1.1 References

• CRAN Task View: Graphic Displays & Dynamic Graphics & Graphic Devices & Visualization: http://cran.r-project.org/web/views/Graphics.html
• CRAN Task View: Web Technologies and Services: http://cran.r-project.org/web/views/WebTechnologies.html
• ggplot2 book (syntax is slightly outdated) (code), web page and on-line docs
• lattice book and web page
• R Graphics book
• R Cookbook and R Graphics Cookbook

1.2 Relevant packages

There are currently 92 Proteomics and 62 MassSpectrometry packages in Bioconductor version 3.5. Other non-Bioconductor packages are described in the RforProteomics vignette.

3.1 The traditional plotting system

par(mfrow = c(1, 2))
plot(M ~ A, data = madata42, main = "4vs2",
     xlab = "A", ylab = "M", col = madata62$class)
plot(M ~ A, data = madata62, main = "6vs2",
     xlab = "A", ylab = "M", col = madata62$class)

3.2 lattice

library("lattice")
latma <- xyplot(M ~ A | data, data = madata,
                groups = madata$class,
                auto.key = TRUE)
print(latma)

3.3 ggplot2

library("ggplot2")
ggma <- ggplot(aes(x = A, y = M, colour = class), data = madata,
               colour = class) +
                   geom_point() +
                       facet_grid(. ~ data)
print(ggma)

3.4 Customization

library("RColorBrewer")
bcols <- brewer.pal(4, "Set1")
cls <- c("Background" = "#12121230",
         "P02769" = bcols[1],
         "P00924" = bcols[2],
         "P62894" = bcols[3],
         "P00489" = bcols[4])

ggma2 <- ggplot(aes(x = A, y = M, colour = class),
                data = madata) + geom_point(shape = 19) +
                    facet_grid(. ~ data) + scale_colour_manual(values = cls) +
                        guides(colour = guide_legend(override.aes = list(alpha = 1)))
print(ggma2)

3.5 The MAplot method for MSnSet instances

MAplot(qnt, cex = .8)

3.6 An interactive shiny app for MA plots

This app is based on Mike Love’s shinyMA application, adapted for a proteomics data. A screen shot is displayed below. To start the application:

shinyMA()

The application is also available online at https://lgatto.shinyapps.io/shinyMA/.

See the excellent shiny web page for tutorials.

3.7 Volcano plots

Below, using the msmsTest package, we load a example MSnSet data with spectral couting data (from the r Biocpkg("msmsEDA") package) and run a statistical test to obtain (adjusted) p-values and fold-changes.

library("msmsEDA")
library("msmsTests")
data(msms.dataset)
## Pre-process expression matrix
e <- pp.msms.data(msms.dataset)
## Models and normalizing condition
null.f <- "y~batch"
alt.f <- "y~treat+batch"
div <- apply(exprs(e), 2, sum)
## Test
res <- msms.glm.qlll(e, alt.f, null.f, div = div)
lst <- test.results(res, e, pData(e)$treat, "U600", "U200 ", div,
                    alpha = 0.05, minSpC = 2, minLFC = log2(1.8),
                    method = "BH")

Here, we produce the volcano plot by hand, with the plot function. In the second plot, we limit the x axis limits and add grid lines.

plot(lst$tres$LogFC, -log10(lst$tres$p.value))

plot(lst$tres$LogFC, -log10(lst$tres$p.value),
     xlim = c(-3, 3))
grid()

Below, we use the res.volcanoplot function from the r Biocpkg("msmsTests") package. This functions uses the sample annotation stored with the quantitative data in the MSnSet object to colour the samples according to their phenotypes.

## Plot
res.volcanoplot(lst$tres,
                max.pval = 0.05,
                min.LFC = 1,
                maxx = 3,
                maxy = NULL,
                ylbls = 4)

3.8 A PCA plot

Using the counts.pca function from the msmsEDA package:

library("msmsEDA")
data(msms.dataset)
msnset <- pp.msms.data(msms.dataset)
lst <- counts.pca(msnset, wait = FALSE)

It is also possible to generate the PCA data using the prcomp. Below, we extract the coordinates of PC1 and PC2 from the counts.pca result and plot them using the plot function.

pcadata <- lst$pca$x[, 1:2]
head(pcadata)

##                  PC1       PC2
## U2.2502.1 -120.26080 -53.55270
## U2.2502.2  -99.90618 -53.89979
## U2.2502.3 -127.35928 -49.29906
## U2.2502.4 -166.04611 -39.27557
## U6.2502.1 -127.18423  37.11614
## U6.2502.2 -117.97016  47.03702

plot(pcadata[, 1], pcadata[, 2],
     xlab = "PCA1", ylab = "PCA2")
grid()

4.1 Scatter plots

See the MA and volcano plot examples.

The default plot type is p, for points. Other important types are l for lines and h for histogram (see below).

4.2 Historams and density plots

We extract the (normalised) intensities of the first sample

x <- exprs(tan2009r1)[, 1]

and plot the distribution with a histogram and a density plot next to each other on the same figure (using the mfrow par plotting paramter)

par(mfrow = c(1, 2))
hist(x)
plot(density(x))

4.3 Box plots and violin plots

we first extract the 888 proteins by r ncol(tan2009r1) samples data matrix and plot the sample distributions next to each other using boxplot and beanplot (from the beanplot package).

library("beanplot")
x <- exprs(tan2009r1)
par(mfrow = c(2, 1))
boxplot(x)
beanplot(x[, 1], x[, 2], x[, 3], x[, 4], log = "")

4.4 Line plots

below, we produce line plots that describe the protein quantitative profiles for two sets of proteins, namely er and mitochondrial proteins using matplot.

we need to transpose the matrix (with t) and set the type to both (b), to display points and lines, the colours to red and steel blue, the point characters to 1 (an empty point) and the line type to 1 (a solid line).

er <- fData(tan2009r1)$markers == "ER"
mt <- fData(tan2009r1)$markers == "mitochondrion"

par(mfrow = c(2, 1))
matplot(t(x[er, ]), type = "b", col = "red", pch = 1, lty = 1)
matplot(t(x[mt, ]), type = "b", col = "steelblue", pch = 1, lty = 1)

In the last section, about spatial proteomics, we use the specialised plotDist function from the pRoloc package to generate such figures.

4.5 Bar and dot charts

To illustrate bar and dot charts, we cound the number of proteins in the respective class using table.

x <- table(fData(tan2009r1)$markers)
x

## 
##  Cytoskeleton            ER         Golgi      Lysosome       Nucleus 
##             7            28            13             8            21 
##            PM    Peroxisome    Proteasome  Ribosome 40S  Ribosome 60S 
##            34             4            15            20            32 
## mitochondrion       unknown 
##            29           677

par(mfrow = c(1, 2))
barplot(x)
dotchart(x)

## Warning in dotchart(x): 'x' is neither a vector nor a matrix: using
## as.numeric(x)

4.6 Heatmaps

The easiest to produce a complete heatmap is with the heatmap function:

heatmap(exprs(tan2009r1))

To produce the a heatmap without the dendrograms, one can use the image function on a matrix or the specialised version for MSnSet objects from the MSnbase package.

par(mfrow = c(1, 2))
x <- matrix(1:9, ncol = 3)
image(x)
image(tan2009r1)

See also gplots’s heatmap.2 function and the Heatplus Bioconductor package for more advanced heatmaps and the corrplot package for correlation matrices.

4.7 Dendrograms

The easiest way to produce and plot a dendrogram is:

d <- dist(t(exprs(tan2009r1))) ## distance between samples
hc <- hclust(d) ## hierarchical clustering
plot(hc) ## visualisation

See also dendextend and this post to illustrate latticeExtra and ggdendro.

4.8 Venn diagrams

• The limma package.
• The VennDiagram package.

5.1 Direct access to the raw data

library("mzR")
mzf <- pxget(px1,
             "TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01-20141210.mzML")

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## 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)

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))

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")

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")

5.2 The MSnbase infrastructure

library("MSnbase")
data(itraqdata)
itraqdata2 <- pickPeaks(itraqdata, verbose = FALSE)
plot(itraqdata[[25]], full = TRUE, reporters = iTRAQ4)

par(oma = c(0, 0, 0, 0))
par(mar = c(4, 4, 1, 1))
plot(itraqdata2[[25]], itraqdata2[[28]], sequences = rep("IMIDLDGTENK", 2))

5.3 The protViz package

library("protViz")
data(msms)

fi <- fragmentIon("TAFDEAIAELDTLNEESYK")
fi.cyz <- as.data.frame(cbind(c=fi[[1]]$c, y=fi[[1]]$y, z=fi[[1]]$z))
     
p <- peakplot("TAFDEAIAELDTLNEESYK",
              spec = msms[[1]],
              fi = fi.cyz,
              itol = 0.6,
              ion.axes = FALSE)

The peakplot function return the annotation of the MSMS spectrum that is plotted:

str(p)

## List of 7
##  $ mZ.Da.error : num [1:57] 215.3 144.27 -2.8 -17.06 2.03 ...
##  $ mZ.ppm.error: num [1:57] 1808046 758830 -8306 -37724 3501 ...
##  $ idx         : num [1:57] 1 1 1 3 16 24 41 52 67 88 ...
##  $ label       : chr [1:57] "c1" "c2" "c3" "c4" ...
##  $ score       : num -1
##  $ sequence    : chr "TAFDEAIAELDTLNEESYK"
##  $ fragmentIon :'data.frame':    19 obs. of  3 variables:
##   ..$ c: num [1:19] 119 190 337 452 581 ...
##   ..$ y: num [1:19] 147 310 397 526 655 ...
##   ..$ z: num [1:19] 130 293 380 509 638 ...

5.4 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

7.1 An interactive shiny app for Imaging mass spectrometry

There is also an interactive MALDIquant IMS shiny app for demonstration purposes. A screen shot is displayed below. To start the application:

library("shiny")
runGitHub("sgibb/ims-shiny")