R/apLCMS.align.R
In BowenNCSU/xMSanalyzer: xMSanalyzer: R package for data analysis, comparison, and annotation of metabolomics data.
#' apLCMS.align
#'
#' Call apLCMS function, cdf.to.ftr, at different parameter settings
#'
#' This utility calls the cdf.to.ftr() function in the apLCMS package and
#' performs serial sample processing at multiple combinations of two
#' parameters: min.run (minimum length of elution time for a series of signals
#' grouped by m/z to be considered a feature; default value: 3) and min.pres
#' (minimum proportion of scans in which the signal was present; default
#' values: 0.3, 0.8). The function allows the user to define parameters such as
#' min.exp (minimum number of samples in which a feature is present). This
#' differs from the original apLCMS in that the original only allows one set of
#' parameters, whereas this function allows multiple sets. The resulting tables
#' containing m/z, retention time, and peak intensities in each sample are
#' stored at each parameter combination.
#'
#' @param cdfloc The folder where all NetCDF/mzML/mzXML/mzData files to be
#' processed are located. For example "C:/experiment1/cdf/"
#' @param apLCMS.outloc The folder where alignment output will be written. For
#' example "C:/experiment1/apLCMSoutput/"
#' @param min.run.list List of values for min.run parameter, eg: c(3,6) would
#' run the cdf.to.ftr function at min.run=3 and min.run=6
#' @param min.pres.list List of values min.pres, eg: c(0.3,0.8) would run the
#' cdf.to.ftr function at min.run=3 and min.run=6
#' @param minexp If a feature is to be included in the final feature table, it
#' must be present in at least this number of spectra, eg:2
#' @param mztol The user can provide the m/z tolerance level for peak
#' identification to override the programs selection of the tolerance level.
#' This value is expressed as the percentage of the m/z value. This value,
#' multiplied by the m/z value, becomes the cutoff level. Please see the help
#' for proc.cdf() for details.
#' @param alignmztol The user can provide the m/z tolerance level for peak
#' alignment to override the programs selection. This value is expressed as the
#' percentage of the m/z value. This value, multiplied by the m/z value,
#' becomes the cutoff level.Please see the help for feature.align() for
#' details.
#' @param alignchrtol The retention time tolerance level for peak alignment.
#' The default is NA, which allows the program to search for the tolerance
#' level based on the data. Default: 10
#' @param numnodes Number of nodes to use for processing.
#' @param run.order.file Name of a tab-delimited file that includes sample
#' names sorted by the order in which they were run (sample names must match
#' the CDF file names)
#' @param subs If not all the CDF files in the folder are to be processed, the
#' user can define a subset using this parameter. For example, subs=15:30, or
#' subs=c(2,4,6,8)
#' @param filepattern File format of processed data files. eg: ".cdf", ".mzXML"
#' @param apLCMSmode "untargeted" or "hybrid"; Default: "untargeted"
#' @param known_table A data frame containing the known metabolite ions and
#' previously found features. Please see documentation of semi.sup() function
#' in apLCMS for more details.
#' @param match_tol_ppm The ppm tolerance to match identified features to known
#' metabolites/features. Used by the semi.sup() function in apLCMS. Default: 5
#' @return Feature table after weak signal recovery. This is the end product of
#' the function cdf.to.ftr.
#' @author Karan Uppal <kuppal2@@emory.edu>
#' @references http://www.sph.emory.edu/apLCMS/
#' @keywords ~apLCMS
apLCMS.align <- function(cdfloc, apLCMS.outloc, min.run.list = c(3,
3), min.pres.list = c(0.3, 0.8), minexp = 2, mztol = 1e-05,
alignmztol = NA, alignchrtol = NA, numnodes = 2,
run.order.file = NA, subs = NA, filepattern = ".cdf",
apLCMSmode = "untargeted", known_table, match_tol_ppm = 5,
ref.mz.list = NA, refMZ.mz.diff = 10, refMZ.time.diff = NA,
target.mz.list = NA) {
setwd(cdfloc)
cdf.files = list.files(cdfloc, filepattern, ignore.case = TRUE)
cdf.files = tolower(cdf.files)
dir.create(apLCMS.outloc, showWarnings = FALSE)
aligned_data_list = new("list")
pcount = 1
if (is.na(subs[1]) == FALSE) {
numsamp = length(subs)
cdf.files = cdf.files[subs]
} else {
numsamp = length(cdf.files)
}
if (length(min.run.list) != length(min.pres.list)) {
stop("Vectors min.run.list and min.pres.list should be of the same length. eg: min.run.list=c(3,3) and min.pres.list=c(0.3,0.8)")
}
for (r in 1:length(min.run.list)) {
runval = min.run.list[r]
p = r
# for(p in 1:length(min.pres.list))
{
features <- new("list")
presval = min.pres.list[p]
par(mfrow = c(2, 2))
fname <- paste("Rplots", runval, presval,
".pdf", sep = "")
pdf(fname)
print(minexp)
if (apLCMSmode == "untargeted") {
aligned <- cdf.to.ftr(cdfloc, subs = subs,
min.exp = minexp, min.run = runval,
min.pres = presval, mz.tol = mztol,
align.mz.tol = alignmztol, align.chr.tol = alignchrtol,
n.nodes = numnodes, file.pattern = filepattern)
} else {
if (apLCMSmode == "hybrid") {
aligned <- semi.sup(folder = cdfloc,
known.table = known_table, match.tol.ppm = match_tol_ppm,
subs = subs, min.exp = minexp,
min.run = runval, min.pres = presval,
mz.tol = mztol, align.mz.tol = alignmztol,
align.chr.tol = alignchrtol, n.nodes = numnodes,
file.pattern = filepattern)
}
}
dev.off()
finalfeatmat = aligned$final.ftrs
fname <- paste(apLCMS.outloc, "/apLCMS_aligned",
"_pres", presval, "_run", runval,
"_", minexp, "exppostrecovery.Rda",
sep = "")
save(aligned, file = fname)
if (is.na(target.mz.list) == FALSE) {
eic_fname <- paste(apLCMS.outloc,
"/EICrun", runval, "pres", presval,
".pdf", sep = "")
pdf(eic_fname)
if (is.na(target.mz.list[1, 1]) ==
FALSE) {
stddata <- target.mz.list
# print(head(stddata))
} else {
Name <- paste("mz", seq(1, dim(finalfeatmat)[1]),
sep = "")
stddata <- cbind(finalfeatmat[,
c(1)], Name)
}
overlapres5ppm <- getVenn(dataA = finalfeatmat,
name_a = paste("Expdata", sep = ""),
dataB = stddata, name_b = "Target",
mz.thresh = 10, time.thresh = NA,
alignment.tool = "apLCMS", xMSanalyzer.outloc = apLCMS.outloc,
plotvenn = FALSE)
if (length(unique(overlapres5ppm$common$index.A)) >
0) {
setwd(cdfloc)
num_samples <- dim(finalfeatmat)[2] -
4
min_samp <- 6
if (num_samples < min_samp) {
min_samp <- num_samples
}
rand_sample_set <- sample(size = num_samples,
x = num_samples, replace = FALSE)
rand_set <- c(1:min_samp, (num_samples -
2):num_samples)
com_ind <- which(rand_sample_set %in%
rand_set)
if (length(com_ind) > 0) {
rand_sample_set <- rand_sample_set[-c(com_ind)]
rand_sample_set <- rand_sample_set[1:min_samp]
rand_set <- c(rand_set, rand_sample_set)
} else {
rand_set <- c(rand_set, rand_sample_set[1:min_samp])
rand_set <- rand_set[order(rand_set)]
}
rand_set <- na.omit(rand_set)
print(rand_set)
# EIC.plot(aligned,rows=c(unique(overlapres5ppm$common$index.A)),min.run=runval,min.pres=presval)
# apLCMS.EIC.plot(aligned, rows =
# c(unique(overlapres5ppm$common$index.A)), colors
# = NA, transform = 'none', subset = rand_set,
# minrt=NA, maxrt=NA,
# min.run=runval,min.pres=presval,
# max.spline.time.points = 1000) rand_set<-c(1:6)
overlap_res <- overlapres5ppm$common
overlap_res <- as.data.frame(overlap_res)
dup_mz_ind <- which(duplicated(overlapres5ppm$common$index.A) ==
TRUE)
if (length(dup_mz_ind) > 0) {
overlap_res <- overlap_res[-c(dup_mz_ind),
]
}
finalfeatmat <- as.data.frame(finalfeatmat)
time.list = finalfeatmat$time[c((overlap_res$index.A))]
mz.list = finalfeatmat$mz[c((overlap_res$index.A))]
chem.names <- stddata$Name[c((overlap_res$index.B))]
custom.EIC.plot(aligned, rows = c((overlap_res$index.A)),
colors = NA, transform = "none",
subset = rand_set, mz.list = mz.list,
time.list = time.list, chem.names = chem.names,
min.run = runval, min.pres = presval,
max.spline.time.points = 1000)
}
dev.off()
}
cnames <- colnames(finalfeatmat[, -c(1:4)])
cnames <- tolower(cnames)
cnames <- gsub(".cdf", "", cnames)
if (is.na(run.order.file) == FALSE) {
fileorder = read.table(run.order.file,
header = FALSE)
fileorder = apply(fileorder, 1, tolower)
cnames = tolower(cnames)
ordlist = sapply(1:length(fileorder),
function(i) {
which(cnames == fileorder[i])
})
ordlist = unlist(ordlist)
ordlist = ordlist + 4
finalfeatmat = finalfeatmat[, c(1:4,
ordlist)]
}
fname <- paste(apLCMS.outloc, "/apLCMS_aligned",
"_pres", presval, "_run", runval,
"_", minexp, "exppostrecovery.txt",
sep = "")
write.table(finalfeatmat, fname, sep = "\t",
row.names = F)
aligned_data_list[[pcount]] <- finalfeatmat
pcount = pcount + 1
}
}
return(aligned_data_list)
}
BowenNCSU/xMSanalyzer documentation built on May 24, 2019, 7:36 a.m.
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#' apLCMS.align
#'
#' Call apLCMS function, cdf.to.ftr, at different parameter settings
#'
#' This utility calls the cdf.to.ftr() function in the apLCMS package and
#' performs serial sample processing at multiple combinations of two
#' parameters: min.run (minimum length of elution time for a series of signals
#' grouped by m/z to be considered a feature; default value: 3) and min.pres
#' (minimum proportion of scans in which the signal was present; default
#' values: 0.3, 0.8). The function allows the user to define parameters such as
#' min.exp (minimum number of samples in which a feature is present). This
#' differs from the original apLCMS in that the original only allows one set of
#' parameters, whereas this function allows multiple sets. The resulting tables
#' containing m/z, retention time, and peak intensities in each sample are
#' stored at each parameter combination.
#'
#' @param cdfloc The folder where all NetCDF/mzML/mzXML/mzData files to be
#' processed are located. For example "C:/experiment1/cdf/"
#' @param apLCMS.outloc The folder where alignment output will be written. For
#' example "C:/experiment1/apLCMSoutput/"
#' @param min.run.list List of values for min.run parameter, eg: c(3,6) would
#' run the cdf.to.ftr function at min.run=3 and min.run=6
#' @param min.pres.list List of values min.pres, eg: c(0.3,0.8) would run the
#' cdf.to.ftr function at min.run=3 and min.run=6
#' @param minexp If a feature is to be included in the final feature table, it
#' must be present in at least this number of spectra, eg:2
#' @param mztol The user can provide the m/z tolerance level for peak
#' identification to override the programs selection of the tolerance level.
#' This value is expressed as the percentage of the m/z value. This value,
#' multiplied by the m/z value, becomes the cutoff level. Please see the help
#' for proc.cdf() for details.
#' @param alignmztol The user can provide the m/z tolerance level for peak
#' alignment to override the programs selection. This value is expressed as the
#' percentage of the m/z value. This value, multiplied by the m/z value,
#' becomes the cutoff level.Please see the help for feature.align() for
#' details.
#' @param alignchrtol The retention time tolerance level for peak alignment.
#' The default is NA, which allows the program to search for the tolerance
#' level based on the data. Default: 10
#' @param numnodes Number of nodes to use for processing.
#' @param run.order.file Name of a tab-delimited file that includes sample
#' names sorted by the order in which they were run (sample names must match
#' the CDF file names)
#' @param subs If not all the CDF files in the folder are to be processed, the
#' user can define a subset using this parameter. For example, subs=15:30, or
#' subs=c(2,4,6,8)
#' @param filepattern File format of processed data files. eg: ".cdf", ".mzXML"
#' @param apLCMSmode "untargeted" or "hybrid"; Default: "untargeted"
#' @param known_table A data frame containing the known metabolite ions and
#' previously found features. Please see documentation of semi.sup() function
#' in apLCMS for more details.
#' @param match_tol_ppm The ppm tolerance to match identified features to known
#' metabolites/features. Used by the semi.sup() function in apLCMS. Default: 5
#' @return Feature table after weak signal recovery. This is the end product of
#' the function cdf.to.ftr.
#' @author Karan Uppal <kuppal2@@emory.edu>
#' @references http://www.sph.emory.edu/apLCMS/
#' @keywords ~apLCMS
apLCMS.align <- function(cdfloc, apLCMS.outloc, min.run.list = c(3,
3), min.pres.list = c(0.3, 0.8), minexp = 2, mztol = 1e-05,
alignmztol = NA, alignchrtol = NA, numnodes = 2,
run.order.file = NA, subs = NA, filepattern = ".cdf",
apLCMSmode = "untargeted", known_table, match_tol_ppm = 5,
ref.mz.list = NA, refMZ.mz.diff = 10, refMZ.time.diff = NA,
target.mz.list = NA) {
setwd(cdfloc)
cdf.files = list.files(cdfloc, filepattern, ignore.case = TRUE)
cdf.files = tolower(cdf.files)
dir.create(apLCMS.outloc, showWarnings = FALSE)
aligned_data_list = new("list")
pcount = 1
if (is.na(subs[1]) == FALSE) {
numsamp = length(subs)
cdf.files = cdf.files[subs]
} else {
numsamp = length(cdf.files)
}
if (length(min.run.list) != length(min.pres.list)) {
stop("Vectors min.run.list and min.pres.list should be of the same length. eg: min.run.list=c(3,3) and min.pres.list=c(0.3,0.8)")
}
for (r in 1:length(min.run.list)) {
runval = min.run.list[r]
p = r
# for(p in 1:length(min.pres.list))
{
features <- new("list")
presval = min.pres.list[p]
par(mfrow = c(2, 2))
fname <- paste("Rplots", runval, presval,
".pdf", sep = "")
pdf(fname)
print(minexp)
if (apLCMSmode == "untargeted") {
aligned <- cdf.to.ftr(cdfloc, subs = subs,
min.exp = minexp, min.run = runval,
min.pres = presval, mz.tol = mztol,
align.mz.tol = alignmztol, align.chr.tol = alignchrtol,
n.nodes = numnodes, file.pattern = filepattern)
} else {
if (apLCMSmode == "hybrid") {
aligned <- semi.sup(folder = cdfloc,
known.table = known_table, match.tol.ppm = match_tol_ppm,
subs = subs, min.exp = minexp,
min.run = runval, min.pres = presval,
mz.tol = mztol, align.mz.tol = alignmztol,
align.chr.tol = alignchrtol, n.nodes = numnodes,
file.pattern = filepattern)
}
}
dev.off()
finalfeatmat = aligned$final.ftrs
fname <- paste(apLCMS.outloc, "/apLCMS_aligned",
"_pres", presval, "_run", runval,
"_", minexp, "exppostrecovery.Rda",
sep = "")
save(aligned, file = fname)
if (is.na(target.mz.list) == FALSE) {
eic_fname <- paste(apLCMS.outloc,
"/EICrun", runval, "pres", presval,
".pdf", sep = "")
pdf(eic_fname)
if (is.na(target.mz.list[1, 1]) ==
FALSE) {
stddata <- target.mz.list
# print(head(stddata))
} else {
Name <- paste("mz", seq(1, dim(finalfeatmat)[1]),
sep = "")
stddata <- cbind(finalfeatmat[,
c(1)], Name)
}
overlapres5ppm <- getVenn(dataA = finalfeatmat,
name_a = paste("Expdata", sep = ""),
dataB = stddata, name_b = "Target",
mz.thresh = 10, time.thresh = NA,
alignment.tool = "apLCMS", xMSanalyzer.outloc = apLCMS.outloc,
plotvenn = FALSE)
if (length(unique(overlapres5ppm$common$index.A)) >
0) {
setwd(cdfloc)
num_samples <- dim(finalfeatmat)[2] -
4
min_samp <- 6
if (num_samples < min_samp) {
min_samp <- num_samples
}
rand_sample_set <- sample(size = num_samples,
x = num_samples, replace = FALSE)
rand_set <- c(1:min_samp, (num_samples -
2):num_samples)
com_ind <- which(rand_sample_set %in%
rand_set)
if (length(com_ind) > 0) {
rand_sample_set <- rand_sample_set[-c(com_ind)]
rand_sample_set <- rand_sample_set[1:min_samp]
rand_set <- c(rand_set, rand_sample_set)
} else {
rand_set <- c(rand_set, rand_sample_set[1:min_samp])
rand_set <- rand_set[order(rand_set)]
}
rand_set <- na.omit(rand_set)
print(rand_set)
# EIC.plot(aligned,rows=c(unique(overlapres5ppm$common$index.A)),min.run=runval,min.pres=presval)
# apLCMS.EIC.plot(aligned, rows =
# c(unique(overlapres5ppm$common$index.A)), colors
# = NA, transform = 'none', subset = rand_set,
# minrt=NA, maxrt=NA,
# min.run=runval,min.pres=presval,
# max.spline.time.points = 1000) rand_set<-c(1:6)
overlap_res <- overlapres5ppm$common
overlap_res <- as.data.frame(overlap_res)
dup_mz_ind <- which(duplicated(overlapres5ppm$common$index.A) ==
TRUE)
if (length(dup_mz_ind) > 0) {
overlap_res <- overlap_res[-c(dup_mz_ind),
]
}
finalfeatmat <- as.data.frame(finalfeatmat)
time.list = finalfeatmat$time[c((overlap_res$index.A))]
mz.list = finalfeatmat$mz[c((overlap_res$index.A))]
chem.names <- stddata$Name[c((overlap_res$index.B))]
custom.EIC.plot(aligned, rows = c((overlap_res$index.A)),
colors = NA, transform = "none",
subset = rand_set, mz.list = mz.list,
time.list = time.list, chem.names = chem.names,
min.run = runval, min.pres = presval,
max.spline.time.points = 1000)
}
dev.off()
}
cnames <- colnames(finalfeatmat[, -c(1:4)])
cnames <- tolower(cnames)
cnames <- gsub(".cdf", "", cnames)
if (is.na(run.order.file) == FALSE) {
fileorder = read.table(run.order.file,
header = FALSE)
fileorder = apply(fileorder, 1, tolower)
cnames = tolower(cnames)
ordlist = sapply(1:length(fileorder),
function(i) {
which(cnames == fileorder[i])
})
ordlist = unlist(ordlist)
ordlist = ordlist + 4
finalfeatmat = finalfeatmat[, c(1:4,
ordlist)]
}
fname <- paste(apLCMS.outloc, "/apLCMS_aligned",
"_pres", presval, "_run", runval,
"_", minexp, "exppostrecovery.txt",
sep = "")
write.table(finalfeatmat, fname, sep = "\t",
row.names = F)
aligned_data_list[[pcount]] <- finalfeatmat
pcount = pcount + 1
}
}
return(aligned_data_list)
}
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