Direct access to the raw data
library("mzR")
mzf <- pxget(px1, 6)
## Downloading 1 file
ms <- openMSfile(mzf)
hd <- header(ms)
ms1 <- which(hd$msLevel == 1)
rtsel <- hd$retentionTime[ms1] / 60 > 30 & hd$retentionTime[ms1] / 60 < 35
library("MSnbase")
(M <- MSmap(ms, ms1[rtsel], 521, 523, .005, hd))
## 1
## Object of class "MSmap"
## Map [75, 401]
## [1] Retention time: 30:1 - 34:58
## [2] M/Z: 521 - 523 (res 0.005)
library("lattice")
ff <- colorRampPalette(c("yellow", "steelblue"))
trellis.par.set(regions=list(col=ff(100)))
plot(M, aspect = 1, allTicks = FALSE)
M@map[msMap(M) == 0] <- NA
plot3D(M, rgl = FALSE)
To produce a version that can be reoriented interactively on the screen discplay, use the rgl
library("rgl")
plot3D(M, rgl = TRUE)
lout <- matrix(NA, ncol = 10, nrow = 8)
lout[1:2, ] <- 1
for (ii in 3:4)
lout[ii, ] <- c(2, 2, 2, 2, 2, 2, 3, 3, 3, 3)
lout[5, ] <- rep(4:8, each = 2)
lout[6, ] <- rep(4:8, each = 2)
lout[7, ] <- rep(9:13, each = 2)
lout[8, ] <- rep(9:13, each = 2)
i <- ms1[which(rtsel)][1]
j <- ms1[which(rtsel)][2]
ms2 <- (i+1):(j-1)
layout(lout)
par(mar=c(4,2,1,1))
chromatogram(ms)
abline(v = hd[i, "retentionTime"], col = "red")
par(mar = c(3, 2, 1, 0))
plot(peaks(ms, i), type = "l", xlim = c(400, 1000))
legend("topright", bty = "n",
legend = paste0(
"Acquisition ", hd[i, "acquisitionNum"], "\n",
"Retention time ", formatRt(hd[i, "retentionTime"])))
abline(h = 0)
abline(v = hd[ms2, "precursorMZ"],
col = c("#FF000080",
rep("#12121280", 9)))
par(mar = c(3, 0.5, 1, 1))
plot(peaks(ms, i), type = "l", xlim = c(521, 522.5),
yaxt = "n")
abline(h = 0)
abline(v = hd[ms2, "precursorMZ"], col = "#FF000080")
##par(mar = omar)
par(mar = c(2, 2, 0, 1))
for (ii in ms2) {
p <- peaks(ms, ii)
plot(p, xlab = "", ylab = "", type = "h", cex.axis = .6)
legend("topright", legend = paste0("Prec M/Z\n",
round(hd[ii, "precursorMZ"], 2)),
bty = "n", cex = .8)
}
M2 <- MSmap(ms, i:j, 100, 1000, 1, hd)
## 1
plot3D(M2)
MS barcoding
par(mar=c(4,1,1,1))
image(t(matrix(hd$msLevel, 1, nrow(hd))),
xlab="Retention time",
xaxt="n", yaxt="n", col=c("black","steelblue"))
k <- round(range(hd$retentionTime) / 60)
nk <- 5
axis(side=1, at=seq(0,1,1/nk), labels=seq(k[1],k[2],k[2]/nk))
Animation
The following animation scrolls over 5 minutes of retention time for a MZ range between 521 and 523.
library("animation")
an1 <- function() {
for (i in seq(0, 5, 0.2)) {
rtsel <- hd$retentionTime[ms1] / 60 > (30 + i) &
hd$retentionTime[ms1] / 60 < (35 + i)
M <- MSmap(ms, ms1[rtsel], 521, 523, .005, hd)
M@map[msMap(M) == 0] <- NA
print(plot3D(M, rgl = FALSE))
}
}
saveGIF(an1(), movie.name = "msanim1.gif")
The code chunk below scrolls of a slice of retention times while keeping the retention time constant between 30 and 35 minutes.
an2 <- function() {
for (i in seq(0, 2.5, 0.1)) {
rtsel <- hd$retentionTime[ms1] / 60 > 30 & hd$retentionTime[ms1] / 60 < 35
mz1 <- 520 + i
mz2 <- 522 + i
M <- MSmap(ms, ms1[rtsel], mz1, mz2, .005, hd)
M@map[msMap(M) == 0] <- NA
print(plot3D(M, rgl = FALSE))
}
}
saveGIF(an2(), movie.name = "msanim2.gif")
Preprocessing of MALDI-MS spectra
The following code chunks demonstrate the usage of the mass spectrometry preprocessing and plotting routines in the r CRANpkg("MALDIquant") package. MALDIquant uses the traditional graphics system. Therefore MALDIquant overloads the traditional functions plot, lines and points for its own data types. These data types represents spectrum and peak lists as S4 classes. Please see the MALDIquant vignette and the corresponding website for more details.
After loading some example data a simple plot draws the raw spectrum.
library("MALDIquant")
data("fiedler2009subset", package="MALDIquant")
plot(fiedler2009subset[[14]])
After some preprocessing, namely variance stabilization and smoothing, we use lines to draw our baseline estimate in our processed spectrum.
transformedSpectra <- transformIntensity(fiedler2009subset, method = "sqrt")
smoothedSpectra <- smoothIntensity(transformedSpectra, method = "SavitzkyGolay")
plot(smoothedSpectra[[14]])
lines(estimateBaseline(smoothedSpectra[[14]]), lwd = 2, col = "red")
After removing the background removal we could use plot again to draw our baseline corrected spectrum.
rbSpectra <- removeBaseline(smoothedSpectra)
plot(rbSpectra[[14]])
detectPeaks returns a MassPeaks object that offers the same traditional graphics functions. The next code chunk demonstrates how to mark the detected peaks in a spectrum.
cbSpectra <- calibrateIntensity(rbSpectra, method = "TIC")
peaks <- detectPeaks(cbSpectra, SNR = 5)
plot(cbSpectra[[14]])
points(peaks[[14]], col = "red", pch = 4, lwd = 2)
Additional there is a special function labelPeaks that allows to draw the M/Z values above the corresponding peaks. Next we mark the 5 top peaks in the spectrum.
top5 <- intensity(peaks[[14]]) %in% sort(intensity(peaks[[14]]),
decreasing = TRUE)[1:5]
labelPeaks(peaks[[14]], index = top5, avoidOverlap = TRUE)
Often multiple spectra have to be recalibrated to be comparable. Therefore MALDIquant warps the spectra according to so called reference or landmark peaks. For debugging the determineWarpingFunctions function offers some warping plots. Here we show only the last 4 plots:
par(mfrow = c(2, 2))
warpingFunctions <-
determineWarpingFunctions(peaks,
tolerance = 0.001,
plot = TRUE,
plotInteractive = TRUE)
par(mfrow = c(1, 1))
warpedSpectra <- warpMassSpectra(cbSpectra, warpingFunctions)
warpedPeaks <- warpMassPeaks(peaks, warpingFunctions)
In the next code chunk we visualise the need and the effect of the recalibration.
sel <- c(2, 10, 14, 16)
xlim <- c(4180, 4240)
ylim <- c(0, 1.9e-3)
lty <- c(1, 4, 2, 6)
par(mfrow = c(1, 2))
plot(cbSpectra[[1]], xlim = xlim, ylim = ylim, type = "n")
for (i in seq(along = sel)) {
lines(peaks[[sel[i]]], lty = lty[i], col = i)
lines(cbSpectra[[sel[i]]], lty = lty[i], col = i)
}
plot(cbSpectra[[1]], xlim = xlim, ylim = ylim, type = "n")
for (i in seq(along = sel)) {
lines(warpedPeaks[[sel[i]]], lty = lty[i], col = i)
lines(warpedSpectra[[sel[i]]], lty = lty[i], col = i)
}
par(mfrow = c(1, 1))
The code chunks above generate plots that are very similar to the figure 7 in the corresponding paper “Visualisation of proteomics data using R”. Please find the code to exactly reproduce the figure at: https://github.com/sgibb/MALDIquantExamples/blob/master/R/createFigure1_color.R
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