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MET_LIBRARY.R
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MET_LIBRARY.R
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################################################################################
################################################################################
## ##
## ##
## MET version 1.0.0, Tools for processing and visualization of ##
## mass spectroscpy files ##
## Copyright (C) 2022 Ian A. Lewis ##
## ##
## This program is free software: you can redistribute it and/or modify ##
## it under the terms of the GNU General Public License as published by ##
## the Free Software Foundation, either version 3 of the License, or ##
## any later version. ##
## ##
## This program is distributed in the hope that it will be useful, ##
## but WITHOUT ANY WARRANTY; without even the implied warranty of ##
## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the ##
## GNU General Public License for more details. ##
## ##
## A copy of the GNU General Public License can be found at: ##
## www.r-project.org/Licenses/GPL-3 ##
## ##
## ##
################################################################################
################################################################################
##################
# Functions List
##################
# METRead
# METWrite
# METStats
# METPlot
# METCluster
##################
################################################################################
## ##
## Simplifed Reading and writing files ##
## ##
################################################################################
# Read Files
METRead <- function( inPath = file.choose()){
return(read.csv( inPath, head = TRUE, stringsAsFactors = FALSE))
}
# Write Files
METWrite <- function( table ){
write.csv( table, file.choose(new=TRUE), quote = FALSE, row.names = FALSE )
}
################################################################################
################################################################################
## ##
## Internal functions ##
## ##
################################################################################
myNaN <- function( x ){
if( any(is.nan(x)) )
x[ is.nan(x) ] <- 0
return(x)
}
## Function for dividing by zero
## x - dataframe with numerator data
## y - dataframe with denominator data
## num - numeric, the value to be assigned for non-zero numerators dived by 0
## den - numeric, the value to be assigned for 0/0
## returns dataframe of dim(x)
myDiv <- function(x, y, num = 0, den = 1){
dX <- dim(x)
dY <- dim(y)
if (all(dX != dY))
stop("x and y must have the same number of rows and columns")
vX <- as.numeric(as.vector(unlist(x)))
vY <- as.numeric(as.vector(unlist(y)))
if(any(vY == 0) )
vY[vY == 0 ] <- den
out <- data.frame(matrix(vX/vY, ncol = ncol(x)))
names(out) <- names(x)
return(out)
}
findPval <- function( sLab, mzData, type = 'p' ){
nr <- nrow( mzData )
pVal <- list()
sLab <- factor(sLab)
for( i in 1:nr ){
tVal <- tryCatch( pVal[[i]] <- aov(as.vector(unlist(mzData[ i, ])) ~ sLab),
error=function(er) return(NA))
if( is.na(tVal[1]) ){
pVal[[i]] <- NA
next
}
if(type == 'p')
pVal[[i]] <- unclass(summary.aov(tVal))[[1]]$Pr[1]
if(type =='f')
pVal[[i]] <- unclass(summary.aov(tVal))[[1]]$"F value"[1]
if(type == 'df')
pVal[[i]] <- unclass(summary.aov(tVal))[[1]]$"rank"
}
return(as.vector(unlist(pVal)))
}
## Internal function heatmap function adapted from base heatmap function in r
## minor modification to handle errors
## see: https://www.rdocumentation.org/packages/stats/versions/3.6.2/topics/heatmap
## for documentation
myheatmap <- function (x, Rowv = NULL, Colv = if (symm) "Rowv" else NULL,
reorderfun = function(d,w) reorder(d, w), add.expr,
symm = FALSE, revC = identical(Colv, "Rowv"), na.rm = TRUE,
grid = TRUE, breaks = NULL, margins = c(5, 5),
ColSideColors, RowSideColors, cexRow = 0.2 +
1/log10(nr), cexCol = 0.2 + 1/log10(nc), labRow = NULL,
labCol = NULL, main = NULL, xlab = NULL, ylab = NULL, keep.dendro = TRUE,
verbose = getOption("verbose"), ...) {
if (length(di <- dim(x)) != 2 || !is.numeric(x))
stop("'x' must be a numeric matrix")
x[ x == Inf ] <- max(x[is.finite(unlist(x))], na.rm = T) * 100
x[ x == -Inf ] <- min(x[is.finite(unlist(x))], na.rm = T) / 100
nr <- di[1L]
nc <- di[2L]
if (nr <= 1 || nc <= 1)
stop("'x' must have at least 2 rows and 2 columns")
if (!is.numeric(margins) || length(margins) != 2L)
stop("'margins' must be a numeric vector of length 2")
doRdend <- !identical(Rowv, NA)
doCdend <- !identical(Colv, NA)
if (!doRdend && identical(Colv, "Rowv"))
doCdend <- FALSE
if (is.null(Rowv))
Rowv <- rowMeans(x, na.rm = na.rm)
if (is.null(Colv))
Colv <- colMeans(x, na.rm = na.rm)
if (doRdend) {
if (inherits(Rowv, "dendrogram"))
ddr <- Rowv
else {
hcr <- hclust(dist(x))
ddr <- as.dendrogram(hcr)
if (!is.logical(Rowv) || Rowv)
ddr <- reorderfun(ddr, Rowv)
}
if (nr != length(rowInd <- order.dendrogram(ddr)))
stop("row dendrogram ordering gave index of wrong length")
}
else rowInd <- 1L:nr
if (doCdend) {
if (inherits(Colv, "dendrogram"))
ddc <- Colv
else if (identical(Colv, "Rowv")) {
if (nr != nc)
stop("Colv = \"Rowv\" but nrow(x) != ncol(x)")
ddc <- ddr
}
else {
hcc <- hclust(dist(if (symm)
x
else t(x)))
ddc <- as.dendrogram(hcc)
if (!is.logical(Colv) || Colv)
ddc <- reorderfun(ddc, Colv)
}
if (nc != length(colInd <- order.dendrogram(ddc)))
stop("column dendrogram ordering gave index of wrong length")
}
else colInd <- 1L:nc
x <- x[rowInd, colInd]
labRow <- if (is.null(labRow))
if (is.null(rownames(x)))
(1L:nr)[rowInd]
else rownames(x)
else labRow[rowInd]
labCol <- if (is.null(labCol))
if (is.null(colnames(x)))
(1L:nc)[colInd]
else colnames(x)
else labCol[colInd]
lmat <- rbind(c(NA, 3), 2:1)
lwid <- c(if (doRdend) 1 else 0.05, 4)
lhei <- c((if (doCdend) 1 else 0.05) + if (!is.null(main)) 0.2 else 0,
4)
if (!missing(ColSideColors)) {
if (!is.character(ColSideColors) || length(ColSideColors) !=
nc)
stop("'ColSideColors' must be a character vector of length ncol(x)")
lmat <- rbind(lmat[1, ] + 1, c(NA, 1), lmat[2, ] + 1)
lhei <- c(lhei[1L], 0.2, lhei[2L])
}
if (!missing(RowSideColors)) {
if (!is.character(RowSideColors) || length(RowSideColors) !=
nr)
stop("'RowSideColors' must be a character vector of length nrow(x)")
lmat <- cbind(lmat[, 1] + 1, c(rep(NA, nrow(lmat) - 1),
1), lmat[, 2] + 1)
lwid <- c(lwid[1L], 0.2, lwid[2L])
}
lmat[is.na(lmat)] <- 0
if (verbose) {
cat("layout: widths = ", lwid, ", heights = ", lhei,
"; lmat=\n")
print(lmat)
}
op <- par(no.readonly = TRUE)
on.exit(par(op))
layout(lmat, widths = lwid, heights = lhei, respect = TRUE)
if (!missing(RowSideColors)) {
par(mar = c(margins[1L], 0, 0, 0.5))
image(rbind(1L:nr), col = RowSideColors[rowInd], axes = FALSE)
}
if (!missing(ColSideColors)) {
par(mar = c(0.5, 0, 0, margins[2L]))
image(cbind(1L:nc), col = ColSideColors[colInd], axes = FALSE)
}
par(mar = c(margins[1L], 0, 0, margins[2L]))
x <- t(x)
if (revC) {
iy <- nr:1
if (doRdend)
ddr <- rev(ddr)
x <- x[, iy]
}
else iy <- 1L:nr
if( !is.null(breaks) && length(breaks) > 1 ){
x[ x > max(breaks) ] <- max(breaks)
x[ x < min(breaks) ] <- min(breaks)
}
image(1L:nc, 1L:nr, x, xlim = 0.5 + c(0, nc), ylim = 0.5 +
c(0, nr), axes = FALSE, xlab = "", ylab = "", breaks = breaks, ...)
axis(1, 1L:nc, labels = labCol, las = 2, line = -0.5, tick = 0,
cex.axis = cexCol, col.axis = par('fg'))
box()
if( grid )
abline(v = 1L:nc + .5, h = iy + .5)
if (!is.null(xlab))
mtext(xlab, side = 1, line = margins[1L] - 1.25)
axis(4, iy, labels = labRow, las = 2, line = -0.5, tick = 0,
cex.axis = cexRow, col.axis = par('fg'))
if (!is.null(ylab))
mtext(ylab, side = 4, line = margins[2L] - 1.25)
if (!missing(add.expr))
eval(substitute(add.expr))
par(mar = c(margins[1L], 0, 0, 0))
if (doRdend)
plot(ddr, horiz = TRUE, axes = FALSE, yaxs = "i", leaflab = "none")
else frame()
par(mar = c(0, 0, if (!is.null(main)) 1 else 0, margins[2L]))
if (doCdend)
plot(ddc, axes = FALSE, xaxs = "i", leaflab = "none")
else if (!is.null(main))
frame()
if (!is.null(main)) {
par(xpd = NA)
title(main, cex.main = 1.5 * op[["cex.main"]])
}
invisible(list(rowInd = rowInd, colInd = colInd, Rowv = if (keep.dendro &&
doRdend) ddr, Colv = if (keep.dendro && doCdend) ddc))
}
foldChange <- function( inTable, normVec, zeroOffset = F, normMin = .01,scale = 'fold' ){
## Calculate standard deviation for normalization vector
if( scale == "zScore"){
if( length(normVec) < 1 )
stop('Z scales require a standard deviation from multiple replicates')
sdTab <- apply(inTable[, normVec], 1, mySd )
normTab <- apply(inTable[, normVec], 1, myMean )
}
else{
if( length(normVec) > 1 )
normTab <- apply(inTable[, normVec], 1, myMean)
else
normTab <- inTable[, normVec ]
}
## Make norm matrix
normTab <- as.vector(unlist(normTab))
normTab <- data.frame(matrix(rep(normTab, ncol(inTable)),
ncol = ncol(inTable)))
## Make sd matrix
if( scale == "zScore"){
sdTab <- as.vector(unlist(sdTab))
sdTab <- data.frame(matrix(rep(sdTab, ncol(inTable)),ncol = ncol(inTable)))
}
## Set all 0 values to 1% of min observed value
if( zeroOffset ){
zSet <- inTable == 0
if( mySum(zSet) ){
tTab <- inTable
tTab[ zSet ] <- NA
mSet <- as.vector(unlist(apply(tTab, 1, myMin )))
for( i in 1:length(mSet) ){
if( any(inTable[i,] == 0, na.rm =T) ){
idx <- which( inTable[i,] == 0 )
inTable[i, idx] <- mSet[i]
}
}
}
}
## Calculate z score
if( scale == 'zScore' ){
out <- (inTable - normTab)/sdTab
#out <- apply( out, 2, myNaN )
return(out)
}
## Normalize
out <- myDiv(inTable, normTab)
if( scale == 'ratio' ){
out <- apply( out, 2, myNaN )
return(out)
}
if( any(out < 1 ) ){
foldFun <- function(x) {
tIdx <- which( x < 1)
x[ tIdx ] <- abs(1/ x[tIdx]) * -1
return(x)
}
out <- apply(out, 2, foldFun)
out <- apply( out, 2, myNaN )
}
return(out)
}
## Function for plotting metabolite heatmaps
metImage <- function( x, col, breaks, cCst = FALSE, rCst = FALSE, grid = TRUE ){
## Generate levels and colors
if( missing(col) || missing(breaks) ){
lDat <- levelFun(x)
col <- lDat$col
breaks <- lDat$breaks
}
## Set col/row dendrogram
Colv <- Rowv <- NA
if( cCst )
Colv <- NULL
if( rCst )
Rowv <- NULL
windows(width = 2)
w <- (3 + par('mar')[2L]) * par("csi") * 2.54
layout(matrix(c(2, 1), ncol = 2L), widths = c(1, lcm(w)))
par( mar = c( 5.1, 1, 4.1, 4.1), las = 1)
plot.new()
plot.window(xlim = c(0, 1), ylim = range(breaks, na.rm = T), xaxs = "i", yaxs = "i")
rect(0, breaks[-length(breaks)], 1, breaks[-1L], col = col)
axis(4, at = breaks)
windows()
out <- myheatmap(as.matrix(x), Colv = Colv, Rowv = Rowv, na.rm = T,
col = col, breaks = breaks, grid = grid )
return(invisible(out))
}
levelFun <- function(x, n = 10, r = c(0, 1), g = c(0,0), b = c(1,0), zlim,
logScale = T, returnInterval = FALSE){
x <- suppressWarnings(as.numeric(as.vector(unlist(x))))
## Remove NA and Inf data
if( any(is.na(x)) )
x[is.na(x)] <- 0
if( any(abs(x) == Inf))
x[ abs(x) == Inf ] <- 0
## Threshold data
if( !missing(zlim) && length(zlim) == 2 && all(is.numeric(zlim)) ){
zlim <- sort(abs(zlim) )
}else
zlim <- c( min(abs(x)), max(abs( x )) )
## Set breaks
if( logScale ){
scale <- 2^(0:50)
xSeq <- scale[which( scale >= zlim[1] )[1] : which( scale >= zlim[2] )[1]]
n <- length(xSeq)
xSeq <- unique(c( -rev(xSeq), xSeq))
}else{
xSeq <- (c(
-rev(seq( zlim[1], zlim[2], length.out = n)),
seq( zlim[1], zlim[2], length.out = n) ))
}
## Set colors
col <- c(
rgb(rep(r[1],(n-1)),
rep(g[1],(n-1)),
rep(b[1],(n-1)), alpha = rev((1:n)[-1] / n)),
'transparent',
rgb(rep(r[2],(n-1)),
rep(g[2],(n-1)),
rep(b[2],(n-1)), alpha = (1:n)[-1] / n)
)
if( !returnInterval )
return( list(breaks = xSeq, col = col))
return(list(breaks = xSeq,
col = col,
interval = findInterval(x, xSeq)))
}
## Internal function for seperating METEN metadata from datasets
## excludeName - character, names of columns to be excluded from analysis
## METNam - character string - default names of METen format metadata
metaSep <- function(inDat,
METNam = c('label', 'metaGroupId', 'groupId', 'goodPeakCount', 'medMz', 'medRt',
'maxQuality', 'note', 'compound', 'compoundId', 'expectedRtDiff',
'ppmDiff', 'parent', 'adductName', 'isotopeLabel', 'formula', 'ctName', 'pVal','grp','nGrp','gPar'), excludeName){
## Check input dataset
if( missing(inDat) || !is.data.frame(inDat) || (nrow(inDat) < 1) )
stop('A METEN format dataset is required')
## Combine default and user-provided names
if( !missing(excludeName) )
METNam <- c(METNam, excludeName)
## Remove all standard METen meta data
mIdx <- na.omit(match(METNam, names(inDat)))
## Find metadaFta
if( length(mIdx) == 1 ){
metaDat <- data.frame(compound = inDat[,mIdx], stringsAsFactors = F)
}else
metaDat <- inDat[,mIdx]
## remove the metaData
outDat <- inDat[,-na.omit(match(METNam, names(inDat)))]
return(list( metaData = metaDat, data = outDat))
}
################################################################################
## ##
## USER FUNCTIONS ##
## ##
################################################################################
## Function for clustering untargeted mz data
## mzTable - dataframe - METen format dataframe from untargeted csv METen output
## mzThresh - numeric - Mass tolerance in ppm
## rtThresh - numeric - Renention time tolerance in minutes
## pW - numeric vector, 0 to 1 - weight given to mass,
## retention time, and correlation
## pThresh - numeric, 0 to 1 - overall probablility threshold
## adList - numeric vector - list of known adducts
## collapse - logical - T collapses groups to highest intensity signal
## clusterOnly - logical - T returns collapsed groups with multiple signals
METStats <- function(inDat, rCst = F, cCst = F, pCst = F, nCst = F,
usrSelect = F, sdRep = F, by = "\\.", excludeName = NULL,
scale = c("log", "durbin", "row", "column", "fold", "ratio", "zScore", "none"),
inverse = F, c = 1, n = 10, maxDup = F, avgRep = F,
na.rm = T, sLab, dThresh, pCalc = F, pThresh = 1, bonferroni = F,
returnMeta = T, rmZero = F, ...){
## check format of peaklist
if( !is.data.frame(inDat) || nrow(inDat) < 1 || ncol(inDat) < 2
|| !any(names(inDat) == 'compound'))
stop("A dataframe with a 'compound' and at least one data column is required")
## Set Scale argument
scale <- if (missing(scale))
"none"
else match.arg(scale)
## Check pThresh
if( avgRep && pThresh < 1)
stop("avgRep and pThresh can not be used in combination")
if( pThresh < 1 && bonferroni )
stop("pThresh and bonferroni can not be used in combination")
if( avgRep && sdRep)
stop("avgRep and sdRep can not be used in combination")
## Make all compound names unique
com <- inDat$compound
if(any(is.na(com)) && any(names(inDat) == 'medMz')
&& any(names(inDat) == 'medRt')){
com[which(is.na(com))] <- paste(inDat$medMz[which(is.na(com))],
inDat$medRt[which(is.na(com))], sep = "@")
}
for( i in unique(com) ){
tCpd <- com[ com == i ]
if( length(tCpd) == 1 )
next
com[com == i] <- paste(tCpd, 1:length(tCpd), sep = '_')
}
inDat$compound <- com
## Remove duplicate metabolite records and find max for each
if(maxDup)
inDat <- findMaxRecord( inDat, excludeName = excludeName )
## Combine any metadata fields to be excluded
tDat <- metaSep(inDat, excludeName = excludeName)
metaDat <- tDat[[1]]
inDat <- tDat[[2]]
## Order samples by name
if (nCst){
inDat <- inDat[,order(names(inDat))]
}
## hold sample names
sam <- names(inDat) ## read tab header to get sample names
rownames(inDat) <- metaDat$compound
## Zero any data below the data threshold
if( !missing(dThresh) )
inDat[ inDat < dThresh ] <- 0
## Remove all zero data
if( any(!apply(inDat, 1, function(x) all(x == 0))) && rmZero ){
inDat <- inDat[- which(apply(inDat, 1, function(x) all(x == 0))),]
}
## Get sample labels
if( (scale == 'zScore' || pCst || pCalc || pThresh < 1) && missing (sLab))
sLab <- sapply( strsplit(sam, split = by), function (x) x[1])
## Average sample replicates
if( avgRep || sdRep ){
tLab <- list(factor(
sapply(strsplit(names(inDat), split = by), function (x) x[1])))
if( avgRep )
inDat <- aggregate(t(inDat), FUN = function(x) mean(x, na.rm = T), by =tLab)
else
inDat <- aggregate(t(inDat), FUN = function(x) sd(x, na.rm = T), by =tLab)
uName <- levels(inDat[,1])
inDat <- data.frame(t(inDat[,-1]))
names(inDat) <- uName
## Reorder colums to match input list
sam <- unique(sapply(strsplit(sam, split = by), function (x) x[1]))
inDat <- inDat[,c(which(names(inDat) == 'compound'),
match(sam, names(inDat)))]
}
## Scale data
if( scale == 'log'){
inDat <- log( inDat )
}else if( scale == 'durbin' ){
inDat <- durbin(inDat, c = c )
}else if (scale == "row") {
inDat <- sweep(inDat, 1L, rowMeans(inDat, na.rm = TRUE), check.margin = FALSE)
sx <- apply(inDat, 1L, sd, na.rm = TRUE)
inDat <- sweep(inDat, 1L, sx, "/", check.margin = FALSE)
}else if (scale == "column") {
inDat <- sweep(inDat, 2L, colMeans(inDat, na.rm = TRUE), check.margin = FALSE)
sx <- apply(inDat, 2L, sd, na.rm = TRUE)
inDat <- sweep(inDat, 2L, sx, "/", check.margin = FALSE)
}else if (scale == "fold" || scale == "ratio" || scale == 'zScore'){
userSel <- select.list( names( inDat), multiple = T,
title = "Select reference:" )
if( !length(userSel) || !nzchar(userSel) )
stop('No file selected')
normCol <- match(userSel, names(inDat))
inDat <- foldChange( inTable = inDat, normVec = normCol, scale = scale)
}
## Remove NA and Inf data
if( na.rm ){
printList <- newList <- NULL
if( any(is.na(inDat)) ){
inDat[is.na(inDat)] <- 0
printList <- c(printList, "NA")
newList <- c(newList, 0)
}
if( any(inDat == Inf)){
inDat[ inDat == Inf ] <- range(unlist(inDat), finite = T)[2]
newList <- c(printList, "Inf")
newList <- c(newList, max(inDat, na.rm = T))
}
if( any(inDat == -Inf)){
inDat[ inDat == -Inf ] <- range(unlist(inDat), finite = T)[1]
printList <- c(printList, "-Inf")
newList <- c(newList, min(inDat, na.rm = T))
}
if(!is.null(printList))
print( paste(c(paste(printList, collapse = ", "),
"data values were changed to:",
paste(newList, collapse = ", ")), collapse = " "))
}
if( inverse )
inDat <- 1/inDat
## Rank values according to ANOVA p value
if( pCst || pCalc || pThresh < 1){
metaDat$pVal <- findPval(sLab = sLab, inDat, type = 'p')
if( pCst ){
sidx <- order(metaDat$pVal)
inDat <- inDat[sidx,]
if(ncol( metaDat) > 1 )
metaDat <- metaDat[sidx,]
else
metaDat <- data.frame( compound = metaDat[sidx,], stringsAsFactors = F)
}
## Calculate pThreshold
if( bonferroni ){
pThresh <- .05 / nrow(inDat)
}
if( pThresh < 1 ){
idxP <- which(metaDat$pVal < pThresh)
inDat <- inDat[idxP,]
if(ncol( metaDat) > 1 )
metaDat <- metaDat[idxP,]
else
metaDat <- data.frame( compound = metaDat[idxP,], stringsAsFactors = F)
}
}
## Allow users to select sub groups of data
if( usrSelect ){
userChoice <- mySelect(names(inDat))
if( length(userChoice) > 0 ){
inDat <- inDat[,userChoice]
sLab <- sLab[userChoice]
}
}
## Add metadata
if( returnMeta ){
inDat <- cbind( metaDat, inDat)
}else{
inDat <- data.frame( compound = row.names(inDat), inDat, stringsAsFactors = F)
names(inDat) <- c('compound', sam)
}
invisible(inDat)
}
## Default MET plotter
## This function takes a METen-format list and makes several types of plots
## inDat - METen format table
## rCst - Logical - TRUE clusters by row
## cCst - Logical - TRUE clusters by column
## pCst - Logical - TRUE ranks rows by P value
## nCst - Logical - TRUE sorts samples by name
## wildMan - Logical - Allows users to select a smaller set of data to plot
## grid - Logical - TRUE plots the a sample grid over the heatmap
## pca - Logical - TRUE makes the PCA plots
## linePlot - Logical - TRUE makes a line plot
## stackPlot - Logical - TRUE makes a staked line plot
## heatMap - Logical - TRUE makes a classic heatmap
## barPlot - Logical - Plots bar figure, note this needs replicates see "by"
## zPlot - Logical - this plots data relative to a reference sample set
## scale - controls how data are normalized
## must be one of the following: "log", "durbin", "row", "column", or "none"
## 'log' applies base 2 log transform
## 'durbin' applies durbin transform
## 'row' normalizes by row
## 'column' noralizes by column
## 'zScore' normalizes relative to a control, sets equal variance
## 'none' does not apply any normalization
## c - integer - constant for durbin trasform (0 is ln)
## n - number of levels used in heatmap
## zlim - numeric vector of length 2 for the c(min, max) plotted value
## if zlim is missing or NULL, then all data are plotted
## logScale - logical - TRUE uses log2 intervals and disable n
## byClass - Logical - TRUE agregates data according to values in the 'notes' column
## excludeName - character, names of columns to be excluded from analysis
## na.rm - Logical - TRUE converts NA to 0, INF to positive max,
## -INF to negative max
## maxDup - logical, finds the maximum record in cases of duplicated compounds
## avgRep - logical, returns the mean intensity for each group
## sdRep - logical, returns the standard deviation for each group
## - note, avgRep and sdRep cannont be both set to T
## inverse - logical, inverts the incoming data (1/data)
## pThresh - logical - excludes data above the pValue threshold
## bonferroni - logical - calculates the bonferroni-corrected pThresh
## returnPCA - logical - TRUE returnes PCA scores
## returnMeta - Logical, if T, will return all input meta data
## note: this cannot be used with column/p clustering or thresholding
## rmZero - logical - removes rows with all zero intensities
## random - Logical, TRUE will generate random number distribution that match
## the mean/sd for each compound
## zJitter - Logical, if true will offset the x scale for zPlots
## returns the dataframe used to plot the data
METPlot <- function(inDat, rCst = F, cCst = F, pCst = F, nCst = F,
usrSelect = F,grid = FALSE, sdRep = F, by = "\\.", excludeName = NULL,
scale = c("log", "durbin", "row", "column", "fold", "ratio", "zScore", "none"),
c = 1, n = 10, zlim, logScale = F, maxDup = F, avgRep = F,
byClass = F, na.rm = T, sLab, dThresh,
pca = F, linePlot = F, stackPlot = F, heatMap = F, barPlot = F, zPlot = F,
vioPlot = F, ylim, xlim,
returnMeta = T, returnPCA = F, rmZero = F,zJitter = F, main = NULL, sub = NULL, xlab = NULL, ylab = NULL, ...){
## check format of peaklist
if( !is.data.frame(inDat) || nrow(inDat) < 1 || ncol(inDat) < 2
|| !any(names(inDat) == 'compound'))
stop("A dataframe with a 'compound' and at least one data column is required")
## Set Scale argument
scale <- if (missing(scale))
"none"
else match.arg(scale)
## Make all compound names unique
com <- inDat$compound
if(any(is.na(com)) && any(names(inDat) == 'medMz')
&& any(names(inDat) == 'medRt')){
com[which(is.na(com))] <- paste(inDat$medMz[which(is.na(com))],
inDat$medRt[which(is.na(com))], sep = "@")
}
for( i in unique(com) ){
tCpd <- com[ com == i ]
if( length(tCpd) == 1 )
next
com[com == i] <- paste(tCpd, 1:length(tCpd), sep = '_')
}
inDat$compound <- com
## Remove duplicate metabolite records and find max for each
if(maxDup)
inDat <- findMaxRecord( inDat, excludeName = excludeName )
## Combine any metadata fields to be excluded
tDat <- metaSep(inDat, excludeName = excludeName)
metaDat <- tDat[[1]]
inDat <- tDat[[2]]
## Order samples by name
if (nCst){
inDat <- inDat[,order(names(inDat))]
}
## hold sample names
sam <- names(inDat) ## read tab header to get sample names
rownames(inDat) <- metaDat$compound
## Zero any data below the data threshold
if( !missing(dThresh) )
inDat[ inDat < dThresh ] <- 0
## Remove all zero data
if( any(!apply(inDat, 1, function(x) all(x == 0))) && rmZero ){
inDat <- inDat[- which(apply(inDat, 1, function(x) all(x == 0))),]
}
## Condenses metabolite signals according to compound classes
if (byClass ){
if( any(is.na(metaDat$note)) )
stop("The note column must contain compound classes")
cpdClass <- metaDat$note
out <- list()
for(i in 1:ncol(inDat))
out[[i]] <- aggregate(inDat[,i], by = list(cpdClass), "sum")
inDat <- data.frame(sapply(out, function(x) x[,2]))
metaDat <- data.frame( compound = out[[1]][,1], stringsAsFactors = F)
names(inDat) <- sam
rownames(inDat) <- metaDat$compound
}
## Get sample labels
if( (barPlot || scale == 'zScore' || pCst || stackPlot || vioPlot ||
linePlot || zPlot) && missing (sLab))
sLab <- sapply( strsplit(sam, split = by), function (x) x[1])
## Average sample replicates
if( avgRep || sdRep ){
tLab <- list(factor(
sapply(strsplit(names(inDat), split = by), function (x) x[1])))
if( avgRep )
inDat <- aggregate(t(inDat), FUN = function(x) mean(x, na.rm = T), by =tLab)
else
inDat <- aggregate(t(inDat), FUN = function(x) sd(x, na.rm = T), by =tLab)
uName <- levels(inDat[,1])
inDat <- data.frame(t(inDat[,-1]))
names(inDat) <- uName
## Reorder colums to match input list
sam <- unique(sapply(strsplit(sam, split = by), function (x) x[1]))
inDat <- inDat[,c(which(names(inDat) == 'compound'),
match(sam, names(inDat)))]
}
## Scale data
if( scale == 'log'){
inDat <- log( inDat )
}else if( scale == 'durbin' ){
inDat <- durbin(inDat, c = c )
}else if (scale == "row") {
inDat <- sweep(inDat, 1L, rowMeans(inDat, na.rm = TRUE), check.margin = FALSE)
sx <- apply(inDat, 1L, sd, na.rm = TRUE)
inDat <- sweep(inDat, 1L, sx, "/", check.margin = FALSE)
}else if (scale == "column") {
inDat <- sweep(inDat, 2L, colMeans(inDat, na.rm = TRUE), check.margin = FALSE)
sx <- apply(inDat, 2L, sd, na.rm = TRUE)
inDat <- sweep(inDat, 2L, sx, "/", check.margin = FALSE)
}else if (scale == "fold" || scale == "ratio" || scale == 'zScore'){
userSel <- select.list( names( inDat), multiple = T,
title = "Select reference:" )
if( !length(userSel) || !nzchar(userSel) )
stop('No file selected')
normCol <- match(userSel, names(inDat))
inDat <- foldChange( inTable = inDat, normVec = normCol, scale = scale)
}
## Remove NA and Inf data
if( na.rm ){
printList <- newList <- NULL
if( any(is.na(inDat)) ){
inDat[is.na(inDat)] <- 0
printList <- c(printList, "NA")
newList <- c(newList, 0)
}
if( any(inDat == Inf)){
inDat[ inDat == Inf ] <- range(unlist(inDat), finite = T)[2]
newList <- c(printList, "Inf")
newList <- c(newList, max(inDat, na.rm = T))
}
if( any(inDat == -Inf)){
inDat[ inDat == -Inf ] <- range(unlist(inDat), finite = T)[1]
printList <- c(printList, "-Inf")
newList <- c(newList, min(inDat, na.rm = T))
}
if(!is.null(printList))
print( paste(c(paste(printList, collapse = ", "),
"data values were changed to:",
paste(newList, collapse = ", ")), collapse = " "))
}
## Allow users to select sub groups of data
if( usrSelect ){
userChoice <- mySelect(names(inDat))
if( length(userChoice) > 0 ){
inDat <- inDat[,userChoice]
sLab <- sLab[userChoice]
}
}
## Generate PCA plots
if( pca ){
outPCA <- pcaPlot( dat = inDat, samples = sam, compounds = metaDat$compound,
transform = FALSE)
title(main = main, sub = sub, xlab = xlab, ylab = ylab)
}
## Get user selections
if( barPlot || linePlot || zPlot || stackPlot || vioPlot){
if( nrow(metaDat) == 1){
usrRow <- 1
}else{
userSel <- select.list( metaDat$compound,
multiple = T, title = "Select compounds:" )
if( !length(userSel) || !nzchar(userSel) ){
print('No compounds selected')
break
}
usrRow <- match(userSel, metaDat$compound )
}
}
## Generate bar plot
if( barPlot ){
userSel <- select.list( unique(sLab), multiple = T )
if( !length(userSel) || !nzchar(userSel) ){
print('No files selected')
break
}
usrCol <- myMatch(userSel, sLab )
plotBar(sLab = sLab[usrCol], y = inDat[usrRow,usrCol],
cpdNames = row.names(inDat)[usrRow], ylim = ylim, ...)
invisible( inDat[usrRow,usrCol] )
}
## Generate XY plot
if(linePlot){
plotXY(sLab = sLab, y = inDat[usrRow,], cpdNames = row.names(inDat)[usrRow],
stack = F, xlim = xlim, ylim = ylim, ...)
title(main = main, sub = sub, xlab = xlab, ylab = ylab)
}
## Generate stacked XY plot
if(stackPlot){
plotXY(sLab = sLab, y = inDat[usrRow,], cpdNames = row.names(inDat)[usrRow],
stack = T, xlim = xlim, ylim = ylim, ...)
title(main = main, sub = sub, xlab = xlab, ylab = ylab)
}
## Generate violin plot
if( vioPlot ){
plotXY(sLab = sLab, y = inDat[usrRow,], cpdNames = row.names(inDat)[usrRow],
stack = F, vioPlot = T, xlim = xlim, ylim = ylim, ...)
title(main = main, sub = sub, xlab = xlab, ylab = ylab)
}
## Generate Z plot
if(zPlot){
plotXY(sLab = sLab, y = inDat[usrRow,], cpdNames = row.names(inDat)[usrRow],
zPlot = T, xlim = xlim, ylim = ylim, zJitter = zJitter, ...)
if( scale == "zScore" )
abline( h=c(-1.644852, 1.644852), lty = 2)
title(main = main, sub = sub, xlab = xlab, ylab = ylab)
}
## Generate heatmap
if( heatMap ){
lDat <- levelFun(inDat, n = n, zlim = zlim, logScale = logScale )
out <- metImage( as.matrix(inDat[nrow(inDat):1,]), col = lDat$col, breaks = lDat$breaks,
rCst = rCst, cCst = cCst, grid = grid )
title(main = main, sub = sub, xlab = xlab, ylab = ylab)
rowInd <- rev(length(out$rowInd)-out$rowInd +1)
colInd <- rev(length(out$colInd)-out$colInd +1)
inDat <- inDat[rowInd, colInd]
if(ncol(metaDat) == 1){
tmp <- data.frame(metaDat[rowInd,], stringsAsFactors = F)
names(tmp) <- names(metaDat)
metaDat <- tmp
}else
metaDat <- metaDat[rowInd,]
}
## Return PCA scores
if(returnPCA)
return(outPCA)
## Add metadata
if( returnMeta ){
inDat <- cbind( metaDat, inDat)
}else{
inDat <- data.frame( compound = row.names(inDat), inDat, stringsAsFactors = F)
names(inDat) <- c('compound', sam)
}
invisible(inDat)
}
## Function for clustering untargeted mz data
## mzTable - dataframe - METen format dataframe from untargeted csv METen output
## mzThresh - numeric - Mass tolerance in ppm
## rtThresh - numeric - Renention time tolerance in minutes
## pW - numeric vector, 0 to 1 - weight given to mass,
## retention time, and correlation
## pThresh - numeric, 0 to 1 - overall probablility threshold
## adList - numeric vector - list of known adducts
## collapse - logical - T collapses groups to highest intensity signal
## clusterOnly - logical - T returns collapsed groups with multiple signals
METCluster <- function( mzTable, mzThresh = 10,rtThresh = .5,pW = c(1,1,1), pThresh = 0.75,
adList = c(0, 1.003355, 1.007825, 22.9897692809, 18.010565, 43.99038, 34.968853),
collapse = FALSE, clusterOnly = FALSE){
mzList <- mzTable$medMz
rtList <- mzTable$medRt
pW <- pW/sum(pW)
## Remove all standard METen meta data