs(Data[,1] - Standards.mz[mt,is])
ppm.match = 10^6 * abs(Data[,1] - Standards.mz[mt,is])/Standards.mz[mt,is]
RT.match = abs(Data[,2] - Standards.RT[mt])
matched.mz = mz.match < max.mz
matched.ppm = ppm.match < max.ppm
matched.RT.narrow = RT.match < max.RT.narrow
matched.RT.wide = RT.match < max.RT.wide
if(sum(matched.mz & matched.ppm & matched.RT.narrow ) > 0){
ranking.ppmfirst.narrow = order(ppm.match[matched.mz & matched.ppm & matched.RT.narrow], RT.match[matched.mz & matched.ppm & matched.RT.narrow], decreasing = FALSE)[1]
ranking.ppmfirst.wide = order(ppm.match[matched.mz & matched.ppm & matched.RT.wide], RT.match[matched.mz & matched.ppm & matched.RT.wide], decreasing = FALSE)[1]
top.peakindex.narrow = which( matched.mz & matched.ppm & matched.RT.narrow >0)[ranking.ppmfirst.narrow]
top.peakindex.wide = which( matched.mz & matched.ppm & matched.RT.wide >0)[ranking.ppmfirst.wide]
standards_groups.onlynarrow[mt,is] = top.peakindex.narrow
if( ppm.match[top.peakindex.wide] < (ppm.match[top.peakindex.narrow] - accept.ppm.diff) ){
standards_groups[mt,is] = top.peakindex.wide
standards_groups.ppmDiff[mt,is] = ppm.match[top.peakindex.wide]
standards_groups.RTDiff[mt,is] = RT.match[top.peakindex.wide]
} else{
standards_groups[mt,is] = top.peakindex.narrow
standards_groups.ppmDiff[mt,is] = ppm.match[top.peakindex.narrow]
standards_groups.RTDiff[mt,is] = RT.match[top.peakindex.narrow]
}
#if(length(closest) > 1){
# closets.
#}
#standards_groups[mt,is] = which( sum(matched.mz & matched.ppm & matched.RT) >0)[ranking.ppmfirst]
} else if(sum(matched.mz & matched.ppm & matched.RT.wide ) > 0){
ranking.ppmfirst.wide = order(ppm.match[matched.mz & matched.ppm & matched.RT.wide], RT.match[matched.mz & matched.ppm & matched.RT.wide], decreasing = FALSE)[1]
top.peakindex.wide = which( matched.mz & matched.ppm & matched.RT.wide >0)[ranking.ppmfirst.wide]
standards_groups[mt,is] = top.peakindex.wide
standards_groups.ppmDiff[mt,is] = ppm.match[top.peakindex.wide]
standards_groups.RTDiff[mt,is] = RT.match[top.peakindex.wide]
}
}
}
}
if(ratioCheck){
if(!xcmsRatio){
for(mt2 in 1:n.stds){
present = !is.na(standards_groups[mt2,])
present.intensities = Data[standards_groups[mt2,present],3]
present.ratios = Standards.ratios[mt2,present]
passed.lower = rep(TRUE, length(present.intensities))
for(jj in 2:length(present.intensities)){
if(present.intensities[jj] >= min(present.intensities[1:(jj-1)]) ){
passed.lower[jj] = FALSE
}
}
standards_groups[mt2,present][!passed.lower] = NA
standards_groups.ppmDiff[mt2,present][!passed.lower] = NA
standards_groups.RTDiff[mt2,present][!passed.lower] = NA
}
} else if (xcmsRatio){
plot.output = list()
for(mt2 in 1:n.stds){
# here we calculate the ratio based on 1 sample, appearing in all features.
present = !is.na(standards_groups[mt2,])
present.index = standards_groups[mt2,present]
present.ratios = Standards.ratios[mt2,present]
samples.in.feature = list()
for(smp in 1:length(present.index)){
sample.peakdata = data.frame(xcmsData@peaks[xcmsData@groupidx[[present.index[smp] ]], ])
samples.in.feature[[smp]] = sample.peakdata$sample[!is.na(sample.peakdata$intb)]
}
possible.samples = Reduce(intersect, samples.in.feature)
if(length(possible.samples) == 0){
possible.samples.sub = rep(NA,(length(present.index)-1))
for(red in 1:(length(present.index)-1) )
possible.samples.sub[red] = length(Reduce(intersect, samples.in.feature[1:(length(present.index) - red)]))
to.reduce = which(possible.samples.sub>0)
standards_groups[mt2,present][(length(present.index) - to.reduce + 1):length(present.index)] = NA
standards_groups.ppmDiff[mt2,present][(length(present.index) - to.reduce + 1):length(present.index)] = NA
standards_groups.RTDiff[mt2,present][(length(present.index) - to.reduce + 1):length(present.index)] = NA
present = !is.na(standards_groups[mt2,])
present.index = standards_groups[mt2,present]
present.ratios = Standards.ratios[mt2,present]
samples.in.feature = list()
for(smp in 1:length(present.index)){
sample.peakdata = data.frame(xcmsData@peaks[xcmsData@groupidx[[present.index[smp] ]], ])
samples.in.feature[[smp]] = sample.peakdata$sample[!is.na(sample.peakdata$intb)]
}
}
if(length(possible.samples)!=0){
possible.samples.peakdata = data.frame(xcmsData@peaks[xcmsData@groupidx[[present.index[1] ]], ])
possible.samples.peakdata = possible.samples.peakdata[possible.samples.peakdata$sample %in% possible.samples,c("into","sample")]
possible.samples.order = order(possible.samples.peakdata$into, decreasing = TRUE)
possible.samples.order = possible.samples.order[!duplicated(possible.samples.peakdata$sample[possible.samples.order])]
start = 1
stop = 0
all.ratio.int = matrix( NA, ncol = 4, nrow = length(possible.samples)*length(present.index))
for(jj in 1:length(possible.samples.order)){
chosen.sample = possible.samples.peakdata$sample[possible.samples.order[jj]]
present.intensities = rep(NA, length(present.index))
for(smp in 1:length(present.index)){
sample.peakdata = data.frame(xcmsData@peaks[xcmsData@groupidx[[present.index[smp] ]], ])
present.intensities[smp] = max(sample.peakdata$into[sample.peakdata$sample == chosen.sample])[1]
}
passed.lower = rep(TRUE, length(present.intensities))
if(length(present.intensities) > 1){
for(jj in 2:length(present.intensities)){
if(present.intensities[jj] >= min(present.intensities[1:(jj-1)]) ){
passed.lower[jj] = FALSE
}
}
}
standards_groups[mt2,present][!passed.lower] = NA
standards_groups.ppmDiff[mt2,present][!passed.lower] = NA
standards_groups.RTDiff[mt2,present][!passed.lower] = NA
stop=stop+length(present.index)
all.ratio.int[start:stop,1] = present.ratios
all.ratio.int[start:stop,2] = present.intensities
all.ratio.int[start:stop,3] = chosen.sample
all.ratio.int[start:stop,4] = as.numeric(passed.lower)
start = stop + 1
}
all.ratio.int = data.frame(all.ratio.int)
colnames(all.ratio.int) = c("ratio", "intensity", "sample", "IR.check")
all.ratio.int$sample = as.factor(all.ratio.int$sample)
plot.output[[mt2]] <- ggplot() +
geom_line(data = subset(all.ratio.int, ~IR.check ==1),
aes(x = ~ratio, y = ~intensity, colour = ~sample)) +
geom_point(data = subset(all.ratio.int, ~IR.check ==1),
aes(x = ~ratio, y = ~intensity, colour = ~sample))+
geom_point(data = subset(all.ratio.int, ~IR.check ==0),
aes(x = ~ratio, y = ~intensity, colour = ~sample), shape = 4)+
scale_x_reverse()+
theme_bw() +
ggtitle(paste("Standard ",as.character(mt2))) +
theme(plot.title = element_text(hjust = 0.5, face = "bold"))
}
}
}
}
#for(mt in 1:n.stds){
# first.na = which(is.na(standards_groups[mt,]))[1]
# standards_groups[mt,first.na:n.isotopes] = NA
#}
if(xcmsRatio & ratioCheck){
Results = list(Matched = standards_groups, ppm.diffs = standards_groups.ppmDiff, RT.diffs = standards_groups.RTDiff, Plots = plot.output)
} else{
Results = list(Matched = standards_groups, ppm.diffs = standards_groups.ppmDiff, RT.diffs = standards_groups.RTDiff)
}
return(Results)
}
Beirnaert/MetaboMeeseeks documentation built on May 20, 2019, 11:09 a.m.
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#' Standards Locater
#'
#' This function searches for XCMS objects (peaks, groups) that match with the provided standards (optionally with isotopic profiles).
#'
#'
#' @param Standards.mz A vector (single standards) or matrix (isotopic profiles) with the standards.
#' @param Standards.RT A vector with the RT values
#' @param Data This can be either a) a matrix with 2 or 3* columns (mz, RT, Intensity*) without missing values, b) an xcmsSet object post grouping (the groups are features, intensity ratio's can be checked) or c) an XCMS@groups matrix object (if only this is supplied there will be no intensity ratio check). Note that an XCMS@peaks object is also supported but the function only returns 1 match per standard (the closest one, or the first of a number of equally close ones)
#' @param Standards.ratios Optional matrix. Matrix with the intensity ratio's
#' @param max.ppm maximal ppm distance
#' @param max.mz maximal mz distance (not necessary if ppm supplied)
#' @param max.RT.narrow preferred maximal RT distance
#' @param max.RT.wide extended RT search area. A peak is only accepted here when the improvement in ppm difference is at least accept.ppm.diff
#' @param accept.ppm.diff The improvement in ppm difference a peak in the wider RT area has to have for it to be accepted
#'
#' @return
#' A list with 3 vector/matrix entries (the same size as the supplied vector/matrix with standards).
#' The first are the matched peaks/groups. The second the ppm differences. The third the RT differences.
#'
#'
#' @author Charlie Beirnaert, \email{charlie.beirnaert@@uantwerpen.be}
#'
#'
#' @export
StdMatchR = function(Standards.mz, Standards.RT, Data, Standards.ratios = NULL, max.ppm = NULL, max.mz = NULL, max.RT.narrow = NULL, max.RT.wide = NULL, accept.ppm.diff = NULL){
ratioCheck = FALSE
if( !is.null(Standards.ratios) ){
if( ! "matrix" %in% class(Standards.ratios) ){
stop("Standards.ratios is not a matrix. Should be of format (Standard X Intensitie ratio)")
}
if(!"xcmsSet" %in% class(Data) ){
if(ncol(Data) != 3){
stop("Data is not an xcmsSet object, nor is it a matrix with 3 columns (mz, RT, Intensity). One of these is necassary for the ratio check")
}
}
if( !all( dim(Standards.ratios) == dim(Standards.mz)) ) {
stop("The dimensions of Standards.ratios is not equal to those of Standards.mz")
}
ratioCheck = TRUE
}
if("matrix" %in% class(Standards.mz)){
n.stds = dim(Standards.mz)[1]
n.isotopes = dim(Standards.mz)[2]
} else if("numeric" %in% class(Standards.mz)){
n.stds = length(Standards.mz)
n.isotopes = 1
Standards.mz = matrix(Standards.mz, nrow = n.stds, ncol = n.isotopes)
}
xcmsRatio = FALSE
if("xcmsSet" %in% class(Data)){
xcmsData = Data
Data = xcmsData@groups[,c(1,4)]
xcmsRatio = TRUE
} else if(ncol(Data) > 3){
Data = Data[,c(1,4)]
ratioCheck = FALSE
} else if(ncol(Data) == 2){
ratioCheck = FALSE
}
if( is.null(max.ppm) & is.null(max.mz) ){
warning("No maximal values submitted for the mz or ppm difference. max.ppm set to 50 and max.mz to 0.2")
max.ppm = 50
max.mz = 0.2
}
if(is.null(max.mz)){
max.mz = max(Data[,1])*max.ppm/(10^6) + 0.1
}
if(is.null(max.ppm)){
max.ppm = 10^6 * max.mz/min(Data[,1])
}
if(is.null(max.RT.narrow)){
max.RT.narrow = 300 # 5 minutes
}
if(is.null(max.RT.wide)){
max.RT.wide = max.RT.narrow
}
if(is.null(accept.ppm.diff)){
accept.ppm.diff = 5
}
standards_groups = matrix(NA, nrow = n.stds, ncol = n.isotopes)
standards_groups.onlynarrow = matrix(NA, nrow = n.stds, ncol = n.isotopes)
standards_groups.ppmDiff = matrix(NA, nrow = n.stds, ncol = n.isotopes)
standards_groups.RTDiff = matrix(NA, nrow = n.stds, ncol = n.isotopes)
for(mt in 1:n.stds){
for(is in 1:n.isotopes){
if( !is.na(Standards.mz[mt,is])){
mz.match = abs(Data[,1] - Standards.mz[mt,is])
ppm.match = 10^6 * abs(Data[,1] - Standards.mz[mt,is])/Standards.mz[mt,is]
RT.match = abs(Data[,2] - Standards.RT[mt])
matched.mz = mz.match < max.mz
matched.ppm = ppm.match < max.ppm
matched.RT.narrow = RT.match < max.RT.narrow
matched.RT.wide = RT.match < max.RT.wide
if(sum(matched.mz & matched.ppm & matched.RT.narrow ) > 0){
ranking.ppmfirst.narrow = order(ppm.match[matched.mz & matched.ppm & matched.RT.narrow], RT.match[matched.mz & matched.ppm & matched.RT.narrow], decreasing = FALSE)[1]
ranking.ppmfirst.wide = order(ppm.match[matched.mz & matched.ppm & matched.RT.wide], RT.match[matched.mz & matched.ppm & matched.RT.wide], decreasing = FALSE)[1]
top.peakindex.narrow = which( matched.mz & matched.ppm & matched.RT.narrow >0)[ranking.ppmfirst.narrow]
top.peakindex.wide = which( matched.mz & matched.ppm & matched.RT.wide >0)[ranking.ppmfirst.wide]
standards_groups.onlynarrow[mt,is] = top.peakindex.narrow
if( ppm.match[top.peakindex.wide] < (ppm.match[top.peakindex.narrow] - accept.ppm.diff) ){
standards_groups[mt,is] = top.peakindex.wide
standards_groups.ppmDiff[mt,is] = ppm.match[top.peakindex.wide]
standards_groups.RTDiff[mt,is] = RT.match[top.peakindex.wide]
} else{
standards_groups[mt,is] = top.peakindex.narrow
standards_groups.ppmDiff[mt,is] = ppm.match[top.peakindex.narrow]
standards_groups.RTDiff[mt,is] = RT.match[top.peakindex.narrow]
}
#if(length(closest) > 1){
# closets.
#}
#standards_groups[mt,is] = which( sum(matched.mz & matched.ppm & matched.RT) >0)[ranking.ppmfirst]
} else if(sum(matched.mz & matched.ppm & matched.RT.wide ) > 0){
ranking.ppmfirst.wide = order(ppm.match[matched.mz & matched.ppm & matched.RT.wide], RT.match[matched.mz & matched.ppm & matched.RT.wide], decreasing = FALSE)[1]
top.peakindex.wide = which( matched.mz & matched.ppm & matched.RT.wide >0)[ranking.ppmfirst.wide]
standards_groups[mt,is] = top.peakindex.wide
standards_groups.ppmDiff[mt,is] = ppm.match[top.peakindex.wide]
standards_groups.RTDiff[mt,is] = RT.match[top.peakindex.wide]
}
}
}
}
if(ratioCheck){
if(!xcmsRatio){
for(mt2 in 1:n.stds){
present = !is.na(standards_groups[mt2,])
present.intensities = Data[standards_groups[mt2,present],3]
present.ratios = Standards.ratios[mt2,present]
passed.lower = rep(TRUE, length(present.intensities))
for(jj in 2:length(present.intensities)){
if(present.intensities[jj] >= min(present.intensities[1:(jj-1)]) ){
passed.lower[jj] = FALSE
}
}
standards_groups[mt2,present][!passed.lower] = NA
standards_groups.ppmDiff[mt2,present][!passed.lower] = NA
standards_groups.RTDiff[mt2,present][!passed.lower] = NA
}
} else if (xcmsRatio){
plot.output = list()
for(mt2 in 1:n.stds){
# here we calculate the ratio based on 1 sample, appearing in all features.
present = !is.na(standards_groups[mt2,])
present.index = standards_groups[mt2,present]
present.ratios = Standards.ratios[mt2,present]
samples.in.feature = list()
for(smp in 1:length(present.index)){
sample.peakdata = data.frame(xcmsData@peaks[xcmsData@groupidx[[present.index[smp] ]], ])
samples.in.feature[[smp]] = sample.peakdata$sample[!is.na(sample.peakdata$intb)]
}
possible.samples = Reduce(intersect, samples.in.feature)
if(length(possible.samples) == 0){
possible.samples.sub = rep(NA,(length(present.index)-1))
for(red in 1:(length(present.index)-1) )
possible.samples.sub[red] = length(Reduce(intersect, samples.in.feature[1:(length(present.index) - red)]))
to.reduce = which(possible.samples.sub>0)
standards_groups[mt2,present][(length(present.index) - to.reduce + 1):length(present.index)] = NA
standards_groups.ppmDiff[mt2,present][(length(present.index) - to.reduce + 1):length(present.index)] = NA
standards_groups.RTDiff[mt2,present][(length(present.index) - to.reduce + 1):length(present.index)] = NA
present = !is.na(standards_groups[mt2,])
present.index = standards_groups[mt2,present]
present.ratios = Standards.ratios[mt2,present]
samples.in.feature = list()
for(smp in 1:length(present.index)){
sample.peakdata = data.frame(xcmsData@peaks[xcmsData@groupidx[[present.index[smp] ]], ])
samples.in.feature[[smp]] = sample.peakdata$sample[!is.na(sample.peakdata$intb)]
}
}
if(length(possible.samples)!=0){
possible.samples.peakdata = data.frame(xcmsData@peaks[xcmsData@groupidx[[present.index[1] ]], ])
possible.samples.peakdata = possible.samples.peakdata[possible.samples.peakdata$sample %in% possible.samples,c("into","sample")]
possible.samples.order = order(possible.samples.peakdata$into, decreasing = TRUE)
possible.samples.order = possible.samples.order[!duplicated(possible.samples.peakdata$sample[possible.samples.order])]
start = 1
stop = 0
all.ratio.int = matrix( NA, ncol = 4, nrow = length(possible.samples)*length(present.index))
for(jj in 1:length(possible.samples.order)){
chosen.sample = possible.samples.peakdata$sample[possible.samples.order[jj]]
present.intensities = rep(NA, length(present.index))
for(smp in 1:length(present.index)){
sample.peakdata = data.frame(xcmsData@peaks[xcmsData@groupidx[[present.index[smp] ]], ])
present.intensities[smp] = max(sample.peakdata$into[sample.peakdata$sample == chosen.sample])[1]
}
passed.lower = rep(TRUE, length(present.intensities))
if(length(present.intensities) > 1){
for(jj in 2:length(present.intensities)){
if(present.intensities[jj] >= min(present.intensities[1:(jj-1)]) ){
passed.lower[jj] = FALSE
}
}
}
standards_groups[mt2,present][!passed.lower] = NA
standards_groups.ppmDiff[mt2,present][!passed.lower] = NA
standards_groups.RTDiff[mt2,present][!passed.lower] = NA
stop=stop+length(present.index)
all.ratio.int[start:stop,1] = present.ratios
all.ratio.int[start:stop,2] = present.intensities
all.ratio.int[start:stop,3] = chosen.sample
all.ratio.int[start:stop,4] = as.numeric(passed.lower)
start = stop + 1
}
all.ratio.int = data.frame(all.ratio.int)
colnames(all.ratio.int) = c("ratio", "intensity", "sample", "IR.check")
all.ratio.int$sample = as.factor(all.ratio.int$sample)
plot.output[[mt2]] <- ggplot() +
geom_line(data = subset(all.ratio.int, ~IR.check ==1),
aes(x = ~ratio, y = ~intensity, colour = ~sample)) +
geom_point(data = subset(all.ratio.int, ~IR.check ==1),
aes(x = ~ratio, y = ~intensity, colour = ~sample))+
geom_point(data = subset(all.ratio.int, ~IR.check ==0),
aes(x = ~ratio, y = ~intensity, colour = ~sample), shape = 4)+
scale_x_reverse()+
theme_bw() +
ggtitle(paste("Standard ",as.character(mt2))) +
theme(plot.title = element_text(hjust = 0.5, face = "bold"))
}
}
}
}
#for(mt in 1:n.stds){
# first.na = which(is.na(standards_groups[mt,]))[1]
# standards_groups[mt,first.na:n.isotopes] = NA
#}
if(xcmsRatio & ratioCheck){
Results = list(Matched = standards_groups, ppm.diffs = standards_groups.ppmDiff, RT.diffs = standards_groups.RTDiff, Plots = plot.output)
} else{
Results = list(Matched = standards_groups, ppm.diffs = standards_groups.ppmDiff, RT.diffs = standards_groups.RTDiff)
}
return(Results)
}
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