1.1 Speed and memory requirements

Raw mass spectrometry file are generally several hundreds of MB large and most of this is used for binary raw spectrum data. As such, data containers can easily grow very large and thus require large amounts of RAM. This requirement is being tackled by avoiding to load the raw data into memory and using on-disk random access to the content of mzXML/mzML data files on demand. When focusing on reporter ion quantitation, a direct solution for this is to trim the spectra using the trimMz method to select the area of interest and thus substantially reduce the size of the Spectrum objects. This is illustrated in section 6.2.

Parallel processing The independent handling of spectra is ideally suited for parallel processing. The quantify method for example performs reporter peaks quantitation in parallel.

Parallel support is provided by the BiocParallel and various backends including multicore (forking, default on Linux), simple networf network of workstations (SNOW, default on Windows) using sockets, forking or MPI among others. We refer readers to the documentation in BiocParallel. Automatic parallel processing of spectra is only established for a certain number of spectra (per file). This value (default is 1000) can be set with the setMSnbaseParallelThresh function.

In sock-based parallel processing, the main worker process has to start new R instances and connect to them via sock. Sometimes these connections can not be established and the processes get stuck. To test this, users can disable parallel processing by disabling parallel processing with register(SerialParam()). To avoid these deadlocks, it is possible to initiate the parallel processing setup explicitly at the beginning of the script using, for example

library("doParallel")
registerDoParallel(3) ## using 3 slave nodes
register(DoparParam(), default = TRUE)

## rest of script comes below

On-disk access Developmenets in version 2 of the package have solved the memory issue by implementing and on-disk version the of data class storing raw data (MSnExp, see section 2.3), where the spectra a accessed on-disk only when required. The benchmarking vignette compares the on-disk and in-memory implemenatations22 in R, open it with vignette("benchmarking") or read it online here. See details below.

2.1 Importing experiments

MSnbase is able to import raw MS data stored in one of the XML-based formats as well as peak lists in the mfg format33 Mascot Generic Format, see http://www.matrixscience.com/help/data_file_help.html#GEN.

Raw data The XML-based formats, mzXML (Pedrioli et al. 2004), mzData (Orchard et al. 2007) and mzML (Martens et al. 2010) can be imported with the readMSData function, as illustrated below (see ?readMSData for more details). To make use of the new on-disk implementation, set mode = "onDisk" in readMSData rather than using the default mode = "inMemory".

file <- dir(system.file(package = "MSnbase", dir = "extdata"),
            full.names = TRUE, pattern = "mzXML$")
rawdata <- readMSData(file, msLevel = 2, verbose = FALSE)

Only spectra of a given MS level can be loaded at a time by setting the msLevel parameter accordingly in readMSData and in-memory data. In this document, we will use the itraqdata data set, provided with MSnbase. It includes feature metadata, accessible with the fData accessor. The metadata includes identification data for the 55 MS2 spectra.

Version 2.0 and later of MSnbase provide a new on-disk data storage model (see the benchmarking vignette for more details). The new data backend is compatible with the orignal in-memory model. To make use of the new infrastructure, read your raw data by setting the mode argument to "onDisk" (the default is still "inMemory" but is likely to change in the future). The new on-disk implementation supports several MS levels in a single raw data object. All existing operations work irrespective of the backend.

Peak lists can often be exported after spectrum processing from vendor-specific software and are also used as input to search engines. Peak lists in mgf format can be imported with the function readMgfData (see ?readMgfData for details) to create experiment objects. Experiments or individual spectra can be exported to an mgf file with the writeMgfData methods (see ?writeMgfData for details and examples).

Experiments with multiple runs Although it is possible to load and process multiple files serially and later merge the resulting quantitation data as show in section 13, it is also feasible to load several raw data files at once. Here, we report the analysis of an LC-MSMS experiment were 14 liquid chromatography (LC) fractions were loaded in memory using readMSData on a 32-cores servers with 128 Gb of RAM. It took about 90 minutes to read the 14 uncentroided mzXML raw files (4.9 Gb on disk in total) and create a 3.3 Gb raw data object (an MSnExp instance, see next section). Quantitation of 9 reporter ions (iTRAQ9 object, see 2.5) for 88690 features was performed in parallel on 16 processors and took 76 minutes. The resulting quantitation data was only 22.1 Mb and could easily be further processed. These number are based on the older in-memory implementation. As shown in the benchmarking vignette, using on-disk data greatly reduces memory requirement and computation time.

See also section 7.2 to import quantitative data stored in spreadsheets into R for further processing using MSnbase. The MSnbase-iovignette[in R, open it with vignette("MSnbase-io") or read it online here] gives a general overview of MSnbase’s input/ouput capabilites.

See section 7.3 for importing chromatographic data of SRM/MRM experiments.

2.2 Exporting experiments/MS data

MSnbase supports also to write MSnExp or OnDiskMSnExp objects to mzML or mzXML files using the writeMSData function. This is specifically useful in workflows in which the MS data was heavily manipulated. Presently, each sample/file is exported into one file.

Below we write the data in mzML format to a temporary file. By setting the optional parameter copy = TRUE general metadata (such as instrument info or all data processing descriptions) are copied over from the originating file.

writeMSData(rawdata, file = paste0(tempfile(), ".mzML"), copy = TRUE)

2.3 MS experiments

Raw data is contained in MSnExp objects, that stores all the spectra of an experiment, as defined by one or multiple raw data files.

library("MSnbase")
itraqdata

## MSn experiment data ("MSnExp")
## Object size in memory: 1.9 Mb
## - - - Spectra data - - -
##  MS level(s): 2 
##  Number of spectra: 55 
##  MSn retention times: 19:9 - 50:18 minutes
## - - - Processing information - - -
## Data loaded: Wed May 11 18:54:39 2011 
## Updated from version 0.3.0 to 0.3.1 [Fri Jul  8 20:23:25 2016] 
##  MSnbase version: 1.1.22 
## - - - Meta data  - - -
## phenoData
##   rowNames: 1
##   varLabels: sampleNames sampleNumbers
##   varMetadata: labelDescription
## Loaded from:
##   dummyiTRAQ.mzXML 
## protocolData: none
## featureData
##   featureNames: X1 X10 ... X9 (55 total)
##   fvarLabels: spectrum ProteinAccession ProteinDescription
##     PeptideSequence
##   fvarMetadata: labelDescription
## experimentData: use 'experimentData(object)'

head(fData(itraqdata))

##     spectrum ProteinAccession                       ProteinDescription
## X1         1              BSA                     bovine serum albumin
## X10       10          ECA1422 glucose-1-phosphate cytidylyltransferase
## X11       11          ECA4030         50S ribosomal subunit protein L4
## X12       12          ECA3882                   chaperone protein DnaK
## X13       13          ECA1364      succinyl-CoA synthetase alpha chain
## X14       14          ECA0871              NADP-dependent malic enzyme
##     PeptideSequence
## X1           NYQEAK
## X10 VTLVDTGEHSMTGGR
## X11           SPIWR
## X12        TAIDDALK
## X13          SILINK
## X14    DFEVVNNESDPR

As illustrated above, showing the experiment textually displays it’s content:

• Information about the raw data, i.e. the spectra.

• Specific information about the experiment processing44 This part will be automatically updated when the object is modified with it’s ad hoc methods, as illustrated later. and package version. This slot can be accessed with the processingData method.

• Other meta data, including experimental phenotype, file name(s) used to import the data, protocol data, information about features (individual spectra here) and experiment data. Most of these are implemented as in the eSet class and are described in more details in their respective manual pages. See ?MSnExp and references therein for additional background information.

  The experiment meta data associated with an MSnExp experiment is of class MIAPE. It stores general information about the experiment as well as MIAPE (Minimum Information About a Proteomics Experiment) information (Taylor et al. 2007, Taylor et al. (2008)). This meta-data can be accessed with the experimentData method. When available, a summary of MIAPE-MS data can be printed with the msInfo method. See ?MIAPE for more details.

2.4 Spectra objects

The raw data is composed of the 55 MS spectra. The spectra are named individually (X1, X10, X11, X12, X13, X14, …) and stored in a environment. They can be accessed individually with itraqdata[["X1"]] or itraqdata[[1]], or as a list with spectra(itraqdata). As we have loaded our experiment specifying msLevel=2, the spectra will all be of level 2 (or higher, if available).

sp <- itraqdata[["X1"]]
sp

## Object of class "Spectrum2"
##  Precursor: 520.7833 
##  Retention time: 19:9 
##  Charge: 2 
##  MSn level: 2 
##  Peaks count: 1922 
##  Total ion count: 26413754

Attributes of individual spectra or of all spectra of an experiment can be accessed with their respective methods: precursorCharge for the precursor charge, rtime for the retention time, mz for the MZ values, intensity for the intensities, … see the Spectrum, Spectrum1 and Spectrum2 manuals for more details.

peaksCount(sp)

## [1] 1922

head(peaksCount(itraqdata))

##   X1  X10  X11  X12  X13  X14 
## 1922 1376 1571 2397 2574 1829

rtime(sp)

## [1] 1149.31

head(rtime(itraqdata))

##      X1     X10     X11     X12     X13     X14 
## 1149.31 1503.03 1663.61 1663.86 1664.08 1664.32

2.5 Reporter ions

Reporter ions are defined with the ReporterIons class. Specific peaks of interest are defined by a MZ value, a with around the expected MZ and a name (and optionally a colour for plotting, see section 3). ReporterIons instances are required to quantify reporter peaks in MSnExp experiments. Instances for the most commonly used isobaric tags like iTRAQ 4-plex and 8-plex and TMT 6- and 10-plex tags are already defined in MSnbase. See ?ReporterIons for details about how to generate new ReporterIons objects.

iTRAQ4

## Object of class "ReporterIons"
## iTRAQ4: '4-plex iTRAQ' with 4 reporter ions
##  - [iTRAQ4.114] 114.1112 +/- 0.05 (red)
##  - [iTRAQ4.115] 115.1083 +/- 0.05 (green)
##  - [iTRAQ4.116] 116.1116 +/- 0.05 (blue)
##  - [iTRAQ4.117] 117.115 +/- 0.05 (yellow)

TMT10

## Object of class "ReporterIons"
## TMT10HCD: '10-plex TMT HCD' with 10 reporter ions
##  - [126] 126.1277 +/- 0.002 (#8DD3C7)
##  - [127N] 127.1248 +/- 0.002 (#FFFFB3)
##  - [127C] 127.1311 +/- 0.002 (#BEBADA)
##  - [128N] 128.1281 +/- 0.002 (#FB8072)
##  - [128C] 128.1344 +/- 0.002 (#80B1D3)
##  - [129N] 129.1315 +/- 0.002 (#FDB462)
##  - [129C] 129.1378 +/- 0.002 (#B3DE69)
##  - [130N] 130.1348 +/- 0.002 (#FCCDE5)
##  - [130C] 130.1411 +/- 0.002 (#D9D9D9)
##  - [131] 131.1382 +/- 0.002 (#BC80BD)

2.6 Chromatogram objects

Chromatographic data, i.e. intensity values along the retention time dimension for a given \(m/z\) range/slice, can be extracted with the chromatogram method. Below we read a file from the msdata package and extract the (MS level 1) chromatogram. Without providing an \(m/z\) and a retention time range the function returns the total ion chromatogram (TIC) for each file within the MSnExp or OnDiskMSnExp object. See also section 7.3 for importing chromatographic data from SRM/MRM experiments.

f <- c(system.file("microtofq/MM14.mzML", package = "msdata"))
mtof <- readMSData(f, mode = "onDisk")
mtof_tic <- chromatogram(mtof)
mtof_tic

## Chromatograms with 1 row and 1 column
##           MM14.mzML
##      <Chromatogram>
## [1,]    length: 112
## phenoData with 1 variables
## featureData with 1 variables

Chromatographic data, represented by the intensity-retention time duplets, is stored in the Chromatogram object. The chromatogram method returns a Chromatograms object (note the s) which holds multiple Chromatogram objects and arranges them in a two-dimensional grid with columns representing files/samples of the MSnExp or OnDiskMSnExp object and rows \(m/z\)-retention time ranges. In the example above the Chromatograms object contains only a single Chromatogram object. Below we access this chromatogram object. Similar to the Spectrum objects, Chromatogram objects provide the accessor functions intensity and rtime to access the data, as well as the mz function, that returns the \(m/z\) range of the chromatogram.

mtof_tic[1, 1]

## Object of class: Chromatogram
## Intensity values aggregated using: sum 
## length of object: 112
## from file: 1
## mz range: [94.80679, 1004.962]
## rt range: [270.334, 307.678]
## MS level: 1

head(intensity(mtof_tic[1, 1]))

## F1.S001 F1.S002 F1.S003 F1.S004 F1.S005 F1.S006 
##   64989   67445   77843  105097  155609  212760

head(rtime(mtof_tic[1, 1]))

## F1.S001 F1.S002 F1.S003 F1.S004 F1.S005 F1.S006 
## 270.334 270.671 271.007 271.343 271.680 272.016

mz(mtof_tic[1, 1])

## [1]   94.80679 1004.96155

To extract the base peak chromatogram (the largest peak along the \(m/z\) dimension for each retention time/spectrum) we set the aggregationFun argument to "max".

mtof_bpc <- chromatogram(mtof, aggregationFun = "max")

See the Chromatogram help page and the vignettes from the xcms package for more details and use cases, also on how to extract chromatograms for specific ions.

3.1 MS data space

The MSmap class can be used to isolate specific slices of interest from a complete MS acquisition by specifying \(m/z\) and retention time ranges. One needs a raw data file in a format supported by mzR’s openMSfile (mzML, mzXML, …). Below we first download a raw data file from the PRIDE repository and create an MSmap containing all the MS1 spectra between acquired between 30 and 35 minutes and peaks between 521 and 523 \(m/z\). See ?MSmap for details.

## downloads the data
library("rpx")
px1 <- PXDataset("PXD000001")
mzf <- pxget(px1, 7)

## reads the data
ms <- openMSfile(mzf)
hd <- header(ms)

## a set of spectra of interest: MS1 spectra eluted
## between 30 and 35 minutes retention time
ms1 <- which(hd$msLevel == 1)
rtsel <- hd$retentionTime[ms1] / 60 > 30 &
    hd$retentionTime[ms1] / 60 < 35

## the map
M <- MSmap(ms, ms1[rtsel], 521, 523, .005, hd, zeroIsNA = TRUE)

M

## Object of class "MSmap"
##  Map [75, 401]
##   [1]  Retention time: 30:1 - 34:58 
##   [2]  M/Z: 521 - 523 (res 0.005)

The M map object can be rendered as a heatmap with plot, as shown on figure 1.

plot(M, aspect = 1, allTicks = FALSE)

Figure 1: Heat map of a chunk of the MS data

One can also render the data in 3 dimension with the plot3D function, as show on figure 2.

plot3D(M)

Figure 2: 3 dimensional represention of MS map data

To produce figure 3, we create a second MSmap object containing the first two MS1 spectra of the first map (object M above) and all intermediate MS2 spectra and display \(m/z\) values between 100 and 1000.

i <- ms1[which(rtsel)][1]
j <- ms1[which(rtsel)][2]
M2 <- MSmap(ms, i:j, 100, 1000, 1, hd)

M2

## Object of class "MSmap"
##  Map [12, 901]
##   [1]  Retention time: 30:1 - 30:5 
##   [2]  M/Z: 100 - 1000 (res 1)

plot3D(M2)

Figure 3: 3 dimensional represention of MS map data
MS1 and MS2 spectra are coloured in blue and magenta respectively.

3.2 MS Spectra

Spectra can be plotted individually or as part of (subset) experiments with the plot method. Full spectra can be plotted (using full=TRUE), specific reporter ions of interest (by specifying with reporters with reporters=iTRAQ4 for instance) or both (see figure 4).

plot(sp, reporters = iTRAQ4, full = TRUE)

Figure 4: Raw MS2 spectrum with details about reporter ions

It is also possible to plot all spectra of an experiment (figure 5). Lets start by subsetting the itraqdata experiment using the protein accession numbers included in the feature metadata, and keep the 6 from the BSA protein.

sel <- fData(itraqdata)$ProteinAccession == "BSA"
bsa <- itraqdata[sel]
bsa

## MSn experiment data ("MSnExp")
## Object size in memory: 0.11 Mb
## - - - Spectra data - - -
##  MS level(s): 2 
##  Number of spectra: 3 
##  MSn retention times: 19:9 - 36:17 minutes
## - - - Processing information - - -
## Data loaded: Wed May 11 18:54:39 2011 
## Updated from version 0.3.0 to 0.3.1 [Fri Jul  8 20:23:25 2016] 
## Data [logically] subsetted 3 spectra: Fri May 31 19:49:03 2019 
##  MSnbase version: 1.1.22 
## - - - Meta data  - - -
## phenoData
##   rowNames: 1
##   varLabels: sampleNames sampleNumbers
##   varMetadata: labelDescription
## Loaded from:
##   dummyiTRAQ.mzXML 
## protocolData: none
## featureData
##   featureNames: X1 X52 X53
##   fvarLabels: spectrum ProteinAccession ProteinDescription
##     PeptideSequence
##   fvarMetadata: labelDescription
## experimentData: use 'experimentData(object)'

as.character(fData(bsa)$ProteinAccession)

## [1] "BSA" "BSA" "BSA"

These can then be visualised together by plotting the MSnExp object, as illustrated on figure 5.

plot(bsa, reporters = iTRAQ4, full = FALSE) + theme_gray(8)

Figure 5: Experiment-wide raw MS2 spectra
The y-axes of the individual spectra are automatically rescaled to the same range. See section 8.2 to rescale peaks identically.

## NULL

Customising your plots The MSnbase plot methods have a logical plot parameter (default is TRUE), that specifies if the plot should be printed to the current device. A plot object is also (invisibly) returned, so that it can be saved as a variable for later use or for customisation.

MSnbase uses the package to generate plots, which can subsequently easily be customised. More details about can be found in (Wickham 2009) (especially chapter 8) and on http://had.co.nz/ggplot2/. Finally, if a plot object has been saved in a variable p, it is possible to obtain a summary of the object with summary(p). To view the data frame used to generate the plot, use p$data.

3.3 MS Chromatogram

Chromatographic data can be plotted using the plot method which, in contrast to the plot method for Spectrum classes, uses R base graphics. The plot method is implemented for Chromatogram and Chromatograms classes. The latter plots all chromatograms for the same \(m/z\)-rt range of all files in an experiment (i.e. for one row in the Chromatograms object) into one plot.

plot(mtof_bpc)

Figure 6: Base peak chromatogram

4.1 Adding identification data

MSnbase is able to integrate identification data from mzIdentML (Jones et al. 2012) files.

We first load two example files shipped with the MSnbase containing raw data (as above) and the corresponding identification results respectively. The raw data is read with the readMSData, as demonstrated above. As can be seen, the default feature data only contain spectra numbers. More data about the spectra is of course available in an MSnExp object, as illustrated in the previous sections. See also ?pSet and ?MSnExp for more details.

## find path to a mzXML file
quantFile <- dir(system.file(package = "MSnbase", dir = "extdata"),
                 full.name = TRUE, pattern = "mzXML$")
## find path to a mzIdentML file
identFile <- dir(system.file(package = "MSnbase", dir = "extdata"),
                 full.name = TRUE, pattern = "dummyiTRAQ.mzid")
## create basic MSnExp
msexp <- readMSData(quantFile, verbose = FALSE)
head(fData(msexp), n = 2)

##       spectrum
## F1.S1        1
## F1.S2        2

The addIdentificationData method takes an MSnExp instance (or an MSnSet instance storing quantitation data, see section 7.1) as first argument and one or multiple mzIdentML file names (as a character vector) as second one55 The identification data can also be passed as dedicated identification objects such as mzRident from the mzR package or mzID from thr mzID package, or as a data.frame - see ?addIdentifionData for details. and updates the MSnExp feature data using the identification data read from the mzIdentML file(s).

msexp <- addIdentificationData(msexp, id = identFile)
head(fData(msexp), n = 2)

##       spectrum acquisition.number          sequence chargeState rank
## F1.S1        1                  1 VESITARHGEVLQLRPK           3    1
## F1.S2        2                  2     IDGQWVTHQWLKK           3    1
##       passThreshold experimentalMassToCharge calculatedMassToCharge modNum
## F1.S1          TRUE                 645.3741               645.0375      0
## F1.S2          TRUE                 546.9586               546.9633      0
##       isDecoy post pre start end DatabaseAccess DBseqLength DatabaseSeq
## F1.S1   FALSE    A   R   170 186        ECA0984         231            
## F1.S2   FALSE    A   K    50  62        ECA1028         275            
##                                                              DatabaseDescription
## F1.S1                                        ECA0984 DNA mismatch repair protein
## F1.S2 ECA1028 2,3,4,5-tetrahydropyridine-2,6-dicarboxylate N-succinyltransferase
##       scan.number.s.          idFile MS.GF.RawScore MS.GF.DeNovoScore
## F1.S1              1 dummyiTRAQ.mzid            -39                77
## F1.S2              2 dummyiTRAQ.mzid            -30                39
##       MS.GF.SpecEValue MS.GF.EValue modName modMass modLocation
## F1.S1     5.527468e-05     79.36958    <NA>      NA          NA
## F1.S2     9.399048e-06     13.46615    <NA>      NA          NA
##       subOriginalResidue subReplacementResidue subLocation nprot npep.prot
## F1.S1               <NA>                  <NA>          NA     1         1
## F1.S2               <NA>                  <NA>          NA     1         1
##       npsm.prot npsm.pep
## F1.S1         1        1
## F1.S2         1        1

Finally we can use idSummary to summarise the percentage of identified features per quantitation/identification pairs.

idSummary(msexp)

##       spectrumFile          idFile coverage
## 1 dummyiTRAQ.mzXML dummyiTRAQ.mzid      0.6

When identification data is present, and hence peptide sequences, one can annotation fragment peaks on the MS2 figure by passing the peptide sequence to the plot method.

itraqdata2 <- pickPeaks(itraqdata, verbose=FALSE)
i <- 14
s <- as.character(fData(itraqdata2)[i, "PeptideSequence"])

plot(itraqdata2[[i]], s, main = s)

Figure 7: Annotated MS2 spectrum

The fragment ions are calculated with the calculateFragments, described in section 4.3.

4.2 Filtering identification data

One can remove the features that have not been identified using removeNoId. This function uses by default the pepseq feature variable to search the presence of missing data (NA values) and then filter these non-identified spectra.

fData(msexp)$sequence

## [1] "VESITARHGEVLQLRPK" "IDGQWVTHQWLKK"     NA                 
## [4] NA                  "LVILLFR"

msexp <- removeNoId(msexp)
fData(msexp)$sequence

## [1] "VESITARHGEVLQLRPK" "IDGQWVTHQWLKK"     "LVILLFR"

idSummary(msexp)

##       spectrumFile          idFile coverage
## 1 dummyiTRAQ.mzXML dummyiTRAQ.mzid        1

Similarly, the removeMultipleAssignment method can be used to filter out non-unique features, i.e. that have been assigned to protein groups with more than one member. This function uses by default the nprot feature variable.

Note that removeNoId and removeMultipleAssignment methods can also be called on MSnExp instances.

4.3 Calculate Fragments

MSnbase is able to calculate theoretical peptide fragments via calculateFragments.

calculateFragments("ACEK",
                   type = c("a", "b", "c", "x", "y", "z"))

##           mz ion type pos z seq
## 1   44.04947  a1    a   1 1   A
## 2  204.08012  a2    a   2 1  AC
## 3  333.12271  a3    a   3 1 ACE
## 4   72.04439  b1    b   1 1   A
## 5  232.07504  b2    b   2 1  AC
## 6  361.11763  b3    b   3 1 ACE
## 7   89.07094  c1    c   1 1   A
## 8  249.10159  c2    c   2 1  AC
## 9  378.14417  c3    c   3 1 ACE
## 10 173.09207  x1    x   1 1   K
## 11 302.13466  x2    x   2 1  EK
## 12 462.16531  x3    x   3 1 CEK
## 13 147.11280  y1    y   1 1   K
## 14 276.15539  y2    y   2 1  EK
## 15 436.18604  y3    y   3 1 CEK
## 16 130.08625  z1    z   1 1   K
## 17 259.12884  z2    z   2 1  EK
## 18 419.15949  z3    z   3 1 CEK
## 19 284.12409 x2_   x_   2 1  EK
## 20 258.14483 y2_   y_   2 1  EK
## 21 241.11828 z2_   z_   2 1  EK
## 22 155.08150 x1_   x_   1 1   K
## 23 444.15474 x3_   x_   3 1 CEK
## 24 129.10224 y1_   y_   1 1   K
## 25 418.17548 y3_   y_   3 1 CEK
## 26 112.07569 z1_   z_   1 1   K
## 27 401.14893 z3_   z_   3 1 CEK

It is also possible to match these fragments against an Spectrum2 object.

pepseq <- fData(msexp)$sequence[1]
calculateFragments(pepseq, msexp[[1]], type=c("b", "y"))

##           mz  intensity  ion type pos z             seq       error
## 1   100.0005       0.00   b1    b   1 1               V  0.07522824
## 2   429.2563 1972344.00   b4    b   4 1            VESI -0.02189010
## 3   512.3044  684918.00  b5_   b_   5 1           VESIT -0.03290132
## 4   513.3047 2574137.00   y4    y   4 1            LRPK  0.04598246
## 5   583.3300 1440833.75  b6_   b_   6 1          VESITA -0.02142609
## 6   754.4504  537234.81   y6    y   6 1          LQLRPK  0.04293155
## 7   836.6139   82364.42  y7*   y*   7 1         VLQLRPK -0.07865960
## 8   982.5354  500159.06   y8    y   8 1        EVLQLRPK  0.06897061
## 9  1080.5867  209363.69  b10    b  10 1      VESITARHGE -0.04344392
## 10 1672.8380   76075.02 b15*   b*  15 1 VESITARHGEVLQLR  0.07488430
## 11 1688.0375  136748.83 y15*   y*  15 1 SITARHGEVLQLRPK -0.07729359

6.1 Cleaning spectra

There are several methods implemented to perform basic raw data processing and manipulation. Low intensity peaks can be set to 0 with the removePeaks method from spectra or whole experiments. The intensity threshold below which peaks are removed is defined by the t parameter. t can be specified directly as a numeric. The default value is the character "min", that will remove all peaks equal to the lowest non null intensity in any spectrum. We observe the effect of the removePeaks method by comparing total ion count (i.e. the total intensity in a spectrum) with the ionCount method before (object itraqdata) and after (object experiment) for spectrum X55. The respective spectra are shown on figure 8.

experiment <- removePeaks(itraqdata, t = 400, verbose = FALSE)
ionCount(itraqdata[["X55"]])

## [1] 555408.8

ionCount(experiment[["X55"]])

## [1] 499769.6

Figure 8: Same spectrum before (left) and after setting peaks <= 400 to 0

Unlike the name might suggest, the removePeaks method does not actually remove peaks from the spectrum; they are set to 0. This can be checked using the peaksCount method, that returns the number of peaks (including 0 intensity peaks) in a spectrum. To effectively remove 0 intensity peaks from spectra, and reduce the size of the data set, one can use the clean method. The effect of the removePeaks and clean methods are illustrated on figure 9.

peaksCount(itraqdata[["X55"]])

## [1] 1726

peaksCount(experiment[["X55"]])

## [1] 1726

experiment <- clean(experiment, verbose = FALSE)
peaksCount(experiment[["X55"]])

## [1] 440

Figure 9: This figure illustrated the effect or the removePeaks and clean methods
The left-most spectrum displays two peaks, of max height 3 and 7 respectively. The middle spectrum shows the result of calling removePeaks with argument t=3, which sets all data points of the first peak, whose maximum height is smaller or equal to t to 0. The second peak is unaffected. Calling clean after removePeaks effectively deletes successive 0 intensities from the spectrum, as shown on the right plot.

6.2 Focusing on specific MZ values

Another useful manipulation method is trimMz, that takes as parameters and MSnExp (or a Spectrum) and a numeric mzlim. MZ values smaller then min(mzlim) or greater then max(mzmax) are discarded. This method is particularly useful when one wants to concentrate on a specific MZ range, as for reporter ions quantification, and generally results in substantial reduction of data size. Compare the size of the full trimmed experiment to the original 1.9 Mb.

range(mz(itraqdata[["X55"]]))

## [1] 100.0002 977.6636

experiment <- filterMz(experiment, mzlim = c(112,120))
range(mz(experiment[["X55"]]))

## [1] 102.0612 473.3372

experiment

## MSn experiment data ("MSnExp")
## Object size in memory: 1.18 Mb
## - - - Spectra data - - -
##  MS level(s): 2 
##  Number of spectra: 55 
##  MSn retention times: 19:9 - 50:18 minutes
## - - - Processing information - - -
## Data loaded: Wed May 11 18:54:39 2011 
## Updated from version 0.3.0 to 0.3.1 [Fri Jul  8 20:23:25 2016] 
## Curves <= 400 set to '0': Fri May 31 19:49:13 2019 
## Spectra cleaned: Fri May 31 19:49:15 2019 
##  MSnbase version: 1.1.22 
## - - - Meta data  - - -
## phenoData
##   rowNames: 1
##   varLabels: sampleNames sampleNumbers
##   varMetadata: labelDescription
## Loaded from:
##   dummyiTRAQ.mzXML 
## protocolData: none
## featureData
##   featureNames: X1 X10 ... X9 (55 total)
##   fvarLabels: spectrum ProteinAccession ProteinDescription
##     PeptideSequence
##   fvarMetadata: labelDescription
## experimentData: use 'experimentData(object)'

As can be seen above, all processing performed on the experiment is recorded and displayed as integral part of the experiment object.

6.3 Spectrum processing

MSnExp and Spectrum2 instances also support standard MS data processing such as smoothing and peak picking, as described in the smooth and pickPeak manual pages. The methods that either single spectra of experiments, process the spectrum/spectra, and return a updated, processed, object. The implementations originate from the package (Gibb and Strimmer 2012).

7.1 Reporter ions quantitation

Quantitation is performed on fixed peaks in the spectra, that are specified with an ReporterIons object. A specific peak is defined by it’s expected mz value and is searched for within mz \(\pm\) width. If no data is found, NA is returned.

mz(iTRAQ4)

## [1] 114.1112 115.1083 116.1116 117.1150

width(iTRAQ4)

## [1] 0.05

The quantify method takes the following parameters: an MSnExp experiment, a character describing the quantification method, the reporters to be quantified and a strict logical defining whether data points ranging outside of mz \(\pm\) width should be considered for quantitation. Additionally, a progress bar can be displaying when setting the verbose parameter to TRUE. Three quantification methods are implemented, as illustrated on figure 10. Quantitation using sum sums all the data points in the peaks to produce, for this example, 7, whereas method max only uses the peak’s maximum intensity, 3. Trapezoidation calculates the area under the peak taking the full with into account (using strict = FALSE gives 0.375) or only the width as defined by the reporter (using strict = TRUE gives 0.1). See ?quantify for more details.

Figure 10: The different quantitation methods
See text for details.

The quantify method returns MSnSet objects, that extend the well-known eSet class defined in the Biobase package. MSnSet instances are very similar to ExpressionSet objects, except for the experiment meta-data that captures MIAPE specific information. The assay data is a matrix of dimensions \(n \times m\), where \(m\) is the number of features/spectra originally in the MSnExp used as parameter in quantify and \(m\) is the number of reporter ions, that can be accessed with the exprs method. The meta data is directly inherited from the MSnExp instance.

qnt <- quantify(experiment,
                method = "trap",
                reporters = iTRAQ4,
                strict = FALSE,
                verbose = FALSE)
qnt

## MSnSet (storageMode: lockedEnvironment)
## assayData: 55 features, 4 samples 
##   element names: exprs 
## protocolData: none
## phenoData
##   sampleNames: iTRAQ4.114 iTRAQ4.115 iTRAQ4.116 iTRAQ4.117
##   varLabels: mz reporters
##   varMetadata: labelDescription
## featureData
##   featureNames: X1 X10 ... X9 (55 total)
##   fvarLabels: spectrum ProteinAccession ... collision.energy (15
##     total)
##   fvarMetadata: labelDescription
## experimentData: use 'experimentData(object)'
## Annotation: No annotation 
## - - - Processing information - - -
## Data loaded: Wed May 11 18:54:39 2011 
## Updated from version 0.3.0 to 0.3.1 [Fri Jul  8 20:23:25 2016] 
## Curves <= 400 set to '0': Fri May 31 19:49:13 2019 
## Spectra cleaned: Fri May 31 19:49:15 2019 
## iTRAQ4 quantification by trapezoidation: Fri May 31 19:49:20 2019 
##  MSnbase version: 1.1.22

head(exprs(qnt))

##     iTRAQ4.114 iTRAQ4.115 iTRAQ4.116 iTRAQ4.117
## X1   1347.6158  2247.3097  3927.6931  7661.1463
## X10   739.9861   799.3501   712.5983   940.6793
## X11 27638.3582 33394.0252 32104.2879 26628.7278
## X12 31892.8928 33634.6980 37674.7272 37227.7119
## X13 26143.7542 29677.4781 29089.0593 27902.5608
## X14  6448.0829  6234.1957  6902.8903  6437.2303

The next figure illustrates the quantitation of the TMT 10-plex isobaric tags using the quantify method and the TMT10 reporter instance. The data on the \(x\) axis has been quantified using method = "max" and centroided data (as generated using ProteoWizard’s msconvert with vendor libraries’ peak picking); on the \(y\) axis, the quantitation method was trapezoidation and strict = TRUE (that’s important for TMT 10-plex) and the profile data. We observe a very good correlation.

TMT 10-plex quantitation.

If no peak is detected for a reporter ion peak, the respective quantitation value is set to NA. In our case, there is 1 such case in row 41. We will remove the offending line using the filterNA method. The pNA argument defines the percentage of accepted missing values per feature. As we do not expect any missing peaks, we set it to be 0 (which is also the detault value).

table(is.na(qnt))

## 
## FALSE  TRUE 
##   219     1

qnt <- filterNA(qnt, pNA = 0)
sum(is.na(qnt))

## [1] 0

The filtering criteria for filterNA can also be defined as a pattern of columns that can have missing values and columns that must not exhibit any. See ?filterNA for details and examples.

The infrastructure around the MSnSet class allows flexible filtering using the [ sub-setting operator. Below, we mimic the behaviour of filterNA(, pNA = 0) by calculating the row indices that should be removed, i.e. those that have at least one NA value and explicitly remove these rows. This method allows one to devise and easily apply any filtering strategy.

whichRow <- which(is.na((qnt))) %% nrow(qnt)
qnt <- qnt[-whichRow, ]

See also the plotNA method to obtain a graphical overview of the completeness of a data set.

7.2 Importing quantitation data

If quantitation data is already available as a spreadsheet, it can be imported, along with additional optional feature and sample (pheno) meta data, with the readMSnSet function. This function takes the respective text-based spreadsheet (comma- or tab-separated) file names as argument to create a valid MSnSet instance.

Note that the quantitation data of MSnSet objects can also be exported to a text-based spreadsheet file using the write.exps method.

MSnbase also supports the mzTab format, a light-weight, tab-delimited file format for proteomics data. mzTab files can be read into R with readMzTabData to create and MSnSet instance.

See the MSnbase-io vignette for a general overview of MSnbase’s input/ouput capabilites.

7.3 Importing chromatographic data from SRM/MRM experiments

Data from SRM/MRM experiments can be imported from mzML files using the readSRMData function. The mzML files are expected to contain chromatographic data for the same precursor and product m/z values. The function returns a Chromatograms object that arranges the data in a two-dimensional array, each column representing the data of one file (sample) and each row the chromatographic data for the same polarity, precursor and product m/z. In the example code below we load a single SRM file using readSRMData.

fl <- proteomics(full.names = TRUE, pattern = "MRM")
srm <- readSRMData(fl)

srm

## Chromatograms with 137 rows and 1 column
##                     1
##        <Chromatogram>
## [1,]      length: 523
## [2,]      length: 523
## ...              ... 
## [136,]    length: 962
## [137,]    length: 962
## phenoData with 1 variables
## featureData with 10 variables

The precursor and product m/z values can be extracted with the precursorMz and productMz functions. These functions always return a matrix, each row providing the lower and upper m/z value of the isolation window (in most cases minimal and maximal m/z will be identical).

head(precursorMz(srm))

##      mzmin mzmax
## [1,]   115   115
## [2,]   115   115
## [3,]   117   117
## [4,]   117   117
## [5,]   133   133
## [6,]   133   133

head(productMz(srm))

##        mzmin   mzmax
## [1,]  26.996  26.996
## [2,]  70.996  70.996
## [3,]  72.996  72.996
## [4,]  98.996  98.996
## [5,] 114.996 114.996
## [6,]  70.996  70.996

7.4 Peak adjustments

Single peak adjustment In certain cases, peak intensities need to be adjusted as a result of peak interferance. For example, the \(+1\) peak of the phenylalanine (F, Phe) immonium ion (with \(m/z\) 120.03) inteferes with the 121.1 TMT reporter ion. Below, we calculate the relative intensity of the +1 peaks compared to the main peak using the Rdisop package.

library(Rdisop)
## Phenylalanine immonium ion
Fim <- getMolecule("C8H10N")
getMass(Fim)

## [1] 120.0813

isotopes <- getIsotope(Fim)
F1 <- isotopes[2, 2]
F1

## [1] 0.08573496

If desired, one can thus specifically quantify the F immonium ion in the MS2 spectrum, estimate the intensity of the +1 ion (0.0857% of the F peak) and substract this calculated value from the 121.1 TMT reporter intensity.

The above principle can also be generalised for a set of overlapping peaks, as described below.

Reporter ions purity correction Impurities in the reporter reagents can also bias the results and can be corrected when manufacturers provide correction coefficients. These generally come as percentages of each reporter ion that have masses differing by -2, -1, +1 and +2 Da from the nominal reporter ion mass due to isotopic variants. The purityCorrect method applies such correction to MSnSet instances. It also requires a square matrix as second argument, impurities, that defines the relative percentage of reporter in the quantified each peak. See ?purityCorrect for more details.

impurities <- matrix(c(0.929, 0.059, 0.002, 0.000,
                       0.020, 0.923, 0.056, 0.001,
                       0.000, 0.030, 0.924, 0.045,
                       0.000, 0.001, 0.040, 0.923),
                     nrow = 4)
qnt.crct <- purityCorrect(qnt, impurities)
head(exprs(qnt))

##     iTRAQ4.114 iTRAQ4.115 iTRAQ4.116 iTRAQ4.117
## X1   1347.6158  2247.3097  3927.6931  7661.1463
## X10   739.9861   799.3501   712.5983   940.6793
## X11 27638.3582 33394.0252 32104.2879 26628.7278
## X12 31892.8928 33634.6980 37674.7272 37227.7119
## X13 26143.7542 29677.4781 29089.0593 27902.5608
## X14  6448.0829  6234.1957  6902.8903  6437.2303

head(exprs(qnt.crct))

##     iTRAQ4.114 iTRAQ4.115 iTRAQ4.116 iTRAQ4.117
## X1   1304.7675  2168.1082  3784.2244  8133.9211
## X10   743.8159   806.5647   696.9024   988.0787
## X11 27547.6515 33592.3997 32319.1803 27413.1833
## X12 32127.1898 33408.8353 37806.0787 38658.7865
## X13 26187.3141 29788.6254 29105.2485 28936.6871
## X14  6533.1862  6184.1103  6945.2074  6666.5633

The makeImpuritiesMatrix can be used to create impurity matrices. It opens a rudimentary spreadsheet that can be directly edited.

8.1 Data imputation

A set of imputation methods are available in the impute method: it takes an MSnSet instance as input, the name of the imputation method to be applied (one of bpca, knn, QRILC, MLE, MinDet, MinProb, min, zero, mixed, nbavg, none), possible additional parameters and returns an updated for MSnSet without any missing values. Below, we apply a deterministic minimum value imputation on the naset example data:

## an example MSnSet containing missing values
data(naset)
table(is.na(naset))

## 
## FALSE  TRUE 
## 10254   770

## number of NAs per protein
table(fData(naset)$nNA)

## 
##   0   1   2   3   4   8   9  10 
## 301 247  91  13   2  23  10   2

x <- impute(naset, "min")
processingData(x)

## - - - Processing information - - -
## Data imputation using min Fri May 31 19:49:22 2019 
##  MSnbase version: 1.15.6

table(is.na(x))

## 
## FALSE 
## 11024

As described in more details in (Lazar et al. 2016), there are two types of mechanisms resulting in missing values in LC/MSMS experiments.

• Missing values resulting from absence of detection of a feature, despite ions being present at detectable concentrations. For example in the case of ion suppression or as a result from the stochastic, data-dependent nature of the MS acquisition method. These missing value are expected to be randomly distributed in the data and are defined as missing at random (MAR) or missing completely at random (MCAR).

• Biologically relevant missing values, resulting from the absence of the low abundance of ions (below the limit of detection of the instrument). These missing values are not expected to be randomly distributed in the data and are defined as missing not at random (MNAR).

MAR and MCAR values can be reasonably well tackled by many imputation methods. MNAR data, however, requires some knowledge about the underlying mechanism that generates the missing data, to be able to attempt data imputation. MNAR features should ideally be imputed with a left-censor (for example using a deterministic or probabilistic minimum value) method. Conversely, it is recommended to use hot deck methods (for example nearest neighbour, maximum likelihood, etc) when data are missing at random.

Figure 11: Mixed imputation method
Black cells represent presence of quantitation values and light grey corresponds to missing data. The two groups of interest are depicted in green and blue along the heatmap columns. Two classes of proteins are annotated on the left: yellow are proteins with randomly occurring missing values (if any) while proteins in brown are candidates for non-random missing value imputation.

It is anticipated that the identification of both classes of missing values will depend on various factors, such as feature intensities and experimental design. Below, we use perform mixed imputation, applying nearest neighbour imputation on the 654 features that are assumed to contain randomly distributed missing values (if any) (yellow on figure 11) and a deterministic minimum value imputation on the 35 proteins that display a non-random pattern of missing values (brown on figure 11).

x <- impute(naset, method = "mixed",
            randna = fData(naset)$randna,
            mar = "knn", mnar = "min")
x

## MSnSet (storageMode: lockedEnvironment)
## assayData: 689 features, 16 samples 
##   element names: exprs 
## protocolData: none
## phenoData
##   sampleNames: M1F1A M1F4A ... M2F11B (16 total)
##   varLabels: nNA
##   varMetadata: labelDescription
## featureData
##   featureNames: AT1G09210 AT1G21750 ... AT4G39080 (689 total)
##   fvarLabels: nNA randna
##   fvarMetadata: labelDescription
## experimentData: use 'experimentData(object)'
## Annotation:  
## - - - Processing information - - -
## Data imputation using mixed Fri May 31 19:49:23 2019 
##   Using default parameters 
##  MSnbase version: 1.15.6

Please read ?impute for a description of the different methods.

8.2 Normalisation

A MSnSet object is meant to be compatible with further downstream packages for data normalisation and statistical analysis. There is also a normalise (also available as normalize) method for expression sets. The method takes and instance of class MSnSet as first argument, and a character to describe the method to be used:

• quantiles: Applies quantile normalisation (Bolstad et al. 2003) as implemented in the normalize.quantiles function of the preprocessCore package.

• quantiles.robust: Applies robust quantile normalisation (Bolstad et al. 2003) as implemented in the normalize.quantiles.robust function of the preprocessCore package.

• vsn: Applies variance stabilisation normalization (Huber et al. 2002) as implemented in the vsn2 function of the vsn package.

• max: Each feature’s reporter intensity is divided by the maximum of the reporter ions intensities.

• sum: Each feature’s reporter intensity is divided by the sum of the reporter ions intensities.

See ?normalise for more methods. A scale method for MSnSet instances, that relies on the base::scale function.

qnt.max <- normalise(qnt, "max")
qnt.sum <- normalise(qnt, "sum")
qnt.quant <- normalise(qnt, "quantiles")
qnt.qrob <- normalise(qnt, "quantiles.robust")
qnt.vsn <- normalise(qnt, "vsn")

The effect of these are illustrated on figure 12 and figure 13 reproduces figure 3 of (Karp et al. 2010) that described the application of vsn on iTRAQ reporter data.

Figure 12: Comparison of the normalisation MSnSet methods
Note that vsn also glog-transforms the intensities.

Figure 13: CV versus signal intensity comparison for log2 and vsn transformed data
Lines indicate running CV medians.

Note that it is also possible to normalise individual spectra or whole MSnExp experiments with the normalise method using the max method. This will rescale all peaks between 0 and 1. To visualise the relative reporter peaks, one should this first trim the spectra using method trimMz as illustrated in section 6, then normalise the MSnExp with normalise using method="max" as illustrated above and plot the data using plot (figure ??).

## NULL

Additional dedicated normalisation method are available for MS2 label-free quantitation, as described in section 10 and in the quantify documentation.

10.1 Peptide counting

Note that if samples are not multiplexed, label-free MS2 quantitation by spectral counting is possible using MSnbase. Once individual spectra have been assigned to peptides and proteins (see section 4), it becomes straightforward to estimate protein quantities using the simple peptide counting method, as illustrated in section 9.

sc <- quantify(msexp, method = "count")
## lets modify out data for demonstration purposes
fData(sc)$DatabaseAccess[1] <- fData(sc)$DatabaseAccess[2]
fData(sc)$DatabaseAccess

## [1] "ECA1028" "ECA1028" "ECA0510"

sc <- combineFeatures(sc, groupBy = fData(sc)$DatabaseAccess,
                      method = "sum")
exprs(sc)

##         dummyiTRAQ.mzXML
## ECA0510                1
## ECA1028                2

Such count data could then be further analyses using dedicated count methods (originally developed for high-throughput sequencing) and directly available for MSnSet instances in the msmsTests Bioconductor package.

10.2 Spectral counting and intensity methods

The spectral abundance factor (SAF) and the normalised form (NSAF) (Paoletti et al. 2006) as well as the spectral index (SI) and other normalised variations (SI\(_{GI}\) and SI\(_N\)) (Griffin et al. 2010) are also available. Below, we illustrate how to apply the normalised SI\(_N\) to the experiment containing identification data produced in section 4.

The spectra that did not match any peptide have already been remove with the removeNoId method. As can be seen in the following code chunk, the first spectrum could not be matched to any single protein. Non-identified spectra and those matching multiple proteins are removed automatically prior to any label-free quantitation. Once can also remove peptide that do not match uniquely to proteins (as defined by the nprot feature variable column) with the removeMultipleAssignment method.

fData(msexp)[, c("DatabaseAccess", "nprot")]

##       DatabaseAccess nprot
## F1.S1        ECA0984     1
## F1.S2        ECA1028     1
## F1.S5        ECA0510     1

Note that the label-free methods implicitely apply feature aggregation (section 9) and normalise (section 8.2) the quantitation values based on the total sample intensity and or the protein lengths (see (Paoletti et al. 2006) and (Griffin et al. 2010) for details).

Let’s now proceed with the quantitation using the quantify, as in section 7.1, this time however specifying the method of interest, SIn (the reporters argument can of course be ignored here). The required peptide-protein mapping and protein lengths are extracted automatically from the feature meta-data using the default accession and length feature variables.

siquant <- quantify(msexp, method = "SIn")
processingData(siquant)

## - - - Processing information - - -
## Data loaded: Fri May 31 19:49:11 2019 
## Filtered 2 unidentified peptides out [Fri May 31 19:49:12 2019] 
## Quantitation by total ion current [Fri May 31 19:49:26 2019] 
## Combined 3 features into 3 using sum: Fri May 31 19:49:26 2019 
## Quantification by SIn [Fri May 31 19:49:26 2019] 
##  MSnbase version: 2.10.1

exprs(siquant)

##         dummyiTRAQ.mzXML
## ECA0510     0.0006553518
## ECA0984     0.0035384487
## ECA1028     0.0002684726

Other label-free methods can be applied by specifiying the appropriate method argument. See ?quantify for more details.

11.1 Plotting two spectra

MSnbase provides functionality to compare spectra against each other. The first notable function is plot. If two Spectrum2 objects are provided plot will draw two plots: the upper and lower panel contain respectively the first and second spectrum. Common peaks are drawn in a slightly darker colour.

centroided <- pickPeaks(itraqdata, verbose = FALSE)
(k <- which(fData(centroided)[, "PeptideSequence"] == "TAGIQIVADDLTVTNPK"))

## [1] 41 42

mzk <- precursorMz(centroided)[k]
zk <- precursorCharge(centroided)[k]
mzk * zk

##      X46      X47 
## 2046.175 2045.169

plot(centroided[[k[1]]], centroided[[k[2]]])

Figure 14: Comparing two MS2 spectra

11.2 Comparison metrics

Currently MSnbase supports three different metrics to compare spectra against each other: common to calculate the number of common peaks, cor to calculate the Pearson correlation and dotproduct to calculate the dot product. See ?compareSpectra to apply other arbitrary metrics.

compareSpectra(centroided[[2]], centroided[[3]],
               fun = "common")

## [1] 8

compareSpectra(centroided[[2]], centroided[[3]],
               fun = "cor")

## [1] 0.1105021

compareSpectra(centroided[[2]], centroided[[3]],
               fun = "dotproduct")

## [1] 0.1185025

compareSpectra supports MSnExp objects as well.

compmat <- compareSpectra(centroided, fun="cor")
compmat[1:10, 1:5]

##             X1        X10        X11        X12        X13
## X1          NA 0.07672973 0.38024702 0.51579989 0.46647324
## X10 0.07672973         NA 0.11050214 0.11162512 0.08611906
## X11 0.38024702 0.11050214         NA 0.47184437 0.47905818
## X12 0.51579989 0.11162512 0.47184437         NA 0.57909089
## X13 0.46647324 0.08611906 0.47905818 0.57909089         NA
## X14 0.09999703 0.01558385 0.12165400 0.12057251 0.11853321
## X15 0.03314059 0.00416184 0.01733228 0.04796236 0.03196115
## X16 0.39140514 0.06634870 0.42259036 0.45624614 0.45469020
## X17 0.37945538 0.07188420 0.52292845 0.44791250 0.43679447
## X18 0.55367861 0.10286983 0.56621755 0.66884285 0.64262061

Below, we illustrate how to compare a set of spectra using a hierarchical clustering.

plot(hclust(as.dist(compmat)))

13.1 Combining identical samples

To simulate this situation, let us use quantiation data from the itraqdata object that is provided with the package as experiment 1 and the data from the rawdata MSnExp instance created at the very beginning of this document. Both experiments share the same default iTRAQ 4-plex reporter names as default sample names, and will thus automatically be combined along rows.

exp1 <- quantify(itraqdata, reporters = iTRAQ4,
                 verbose = FALSE)
sampleNames(exp1)

## [1] "iTRAQ4.114" "iTRAQ4.115" "iTRAQ4.116" "iTRAQ4.117"

centroided(rawdata) <- FALSE
exp2 <- quantify(rawdata, reporters = iTRAQ4,
                 verbose = FALSE)
sampleNames(exp2)

## [1] "iTRAQ4.114" "iTRAQ4.115" "iTRAQ4.116" "iTRAQ4.117"

It important to note that the features of these independent experiments share the same default anonymous names: X1, X2, X3, …, that however represent quantitation of distinct physical analytes. If the experiments were to be combined as is, it would result in an error because data points for the same feature name (say X1) and the same sample name (say iTRAQ4.114) have different values. We thus first update the feature names to explicitate that they originate from different experiment and represent quantitation from different spectra using the convenience function updateFeatureNames. Note that updating the names of one experiment would suffice here.

head(featureNames(exp1))

## [1] "X1"  "X10" "X11" "X12" "X13" "X14"

exp1 <- updateFeatureNames(exp1)
head(featureNames(exp1))

## [1] "X1.exp1"  "X10.exp1" "X11.exp1" "X12.exp1" "X13.exp1" "X14.exp1"

head(featureNames(exp2))

## [1] "F1.S1" "F1.S2" "F1.S3" "F1.S4" "F1.S5"

exp2 <- updateFeatureNames(exp2)
head(featureNames(exp2))

## [1] "F1.S1.exp2" "F1.S2.exp2" "F1.S3.exp2" "F1.S4.exp2" "F1.S5.exp2"

The two experiments now share the same sample names and have different feature names and will be combined along the row. Note that all meta-data is correctly combined along the quantitation values.

exp12 <- combine(exp1, exp2)

## Warning in combine(experimentData(x), experimentData(y)): 
##   unknown or conflicting information in MIAPE field 'email'; using information from first object 'x'

dim(exp1)

## [1] 55  4

dim(exp2)

## [1] 5 4

dim(exp12)

## [1] 60  4

13.2 Combine different samples

Lets now create two MSnSets from the same raw data to simulate two different independent experiments that share some features. As done previously (see section 9), we combine the spectra based on the proteins they have been identified to belong to. Features can thus naturally be named using protein accession numbers. Alternatively, if peptide sequences would have been used as grouping factor in combineFeatures, then these would be good feature name candidates.

set.seed(1)
i <- sample(length(itraqdata), 35)
j <- sample(length(itraqdata), 35)
exp1 <- quantify(itraqdata[i], reporters = iTRAQ4,
                 verbose = FALSE)
exp2 <- quantify(itraqdata[j], reporters = iTRAQ4,
                 verbose = FALSE)
exp1 <- droplevels(exp1)
exp2 <- droplevels(exp2)
table(featureNames(exp1) %in% featureNames(exp2))

## 
## FALSE  TRUE 
##    14    21

exp1 <- combineFeatures(exp1,
                        groupBy = fData(exp1)$ProteinAccession)
exp2 <- combineFeatures(exp2,
                        groupBy = fData(exp2)$ProteinAccession)

## Your data contains missing values. Please read the relevant section
## in the combineFeatures manual page for details the effects of
## missing values on data aggregation.

head(featureNames(exp1))

## [1] "BSA"     "ECA0172" "ECA0469" "ECA0631" "ECA0691" "ECA0871"

head(featureNames(exp2))

## [1] "BSA"     "ECA0435" "ECA0469" "ECA0621" "ECA0871" "ECA1032"

The droplevels drops the unused featureData levels. This is required to avoid passing absent levels as groupBy in combineFeatures. Alternatively, one could also use factor(fData(exp1)\$ProteinAccession) as groupBy argument.

The feature names are updated automatically by combineFeatures, using the groupBy argument. Proper feature names, reflecting the nature of the features (spectra, peptides or proteins) is critical when multiple experiments are to be combined, as this is done using common features as defined by their names (see below).

Sample names should also be updated to replace anonymous reporter names with relevant identifiers; the individual reporter data is stored in the phenoData and is not lost. A convenience function updateSampleNames is provided to append the MSnSet’s variable name to the already defined names, although in general, biologically relevant identifiers are preferred.

sampleNames(exp1)

## [1] "iTRAQ4.114" "iTRAQ4.115" "iTRAQ4.116" "iTRAQ4.117"

exp1 <- updateSampleNames(exp1)
sampleNames(exp1)

## [1] "iTRAQ4.114.exp1" "iTRAQ4.115.exp1" "iTRAQ4.116.exp1" "iTRAQ4.117.exp1"

sampleNames(exp1) <- c("Ctrl1", "Cond1", "Ctrl2", "Cond2")
sampleNames(exp2) <- c("Ctrl3", "Cond3", "Ctrl4", "Cond4")

At this stage, it is not yet possible to combine the two experiments, because their feature data is not compatible yet; they share the same feature variable labels, i.e. the feature data column names (spectrum, ProteinAccession, ProteinDescription, …), but the part of the content is different because the original data was (in particular all the spectrum centric data: identical peptides in different runs will have different retention times, precursor intensities, …). Feature data with identical labels (columns in the data frame) and names (row in the data frame) are expected to have the same data and produce an error if not conform.

stopifnot(all(fvarLabels(exp1) == fvarLabels(exp2)))
fData(exp1)["BSA", 1:4]

##     spectrum ProteinAccession   ProteinDescription PeptideSequence
## BSA        1              BSA bovine serum albumin          NYQEAK

fData(exp2)["BSA", 1:4]

##     spectrum ProteinAccession   ProteinDescription PeptideSequence
## BSA        1              BSA bovine serum albumin          NYQEAK

Instead of removing these identical feature data columns, one can use a second convenience function, updateFvarLabels, to update feature labels based on the experiements variable name and maintain all the metadata.

exp1 <- updateFvarLabels(exp1)
exp2 <- updateFvarLabels(exp2)
head(fvarLabels(exp1))

## [1] "spectrum.exp1"           "ProteinAccession.exp1"  
## [3] "ProteinDescription.exp1" "PeptideSequence.exp1"   
## [5] "fileIdx.exp1"            "retention.time.exp1"

head(fvarLabels(exp2))

## [1] "spectrum.exp2"           "ProteinAccession.exp2"  
## [3] "ProteinDescription.exp2" "PeptideSequence.exp2"   
## [5] "fileIdx.exp2"            "retention.time.exp2"

It is now possible to combine exp1 and exp2, including all the meta-data, with the combine method. The new experiment will contain the union of the feature names of the individual experiments with missing values inserted appropriately.

exp12 <- combine(exp1, exp2)
dim(exp12)

## [1] 36  8

pData(exp12)

##             mz reporters
## Ctrl1 114.1112    iTRAQ4
## Cond1 115.1083    iTRAQ4
## Ctrl2 116.1116    iTRAQ4
## Cond2 117.1150    iTRAQ4
## Ctrl3 114.1112    iTRAQ4
## Cond3 115.1083    iTRAQ4
## Ctrl4 116.1116    iTRAQ4
## Cond4 117.1150    iTRAQ4

exprs(exp12)[25:28, ]

##             Ctrl1     Cond1     Ctrl2     Cond2     Ctrl3     Cond3
## ECA4513 10154.953 10486.943 11018.191 11289.552        NA        NA
## ECA4514  6516.397  6746.337  6658.973  6838.491 12457.173 12695.491
## ENO     77239.040 49352.793 22493.050 11187.588 39965.733 24967.397
## ECA0435        NA        NA        NA        NA  4923.628  5557.818
##             Ctrl4     Cond4
## ECA4513        NA        NA
## ECA4514 14423.640 14556.855
## ENO            NA  5925.663
## ECA0435  5775.203  5079.295

exp12

## MSnSet (storageMode: lockedEnvironment)
## assayData: 36 features, 8 samples 
##   element names: exprs 
## protocolData: none
## phenoData
##   sampleNames: Ctrl1 Cond1 ... Cond4 (8 total)
##   varLabels: mz reporters
##   varMetadata: labelDescription
## featureData
##   featureNames: BSA ECA0172 ... ECA4037 (36 total)
##   fvarLabels: spectrum.exp1 ProteinAccession.exp1 ...
##     CV.iTRAQ4.117.exp2 (38 total)
##   fvarMetadata: labelDescription
## experimentData: use 'experimentData(object)'
## Annotation:  
## - - - Processing information - - -
## Combined [27,4] and [24,4] MSnSets Fri May 31 19:49:42 2019 
##  MSnbase version: 2.10.1

In summary, when experiments with different samples need to be combined (along the columns), one needs to (1) clarify the sample names using updateSampleNames or better manually, for biological relevance and (2) update the feature data variable labels with updateFvarLabels. The individual experiments (there can be more than 2) can then easily be combined with the combine method while retaining the meta-data.

If runs for the same sample (different fractions for example) need to be combines, one needs to (1) differentiate the feature provenance with updateFeatureNames prior to use combine.

13.3 Splitting and unsplitting MSnSet instances

A single MSnSet can also be split along the features/rows or samples/columns using the split method and a factor defining the splitting groups, resulting in an instance of class MSnSetList:

data(dunkley2006)
head(pData(dunkley2006))

##        membrane.prep fraction replicate
## M1F1A              1        1         A
## M1F4A              1        4         A
## M1F7A              1        7         A
## M1F11A             1       11         A
## M1F2B              1        2         B
## M1F5B              1        5         B

split(dunkley2006, dunkley2006$replicate)

## Instance of class 'MSnSetList' containig 2 objects.

## or, defining the appropriate annotation variable name
dun <- split(dunkley2006, "replicate")

Above, we split along the columns/samples, but the function would equally work with a factor of length equal to the number of rows of the MSnSet (or a feature variable name) to split along the rows/features.

Finally, the effect of split can be reverted by unsplit.

dun2 <- unsplit(dun, pData(dunkley2006)$replicate)
compareMSnSets(dunkley2006, dun2)

## [1] TRUE

See ?MSnSetList for more details about the class, split and unsplit and comments about storing multiple assays pertaining the same experiment.

13.4 Averaging MSnSet instances

It is sometimes useful to average a set of replicated experiments to facilitate their visualisation. This can be easily achieved with the averageMSnSet function, which takes a list of valid MSnSet instances as input and creates a new object whose expression values are an average of the original values. A value of dispersion (disp) and a count of missing values (nNA) is recorded in the feature metadata slot. The average and dispersion are computed by default as the median and (non-parametric) coefficient of variation (see ?npcv for details), although this can easily be parametrised, as described in ?averageMSnSet.

The next code chunk illustrates the averaging function using three replicated experiments from (Tan et al. 2009) available in the pRolocdata package.

library("pRolocdata")
data(tan2009r1)
data(tan2009r2)
data(tan2009r3)
msnl <- MSnSetList(list(tan2009r1, tan2009r2, tan2009r3))
avgtan <- averageMSnSet(msnl)
head(exprs(avgtan))

##             X114      X115      X116      X117
## P20353 0.3605000 0.3035000 0.2095000 0.1265000
## P53501 0.4299090 0.1779700 0.2068280 0.1852625
## Q7KU78 0.1704443 0.1234443 0.1772223 0.5290000
## P04412 0.2567500 0.2210000 0.3015000 0.2205000
## Q7KJ73 0.2160000 0.1830000 0.3420000 0.2590000
## Q7JZN0 0.0965000 0.2509443 0.4771667 0.1750557

head(fData(avgtan)$disp)

##               X114      X115        X116       X117
## P20353 0.076083495 0.1099127 0.109691169 0.14650198
## P53501 0.034172542 0.2640556 0.005139653 0.17104568
## Q7KU78 0.023198743 0.4483795 0.027883087 0.04764499
## P04412 0.053414021 0.2146751 0.090972139 0.27903810
## Q7KJ73 0.000000000 0.0000000 0.000000000 0.00000000
## Q7JZN0 0.007681865 0.1959534 0.097873350 0.06210542

head(fData(avgtan)$nNA)

##        X114 X115 X116 X117
## P20353    1    1    1    1
## P53501    1    1    1    1
## Q7KU78    0    0    0    0
## P04412    1    1    1    1
## Q7KJ73    2    2    2    2
## Q7JZN0    0    0    0    0

We are going to visualise the average data on a principle component (PCA) plot using the plot2D function from the pRoloc package (Gatto et al. 2014). In addition, we are going to use the measure of dispersion to highlight averages with high variability by taking, for each protein, the maximum observed dispersion in the 4 samples. Note that in the default implementation, dispersions estimated from a single measurement (i.e. that had 2 missing values in our example) are set to 0; we will set these to the overal maximum observed dispersion.

disp <- rowMax(fData(avgtan)$disp)
disp[disp == 0] <- max(disp)
range(disp)

## [1] 0.01152877 1.20888923

library("pRoloc")
plot2D(avgtan, cex = 3 * disp)

Figure 16: PCA plot of the averaged MSnSet
The point sizes are proportional to the dispersion of the protein quantitation across the averaged data.