Using R for proteomics data analysis
1 September 2022
Abstract
This is the companion vignette to the ‘Visualisation of proteomics data using R and Bioconductor’ manuscript that presents an overview of R and Bioconductor software for mass spectrometry and proteomics data. It provides the code to reproduce the figures in the paper.Package
RforProteomics 1.34.1
1 Introduction
This document illustrates some existing R infrastructure for the analysis of proteomics data. It presents the code for the use cases taken from (Gatto and Christoforou 2013, Gatto:2015). A pre-print of (Gatto and Christoforou 2013) available on arXiv and (Gatto et al. 2015) is open access.
There are however numerous additional R resources distributed by the Bioconductor and CRAN repositories, as well as packages hosted on personal websites. Section 8 tries to provide a wider picture of available packages, without going into details.
NB: I you are interested in R packages for mass spectrometry-based proteomics and metabolomics, see also the R for Mass Spectrometry initiative packages and the tutorial book. It provides more up-to-date packages and solutions for several of the tasks described below.
1.1 General R resources
The reader is expected to have basic R knowledge to find the document helpful. There are numerous R introductions freely available, some of which are listed below.
From the R project web-page:
- An Introduction to R is based on the former Notes on R, gives an introduction to the language and how to use R for doing statistical analysis and graphics (html and pdf.
- Several introductory tutorials in the contributed documentation section.
- The TeachingMaterial repository contains several sets of slides and vignettes about R programming.
Relevant background on the R software and its application to computational biology in general and proteomics in particular can also be found in (Gatto and Christoforou 2013). For details about the Bioconductor project, the reader is referred to (Gentleman et al. 2004).
1.2 Bioconductor resources
The Bioconductor offers many educational resources on its help page, in addition the package’s vignettes (vignettes are a requirement for Bioconductor packages). We want to draw the attention to the Bioconductor work flows that offer a cross-package overview about a specific topic. In particular, there is now a Mass spectrometry and proteomics data analysis work flow.
1.3 Getting help
All R packages come with ample documentation. Every command
(function, class or method) a user is susceptible to use is
documented. The documentation can be accessed by preceding the command
by a ? in the R console. For example, to obtain help about
the library function, that will be used in the next
section, one would type ?library. In addition, all
Bioconductor packages come with at least one vignette (this document
is the vignette that comes with the RforProteomics
package), a document that combines text and R code that is executed
before the pdf is assembled. To look up all vignettes that come with a
package, say RforProteomics and then open the vignette of
interest, one uses the vignette function as illustrated
below. More details can be found in ?vignette.
## list all the vignettes in the RforProteomics package
vignette(package = "RforProteomics")
## Open the vignette called RforProteomics
vignette("RforProteomics", package = "RforProteomics")
## or just
vignette("RforProteomics")R has several mailing lists. The
most relevant here being the main R-help list, for discussion about
problem and solutions using R, ideal for general R content and is not
suitable for bioinformatics or proteomics questions. Bioconductor also
offers several resources dedicated to bioinformatics matters and
Bioconductor packages, in particular the
main Bioconductor support forum
for Bioconductor-related queries.
It is advised to read and comply to
the posting guides
(and here
to maximise the chances to obtain good responses. It is important to
specify the software versions using the sessionInfo() functions (see
an example output at the end of this document. It the question
involves some code, make sure to isolate the relevant portion and
report it with your question, trying to make
your
code/example reproducible.
1.4 Installation
The package should be installed using as described below:
## only first time you install Bioconductor packages
if (!requireNamespace("BiocManager", quietly=TRUE))
install.packages("BiocManager")
## else
library("BiocManager")
BiocManager::install("RforProteomics")To install all dependencies and reproduce the code in the vignette, replace the last line in the code chunk above with:)
BiocManager::install("RforProteomics", dependencies = TRUE)Finally, the package can be loaded with
library("RforProteomics")See also the RforProteomics web page for more information on installation.
1.5 External dependencies
Some packages used in the document depend on external libraries that need to be installed prior to the R packages:
- mzR depends on
the Common Data Format (CDF) to
CDF-based raw mass-spectrometry data. On Linux, the
libcdflibrary is required. On Debian-based systems, for instance, one needs to install thelibnetcdf-devpackage. - several packages depend on the XML package which
requires the
libxml2infrastructure on Linux. On Debian-based systems, one needs to installlibxml2-dev. - biomaRt performs on-line requests using
the
curlinfrastructure. On Debian-based systems, you one needs to installlibcurl-devorlibcurl4-openssl-dev. - MSGFplus is based on the
MS-GF+java program and thus requires Java 1.7 in order to work.
1.6 Obtaining the code
The code in this document describes all the examples presented in
(Gatto and Christoforou 2013) and can be copy, pasted and executed. It is however more
convenient to have it in a separate text file for better interaction
with R to easily modify and explore it. This can be achieved with the
Stangle function. One needs the Sweave source of this document (a
document combining the narration and the R code) and the Stangle
then specifically extracts the code chunks and produces a clean R
source file. If the package is installed, the following code chunk
will direct you to the RforProteomics.R file containing all the
annotated source code contained in this document.
## gets the vignette source
rfile <- system.file("doc/RforProteomics.R",
package = "RforProteomics")
rfile## [1] ""1.7 Prepare the working environment
The packages that we will depend on to execute the examples will be loaded in the respective sections. Here, we pre-load packages that provide general functionality used throughout the document.
library("RColorBrewer") ## Color palettes
library("ggplot2") ## Convenient and nice plotting
library("reshape2") ## Flexibly reshape data2 Data standards and input/output
2.1 The mzR package
2.1.1 Raw MS data
The mzR package (Chambers et al. 2012) provides a unified
interface to various mass spectrometry open formats. This code chunk,
taken from the openMSfile documentation, illustrated how
to open a connection to an raw data file. The example mzML
data is taken from the msdata data package. The code
below would also be applicable to an mzXML, mzData
or netCDF file.
## load the required packages
library("mzR") ## the software package
library("msdata") ## the data package
## below, we extract the releavant example file
## from the local 'msdata' installation
filepath <- system.file("microtofq", package = "msdata")
file <- list.files(filepath, pattern="MM14.mzML",
full.names=TRUE, recursive = TRUE)
## creates a commection to the mzML file
mz <- openMSfile(file)
## demonstraction of data access
basename(fileName(mz))## [1] "MM14.mzML"runInfo(mz)## $scanCount
## [1] 112
##
## $lowMz
## [1] 0
##
## $highMz
## [1] 0
##
## $dStartTime
## [1] 270.334
##
## $dEndTime
## [1] 307.678
##
## $msLevels
## [1] 1
##
## $startTimeStamp
## [1] NAinstrumentInfo(mz)## $manufacturer
## [1] "Unknown"
##
## $model
## [1] "instrument model"
##
## $ionisation
## [1] "electrospray ionization"
##
## $analyzer
## [1] "mass analyzer type"
##
## $detector
## [1] "detector type"
##
## $software
## [1] "so_in_0 "
##
## $sample
## [1] "MM14_20uMsa_0"
##
## $source
## [1] ""## once finished, it is good to explicitely
## close the connection
close(mz)mzR is used by other packages, like MSnbase (Gatto and Lilley 2012), TargetSearch (Cuadros-Inostroza et al. 2009) and xcms (Smith et al. 2006, Benton2008, Tautenhahn2008), that provide a higher level abstraction to the data.
2.1.2 Identification data
The mzR package also provides very fast access to
mzIdentML data by leveraging proteowizard’s C++
parser.
file <- system.file("mzid", "Tandem.mzid.gz", package="msdata")
mzid <- openIDfile(file)
mzid## Identification file handle.
## Filename: Tandem.mzid.gz
## Number of psms: 171Once and mzRident identification file handle has been
established, various data and metadata can be extracted, as
illustrated below.
softwareInfo(mzid)## [1] "xtandem x! tandem CYCLONE (2010.06.01.5) "
## [2] "ProteoWizard MzIdentML 3.0.21263 ProteoWizard"enzymes(mzid)## name nTermGain cTermGain minDistance missedCleavages
## 1 Trypsin H OH 0 1names(psms(mzid))## [1] "spectrumID" "chargeState"
## [3] "rank" "passThreshold"
## [5] "experimentalMassToCharge" "calculatedMassToCharge"
## [7] "sequence" "peptideRef"
## [9] "modNum" "isDecoy"
## [11] "post" "pre"
## [13] "start" "end"
## [15] "DatabaseAccess" "DBseqLength"
## [17] "DatabaseSeq" "DatabaseDescription"
## [19] "spectrum.title" "acquisitionNum"head(psms(mzid))[, 1:13]## spectrumID chargeState rank passThreshold experimentalMassToCharge
## 1 index=12 3 1 FALSE 903.7209
## 2 index=285 3 1 FALSE 792.3792
## 3 index=83 3 1 FALSE 792.5295
## 4 index=21 3 1 FALSE 850.0782
## 5 index=198 3 1 FALSE 527.2592
## 6 index=13 2 1 FALSE 724.8816
## calculatedMassToCharge sequence
## 1 903.4032 LCYIALDFDEEMKAAEDSSDIEK
## 2 792.3899 KDLYGNVVLSGGTTMYEGIGER
## 3 792.3899 KDLYGNVVLSGGTTMYEGIGER
## 4 849.7635 VIDENFGLVEGLMTTVHAATGTQK
## 5 527.2849 GVGGAIVLVLYDEMK
## 6 724.3771 HAVGGRYSSLLCK
## peptideRef modNum isDecoy post pre
## 1 LCYIALDFDEEMKAAEDSSDIEK_15.9949@M$12;_57.0215@C$2;_ 2 FALSE S K
## 2 KDLYGNVVLSGGTTMYEGIGER_15.9949@M$15;__ 1 FALSE L R
## 3 KDLYGNVVLSGGTTMYEGIGER_15.9949@M$15;__ 1 FALSE L R
## 4 VIDENFGLVEGLMTTVHAATGTQK_15.9949@M$13;__ 1 FALSE V K
## 5 GVGGAIVLVLYDEMK_15.9949@M$14;__ 1 FALSE R R
## 6 HAVGGRYSSLLCK__57.0215@C$12;_ 1 TRUE D K
## start
## 1 217
## 2 292
## 3 292
## 4 842
## 5 297
## 6 3922.2 Handling MS2 identification data with mzID
The mzID package allows to load and manipulate MS2 data
in the mzIdentML format. The main mzID function
reads such a file and constructs an instance of class mzID.
library("mzID")
mzids <- list.files(system.file('extdata', package = 'mzID'),
pattern = '*.mzid', full.names = TRUE)
mzids## [1] "/home/biocbuild/bbs-3.15-bioc/R/library/mzID/extdata/55merge_omssa.mzid"
## [2] "/home/biocbuild/bbs-3.15-bioc/R/library/mzID/extdata/55merge_tandem.mzid"
## [3] "/home/biocbuild/bbs-3.15-bioc/R/library/mzID/extdata/MPC_example_Multiple_search_engines.mzid"
## [4] "/home/biocbuild/bbs-3.15-bioc/R/library/mzID/extdata/Mascot_MSMS_example.mzid"
## [5] "/home/biocbuild/bbs-3.15-bioc/R/library/mzID/extdata/Mascot_MSMS_example1.0.mzid"
## [6] "/home/biocbuild/bbs-3.15-bioc/R/library/mzID/extdata/Mascot_NA_example.mzid"
## [7] "/home/biocbuild/bbs-3.15-bioc/R/library/mzID/extdata/Mascot_top_down_example.mzid"
## [8] "/home/biocbuild/bbs-3.15-bioc/R/library/mzID/extdata/Sequest_example_ver1.1.mzid"
## [9] "/home/biocbuild/bbs-3.15-bioc/R/library/mzID/extdata/mascot_pmf_example.mzid"id <- mzID(mzids[1])## reading 55merge_omssa.mzid... DONE!id## An mzID object
##
## Software used: OMSSA (version: NA)
##
## Rawfile: D:/TestSpace/NeoTestMarch2011/55merge.mgf
##
## Database: D:/Software/Databases/Neospora_3rndTryp/Neo_rndTryp_3times.fasta
##
## Number of scans: 39
## Number of PSM's: 99Multiple files can be parsed in one go, possibly in parallel if the environment supports it. When this is done an mzIDCollection object is returned:
ids <- mzID(mzids[1:2])
ids## An mzIDCollection object containing 2 samplesPeptides, scans, parameters, … can be extracted with the
respective peptides, scans,
parameters, … functions. The mzID object
can also be converted into a data.frame using the
flatten function.
fid <- flatten(id)
names(fid)## [1] "spectrumid" "spectrum title"
## [3] "acquisitionnum" "passthreshold"
## [5] "rank" "calculatedmasstocharge"
## [7] "experimentalmasstocharge" "chargestate"
## [9] "omssa:evalue" "omssa:pvalue"
## [11] "isdecoy" "post"
## [13] "pre" "end"
## [15] "start" "accession"
## [17] "length" "description"
## [19] "pepseq" "modified"
## [21] "modification" "idFile"
## [23] "spectrumFile" "databaseFile"dim(fid)## [1] 101 243 Raw data abstraction with MSnExp objects
MSnbase (Gatto and Lilley 2012) provides base functions and classes for MS-based proteomics that allow facile data and meta-data processing, manipulation and plotting (see for instance figure below).
library("MSnbase")
## uses a simple dummy test included in the package
mzXML <- dir(system.file(package="MSnbase",dir="extdata"),
full.name=TRUE,
pattern="mzXML$")
basename(mzXML)## [1] "dummyiTRAQ.mzXML"## reads the raw data into and MSnExp instance
raw <- readMSData(mzXML, verbose = FALSE, centroided = TRUE)
raw## MSn experiment data ("MSnExp")
## Object size in memory: 0.18 Mb
## - - - Spectra data - - -
## MS level(s): 2
## Number of spectra: 5
## MSn retention times: 25:01 - 25:02 minutes
## - - - Processing information - - -
## Data loaded: Thu Sep 1 10:37:43 2022
## MSnbase version: 2.22.0
## - - - Meta data - - -
## phenoData
## rowNames: dummyiTRAQ.mzXML
## varLabels: sampleNames
## varMetadata: labelDescription
## Loaded from:
## dummyiTRAQ.mzXML
## protocolData: none
## featureData
## featureNames: F1.S1 F1.S2 ... F1.S5 (5 total)
## fvarLabels: spectrum
## fvarMetadata: labelDescription
## experimentData: use 'experimentData(object)'## Extract a single spectrum
raw[[3]]## Object of class "Spectrum2"
## Precursor: 645.3741
## Retention time: 25:02
## Charge: 2
## MSn level: 2
## Peaks count: 2125
## Total ion count: 150838188plot(raw, full = TRUE)
plot(raw[[3]], full = TRUE, reporters = iTRAQ4)
Figure 1: The plot method can be used on experiments, i.e. spectrum collections (top), or individual spectra (bottom)
3.1 mgf read/write support
Read and write support for data in the
mgf
and mzTab formats are available via the readMgfData/writeMgfData
and readMzTabData/writeMzTabData functions, respectively. An
example for the latter is shown in the next section.
4 Quantitative proteomics
As an running example throughout this document, we will use a TMT
6-plex data set, PXD000001 to illustrate quantitative data
processing. The code chunk below first downloads this data file from
the ProteomeXchange server using the rpx package.
4.1 The mzTab format
The first code chunk downloads the mzTab data from the
ProteomeXchange repository (Vizcaino et al. 2014).
## Experiment information
library("rpx")
px1 <- PXDataset("PXD000001")## Loading PXD000001 from cache.px1## Project PXD000001 with 10 files
## ## Resource ID BFC2 in cache in /home/biocbuild/.cache/R/rpx.## [1] 'erwinia_carotovora.fasta' ... [10] 'PRIDE_Exp_Complete_Ac_22134.pride.mgf.gz'
## Use 'pxfiles(.)' to see all files.pxfiles(px1)## Project PXD000001 files (10):
## [local] erwinia_carotovora.fasta
## [remote] F063721.dat
## [local] F063721.dat-mztab.txt
## [remote] PRIDE_Exp_Complete_Ac_22134.xml.gz
## [remote] TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01.raw
## [local] TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01.mzXML
## [remote] TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01-20141210.mzXML
## [local] TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01-20141210.mzML
## [remote] PRIDE_Exp_Complete_Ac_22134.pride.mztab.gz
## [remote] PRIDE_Exp_Complete_Ac_22134.pride.mgf.gz## Downloading the mzTab data
mztab <- pxget(px1, "F063721.dat-mztab.txt")## Loading F063721.dat-mztab.txt from cache.mztab## [1] "/home/biocbuild/.cache/R/rpx/273f69193e968b_F063721.dat-mztab.txt"The code below loads the mzTab file into R and generates an MSnSet
instance1 Here, we specify mzTab format version 0.9. Recent files
have been generated according to the latest specifications, version
1.0, and the version does not need to be specified explicitly.,
removes missing values and calculates protein intensities by summing
the peptide quantitation data. The figure below illustrates the
intensities for 5 proteins.
## Load mzTab peptide data
qnt <- readMzTabData(mztab, what = "PEP", version = "0.9")## Warning: Version 0.9 is deprecated. Please see '?readMzTabData' and '?MzTab' for
## details.sampleNames(qnt) <- reporterNames(TMT6)
head(exprs(qnt))## TMT6.126 TMT6.127 TMT6.128 TMT6.129 TMT6.130 TMT6.131
## 1 10630132 11238708 12424917 10997763 9928972 10398534
## 2 11105690 12403253 13160903 12229367 11061660 10131218
## 3 1183431 1322371 1599088 1243715 1306602 1159064
## 4 5384958 5508454 6883086 6136023 5626680 5213771
## 5 18033537 17926487 21052620 19810368 17381162 17268329
## 6 9873585 10299931 11142071 10258214 9664315 9518271## remove missing values
qnt <- filterNA(qnt)
processingData(qnt)## - - - Processing information - - -
## mzTab read: Thu Sep 1 10:37:44 2022
## Subset [1528,6][1528,6] Thu Sep 1 10:37:44 2022
## Removed features with more than 0 NAs: Thu Sep 1 10:37:44 2022
## Dropped featureData's levels Thu Sep 1 10:37:44 2022
## MSnbase version: 2.22.0## combine into proteins
## - using the 'accession' feature meta data
## - sum the peptide intensities
protqnt <- combineFeatures(qnt,
groupBy = fData(qnt)$accession,
method = sum)cls <- brewer.pal(5, "Set1")
matplot(t(tail(exprs(protqnt), n = 5)), type = "b",
lty = 1, col = cls,
ylab = "Protein intensity (summed peptides)",
xlab = "TMT reporters")
legend("topright", tail(featureNames(protqnt), n=5),
lty = 1, bty = "n", cex = .8, col = cls)
Figure 2: Protein quantitation data
qntS <- normalise(qnt, "sum")
qntV <- normalise(qntS, "vsn")
qntV2 <- normalise(qnt, "vsn")
acc <- c("P00489", "P00924",
"P02769", "P62894",
"ECA")
idx <- sapply(acc, grep, fData(qnt)$accession)
idx2 <- sapply(idx, head, 3)
small <- qntS[unlist(idx2), ]
idx3 <- sapply(idx, head, 10)
medium <- qntV[unlist(idx3), ]
m <- exprs(medium)
colnames(m) <- c("126", "127", "128",
"129", "130", "131")
rownames(m) <- fData(medium)$accession
rownames(m)[grep("CYC", rownames(m))] <- "CYT"
rownames(m)[grep("ENO", rownames(m))] <- "ENO"
rownames(m)[grep("ALB", rownames(m))] <- "BSA"
rownames(m)[grep("PYGM", rownames(m))] <- "PHO"
rownames(m)[grep("ECA", rownames(m))] <- "Background"
cls <- c(brewer.pal(length(unique(rownames(m)))-1, "Set1"),
"grey")
names(cls) <- unique(rownames(m))
wbcol <- colorRampPalette(c("white", "darkblue"))(256)heatmap(m, col = wbcol, RowSideColors=cls[rownames(m)])
Figure 3: A heatmap
dfr <- data.frame(exprs(small),
Protein = as.character(fData(small)$accession),
Feature = featureNames(small),
stringsAsFactors = FALSE)
colnames(dfr) <- c("126", "127", "128", "129", "130", "131",
"Protein", "Feature")
dfr$Protein[dfr$Protein == "sp|P00924|ENO1_YEAST"] <- "ENO"
dfr$Protein[dfr$Protein == "sp|P62894|CYC_BOVIN"] <- "CYT"
dfr$Protein[dfr$Protein == "sp|P02769|ALBU_BOVIN"] <- "BSA"
dfr$Protein[dfr$Protein == "sp|P00489|PYGM_RABIT"] <- "PHO"
dfr$Protein[grep("ECA", dfr$Protein)] <- "Background"
dfr2 <- melt(dfr)## Using Protein, Feature as id variablesggplot(aes(x = variable, y = value, colour = Protein),
data = dfr2) +
geom_point() +
geom_line(aes(group=as.factor(Feature)), alpha = 0.5) +
facet_grid(. ~ Protein) + theme(legend.position="none") +
labs(x = "Reporters", y = "Normalised intensity")
Figure 4: Spikes plot using r CRANpkg('ggplot2')
4.2 Third-party data
It is possible to import any arbitrary text-based spreadsheet as
MSnSet object using either readMSnSet or
readMSnSet2. The former takes three spreadsheets as input
(for the expression data and the feature and sample meta-data). The
latter uses a single spreadsheet and a vector of expression columns to
populate the assay data and the feature meta-data. Detailed examples
are provided in the MSnbase-io vignette, that can be
consulted from R with vignette("MSnbase-io") or
online.
4.3 Working with raw data
We reuse our dedicated px1 ProteomeXchange data object to
download the raw data (in mzXML format) and load it with the
readMSData from the MSnbase package that
produces a raw data experiment object of class MSnExp (a new
on-disk infrastructure is now available to access the raw
data on disk on demand, rather than loading it all in memory, enabling
the management of more and larger files - see the
benchmarking vignette in the MSnbase package for
details). The raw data is then quantified using the
quantify method specifying the TMT 6-plex isobaric tags
and a 7th peak of interest corresponding to the un-dissociated
reporter tag peaks (see the MSnbase-demo vignette in
MSnbase for details).
mzxml <- pxget(px1, "TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01.mzXML")## Loading TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01.mzXML from cache.rawms <- readMSData(mzxml, centroided = TRUE, verbose = FALSE)
qntms <- quantify(rawms, reporters = TMT7, method = "max")
qntms## MSnSet (storageMode: lockedEnvironment)
## assayData: 6103 features, 7 samples
## element names: exprs
## protocolData: none
## phenoData
## sampleNames: TMT7.126 TMT7.127 ... TMT7.230 (7 total)
## varLabels: mz reporters
## varMetadata: labelDescription
## featureData
## featureNames: F1.S0001 F1.S0002 ... F1.S6103 (6103 total)
## fvarLabels: spectrum fileIdx ... collision.energy (12 total)
## fvarMetadata: labelDescription
## experimentData: use 'experimentData(object)'
## Annotation: No annotation
## - - - Processing information - - -
## Data loaded: Thu Sep 1 10:38:22 2022
## TMT7 quantification by max: Thu Sep 1 10:39:34 2022
## MSnbase version: 2.22.0Identification data in the mzIdentML format can be added to
MSnExp or MSnSet instances with the
addIdentificationData function. See the function
documentation for examples.
d <- data.frame(Signal = rowSums(exprs(qntms)[, 1:6]),
Incomplete = exprs(qntms)[, 7])
d <- log(d)
cls <- rep("#00000050", nrow(qnt))
pch <- rep(1, nrow(qnt))
cls[grep("P02769", fData(qnt)$accession)] <- "gold4" ## BSA
cls[grep("P00924", fData(qnt)$accession)] <- "dodgerblue" ## ENO
cls[grep("P62894", fData(qnt)$accession)] <- "springgreen4" ## CYT
cls[grep("P00489", fData(qnt)$accession)] <- "darkorchid2" ## PHO
pch[grep("P02769", fData(qnt)$accession)] <- 19
pch[grep("P00924", fData(qnt)$accession)] <- 19
pch[grep("P62894", fData(qnt)$accession)] <- 19
pch[grep("P00489", fData(qnt)$accession)] <- 19mzp <- plotMzDelta(rawms, reporters = TMT6, verbose = FALSE) + ggtitle("")mzp## Warning: Removed 2 rows containing missing values (geom_bar).## Warning: Removed 2 rows containing missing values (geom_vline).## Warning: Removed 2 rows containing missing values (geom_text).
Figure 5: A m/z delta plot
plot(Signal ~ Incomplete, data = d,
xlab = expression(Incomplete~dissociation),
ylab = expression(Sum~of~reporters~intensities),
pch = 19,
col = "#4582B380")
grid()
abline(0, 1, lty = "dotted")
abline(lm(Signal ~ Incomplete, data = d), col = "darkblue")
Figure 6: Incomplete dissociation
MAplot(qnt[, c(4, 2)], cex = .9, col = cls, pch = pch, show.statistics = FALSE)
Figure 7: MAplot on an MSnSet instance
4.4 The MALDIquant package
This section illustrates some of MALDIquant’s data
processing capabilities (Gibb and Strimmer 2012). The code is taken from the
processing-peaks.R script downloaded from
the package homepage.
Loading the data
## load packages
library("MALDIquant")
library("MALDIquantForeign")
## getting test data
datapath <-
file.path(system.file("Examples",
package = "readBrukerFlexData"),
"2010_05_19_Gibb_C8_A1")
dir(datapath)## [1] "0_A1" "0_A2"sA1 <- importBrukerFlex(datapath, verbose=FALSE)
# in the following we use only the first spectrum
s <- sA1[[1]]
summary(mass(s))## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 999.9 2373.3 4331.4 4721.3 6874.2 10001.9summary(intensity(s))## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 4 180 1562 2841 4656 32594head(as.matrix(s))## mass intensity
## [1,] 999.9388 11278
## [2,] 1000.1316 11350
## [3,] 1000.3244 10879
## [4,] 1000.5173 10684
## [5,] 1000.7101 10740
## [6,] 1000.9030 10947plot(s)
Figure 8: Spectrum plotting in r CRANpkg('MALDIquant')
{Preprocessing}
## sqrt transform (for variance stabilization)
s2 <- transformIntensity(s, method="sqrt")
s2## S4 class type : MassSpectrum
## Number of m/z values : 22431
## Range of m/z values : 999.939 - 10001.925
## Range of intensity values: 2e+00 - 1.805e+02
## Memory usage : 361.602 KiB
## Name : 2010_05_19_Gibb_C8_A1.A1
## File : /home/biocbuild/bbs-3.15-bioc/R/library/readBrukerFlexData/Examples/2010_05_19_Gibb_C8_A1/0_A1/1/1SLin/fid## smoothing - 5 point moving average
s3 <- smoothIntensity(s2, method="MovingAverage", halfWindowSize=2)
s3## S4 class type : MassSpectrum
## Number of m/z values : 22431
## Range of m/z values : 999.939 - 10001.925
## Range of intensity values: 3.606e+00 - 1.792e+02
## Memory usage : 361.602 KiB
## Name : 2010_05_19_Gibb_C8_A1.A1
## File : /home/biocbuild/bbs-3.15-bioc/R/library/readBrukerFlexData/Examples/2010_05_19_Gibb_C8_A1/0_A1/1/1SLin/fid## baseline subtraction
s4 <- removeBaseline(s3, method="SNIP")
s4## S4 class type : MassSpectrum
## Number of m/z values : 22431
## Range of m/z values : 999.939 - 10001.925
## Range of intensity values: 0e+00 - 1.404e+02
## Memory usage : 361.602 KiB
## Name : 2010_05_19_Gibb_C8_A1.A1
## File : /home/biocbuild/bbs-3.15-bioc/R/library/readBrukerFlexData/Examples/2010_05_19_Gibb_C8_A1/0_A1/1/1SLin/fidPeak picking
## peak picking
p <- detectPeaks(s4)
length(p) # 181## [1] 186peak.data <- as.matrix(p) # extract peak informationpar(mfrow=c(2,3))
xl <- range(mass(s))
# use same xlim on all plots for better comparison
plot(s, sub="", main="1: raw", xlim=xl)
plot(s2, sub="", main="2: variance stabilisation", xlim=xl)
plot(s3, sub="", main="3: smoothing", xlim=xl)
plot(s4, sub="", main="4: base line correction", xlim=xl)
plot(s4, sub="", main="5: peak detection", xlim=xl)
points(p)
top20 <- intensity(p) %in% sort(intensity(p), decreasing=TRUE)[1:20]
labelPeaks(p, index=top20, underline=TRUE)
plot(p, sub="", main="6: peak plot", xlim=xl)
labelPeaks(p, index=top20, underline=TRUE)
Figure 9: Spectrum plotting in r CRANpkg('MALDIquant')
4.5 Working with peptide sequences
library(BRAIN)
atoms <- getAtomsFromSeq("SIVPSGASTGVHEALEMR")
unlist(atoms)## C H N O S
## 77 129 23 27 1library(Rdisop)
pepmol <- getMolecule(paste0(names(atoms),
unlist(atoms),
collapse = ""))
pepmol## $formula
## [1] "C77H129N23O27S"
##
## $score
## [1] 1
##
## $exactmass
## [1] 1839.915
##
## $charge
## [1] 0
##
## $parity
## [1] "e"
##
## $valid
## [1] "Valid"
##
## $DBE
## [1] 25
##
## $isotopes
## $isotopes[[1]]
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1839.9148973 1840.9177412 1841.9196777 1.842921e+03 1.843923e+03
## [2,] 0.3427348 0.3353456 0.1960976 8.474135e-02 2.952833e-02
## [,6] [,7] [,8] [,9] [,10]
## [1,] 1.844925e+03 1.845927e+03 1.846928e+03 1.847930e+03 1.848932e+03
## [2,] 8.691735e-03 2.226358e-03 5.066488e-04 1.040196e-04 1.949686e-05##
library(OrgMassSpecR)## Loading required package: grid##
## Attaching package: 'grid'## The following object is masked from 'package:Biostrings':
##
## patterndata(itraqdata)
simplottest <-
itraqdata[featureNames(itraqdata) %in% paste0("X", 46:47)]
sim <- SpectrumSimilarity(as(simplottest[[1]], "data.frame"),
as(simplottest[[2]], "data.frame"),
top.lab = "itraqdata[['X46']]",
bottom.lab = "itraqdata[['X47']]",
b = 25)
title(main = paste("Spectrum similarity", round(sim, 3)))
MonoisotopicMass(formula = list(C = 2, O = 1, H=6))## [1] 46.04186molecule <- getMolecule("C2H5OH")
molecule$exactmass## [1] 46.04186## x11()
## plot(t(.pepmol$isotopes[[1]]), type = "h")
## x <- IsotopicDistribution(formula = list(C = 2, O = 1, H=6))
## t(molecule$isotopes[[1]])
## par(mfrow = c(2,1))
## plot(t(molecule$isotopes[[1]]), type = "h")
## plot(x[, c(1,3)], type = "h")
## data(myo500)
## masses <- c(147.053, 148.056)
## intensities <- c(93, 5.8)
## molecules <- decomposeIsotopes(masses, intensities)
## experimental eno peptides
exppep <-
as.character(fData(qnt[grep("ENO", fData(qnt)[, 2]), ])[, 1]) ## 13
minlength <- min(nchar(exppep))
if (!file.exists("P00924.fasta"))
eno <- download.file("http://www.uniprot.org/uniprot/P00924.fasta",
destfile = "P00924.fasta")
eno <- paste(readLines("P00924.fasta")[-1], collapse = "")
enopep <- Digest(eno, missed = 1)
nrow(enopep) ## 103## [1] 103sum(nchar(enopep$peptide) >= minlength) ## 68## [1] 68pepcnt <- enopep[enopep[, 1] %in% exppep, ]
nrow(pepcnt) ## 13## [1] 13The following code chunks demonstrate how to use the cleaver package for in-silico cleavage of polypeptides, e.g. cleaving of Gastric juice peptide 1 (P01358) using Trypsin:
library(cleaver)
cleave("LAAGKVEDSD", enzym = "trypsin")## $LAAGKVEDSD
## [1] "LAAGK" "VEDSD"Sometimes cleavage is not perfect and the enzym miss some cleavage positions:
## miss one cleavage position
cleave("LAAGKVEDSD", enzym = "trypsin", missedCleavages = 1)## $LAAGKVEDSD
## [1] "LAAGKVEDSD"## miss zero or one cleavage positions
cleave("LAAGKVEDSD", enzym = "trypsin", missedCleavages = 0:1)## $LAAGKVEDSD
## [1] "LAAGK" "VEDSD" "LAAGKVEDSD"Example code to generate an Texshade image to be included directly in a Latex document or R vignette is presented below. The R code generates a Texshade environment and the annotated sequence display code that is written to a TeX file that can itself be included into a LaTeX or Sweave document.
seq1file <- "seq1.tex"
cat("\\begin{texshade}{Figures/P00924.fasta}
\\setsize{numbering}{footnotesize}
\\setsize{residues}{footnotesize}
\\residuesperline*{70}
\\shadingmode{functional}
\\hideconsensus
\\vsepspace{1mm}
\\hidenames
\\noblockskip\n", file = seq1file)
tmp <- sapply(1:nrow(pepcnt), function(i) {
col <- ifelse((i %% 2) == 0, "Blue", "RoyalBlue")
cat("\\shaderegion{1}{", pepcnt$start[i], "..", pepcnt$stop[i], "}{White}{", col, "}\n",
file = seq1file, append = TRUE)
})
cat("\\end{texshade}
\\caption{Visualising observed peptides for the Yeast enolase protein. Peptides are shaded in blue and black.
The last peptide is a mis-cleavage and overlaps with \`IEEELGDNAVFAGENFHHGDK`.}
\ {#fig:seq}
\\end{center}
\\end{figure}\n\n",
file = seq1file, append = TRUE)N15 incorporation
## 15N incorporation rates from 0, 0.1, ..., 0.9, 0.95, 1
incrate <- c(seq(0, 0.9, 0.1), 0.95, 1)
inc <- lapply(incrate, function(inc)
IsotopicDistributionN("YEVQGEVFTKPQLWP", inc))
par(mfrow = c(4,3))
for (i in 1:length(inc))
plot(inc[[i]][, c(1, 3)], xlim = c(1823, 1848), type = "h",
main = paste0("15N incorporation at ", incrate[i]*100, "%"))
Figure 10: Isotopic envelope for the YEVQGEVFTKPQLWP peptide at different N15 incorporation rates
4.6 The isobar package
The isobar package (Breitwieser et al. 2011) provides methods for the statistical analysis of isobarically tagged MS2 experiments. Please refer to the package vignette for more details.
4.7 The DEP package
The DEP package supports analysis of label-free and TMT
pipelines using, as described in its vignette. These can be used with
MSnSet objects by converting them to/from SummarizedExperiment
objects:
data(msnset)
se <- as(msnset, "SummarizedExperiment")## Loading required namespace: SummarizedExperimentse## class: SummarizedExperiment
## dim: 55 4
## metadata(3): MSnbaseFiles MSnbaseProcessing MSnbaseVersion
## assays(1): ''
## rownames(55): X1 X10 ... X8 X9
## rowData names(15): spectrum ProteinAccession ... acquisition.number
## collision.energy
## colnames(4): iTRAQ4.114 iTRAQ4.115 iTRAQ4.116 iTRAQ4.117
## colData names(2): mz reportersms <- as(se, "MSnSet")
ms## 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:
## - - - Processing information - - -
## MSnbase version: 2.22.04.8 The synapter package
The synapter (Bond et al. 2013) package comes with a detailed
vignette that describes how to prepare the MSE data and then
process it in R. Several interfaces are available provided the user
with maximum control, easy batch processing capabilities or a
graphical user interface. The conversion into MSnSet
instances and filter and combination thereof as well as statistical
analysis are also described.
## open the synapter vignette
library("synapter")
synapterGuide()5 MS2 spectra identification
5.1 Post-search Filtering of MS/MS IDs Using MSnID
The main purpose of MSnID package is to make sure that the peptide and protein identifications resulting from MS/MS searches are sufficiently confident for a given application.} MS/MS peptide and protein identification is a process that prone to uncertanities. A typical and currently most reliable way to quantify uncertainty in the list of identify spectra, peptides or proteins relies on so-called decoy database. For bottom-up (i.e. involving protein digestion) approaches a common way to construct a decoy database is simple inversion of protein amino-acid sequences. If the spectrum matches to normal protein sequence it can be true or false match. Matches to decoy part of the database are false only (excluding the palindromes). Therefore the false discovery rate (FDR) of identifications can be estimated as ratio of hits to decoy over normal parts of the protein sequence database. There are multiple levels of identification that FDR can be estimated for. First, is at the level of peptide/protein- to-spectrum matches. Second is at the level of unique peptide sequences. Note, true peptides tend to be identified by more then one spectrum. False peptide tend to be sporadic. Therefore, after collapsing the redundant peptide identifications from multiple spectra to the level of unique peptide sequence, the FDR typically increases. The extend of FDR increase depends on the type and complexity of the sample. The same trend is true for estimating the identification FDR at the protein level. True proteins tend to be identified with multiple peptides, while false protein identifications are commonly covered only by one peptide. Therefore FDR estimate tend to be even higher for protein level compare to peptide level. The estimation of the FDR is also affected by the number of LC-MS (runs) datasets in the experiment. Again, true identifications tend to be more consistent from run to run, while false are sporadic. After collapsing the redundancy across the runs, the number of true identification reduces much stronger compare to false identifications. Therefore, the peptide and protein FDR estimates need to be re-evaluated. The main objective of the MSnID package is to provide convenience tools for handling tasks on estimation of FDR, defining and optimizing the filtering criteria and ensuring confidence in MS/MS identification data. The user can specify the criteria for filtering the data (e.g. goodness or p-value of matching of experimental and theoretical fragmentation mass spectrum, deviation of theoretical from experimentally measured mass, presence of missed cleavages in the peptide sequence, etc), evaluate the performance of the filter judging by FDRs at spectrum, peptide and protein levels, and finally optimize the filter to achieve the maximum number of identifications while not exceeding maximally allowed FDR upper threshold.
Starting Project and Importing Data
To start a project one have to specify a directory. Currently the only use of the directory is for storing cached results.
library("MSnID")##
## Attaching package: 'MSnID'## The following object is masked from 'package:ProtGenerics':
##
## peptidesmsnid <- MSnID(".")## Note, the anticipated/suggested columns in the
## peptide-to-spectrum matching results are:
## -----------------------------------------------
## accession
## calculatedMassToCharge
## chargeState
## experimentalMassToCharge
## isDecoy
## peptide
## spectrumFile
## spectrumIDData can imported as data.frame or read from mzIdentML file.
PSMresults <- read.delim(system.file("extdata", "human_brain.txt",
package="MSnID"),
stringsAsFactors=FALSE)
psms(msnid) <- PSMresults
show(msnid)## MSnID object
## Working directory: "."
## #Spectrum Files: 1
## #PSMs: 767 at 49 % FDR
## #peptides: 687 at 57 % FDR
## #accessions: 665 at 65 % FDRmzids <- system.file("extdata", "c_elegans.mzid.gz", package="MSnID")
msnid <- read_mzIDs(msnid, mzids)## Reading from mzIdentMLs ...## reading c_elegans.mzid.gz...## Warning in type.convert.default(...): 'as.is' should be specified by the caller;
## using TRUE
## Warning in type.convert.default(...): 'as.is' should be specified by the caller;
## using TRUE
## Warning in type.convert.default(...): 'as.is' should be specified by the caller;
## using TRUE
## Warning in type.convert.default(...): 'as.is' should be specified by the caller;
## using TRUE
## Warning in type.convert.default(...): 'as.is' should be specified by the caller;
## using TRUE
## Warning in type.convert.default(...): 'as.is' should be specified by the caller;
## using TRUE
## Warning in type.convert.default(...): 'as.is' should be specified by the caller;
## using TRUE
## Warning in type.convert.default(...): 'as.is' should be specified by the caller;
## using TRUE
## Warning in type.convert.default(...): 'as.is' should be specified by the caller;
## using TRUE
## Warning in type.convert.default(...): 'as.is' should be specified by the caller;
## using TRUE
## Warning in type.convert.default(...): 'as.is' should be specified by the caller;
## using TRUE
## Warning in type.convert.default(...): 'as.is' should be specified by the caller;
## using TRUE## DONE!show(msnid)## MSnID object
## Working directory: "."
## #Spectrum Files: 1
## #PSMs: 12263 at 36 % FDR
## #peptides: 9489 at 44 % FDR
## #accessions: 7414 at 76 % FDRAnalysis of Peptide Sequences
A particular properties of peptide sequences we are interested in are
- irregular cleavages at the termini of the peptides and
- missing cleavage site within the peptide sequences.
A particular properties of peptide sequences we are interested in are (1) irregular cleavages at the termini of the peptides and (2) missing cleavage site within the peptide sequences:
- Counting the number of irregular cleavage termimi (0, 1 or 2) in
peptides sequence creates a new column
numIrregCleavages. - Counting the number of missed cleavages in peptides sequence
correspondingly creates a
numMissCleavagescolumn.
The default regular expressions for the validCleavagePattern
and missedCleavagePattern correspond to trypsin specificity.
msnid <- assess_termini(msnid, validCleavagePattern="[KR]\\.[^P]")
msnid <- assess_missed_cleavages(msnid, missedCleavagePattern="[KR](?=[^P$])")
prop.table(table(msnid$numIrregCleavages))##
## 0 1 2
## 0.801574390 0.189294149 0.009131462Now the object has two more columns, numIrregCleavages and
numMissCleavages, evidently corresponding to the number of
termini with irregular cleavages and number of missed cleavages within
the peptide sequence. The figure below shows that peptides with 2 or
more missed cleavages are likely to be false identifications.
pepCleav <- unique(psms(msnid)[,c("numMissCleavages", "isDecoy", "peptide")])
pepCleav <- as.data.frame(table(pepCleav[,c("numMissCleavages", "isDecoy")]))
library("ggplot2")
ggplot(pepCleav, aes(x=numMissCleavages, y=Freq, fill=isDecoy)) +
geom_bar(stat='identity', position='dodge') +
ggtitle("Number of Missed Cleavages")
Defining the Filter
The criteria that will be used for filtering the MS/MS data has to be present
in the MSnID object. We will use -log10 transformed MS-GF+
Spectrum E-value, reflecting the goodness of match experimental and
theoretical fragmentation patterns as one the filtering criteria.
Let’s store it under the “msmsScore” name. The score density distribution
shows that it is a good discriminant between non-decoy (red)
and decoy hits (green).
For alternative MS/MS search engines refer to the engine-specific manual for
the names of parameters reflecting the quality of MS/MS spectra matching.
Examples of such parameters are E-Value for X!Tandem
and XCorr and $\Delta$Cn2 for SEQUEST.
As a second criterion we will be using the absolute mass measurement error (in ppm units) of the parent ion. The mass measurement errors tend to be small for non-decoy (enriched with real identificaiton) hits (red line) and is effectively uniformly distributed for decoy hits.
msnid$msmsScore <- -log10(msnid$`MS-GF:SpecEValue`)
msnid$absParentMassErrorPPM <- abs(mass_measurement_error(msnid))MS/MS fiters are handled by a special MSnIDFilter class
objects. Individual filtering criteria can be set by name (that is
present in names(msnid)), comparison operator (>, <, = , …)
defining if we should retain hits with higher or lower given the
threshold and finally the threshold value itself. The filter below set
in such a way that retains only those matches that has less then 5 ppm
of parent ion mass measurement error and more the
\(10^7\) MS-GF:SpecEValue.
filtObj <- MSnIDFilter(msnid)
filtObj$absParentMassErrorPPM <- list(comparison="<", threshold=5.0)
filtObj$msmsScore <- list(comparison=">", threshold=8.0)
show(filtObj)## MSnIDFilter object
## (absParentMassErrorPPM < 5) & (msmsScore > 8)The stringency of the filter can be evaluated at different levels.
evaluate_filter(msnid, filtObj, level="PSM")## fdr n
## PSM 0.00272745 5147evaluate_filter(msnid, filtObj, level="peptide")## fdr n
## peptide 0.00424371 3313evaluate_filter(msnid, filtObj, level="accession")## fdr n
## accession 0.01770658 1207Optimizing the Filter
The threshold values in the example above are not necessarily optimal and set just be in the range of probable values. Filters can be optimized to ensure maximum number of identifications (peptide-to-spectrum matches, unique peptide sequences or proteins) within a given FDR upper limit.
First, the filter can be optimized simply by stepping through
individual parameters and their combinations. The idea has been described in
(Piehowski et al. 2013). The resulting MSnIDFilter object can be
used for final data filtering or can be used as a good starting parameters for
follow-up refining optimizations with more advanced algorithms.
filtObj.grid <- optimize_filter(filtObj, msnid, fdr.max=0.01,
method="Grid", level="peptide",
n.iter=500)
show(filtObj.grid)## MSnIDFilter object
## (absParentMassErrorPPM < 10) & (msmsScore > 7.8)The resulting filtObj.grid can be further fine tuned with such
optimization routines as simulated annealing or Nelder-Mead optimization.
filtObj.nm <- optimize_filter(filtObj.grid, msnid, fdr.max=0.01,
method="Nelder-Mead", level="peptide",
n.iter=500)
show(filtObj.nm)## MSnIDFilter object
## (absParentMassErrorPPM < 10) & (msmsScore > 7.8)Evaluate non-optimized and optimized filters.
evaluate_filter(msnid, filtObj, level="peptide")## fdr n
## peptide 0.00424371 3313evaluate_filter(msnid, filtObj.grid, level="peptide")## fdr n
## peptide 0.009220702 3393evaluate_filter(msnid, filtObj.nm, level="peptide")## fdr n
## peptide 0.009777778 3408Finally applying filter to remove predominantly false identifications.
msnid <- apply_filter(msnid, filtObj.nm)
show(msnid)## MSnID object
## Working directory: "."
## #Spectrum Files: 1
## #PSMs: 5288 at 0.63 % FDR
## #peptides: 3408 at 0.98 % FDR
## #accessions: 1253 at 3.8 % FDRRemoving hits to decoy and contaminant sequences using the same
apply_filter method.
msnid <- apply_filter(msnid, "isDecoy == FALSE")
show(msnid)## MSnID object
## Working directory: "."
## #Spectrum Files: 1
## #PSMs: 5255 at 0 % FDR
## #peptides: 3375 at 0 % FDR
## #accessions: 1207 at 0 % FDRmsnid <- apply_filter(msnid, "!grepl('Contaminant',accession)")
show(msnid)## MSnID object
## Working directory: "."
## #Spectrum Files: 1
## #PSMs: 5246 at 0 % FDR
## #peptides: 3368 at 0 % FDR
## #accessions: 1205 at 0 % FDRInterface with Other Bioconductor Packages
One can extract the entire PSMs tables as data.frame or data.table
psm.df <- psms(msnid)
psm.dt <- as(msnid, "data.table")If only interested in the non-redundant list of confidently identified peptides or proteins
peps <- MSnID::peptides(msnid)
head(peps)## [1] "K.AISQIQEYVDYYGGSGVQHIALNTSDIITAIEALR.A"
## [2] "K.SAGSGYLVGDSLTFVDLLVAQHTADLLAANAALLDEFPQFK.A"
## [3] "K.NSIFTNVAETANGEYFWEGLEDEIADKNVDITTWLGEK.W"
## [4] "R.VFCLLGDGESAEGSVWEAAAFASIYKLDNLVAIVDVNR.L"
## [5] "R.TTDSDGNNTGLDLYTVDQVEHSNYVEQNFLDFIFVFR.K"
## [6] "R.KFDADGSGKLEFDEFCALVYTVANTVDKETLEKELR.E"prots <- accessions(msnid)
head(prots)## [1] "CE02347" "CE07055" "CE12728" "CE36358" "CE36359" "CE36360"prots <- proteins(msnid) # may be more intuitive then accessions
head(prots)## [1] "CE02347" "CE07055" "CE12728" "CE36358" "CE36359" "CE36360"The MSnID package is aimed at providing convenience functionality to handle MS/MS identifications. Quantification per se is outside of the scope of the package. The only type of quantitation that can be seamlessly tied with MS/MS identification analysis is so-called spectral counting approach. In such an approach a peptide abundance is considered to be directly proportional to the number of matched MS/MS spectra. In its turn protein abunance is proportional to the sum of the number of spectra of the matching peptides. The MSnID object can be converted to an MSnSet object defined in MSnbase that extends generic Bioconductor eSet class to quantitative proteomics data. The spectral count data can be analyzed with msmsEDA, msmsTests or DESeq packages.
msnset <- as(msnid, "MSnSet")
library("MSnbase")
head(fData(msnset))## peptide
## -.APPSQDVLKEIFNLYDEELDGK.I -.APPSQDVLKEIFNLYDEELDGK.I
## -.APPSQDVLKEIFNLYDEELDGKIDGTQVGDVAR.A -.APPSQDVLKEIFNLYDEELDGKIDGTQVGDVAR.A
## -.APPTFADLGK.S -.APPTFADLGK.S
## -.GFQNLWFSHPR.K -.GFQNLWFSHPR.K
## -.GIDINHKHDR.V -.GIDINHKHDR.V
## -.MFSNLFIFL.V -.MFSNLFIFL.V
## accession
## -.APPSQDVLKEIFNLYDEELDGK.I CE01236, CE30652
## -.APPSQDVLKEIFNLYDEELDGKIDGTQVGDVAR.A CE01236, CE30652
## -.APPTFADLGK.S CE29443
## -.GFQNLWFSHPR.K CE26849
## -.GIDINHKHDR.V CE16650
## -.MFSNLFIFL.V CE21589head(exprs(msnset))## c_elegans_A_3_1_21Apr10_Draco_10-03-04_dta.txt
## -.APPSQDVLKEIFNLYDEELDGK.I 1
## -.APPSQDVLKEIFNLYDEELDGKIDGTQVGDVAR.A 4
## -.APPTFADLGK.S 2
## -.GFQNLWFSHPR.K 1
## -.GIDINHKHDR.V 2
## -.MFSNLFIFL.V 1Note, the convertion from MSnID to MSnSet uses
peptides as features. The number of redundant peptide observations
represent so-called spectral count that can be used for rough
quantitative analysis. Summing of all of the peptide counts to a
proteins level can be done with combineFeatures function from
MSnbase package.
msnset <- combineFeatures(msnset,
fData(msnset)$accession,
redundancy.handler="unique",
fun="sum",
cv=FALSE)## Warning: Parameter 'fun' is deprecated. Please use 'method' insteadhead(fData(msnset))## peptide accession
## CE00078 K.RLPVAPR.G CE00078
## CE00103 K.LPNDDIGVQVSYLGEPHTFTPEQVLAALLTK.L CE00103
## CE00134 I.PAEVAEHLK.A CE00134
## CE00209 K.ALEGPGPGEDAAHSENNPPR.N CE00209
## CE00302 K.LTYFDIHGLAEPIR.L CE00302
## CE00318 K.ALNALCAQLMTELADALEVLDTDK.S CE00318head(exprs(msnset))## c_elegans_A_3_1_21Apr10_Draco_10-03-04_dta.txt
## CE00078 1.0
## CE00103 1.0
## CE00134 1.0
## CE00209 2.0
## CE00302 1.0
## CE00318 2.26 Quality control
Quality control (QC) is an essential part of any high throughput data driven approach. Bioconductor has a rich history of QC for various genomics data and currently two packages support proteomics QC.
proteoQC provides a dedicated a dedicated pipeline that will produce a dynamic and extensive html report. It uses the rTANDEM package to automate the generation of identification data and uses information about the experimental/replication design.
The qcmetrics package is a general framework to define QC metrics and bundle them together to generate html or pdf reports. It provides some ready made metrics for MS data and N15 labelled data.
7 Annotation
In this section, we briefly present some Bioconductor annotation infrastructure.
We start with the hpar package, an interface to the
Human Protein Atlas (Uhlén et al. 2005, Uhlen2010), to retrieve
subcellular localisation information for the ENSG00000002746
ensemble gene.
id <- "ENSG00000105323"
library("hpar")
getHpa(id, "hpaSubcellularLoc")## Gene Gene.name Reliability Main.location Additional.location
## 2357 ENSG00000105323 HNRNPUL1 Enhanced Nucleoplasm
## Extracellular.location Enhanced Supported Approved Uncertain
## 2357 Nucleoplasm
## Single.cell.variation.intensity Single.cell.variation.spatial
## 2357
## Cell.cycle.dependency GO.id
## 2357 Nucleoplasm (GO:0005654)Below, we make use of the human annotation package org.Hs.eg.db and the Gene Ontology annotation package GO.db to retrieve compatible information with above.
library("org.Hs.eg.db")
library("GO.db")
ans <- AnnotationDbi::select(org.Hs.eg.db,
keys = id,
columns = c("ENSEMBL", "GO", "ONTOLOGY"),
keytype = "ENSEMBL")## 'select()' returned 1:many mapping between keys and columnsans <- ans[ans$ONTOLOGY == "CC", ]
ans## ENSEMBL GO EVIDENCE ONTOLOGY
## 4 ENSG00000105323 GO:0005634 IDA CC
## 5 ENSG00000105323 GO:0005654 IBA CC
## 6 ENSG00000105323 GO:0005654 IDA CC
## 7 ENSG00000105323 GO:0005654 TAS CCsapply(as.list(GOTERM[ans$GO]), slot, "Term")## GO:0005634 GO:0005654 GO:0005654 GO:0005654
## "nucleus" "nucleoplasm" "nucleoplasm" "nucleoplasm"Finally, this information can also be retrieved from on-line databases using the biomaRt package (Durinck et al. 2005).
library("biomaRt")
ensembl <- useMart("ensembl", dataset="hsapiens_gene_ensembl")efilter <- "ensembl_gene_id"
eattr <- c("go_id", "name_1006", "namespace_1003")
bmres <- getBM(attributes=eattr, filters = efilter, values = id, mart = ensembl)
bmres[bmres$namespace_1003 == "cellular_component", "name_1006"]## [1] "nucleoplasm" "nucleus"8 Other packages
8.1 Bioconductor packages
This section provides a complete list of packages available in the
relevant Bioconductor version 3.15
biocView categories.
the tables below represent the packages for the Proteomics
(167 packages), MassSpectrometry
(124 packages) and
MassSpectrometryData (25
experiment packages) categories.
The tables can easily be generated with the proteomicsPackages,
massSpectrometryPackages and massSpectrometryDataPackages
functions. The respective package tables can then be interactively
explored using the display function.
pp <- proteomicsPackages()
display(pp)8.2 Other CRAN packages
The CRAN task view on Chemometrics and Computational Physics is another useful ressource listing additional packages, including a set of packages for mass spectrometry and proteomics, some of which are illustrated in this document.
- MALDIquant provides tools for quantitative analysis of MALDI-TOF mass spectrometry data, with support for baseline correction, peak detection and plotting of mass spectra.
- OrgMassSpecR is for organic/biological mass spectrometry, with a focus on graphical display, quantification using stable isotope dilution, and protein hydrogen/deuterium exchange experiments.
- FTICRMS provides functions for Analyzing Fourier Transform-Ion Cyclotron Resonance Mass Spectrometry Data.
- titan provides a GUI to analyze mass spectrometric data on the relative abundance of two substances from a titration series.
- digeR provides a GUI interface for analysing 2D DIGE data. It allows to perform correlation analysis, score plot, classification, feature selection and power analysis for 2D DIGE experiment data.
- protViz helps with quality checks, visualizations and analysis of mass spectrometry data, coming from proteomics experiments. The package is developed, tested and used at the Functional Genomics Center Zurich.
Suggestions for additional R packages are welcome and will be added to the vignette. Please send suggestions and possibly a short description and/or a example utilisation with code to the RforProteomics package maintainer. The only requirement is that the package must be available on an official package channel (CRAN, Bioconductor, R-forge, Omegahat), i.e. not only available through a personal web page.
9 Session information
All software and respective versions used in this document, as
returned by sessionInfo() are detailed below.
## R version 4.2.1 (2022-06-23)
## 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] MSnID_1.30.0 cleaver_1.34.0 OrgMassSpecR_0.5-3
## [4] msdata_0.36.0 reshape2_1.4.4 biomaRt_2.52.0
## [7] Rdisop_1.56.0 GO.db_3.15.0 org.Hs.eg.db_3.15.0
## [10] BRAIN_1.42.0 Biostrings_2.64.1 GenomeInfoDb_1.32.3
## [13] XVector_0.36.0 PolynomF_2.0-5 hpar_1.38.0
## [16] rols_2.24.3 mzID_1.34.0 beanplot_1.3.1
## [19] ggplot2_3.3.6 lattice_0.20-45 e1071_1.7-11
## [22] msmsTests_1.34.0 msmsEDA_1.34.0 pRolocdata_1.34.0
## [25] pRoloc_1.36.0 BiocParallel_1.30.3 MLInterfaces_1.76.0
## [28] cluster_2.1.4 annotate_1.74.0 XML_3.99-0.10
## [31] AnnotationDbi_1.58.0 IRanges_2.30.1 MALDIquantForeign_0.13
## [34] MALDIquant_1.21 RColorBrewer_1.1-3 xtable_1.8-4
## [37] rpx_2.4.1 knitr_1.40 DT_0.24
## [40] protViz_0.7.3 BiocManager_1.30.18 RforProteomics_1.34.1
## [43] MSnbase_2.22.0 ProtGenerics_1.28.0 S4Vectors_0.34.0
## [46] mzR_2.30.0 Rcpp_1.0.9 Biobase_2.56.0
## [49] BiocGenerics_0.42.0 BiocStyle_2.24.0
##
## loaded via a namespace (and not attached):
## [1] readMzXmlData_2.8.1 rappdirs_0.3.3
## [3] ModelMetrics_1.2.2.2 R.methodsS3_1.8.2
## [5] coda_0.19-4 bit64_4.0.5
## [7] DelayedArray_0.22.0 R.utils_2.12.0
## [9] data.table_1.14.2 rpart_4.1.16
## [11] KEGGREST_1.36.3 hardhat_1.2.0
## [13] RCurl_1.98-1.8 doParallel_1.0.17
## [15] generics_0.1.3 preprocessCore_1.58.0
## [17] RSQLite_2.2.16 proxy_0.4-27
## [19] future_1.27.0 bit_4.0.4
## [21] httpuv_1.6.5 xml2_1.3.3
## [23] lubridate_1.8.0 SummarizedExperiment_1.26.1
## [25] assertthat_0.2.1 viridis_0.6.2
## [27] gower_1.0.0 xfun_0.32
## [29] hms_1.1.2 jquerylib_0.1.4
## [31] promises_1.2.0.1 evaluate_0.16
## [33] fansi_1.0.3 progress_1.2.2
## [35] caTools_1.18.2 dendextend_1.16.0
## [37] dbplyr_2.2.1 DBI_1.1.3
## [39] htmlwidgets_1.5.4 purrr_0.3.4
## [41] ellipsis_0.3.2 crosstalk_1.2.0
## [43] dplyr_1.0.9 bookdown_0.28
## [45] MatrixGenerics_1.8.1 vctrs_0.4.1
## [47] caret_6.0-93 cachem_1.0.6
## [49] withr_2.5.0 prettyunits_1.1.1
## [51] mclust_5.4.10 segmented_1.6-0
## [53] crayon_1.5.1 edgeR_3.38.4
## [55] recipes_1.0.1 pkgconfig_2.0.3
## [57] labeling_0.4.2 nlme_3.1-159
## [59] nnet_7.3-17 rlang_1.0.4
## [61] globals_0.16.1 lifecycle_1.0.1
## [63] LaplacesDemon_16.1.6 filelock_1.0.2
## [65] affyio_1.66.0 BiocFileCache_2.4.0
## [67] AnnotationHub_3.4.0 randomForest_4.7-1.1
## [69] matrixStats_0.62.0 graph_1.74.0
## [71] Matrix_1.4-1 base64enc_0.1-3
## [73] png_0.1-7 viridisLite_0.4.1
## [75] bitops_1.0-7 R.oo_1.25.0
## [77] readBrukerFlexData_1.9.0 KernSmooth_2.23-20
## [79] pROC_1.18.0 blob_1.2.3
## [81] stringr_1.4.1 qvalue_2.28.0
## [83] parallelly_1.32.1 R.cache_0.16.0
## [85] scales_1.2.1 lpSolve_5.6.15
## [87] memoise_2.0.1 magrittr_2.0.3
## [89] plyr_1.8.7 hexbin_1.28.2
## [91] gplots_3.1.3 gdata_2.18.0.1
## [93] zlibbioc_1.42.0 compiler_4.2.1
## [95] pcaMethods_1.88.0 clue_0.3-61
## [97] cli_3.3.0 affy_1.74.0
## [99] listenv_0.8.0 MASS_7.3-58.1
## [101] tidyselect_1.1.2 vsn_3.64.0
## [103] stringi_1.7.8 highr_0.9
## [105] yaml_2.3.5 locfit_1.5-9.6
## [107] biocViews_1.64.1 sass_0.4.2
## [109] tools_4.2.1 future.apply_1.9.0
## [111] parallel_4.2.1 MsCoreUtils_1.8.0
## [113] foreach_1.5.2 gridExtra_2.3
## [115] prodlim_2019.11.13 farver_2.1.1
## [117] digest_0.6.29 shiny_1.7.2
## [119] FNN_1.1.3.1 lava_1.6.10
## [121] GenomicRanges_1.48.0 later_1.3.0
## [123] BiocVersion_3.15.2 ncdf4_1.19
## [125] httr_1.4.4 kernlab_0.9-31
## [127] colorspace_2.0-3 splines_4.2.1
## [129] RBGL_1.72.0 jsonlite_1.8.0
## [131] timeDate_4021.104 ipred_0.9-13
## [133] R6_2.5.1 RUnit_0.4.32
## [135] mime_0.12 pillar_1.8.1
## [137] htmltools_0.5.3 glue_1.6.2
## [139] fastmap_1.1.0 interactiveDisplayBase_1.34.0
## [141] class_7.3-20 codetools_0.2-18
## [143] mvtnorm_1.1-3 utf8_1.2.2
## [145] bslib_0.4.0 tibble_3.1.8
## [147] mixtools_1.2.0 curl_4.3.2
## [149] gtools_3.9.3 magick_2.7.3
## [151] survival_3.4-0 limma_3.52.2
## [153] rmarkdown_2.16 sampling_2.9
## [155] munsell_0.5.0 GenomeInfoDbData_1.2.8
## [157] iterators_1.0.14 impute_1.70.0
## [159] gtable_0.3.0References
Bond, Nicholas J., Pavel V. Shliaha, Kathryn S. Lilley, and Laurent Gatto. 2013. “Improving Qualitative and Quantitative Performance for Label Free Proteomics.” J. Proteome Res. https://doi.org/10.1021/pr300776t.
Breitwieser, F P, A Müller, L Dayon, T Köcher, A Hainard, P Pichler, U Schmidt-Erfurth, et al. 2011. “General Statistical Modeling of Data from Protein Relative Expression Isobaric Tags.” J Proteome Res 10 (6): 2758–66. https://doi.org/10.1021/pr1012784.
Chambers, M C, B Maclean, R Burke, D Amodei, D L Ruderman, S Neumann, L Gatto, et al. 2012. “A Cross-Platform Toolkit for Mass Spectrometry and Proteomics.” Nat Biotechnol 30 (10): 918–20. https://doi.org/10.1038/nbt.2377.
Cuadros-Inostroza, Alvaro, Camila Caldana, Henning Redestig, Jan Lisec, Hugo Pena-Cortes, Lothar Willmitzer, and Matthew A Hannah. 2009. “TargetSearch - a Bioconductor Package for the Efficient Pre-Processing of GC-MS Metabolite Profiling Data.” BMC Bioinformatics 10: 428. https://doi.org/10.1186/1471-2105-10-428.
Durinck, S, Y Moreau, A Kasprzyk, S Davis, B De Moor, A Brazma, and W Huber. 2005. “BioMart and Bioconductor: A Powerful Link Between Biological Databases and Microarray Data Analysis.” Bioinformatics 21 (16): 3439–40. https://doi.org/10.1093/bioinformatics/bti525.
Gatto, Laurent, and Andy Christoforou. 2013. “Using R and Bioconductor for Proteomics Data Analysis.” BBA - Proteins and Proteomics. https://doi.org/10.1016/j.bbapap.2013.04.032.
Gatto, L, L M Breckels, T Naake, and S Gibb. 2015. “Visualisation of proteomics data using R and Bioconductor.” Proteomics, February. https://doi.org/10.1002/pmic.201400392.
Gatto, L, and K S Lilley. 2012. “MSnbase – an R/Bioconductor Package for Isobaric Tagged Mass Spectrometry Data Visualization, Processing and Quantitation.” Bioinformatics 28 (2): 288–9. https://doi.org/10.1093/bioinformatics/btr645.
Gentleman, Robert C., Vincent J. Carey, Douglas M. Bates, Ben Bolstad, Marcel Dettling, Sandrine Dudoit, Byron Ellis, et al. 2004. “Bioconductor: Open Software Development for Computational Biology and Bioinformatics.” Genome Biol 5 (10): –80. https://doi.org/10.1186/gb-2004-5-10-r80.
Gibb, S, and K Strimmer. 2012. “MALDIquant: A Versatile R Package for the Analysis of Mass Spectrometry Data.” Bioinformatics 28 (17): 2270–1. https://doi.org/10.1093/bioinformatics/bts447.
Piehowski, Paul D., Vladislav A. Petyuk, John D. Sandoval, Kristin E. Burnum, Gary R. Kiebel, Matthew E. Monroe, Gordon A. Anderson, David G Camp 2nd, and Richard D. Smith. 2013. “STEPS: A Grid Search Methodology for Optimized Peptide Identification Filtering of Ms/Ms Database Search Results.” Proteomics 13 (5): 766–70. https://doi.org/10.1002/pmic.201200096.
Smith, C A, E J Want, G O’Maille, R Abagyan, and G Siuzdak. 2006. “XCMS: Processing Mass Spectrometry Data for Metabolite Profiling Using Nonlinear Peak Alignment, Matching, and Identification.” Anal Chem 78 (3): 779–87. https://doi.org/10.1021/ac051437y.
Uhlén, Mathias, Erik Björling, Charlotta Agaton, Cristina Al-Khalili A. Szigyarto, Bahram Amini, Elisabet Andersen, Ann-Catrin C. Andersson, et al. 2005. “A human protein atlas for normal and cancer tissues based on antibody proteomics.” Molecular & Cellular Proteomics : MCP 4 (12): 1920–32. https://doi.org/10.1074/mcp.M500279-MCP200.
Vizcaino, Juan A, Eric W Deutsch, Rui Wang, Attila Csordas, Florian Reisinger, Daniel Rios, Jose A Dianes, et al. 2014. “ProteomeXchange Provides Globally Coordinated Proteomics Data Submission and Dissemination.” Nat Biotech 32 (3): 223–26. https://doi.org/10.1038/nbt.2839.