MSnbase: MS data processing, visualisation and quantification
25 April 2022
Abstract
This vignette describes the functionality implemented in the MSnbase package. MSnbase aims at (1) facilitating the import, processing, visualisation and quantification of mass spectrometry data into the R environment (R Development Core Team 2011) by providing specific data classes and methods and (2) enabling the utilisation of throughput-high data analysis pipelines provided by the Bioconductor (Gentleman et al. 2004) project.Package
MSnbase 2.21.7
Foreword
This software is free and open-source software. If you use it, please support the project by citing it in publications:
Gatto L, Lilley KS. MSnbase-an R/Bioconductor package for isobaric tagged mass spectrometry data visualization, processing and quantitation. Bioinformatics. 2012 Jan 15;28(2):288-9. doi: 10.1093/bioinformatics/btr645. PMID: 22113085.
MSnbase, efficient and elegant R-based processing and visualisation of raw mass spectrometry data. Laurent Gatto, Sebastian Gibb, Johannes Rainer. bioRxiv 2020.04.29.067868; doi: https://doi.org/10.1101/2020.04.29.067868
Questions and bugs
For bugs, typos, suggestions or other questions, please file an issue
in our tracking system (https://github.com/lgatto/MSnbase/issues)
providing as much information as possible, a reproducible example and
the output of sessionInfo().
If you don’t have a GitHub account or wish to reach a broader audience for general questions about proteomics analysis using R, you may want to use the Bioconductor support site: https://support.bioconductor.org/.
1 Introduction
MSnbase (Gatto and Lilley 2012) aims are providing a reproducible research framework to proteomics data analysis. It should allow researcher to easily mine mass spectrometry data, explore the data and its statistical properties and visually display these.
MSnbase also aims at being compatible with the infrastructure implemented in Bioconductor, in particular Biobase. As such, classes developed specifically for proteomics mass spectrometry data are based on the eSet and ExpressionSet classes. The main goal is to assure seamless compatibility with existing meta data structure, accessor methods and normalisation techniques.
This vignette illustrates MSnbase utility using a dummy
data sets provided with the package without describing the underlying
data structures. More details can be found in the package, classes,
method and function documentations. A description of the classes is
provided in the MSnbase-development
vignette1 in R, open it with vignette("MSnbase-development") or read it online here.
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 belowOn-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
implemenatations2 in R, open it with vignette("benchmarking") or
read it online
here. See
details below.
2 Data structure and content
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
format3 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: 19:09 - 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 DFEVVNNESDPRAs illustrated above, showing the experiment textually displays it’s content:
Information about the raw data, i.e. the spectra.
Specific information about the experiment processing4 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
processingDatamethod.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
?MSnExpand 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, @Taylor2008). This meta-data can be accessed with the
experimentDatamethod. When available, a summary of MIAPE-MS data can be printed with themsInfomethod. See?MIAPEfor 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: 19:09
## Charge: 2
## MSn level: 2
## Peaks count: 1922
## Total ion count: 26413754Attributes 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] 1922head(peaksCount(itraqdata))## X1 X10 X11 X12 X13 X14
## 1922 1376 1571 2397 2574 1829rtime(sp)## [1] 1149.31head(rtime(itraqdata))## X1 X10 X11 X12 X13 X14
## 1149.31 1503.03 1663.61 1663.86 1664.08 1664.322.5 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)TMT16## Object of class "ReporterIons"
## TMT16HCD: '16-plex TMT HCD' with 16 reporter ions
## - [126] 126.1277 +/- 0.002 (#E55400)
## - [127N] 127.1248 +/- 0.002 (#E28100)
## - [127C] 127.1311 +/- 0.002 (#DFAC00)
## - [128N] 128.1281 +/- 0.002 (#DDD700)
## - [128C] 128.1344 +/- 0.002 (#B5DA00)
## - [129N] 129.1315 +/- 0.002 (#87D8000)
## - [129C] 129.1378 +/- 0.002 (#5BD500)
## - [130N] 130.1348 +/- 0.002 (#30D300)
## - [130C] 130.1411 +/- 0.002 (#05D000)
## - [131N] 131.1382 +/- 0.002 (#00CE23)
## - [131C] 131.1445 +/- 0.002 (#00CB4B)
## - [132N] 132.1415 +/- 0.002 (#00C972)
## - [132C] 132.1479 +/- 0.002 (#00C699)
## - [133N] 133.1449 +/- 0.002 (#00C4BE)
## - [133C] 133.1512 +/- 0.002 (#00A0C1)
## - [134N] 134.1482 +/- 0.002 (#0078BF)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## MChromatograms with 1 row and 1 column
## MM14.mzML
## <Chromatogram>
## [1,] length: 112
## phenoData with 1 variables
## featureData with 1 variablesChromatographic 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: 1head(intensity(mtof_tic[1, 1]))## F1.S001 F1.S002 F1.S003 F1.S004 F1.S005 F1.S006
## 64989 67445 77843 105097 155609 212760head(rtime(mtof_tic[1, 1]))## F1.S001 F1.S002 F1.S003 F1.S004 F1.S005 F1.S006
## 270.334 270.671 271.007 271.343 271.680 272.016mz(mtof_tic[1, 1])## [1] 94.80679 1004.96155To 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 Plotting raw data
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: 30:01 - 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: 30:01 - 30:05
## [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)$ProteinAccession == "BSA"
bsa <- itraqdata[sel]
bsa## MSn experiment data ("MSnExp")
## Object size in memory: 0.11 Mb
## - - - Spectra data - - -
## MS level(s): 2
## Number of spectra: 3
## MSn retention times: 19:09 - 36:17 minutes
## - - - Processing information - - -
## Data loaded: Wed May 11 18:54:39 2011
## Updated from version 0.3.0 to 0.3.1 [Fri Jul 8 20:23:25 2016]
## Data [logically] subsetted 3 spectra: Mon Apr 25 17:08:49 2022
## MSnbase version: 1.1.22
## - - - Meta data - - -
## phenoData
## rowNames: 1
## varLabels: sampleNames sampleNumbers
## varMetadata: labelDescription
## Loaded from:
## dummyiTRAQ.mzXML
## protocolData: none
## featureData
## featureNames: X1 X52 X53
## fvarLabels: spectrum ProteinAccession ProteinDescription
## PeptideSequence
## fvarMetadata: labelDescription
## experimentData: use 'experimentData(object)'as.character(fData(bsa)$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.
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 MChromatograms 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 MChromatograms object) into one plot.
plot(mtof_bpc)
Figure 6: Base peak chromatogram
4 Tandem MS identification data
Typically, identification data is produced by a search engine and
serialised to disk in the mzIdentML (or mzid) file format. This
format can be parsed by openIDfile from the mzR
package or mzID from the mzID package. The MSnbase
package relies on the former (which is faster) and offers a simplified
interface by converting the dedicated identification data objects into
data.frames.
library("msdata")
f <- "TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01-20141210.mzid"
idf <- msdata::ident(full.names = TRUE, pattern = f)
iddf <- readMzIdData(idf)
str(iddf)## 'data.frame': 5802 obs. of 35 variables:
## $ sequence : chr "RQCRTDFLNYLR" "ESVALADQVTCVDWRNRKATKK" "KELLCLAMQIIR" "QRMARTSDKQQSIRFLERLCGR" ...
## $ spectrumID : chr "controllerType=0 controllerNumber=1 scan=2949" "controllerType=0 controllerNumber=1 scan=6534" "controllerType=0 controllerNumber=1 scan=5674" "controllerType=0 controllerNumber=1 scan=4782" ...
## $ chargeState : int 3 2 2 3 3 3 2 3 3 2 ...
## $ rank : int 1 1 1 1 1 1 1 1 1 1 ...
## $ passThreshold : logi TRUE TRUE TRUE TRUE TRUE TRUE ...
## $ experimentalMassToCharge: num 548 1288 744 913 927 ...
## $ calculatedMassToCharge : num 548 1288 744 913 926 ...
## $ peptideRef : chr "Pep77" "Pep108" "Pep136" "Pep163" ...
## $ modNum : int 1 1 1 1 1 1 1 2 2 1 ...
## $ isDecoy : logi FALSE FALSE TRUE FALSE TRUE FALSE ...
## $ post : chr "V" "G" "Q" "D" ...
## $ pre : chr "R" "R" "R" "R" ...
## $ start : int 574 69 131 182 135 310 182 201 201 121 ...
## $ end : int 585 90 142 203 158 334 203 233 233 140 ...
## $ DatabaseAccess : chr "ECA2006" "ECA1676" "XXX_ECA2855" "ECA3009" ...
## $ DBseqLength : int 1295 110 157 437 501 477 437 1204 1204 210 ...
## $ DatabaseSeq : chr "" "" "" "" ...
## $ DatabaseDescription : chr "ECA2006 ATP-dependent helicase" "ECA1676 putative growth inhibitory protein" "" "ECA3009 putative coproporphyrinogen oxidase" ...
## $ scan.number.s. : num 2949 6534 5674 4782 5839 ...
## $ acquisitionNum : num 2949 6534 5674 4782 5839 ...
## $ spectrumFile : chr "TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01-20141210.mzML" "TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01-20141210.mzML" "TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01-20141210.mzML" "TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01-20141210.mzML" ...
## $ idFile : chr "TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01-20141210.mzid" "TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01-20141210.mzid" "TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01-20141210.mzid" "TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01-20141210.mzid" ...
## $ MS.GF.RawScore : num 10 12 8 -5 8 7 21 -31 -31 -3 ...
## $ MS.GF.DeNovoScore : num 101 121 74 160 241 214 196 165 165 59 ...
## $ MS.GF.SpecEValue : num 4.62e-08 7.26e-08 9.34e-08 1.27e-07 1.32e-07 ...
## $ MS.GF.EValue : num 0.132 0.209 0.267 0.366 0.379 ...
## $ MS.GF.QValue : num 0.525 0.61 0.625 0.717 0.736 ...
## $ MS.GF.PepQValue : num 0.549 0.623 0.636 0.724 0.745 ...
## $ modPeptideRef : chr "Pep77" "Pep108" "Pep136" "Pep163" ...
## $ modName : chr "Carbamidomethyl" "Carbamidomethyl" "Carbamidomethyl" "Carbamidomethyl" ...
## $ modMass : num 57 57 57 57 57 ...
## $ modLocation : int 3 11 5 20 20 21 20 1 28 4 ...
## $ subOriginalResidue : chr NA NA NA NA ...
## $ subReplacementResidue : chr NA NA NA NA ...
## $ subLocation : int NA NA NA NA NA NA NA NA NA NA ...The spectra along the rows are duplicated when the PSM can be assigned to multiple proteins, such as
## spectrumID sequence DatabaseAccess
## 3794 controllerType=0 controllerNumber=1 scan=5291 RKAYLLRMRR XXX_ECA2052
## 4886 controllerType=0 controllerNumber=1 scan=5291 ILLHPLRTLMR ECA1281of when there are multiple modifications in a PSM, such as
## spectrumID
## 411 controllerType=0 controllerNumber=1 scan=4936
## 412 controllerType=0 controllerNumber=1 scan=4936
## sequence modName modLocation
## 411 ICSAILRIISPEWWGRKLWRLRCEWRENQFRAIGVIHKK Carbamidomethyl 2
## 412 ICSAILRIISPEWWGRKLWRLRCEWRENQFRAIGVIHKK Carbamidomethyl 23At this stage, it is useful to perform some exploratory data analysis and visualisation on the identification data. For example
table(iddf$isDecoy)##
## FALSE TRUE
## 2906 2896table(iddf$chargeState)##
## 2 3 4 5 6
## 3312 2064 400 23 3library("ggplot2")
ggplot(data = iddf, aes(x = MS.GF.RawScore, colour = isDecoy)) +
geom_density() +
facet_wrap(~chargeState)
The filterIdentificationDataFrame function can be used to remove
- PSMs that match decoy entries
- PSMs of rank > 1
- PSMs that match non-proteotypic proteins
iddf <- filterIdentificationDataFrame(iddf)This data.frame can be now be further reduced so that individual
rows represent unique spectra, which can be done with the reduce
method.
iddf2 <- reduce(iddf, key = "spectrumID")This reduces the number of rows from 2710 to 2646.
The first duplicated spectrum mentioned above is now unique as is
matched a decoy protein that was filtered out with
filterIdentificationDataFrame.
## spectrumID sequence DatabaseAccess
## 1808 controllerType=0 controllerNumber=1 scan=5291 ILLHPLRTLMR ECA1281The matches to multiple modification in the same peptide are now combined into a single row and documented as semicolon-separated values.
## spectrumID
## 1659 controllerType=0 controllerNumber=1 scan=4936
## sequence
## 1659 ICSAILRIISPEWWGRKLWRLRCEWRENQFRAIGVIHKK;ICSAILRIISPEWWGRKLWRLRCEWRENQFRAIGVIHKK
## modName modLocation
## 1659 Carbamidomethyl;Carbamidomethyl 2;23This is the form that is used when combined to raw data, as described in the next section.
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 2The 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 one5 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 peptideRef
## F1.S1 TRUE 645.3741 645.0375 Pep2
## F1.S2 TRUE 546.9586 546.9633 Pep1
## modNum isDecoy post pre start end DatabaseAccess DBseqLength DatabaseSeq
## F1.S1 0 FALSE A R 170 186 ECA0984 231
## F1.S2 0 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 modPeptideRef modName modMass modLocation
## F1.S1 5.527468e-05 79.36958 <NA> <NA> NA NA
## F1.S2 9.399048e-06 13.46615 <NA> <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 1Finally 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 0.6When 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)$sequence## [1] "VESITARHGEVLQLRPK" "IDGQWVTHQWLKK" "LVILLFR"idSummary(msexp)## spectrumFile idFile coverage
## 1 dummyiTRAQ.mzXML dummyiTRAQ.mzid 1Similarly, the removeMultipleAssignment method can be used
to filter out non-unique features, i.e. that have been assigned to
protein groups with more than one member. This function uses by
default the nprot feature variable.
Note that removeNoId and removeMultipleAssignment methods can also
be called on MSnExp instances.
4.3 Calculate Fragments
MSnbase is able to calculate theoretical peptide fragments via
calculateFragments.
calculateFragments("ACEK",
type = c("a", "b", "c", "x", "y", "z"))## mz ion type pos z seq
## 1 44.04947 a1 a 1 1 A
## 2 204.08012 a2 a 2 1 AC
## 3 333.12271 a3 a 3 1 ACE
## 4 72.04439 b1 b 1 1 A
## 5 232.07504 b2 b 2 1 AC
## 6 361.11763 b3 b 3 1 ACE
## 7 89.07094 c1 c 1 1 A
## 8 249.10159 c2 c 2 1 AC
## 9 378.14417 c3 c 3 1 ACE
## 10 173.09207 x1 x 1 1 K
## 11 302.13466 x2 x 2 1 EK
## 12 462.16531 x3 x 3 1 CEK
## 13 147.11280 y1 y 1 1 K
## 14 276.15539 y2 y 2 1 EK
## 15 436.18604 y3 y 3 1 CEK
## 16 130.08625 z1 z 1 1 K
## 17 259.12884 z2 z 2 1 EK
## 18 419.15949 z3 z 3 1 CEK
## 19 284.12409 x2_ x_ 2 1 EK
## 20 258.14483 y2_ y_ 2 1 EK
## 21 241.11828 z2_ z_ 2 1 EK
## 22 155.08150 x1_ x_ 1 1 K
## 23 444.15474 x3_ x_ 3 1 CEK
## 24 129.10224 y1_ y_ 1 1 K
## 25 418.17548 y3_ y_ 3 1 CEK
## 26 112.07569 z1_ z_ 1 1 K
## 27 401.14893 z3_ z_ 3 1 CEKIt is also possible to match these fragments against an Spectrum2 object.
pepseq <- fData(msexp)$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 -0.077293595 Quality control
The current section is not executed dynamically for package size and processing time constrains. The figures and tables have been generated with the respective methods and included statically in the vignette for illustration purposes.
MSnbase allows easy and flexible access to the data, which allows to visualise data features to assess it’s quality. Some methods are readily available, although many QC approaches will be experiment specific and users are encourage to explore their data.
The plot2d method takes one MSnExp instance as first argument to
produce retention time vs. precursor MZ scatter plots. Points
represent individual MS2 spectra and can be coloured based on
precursor charge (with second argument z="charge"), total ion count
(z="ionCount"), number of peaks in the MS2 spectra
z="peaks.count") or, when multiple data files were loaded, file
z="file"), as illustrated on the next figure. The
lower right panel is produced for only a subset of proteins. See the
method documentation for more details.
Illustration of the plot2d output.
The plotDensity method illustrates the distribution of several
parameters of interest (see figure below).
Similarly to plot2d, the first argument is an MSnExp instance.
The second is one of precursor.mz, peaks.count or ionCount,
whose density will be plotted. An optional third argument specifies
whether the x axes should be logged.
Illustration of the plotDensity output.
The plotMzDelta
method6 The code to generate the histograms has been contributed by Guangchuang Yu.
implements the \(m/z\) delta plot from (Foster et al. 2011) The \(m/z\) delta plot
illustrates the suitability of MS2 spectra for identification by
plotting the \(m/z\) differences of the most intense peaks. The
resulting histogram should optimally shown outstanding bars at amino
acid residu masses. More details and parameters are described in the
method documentation (?plotMzDelta). The
next figure has been generated using the PRIDE
experiment 12011, as in (Foster et al. 2011).
Illustration of the plotMzDelta output for the PRIDE experiment 12011, as in figure 4A from (Foster et al. 2011).
In section 12, we illustrate how to assess incomplete reporter ion dissociation.
6 Raw data processing
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] 555408.8ionCount(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] 1726peaksCount(experiment[["X55"]])## [1] 1726experiment <- 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 977.6636experiment <- filterMz(experiment, mzlim = c(112,120))
range(mz(experiment[["X55"]]))## [1] 102.0612 473.3372experiment## MSn experiment data ("MSnExp")
## Object size in memory: 1.18 Mb
## - - - Spectra data - - -
## MS level(s): 2
## Number of spectra: 55
## MSn retention times: 19:09 - 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': Mon Apr 25 17:08:56 2022
## Spectra cleaned: Mon Apr 25 17:08:57 2022
## 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 MS2 isobaric tagging quantitation
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 117.1150width(iTRAQ4)## [1] 0.05The 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': Mon Apr 25 17:08:56 2022
## Spectra cleaned: Mon Apr 25 17:08:57 2022
## iTRAQ4 quantification by trapezoidation: Mon Apr 25 17:09:00 2022
## MSnbase version: 1.1.22head(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 6437.2303The 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 1qnt <- filterNA(qnt, pNA = 0)
sum(is.na(qnt))## [1] 0The 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
MChromatograms 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## MChromatograms 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 variablesThe 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 133head(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 70.9967.4 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] 120.0813isotopes <- getIsotope(Fim)
F1 <- isotopes[2, 2]
F1## [1] 0.08573496If 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 6437.2303head(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 6666.5633The makeImpuritiesMatrix can be used to create impurity
matrices. It opens a rudimentary spreadsheet that can be directly
edited.
8 Processing quantitative data
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, with, 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 2x <- impute(naset, "min")
processingData(x)## - - - Processing information - - -
## Data imputation using min Mon Apr 25 17:09:02 2022
## MSnbase version: 1.15.6table(is.na(x))##
## FALSE
## 11024As 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 Mon Apr 25 17:09:02 2022
## MSnbase version: 1.15.6Please read ?MsCoreUtils::impute_matix() 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 thenormalize.quantilesfunction of the preprocessCore package.quantiles.robust: Applies robust quantile normalisation (Bolstad et al. 2003) as implemented in thenormalize.quantiles.robustfunction of the preprocessCore package.vsn: Applies variance stabilisation normalization (Huber et al. 2002) as implemented in thevsn2function 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 14).
Figure 14: Experiment-wide normalised MS2 spectra
The y-axes of the individual spectra is now rescaled between 0 and 1 (highest peak), as opposed to figure 5.
Additional dedicated normalisation method are available for MS2
label-free quantitation, as described in section 10 and in
the quantify documentation.
9 Feature aggregation
The above quantitation and normalisation has been performed on quantitative data obtained from individual spectra. However, the biological unit of interest is not the spectrum but the peptide or the protein. As such, it is important to be able to summarise features that belong to a same group, i.e. spectra from one peptide, peptides that originate from one protein, or directly combine all spectra that have been uniquely associated to one protein.
MSnbase provides one function, combineFeatures,
that allows to aggregate features stored in an MSnSet using
build-in or user defined summary function and return a new
MSnSet instance. The three main arguments are described
below. Additional details can be found in the method documentation.
combineFeatures’s first argument, object, is an instance of class
MSnSet, as has been created in the section 7.1 for
instance. The second argument, groupBy, is a factor than has as
many elements as there are features in the MSnSet object
argument. The features corresponding to the groupBy levels will be
aggregated so that the resulting MSnSet output will have
length(levels(groupBy)) features. Here, we will combine individual
MS2 spectra based on the protein they originate from. As shown below,
this will result in 40 new aggregated features.
gb <- fData(qnt)$ProteinAccession
table(gb)## gb
## BSA ECA0172 ECA0435 ECA0452 ECA0469 ECA0621 ECA0631 ECA0691 ECA0871 ECA0978
## 3 1 2 1 2 1 1 1 1 1
## ECA1032 ECA1093 ECA1104 ECA1294 ECA1362 ECA1363 ECA1364 ECA1422 ECA1443 ECA2186
## 1 1 1 1 1 1 1 1 1 1
## ECA2391 ECA2421 ECA2831 ECA3082 ECA3175 ECA3349 ECA3356 ECA3377 ECA3566 ECA3882
## 1 1 1 1 1 2 1 1 2 1
## ECA3929 ECA3969 ECA4013 ECA4026 ECA4030 ECA4037 ECA4512 ECA4513 ECA4514 ENO
## 1 1 1 2 1 1 1 1 6 3length(unique(gb))## [1] 40The third argument, method, defined how to combine the
features. Predefined functions are readily available and can be
specified as strings (method="mean", method="median", method="sum",
method="weighted.mean" or method="medianpolish" to compute respectively
the mean, media, sum, weighted mean or median polish of the features
to be aggregated). Alternatively, is is possible to supply user
defined functions with method=function(x) { ... }. We will use the
median here.
qnt2 <- combineFeatures(qnt, groupBy = gb, method = "median")
qnt2## MSnSet (storageMode: lockedEnvironment)
## assayData: 40 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: BSA ECA0172 ... ENO (40 total)
## fvarLabels: spectrum ProteinAccession ... CV.iTRAQ4.117 (19 total)
## fvarMetadata: labelDescription
## experimentData: use 'experimentData(object)'
## 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': Mon Apr 25 17:08:56 2022
## Spectra cleaned: Mon Apr 25 17:08:57 2022
## iTRAQ4 quantification by trapezoidation: Mon Apr 25 17:09:00 2022
## Subset [55,4][54,4] Mon Apr 25 17:09:01 2022
## Removed features with more than 0 NAs: Mon Apr 25 17:09:01 2022
## Dropped featureData's levels Mon Apr 25 17:09:01 2022
## Combined 54 features into 40 using median: Mon Apr 25 17:09:05 2022
## MSnbase version: 2.21.7Of interest is also the iPQF spectra-to-protein summarisation
method, which integrates peptide spectra characteristics and
quantitative values for protein quantitation estimation. See ?iPQF
and references therein for details.
10 Label-free MS2 quantitation
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 2Such 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 1Note 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: Mon Apr 25 17:08:55 2022
## Filtered 2 unidentified peptides out [Mon Apr 25 17:08:56 2022]
## Quantitation by total ion current [Mon Apr 25 17:09:05 2022]
## Combined 3 features into 3 using sum: Mon Apr 25 17:09:05 2022
## Quantification by SIn [Mon Apr 25 17:09:05 2022]
## MSnbase version: 2.21.7exprs(siquant)## dummyiTRAQ.mzXML
## ECA0510 0.0006553518
## ECA0984 0.0035384487
## ECA1028 0.0002684726Other label-free methods can be applied by specifiying the appropriate
method argument. See ?quantify for more details.
11 Spectra comparison
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 42mzk <- precursorMz(centroided)[k]
zk <- precursorCharge(centroided)[k]
mzk * zk## X46 X47
## 2046.175 2045.169plot(centroided[[k[1]]], centroided[[k[2]]])
Figure 15: 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] 8compareSpectra(centroided[[2]], centroided[[3]],
fun = "cor")## [1] 0.1105021compareSpectra(centroided[[2]], centroided[[3]],
fun = "dotproduct")## [1] 0.1185025compareSpectra 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 0.64262061Below, we illustrate how to compare a set of spectra using a hierarchical clustering.
plot(hclust(as.dist(compmat)))
12 Quantitative assessment of incomplete dissociation
Quantitation using isobaric reporter tags assumes complete dissociation between the reporter group (red on the figure below), balance group (blue) and peptide (the peptide reactive group is drawn in green). However, incomplete dissociation does occur and results in an isobaric tag (i.e reporter and balance groups) specific peaks.
iTRAQ 4-plex isobaric tags reagent consist of three parts: (1) a charged reporter group (MZ of 114, 115, 116 and 117) that is unique to each of the four reagents (red), (2) an uncharged mass balance group (28-31 Da) (blue)and (3) a peptide reactive group (NHS ester) that binds to the peptide. In case of incomplete dissociation, the reporter and balance groups produce a specific peaks at MZ 145.
MSnbase provides, among others, a ReporterIons object for iTRAQ 4-plex that includes the 145 peaks, called iTRAQ5. This can then be used to quantify the experiment as show in section 7.1 to estimate incomplete dissociation for each spectrum.
iTRAQ5## Object of class "ReporterIons"
## iTRAQ5: '4-plex iTRAQ and reporter + balance group' with 5 reporter ions
## - [iTRAQ5.114] 114.1112 +/- 0.05 (red)
## - [iTRAQ5.115] 115.1083 +/- 0.05 (green)
## - [iTRAQ5.116] 116.1116 +/- 0.05 (blue)
## - [iTRAQ5.117] 117.115 +/- 0.05 (yellow)
## - [iTRAQ5.145] 145.1 +/- 0.05 (grey)incompdiss <- quantify(itraqdata,
method = "trap",
reporters = iTRAQ5,
strict = FALSE,
verbose = FALSE)
head(exprs(incompdiss))## iTRAQ5.114 iTRAQ5.115 iTRAQ5.116 iTRAQ5.117 iTRAQ5.145
## X1 1347.6158 2247.3097 3927.6931 7661.1463 2063.8947
## X10 739.9861 799.3501 712.5983 940.6793 467.3615
## X11 27638.3582 33394.0252 32104.2879 26628.7278 13543.4565
## X12 31892.8928 33634.6980 37674.7272 37227.7119 11839.2558
## X13 26143.7542 29677.4781 29089.0593 27902.5608 12206.5508
## X14 6448.0829 6234.1957 6902.8903 6437.2303 427.6654Figure 16 compares these intensities for the whole experiment.
## `geom_smooth()` using formula 'y ~ x'
Figure 16: Boxplot and scatterplot comparing intensities of the 4 reporter ions (or their sum, on the right) and the incomplete dissociation specific peak
13 Combining MSnSet instances
Combining mass spectrometry runs can be done in two different ways depending on the nature of these runs. If the runs represent repeated measures of identical samples, for instance multiple fractions, the data has to be combine along the row of the quantitation matrix: all the features (along the rows) represent measurements of the same set of samples (along the columns). In this situation, described in section 13.1, two experiments of dimensions \(n_1\) (rows) by \(m\) (columns and \(n_2\) by \(m\) will produce a new experiment of dimensions \(n_1 + n_2\) by \(m\).
When however, different sets of samples have been analysed in
different mass spectrometry runs, the data has to be combined along
the columns of the quantitation matrix: some features will be shared
across experiments and should thus be aligned on a same row in the new
data set, whereas unique features to one experiment should be set as
missing in the other one. In this situation, described in section
13.2, two experiments of dimensions \(n_1\) by \(m_1\) and
\(n_2\) by \(m_2\) will produce a new experiment of dimensions
\(unique_{n_1} + unique_{n_2} + shared_{n_1, n_2}\) by \(m_1 + m_2\). The
two first terms of the first dimension will be complemented by
NA values.
Default MSnSet feature names (X1, X2, …) and sample names
(iTRAQ4.114, iTRAQ4.115, iTRAQ4.116, …) are not informative.
The features and samples of these anonymous quantitative data-sets
should be updated before being combined, to guide how to meaningfully
merge them.
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 4dim(exp2)## [1] 5 4dim(exp12)## [1] 60 413.2 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 21exp1 <- 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 on 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 NYQEAKfData(exp2)["BSA", 1:4]## spectrum ProteinAccession ProteinDescription PeptideSequence
## BSA 1 BSA bovine serum albumin NYQEAKInstead 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 8pData(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 iTRAQ4exprs(exp12)[25:28, ]## Ctrl1 Cond1 Ctrl2 Cond2 Ctrl3 Cond3 Ctrl4
## ECA4513 10154.953 10486.943 11018.191 11289.552 NA NA NA
## ECA4514 6516.397 6746.337 6658.973 6838.491 12457.173 12695.491 14423.640
## ENO 77239.040 49352.793 22493.050 11187.588 39965.733 24967.397 NA
## ECA0435 NA NA NA NA 4923.628 5557.818 5775.203
## Cond4
## ECA4513 NA
## ECA4514 14556.855
## ENO 5925.663
## ECA0435 5079.295exp12## 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 Mon Apr 25 17:09:15 2022
## MSnbase version: 2.21.7In 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 Bsplit(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] TRUESee ?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 0.1750557head(fData(avgtan)$disp)## X114 X115 X116 X117
## P20353 0.076083495 0.1099127 0.109691169 0.14650198
## P53501 0.034172542 0.2640556 0.005139653 0.17104568
## Q7KU78 0.023198743 0.4483795 0.027883087 0.04764499
## P04412 0.053414021 0.2146751 0.090972139 0.27903810
## Q7KJ73 0.000000000 0.0000000 0.000000000 0.00000000
## Q7JZN0 0.007681865 0.1959534 0.097873350 0.06210542head(fData(avgtan)$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 0We 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 1.20888923library("pRoloc")
plot2D(avgtan, cex = 3 * disp)
Figure 17: PCA plot of the averaged MSnSet
The point sizes are proportional to the dispersion of the protein quantitation across the averaged data.
14 MSE data processing
MSnbase can also be used for MSE data independent
acquisition from Waters instrument. The MSE pipeline depends on
the Bioconductor synapter package (Bond et al. 2013) that
produces MSnSet instances for indvidual acquisitions.
The MSnbase infrastructure can subsequently be used to
further combine experiments, as shown in section 13.2 and
apply top3 quantitation using the topN method.
Session information
## R version 4.2.0 RC (2022-04-19 r82224)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.4 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.15-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.15-bioc/R/lib/libRlapack.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] grid stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] gplots_3.1.3 msdata_0.35.3 pRoloc_1.35.0
## [4] BiocParallel_1.29.21 MLInterfaces_1.75.0 cluster_2.1.3
## [7] annotate_1.73.0 XML_3.99-0.9 AnnotationDbi_1.57.1
## [10] IRanges_2.29.1 pRolocdata_1.33.1 Rdisop_1.55.0
## [13] zoo_1.8-10 MSnbase_2.21.7 ProtGenerics_1.27.2
## [16] S4Vectors_0.33.17 mzR_2.29.6 Rcpp_1.0.8.3
## [19] Biobase_2.55.2 BiocGenerics_0.41.2 ggplot2_3.3.5
## [22] BiocStyle_2.23.1
##
## loaded via a namespace (and not attached):
## [1] BiocFileCache_2.3.5 plyr_1.8.7 splines_4.2.0
## [4] listenv_0.8.0 GenomeInfoDb_1.31.10 digest_0.6.29
## [7] foreach_1.5.2 htmltools_0.5.2 magick_2.7.3
## [10] viridis_0.6.2 fansi_1.0.3 magrittr_2.0.3
## [13] memoise_2.0.1 doParallel_1.0.17 mixtools_1.2.0
## [16] limma_3.51.8 globals_0.14.0 recipes_0.2.0
## [19] Biostrings_2.63.3 gower_1.0.0 hardhat_0.2.0
## [22] lpSolve_5.6.15 prettyunits_1.1.1 colorspace_2.0-3
## [25] blob_1.2.3 rappdirs_0.3.3 xfun_0.30
## [28] dplyr_1.0.8 crayon_1.5.1 RCurl_1.98-1.6
## [31] jsonlite_1.8.0 hexbin_1.28.2 impute_1.69.0
## [34] survival_3.3-1 iterators_1.0.14 glue_1.6.2
## [37] gtable_0.3.0 ipred_0.9-12 zlibbioc_1.41.0
## [40] XVector_0.35.0 kernlab_0.9-30 future.apply_1.8.1
## [43] scales_1.2.0 vsn_3.63.0 mvtnorm_1.1-3
## [46] DBI_1.1.2 viridisLite_0.4.0 xtable_1.8-4
## [49] progress_1.2.2 clue_0.3-60 bit_4.0.4
## [52] proxy_0.4-26 mclust_5.4.9 preprocessCore_1.57.1
## [55] MsCoreUtils_1.7.5 lava_1.6.10 prodlim_2019.11.13
## [58] sampling_2.9 httr_1.4.2 FNN_1.1.3
## [61] RColorBrewer_1.1-3 ellipsis_0.3.2 farver_2.1.0
## [64] pkgconfig_2.0.3 nnet_7.3-17 sass_0.4.1
## [67] dbplyr_2.1.1 utf8_1.2.2 caret_6.0-92
## [70] labeling_0.4.2 reshape2_1.4.4 tidyselect_1.1.2
## [73] rlang_1.0.2 munsell_0.5.0 tools_4.2.0
## [76] LaplacesDemon_16.1.6 cachem_1.0.6 cli_3.3.0
## [79] generics_0.1.2 RSQLite_2.2.12 evaluate_0.15
## [82] stringr_1.4.0 fastmap_1.1.0 mzID_1.33.0
## [85] yaml_2.3.5 ModelMetrics_1.2.2.2 knitr_1.38
## [88] bit64_4.0.5 caTools_1.18.2 randomForest_4.7-1
## [91] purrr_0.3.4 KEGGREST_1.35.0 dendextend_1.15.2
## [94] ncdf4_1.19 future_1.25.0 nlme_3.1-157
## [97] xml2_1.3.3 biomaRt_2.51.4 compiler_4.2.0
## [100] filelock_1.0.2 curl_4.3.2 png_0.1-7
## [103] e1071_1.7-9 affyio_1.65.0 tibble_3.1.6
## [106] bslib_0.3.1 stringi_1.7.6 highr_0.9
## [109] lattice_0.20-45 Matrix_1.4-1 vctrs_0.4.1
## [112] pillar_1.7.0 lifecycle_1.0.1 BiocManager_1.30.17
## [115] jquerylib_0.1.4 MALDIquant_1.21 data.table_1.14.2
## [118] bitops_1.0-7 R6_2.5.1 pcaMethods_1.87.0
## [121] affy_1.73.0 bookdown_0.26 KernSmooth_2.23-20
## [124] gridExtra_2.3 parallelly_1.31.1 codetools_0.2-18
## [127] gtools_3.9.2 MASS_7.3-57 assertthat_0.2.1
## [130] withr_2.5.0 GenomeInfoDbData_1.2.8 mgcv_1.8-40
## [133] parallel_4.2.0 hms_1.1.1 rpart_4.1.16
## [136] timeDate_3043.102 coda_0.19-4 class_7.3-20
## [139] rmarkdown_2.14 segmented_1.5-0 pROC_1.18.0
## [142] lubridate_1.8.0References
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