Mass spectrometry and proteomics data analysis
- Setup
- Introduction
- Exploring available infrastructure
- Mass spectrometry data
- Getting data from proteomics repositories
- Handling raw MS data
- Handling identification data
- MS/MS database search
- Analysing search results
- High-level data interface
- Quantitative proteomics
- Importing third-party quantitation data
- Data processing and analysis
- Statistical analysis
- Machine learning
- Annotation
- Other relevant packages/pipelines
- Session information
Setup
The follow packages will be used throughout this documents. R version 3.3.1 or higher is required to install all the packages using BiocInstaller::biocLite.
library("mzR")
library("mzID")
library("MSnID")
library("MSnbase")
library("rpx")
library("MLInterfaces")
library("pRoloc")
library("pRolocdata")
library("MSGFplus")
library("rols")
library("hpar")The most convenient way to install all the tutorials requirement (and more related content), is to install RforProteomics with all its dependencies.
library("BiocInstaller")
biocLite("RforProteomics", dependencies = TRUE)Other packages of interest, such as rTANDEM or MSGFgui will be described later in the document but are not required to execute the code in this workflow.
Introduction
This workflow illustrates R / Bioconductor infrastructure for proteomics. Topics covered focus on support for open community-driven formats for raw data and identification results, packages for peptide-spectrum matching, data processing and analysis:
- Exploring available infrastructure
- Mass spectrometry data
- Getting data from proteomics repositories
- Handling raw MS data
- Handling identification data
- MS/MS database search
- Analysing search results
- High-level data interface
- Quantitative proteomics
- Importing third-party quantitation data
- Data processing and analysis
- Statistical analysis
- Machine learning
- Annotation
- Other relevant packages/pipelines
Links to other packages and references are also documented. In particular, the vignettes included in the RforProteomics package also contains relevant material.
Exploring available infrastructure
In Bioconductor version 3.5, there are respectively 94 proteomics, 64 mass spectrometry software packages and 17 mass spectrometry experiment packages. These respective packages can be extracted with the proteomicsPackages(), massSpectrometryPackages() and massSpectrometryDataPackages() and explored interactively.
library("RforProteomics")
pp <- proteomicsPackages()
display(pp)Mass spectrometry data
Most community-driven formats are supported in R, as detailed in the table below.
| Type | Format | Package |
|---|---|---|
| raw | mzML, mzXML, netCDF, mzData | mzR (read) |
| identification | mzIdentML | mzR (read) and mzID (read) |
| quantitation | mzQuantML | |
| peak lists | mgf | MSnbase (read/write) |
| other | mzTab | MSnbase (read) |
Getting data from proteomics repositories
MS-based proteomics data is disseminated through the ProteomeXchange infrastructure, which centrally coordinates submission, storage and dissemination through multiple data repositories, such as the PRIDE data base at the EBI for MS/MS experiments, PASSEL at the ISB for SRM data and the MassIVE resource. The rpx is an interface to ProteomeXchange and provides a basic access to PX data.
library("rpx")
pxannounced()## 15 new ProteomeXchange annoucements## Data.Set Publication.Data Message
## 1 PXD006301 2017-06-20 11:27:31 New
## 2 PXD005647 2017-06-20 11:15:11 New
## 3 PXD006658 2017-06-20 10:58:18 New
## 4 PXD005304 2017-06-20 10:38:11 New
## 5 PXD003469 2017-06-20 07:20:35 Updated information
## 6 PXD004574 2017-06-19 15:20:30 Updated information
## 7 PXD006057 2017-06-19 15:18:03 Updated information
## 8 PXD004573 2017-06-19 15:15:42 Updated information
## 9 PXD006056 2017-06-19 15:14:48 Updated information
## 10 PXD004989 2017-06-19 09:05:42 New
## 11 PXD003469 2017-06-19 08:59:33 New
## 12 PXD006113 2017-06-19 08:39:54 New
## 13 PXD006434 2017-06-19 08:34:47 New
## 14 PXD005941 2017-06-19 08:29:19 New
## 15 PXD005781 2017-06-19 08:14:13 NewUsing the unique PXD000001 identifier, we can retrieve the relevant metadata that will be stored in a PXDataset object. The names of the files available in this data can be retrieved with the pxfiles accessor function.
px <- PXDataset("PXD000001")
px## Object of class "PXDataset"
## Id: PXD000001 with 12 files
## [1] 'F063721.dat' ... [12] 'generated'
## Use 'pxfiles(.)' to see all files.pxfiles(px)## [1] "F063721.dat"
## [2] "F063721.dat-mztab.txt"
## [3] "PRIDE_Exp_Complete_Ac_22134.xml.gz"
## [4] "PRIDE_Exp_mzData_Ac_22134.xml.gz"
## [5] "PXD000001_mztab.txt"
## [6] "README.txt"
## [7] "TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01-20141210.mzML"
## [8] "TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01-20141210.mzXML"
## [9] "TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01.mzXML"
## [10] "TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01.raw"
## [11] "erwinia_carotovora.fasta"
## [12] "generated"Other metadata for the px data set:
pxtax(px)## [1] "Erwinia carotovora"pxurl(px)## [1] "ftp://ftp.pride.ebi.ac.uk/pride/data/archive/2012/03/PXD000001"pxref(px)## [1] "Gatto L, Christoforou A. Using R and Bioconductor for proteomics data analysis. Biochim Biophys Acta. 2014 Jan;1844(1 Pt A):42-51. Review"Data files can then be downloaded with the pxget function. Below, we retrieve the raw data file. The file is downloaded in the working directory and the name of the file is return by the function and stored in the mzf variable for later use.
fn <- "TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01-20141210.mzML"
mzf <- pxget(px, fn)## Downloading 1 filemzf## [1] "C:/Windows/Temp/RtmpeCT0vk/Rbuild230405154a4/proteomics/vignettes/TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01-20141210.mzML"Handling raw MS data
The mzR package provides an interface to the proteowizard C/C++ code base to access various raw data files, such as mzML, mzXML, netCDF, and mzData. The data is accessed on-disk, i.e it is not loaded entirely in memory by default but only when explicitly requested. The three main functions are openMSfile to create a file handle to a raw data file, header to extract metadata about the spectra contained in the file and peaks to extract one or multiple spectra of interest. Other functions such as instrumentInfo, or runInfo can be used to gather general information about a run.
Below, we access the raw data file downloaded in the previous section and open a file handle that will allow us to extract data and metadata of interest.
library("mzR")
ms <- openMSfile(mzf)
ms## Mass Spectrometry file handle.
## Filename: TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01-20141210.mzML
## Number of scans: 7534The header function returns the metadata of all available peaks:
hd <- header(ms)
dim(hd)## [1] 7534 21names(hd)## [1] "seqNum" "acquisitionNum"
## [3] "msLevel" "polarity"
## [5] "peaksCount" "totIonCurrent"
## [7] "retentionTime" "basePeakMZ"
## [9] "basePeakIntensity" "collisionEnergy"
## [11] "ionisationEnergy" "lowMZ"
## [13] "highMZ" "precursorScanNum"
## [15] "precursorMZ" "precursorCharge"
## [17] "precursorIntensity" "mergedScan"
## [19] "mergedResultScanNum" "mergedResultStartScanNum"
## [21] "mergedResultEndScanNum"We can extract metadata and scan data for scan 1000 as follows:
hd[1000, ]## seqNum acquisitionNum msLevel polarity peaksCount totIonCurrent
## 1000 1000 1000 2 0 274 1048554
## retentionTime basePeakMZ basePeakIntensity collisionEnergy
## 1000 1106.916 136.061 164464 45
## ionisationEnergy lowMZ highMZ precursorScanNum precursorMZ
## 1000 0 104.5467 1370.758 992 683.0817
## precursorCharge precursorIntensity mergedScan mergedResultScanNum
## 1000 2 689443.7 0 0
## mergedResultStartScanNum mergedResultEndScanNum
## 1000 0 0head(peaks(ms, 1000))## [,1] [,2]
## [1,] 104.5467 308.9326
## [2,] 104.5684 308.6961
## [3,] 108.8340 346.7183
## [4,] 109.3928 365.1236
## [5,] 110.0345 616.7905
## [6,] 110.0703 429.1975plot(peaks(ms, 1000), type = "h")
Below we reproduce the example from the MSmap function from the MSnbase package to plot a specific slice of the raw data using the mzR functions we have just described.
## 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)## 1plot(M, aspect = 1, allTicks = FALSE)
plot3D(M)
## With some MS2 spectra
i <- ms1[which(rtsel)][1]
j <- ms1[which(rtsel)][2]
M2 <- MSmap(ms, i:j, 100, 1000, 1, hd)## 1plot3D(M2)
Handling identification data
The RforProteomics package distributes a small identification result file (see ?TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01.mzid) that we load and parse using infrastructure from the mzID package.
library("mzID")
f <- dir(system.file("extdata", package = "RforProteomics"),
pattern = "mzid", full.names=TRUE)
basename(f)## [1] "TMT_Erwinia.mzid.gz"id <- mzID(f)## reading TMT_Erwinia.mzid.gz... DONE!id## An mzID object
##
## Software used: MS-GF+ (version: Beta (v10072))
##
## Rawfile: /home/lgatto/dev/00_github/RforProteomics/sandbox/TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01.mzXML
##
## Database: /home/lgatto/dev/00_github/RforProteomics/sandbox/erwinia_carotovora.fasta
##
## Number of scans: 5287
## Number of PSM's: 5563Various data can be extracted from the mzID object, using one the accessor functions such as database, scans, peptides, … The object can also be converted into a data.frame using the flatten function.
The mzR package also provides support fasta parsing mzIdentML files with the openIDfile function. As for raw data, the underlying C/C++ code comes from the proteowizard.
library("mzR")
f <- dir(system.file("extdata", package = "RforProteomics"),
pattern = "mzid", full.names=TRUE)
id1 <- openIDfile(f)
fid1 <- mzR::psms(id1)
head(fid1)## spectrumID chargeState rank passThreshold experimentalMassToCharge
## 1 scan=5782 3 1 TRUE 1080.2325
## 2 scan=6037 3 1 TRUE 1002.2089
## 3 scan=5235 3 1 TRUE 1189.2836
## 4 scan=5397 3 1 TRUE 960.5365
## 5 scan=6075 3 1 TRUE 1264.3409
## 6 scan=5761 2 1 TRUE 1268.6429
## calculatedMassToCharge sequence modNum
## 1 1080.2321 PVQIQAGEDSNVIGALGGAVLGGFLGNTIGGGSGR 0
## 2 1002.2115 TQVLDGLINANDIEVPVALIDGEIDVLR 0
## 3 1189.2800 TKGLNVMQNLLTAHPDVQAVFAQNDEMALGALR 0
## 4 960.5365 SQILQQAGTSVLSQANQVPQTVLSLLR 0
## 5 1264.3419 PIIGDNPFVVVLPDVVLDESTADQTQENLALLISR 0
## 6 1268.6501 WTSQSSLDLGEPLSLITESVFAR 0
## isDecoy post pre start end DatabaseAccess DBseqLength DatabaseSeq
## 1 FALSE S R 50 84 ECA1932 155
## 2 FALSE R K 288 315 ECA1147 434
## 3 FALSE A R 192 224 ECA0013 295
## 4 FALSE - R 264 290 ECA1731 290
## 5 FALSE F R 119 153 ECA1443 298
## 6 FALSE Y K 264 286 ECA1444 468
## DatabaseDescription acquisitionNum
## 1 ECA1932 outer membrane lipoprotein 5782
## 2 ECA1147 trigger factor 6037
## 3 ECA0013 ribose-binding periplasmic protein 5235
## 4 ECA1731 flagellin 5397
## 5 ECA1443 UTP--glucose-1-phosphate uridylyltransferase 6075
## 6 ECA1444 6-phosphogluconate dehydrogenase, decarboxylating 5761MS/MS database search
While searches are generally performed using third-party software independently of R or can be started from R using a system call, the rTANDEM package allows one to execute such searches using the X!Tandem engine. The shinyTANDEM provides an experimental interactive interface to explore the search results.
library("rTANDEM")
?rtandem
library("shinyTANDEM")
?shinyTANDEMSimilarly, the MSGFplus package enables to perform a search using the MSGF+ engine, as illustrated below.
We search the C:/Windows/Temp/RtmpeCT0vk/Rbuild230405154a4/proteomics/vignettes/TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01-20141210.mzML file against the fasta file from PXD000001 using MSGFplus.
We first download the fasta files:
fas <- pxget(px, "erwinia_carotovora.fasta")## Downloading 1 filebasename(fas)## [1] "erwinia_carotovora.fasta"library("MSGFplus")
msgfpar <- msgfPar(database = fas,
instrument = 'HighRes',
tda = TRUE,
enzyme = 'Trypsin',
protocol = 'iTRAQ')
idres <- runMSGF(msgfpar, mzf, memory=1000)## "C:\Program Files\Java\jdk1.8.0_31\bin\java.exe" -Xmx1000M -jar "C:/Program Files/R/R-3.4.0/library/MSGFplus/MSGFPlus/MSGFPlus.jar" -s "C:/Windows/Temp/RtmpeCT0vk/Rbuild230405154a4/proteomics/vignettes/TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01-20141210.mzML" -o "C:/Windows/Temp/RtmpeCT0vk/Rbuild230405154a4/proteomics/vignettes/TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01-20141210.mzid" -d "C:/Windows/Temp/RtmpeCT0vk/Rbuild230405154a4/proteomics/vignettes/erwinia_carotovora.fasta" -tda 1 -inst 1 -e 1 -protocol 2
##
## reading TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01-20141210.mzid... DONE!idres## An mzID object
##
## Software used: MS-GF+ (version: Beta (v10072))
##
## Rawfile: C:\Windows\Temp\RtmpeCT0vk\Rbuild230405154a4\proteomics\vignettes\TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01-20141210.mzML
##
## Database: C:\Windows\Temp\RtmpeCT0vk\Rbuild230405154a4\proteomics\vignettes\erwinia_carotovora.fasta
##
## Number of scans: 5343
## Number of PSM's: 5656## identification file (needed below)
basename(mzID::files(idres)$id)## [1] "TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01-20141210.mzid"(Note that in the runMSGF call above, I explicitly reduce the memory allocated to the java virtual machine to 3.5GB. In general, there is no need to specify this argument, unless you experience an error regarding the maximum heap size).
A graphical interface to perform the search the data and explore the results is also available:
library("MSGFgui")
MSGFgui()Analysing search results
The MSnID package can be used for post-search filtering of MS/MS identifications. One starts with the construction of an MSnID object that is populated with identification results that can be imported from a data.frame or from mzIdenML files. Here, we will use the example identification data provided with the package.
mzids <- system.file("extdata", "c_elegans.mzid.gz", package="MSnID")
basename(mzids)## [1] "c_elegans.mzid.gz"We start by loading the package, initialising the MSnID object, and add the identification result from our mzid file (there could of course be more that one).
library("MSnID")
msnid <- MSnID(".")## Note, the anticipated/suggested columns in the
## peptide-to-spectrum matching results are:
## -----------------------------------------------
## accession
## calculatedMassToCharge
## chargeState
## experimentalMassToCharge
## isDecoy
## peptide
## spectrumFile
## spectrumIDmsnid <- read_mzIDs(msnid, mzids)## Reading from mzIdentMLs ...## reading c_elegans.mzid.gz... DONE!show(msnid)## MSnID object
## Working directory: "."
## #Spectrum Files: 1
## #PSMs: 12263 at 36 % FDR
## #peptides: 9489 at 44 % FDR
## #accessions: 7414 at 76 % FDRPrinting the MSnID object returns some basic information such as
- Working directory.
- Number of spectrum files used to generate data.
- Number of peptide-to-spectrum matches and corresponding FDR.
- Number of unique peptide sequences and corresponding FDR.
- Number of unique proteins or amino acid sequence accessions and corresponding FDR.
The package then enables to define, optimise and apply filtering based for example on missed cleavages, identification scores, precursor mass errors, etc. and assess PSM, peptide and protein FDR levels. To properly function, it expects to have access to the following data
## [1] "accession" "calculatedMassToCharge"
## [3] "chargeState" "experimentalMassToCharge"
## [5] "isDecoy" "peptide"
## [7] "spectrumFile" "spectrumID"which are indeed present in our data:
names(msnid)## [1] "spectrumID" "scan number(s)"
## [3] "acquisitionNum" "passThreshold"
## [5] "rank" "calculatedMassToCharge"
## [7] "experimentalMassToCharge" "chargeState"
## [9] "MS-GF:DeNovoScore" "MS-GF:EValue"
## [11] "MS-GF:PepQValue" "MS-GF:QValue"
## [13] "MS-GF:RawScore" "MS-GF:SpecEValue"
## [15] "AssumedDissociationMethod" "IsotopeError"
## [17] "isDecoy" "post"
## [19] "pre" "end"
## [21] "start" "accession"
## [23] "length" "description"
## [25] "pepSeq" "modified"
## [27] "modification" "idFile"
## [29] "spectrumFile" "databaseFile"
## [31] "peptide"Here, we summarise a few steps and redirect the reader to the package’s vignette for more details:
Analysis of peptide sequences
Cleaning irregular cleavages at the termini of the peptides and missing cleavage site within the peptide sequences. The following two function call create the new numMisCleavages and numIrrCleabages columns in the MSnID object
msnid <- assess_termini(msnid, validCleavagePattern="[KR]\\.[^P]")
msnid <- assess_missed_cleavages(msnid, missedCleavagePattern="[KR](?=[^P$])")Trimming the data
Now, we can use the apply_filter function to effectively apply filters. The strings passed to the function represent expressions that will be evaludated, this keeping only PSMs that have 0 irregular cleavages and 2 or less missed cleavages.
msnid <- apply_filter(msnid, "numIrregCleavages == 0")
msnid <- apply_filter(msnid, "numMissCleavages <= 2")
show(msnid)## MSnID object
## Working directory: "."
## #Spectrum Files: 1
## #PSMs: 7838 at 17 % FDR
## #peptides: 5598 at 23 % FDR
## #accessions: 3759 at 53 % FDRParent ion mass errors
Using "calculatedMassToCharge" and "experimentalMassToCharge", the mass_measurement_error function calculates the parent ion mass measurement error in parts per million.
summary(mass_measurement_error(msnid))## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -2184.0640 -0.6992 0.0000 17.6146 0.7512 2012.5178We then filter any matches that do not fit the +/- 20 ppm tolerance
msnid <- apply_filter(msnid, "abs(mass_measurement_error(msnid)) < 20")
summary(mass_measurement_error(msnid))## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -19.7797 -0.5866 0.0000 -0.2970 0.5713 19.6758Filtering criteria
Filtering of the identification data will rely on
- -log10 transformed MS-GF+ Spectrum E-value, reflecting the goodness of match experimental and theoretical fragmentation patterns
msnid$msmsScore <- -log10(msnid$`MS-GF:SpecEValue`)- the absolute mass measurement error (in ppm units) of the parent ion
msnid$absParentMassErrorPPM <- abs(mass_measurement_error(msnid))MS2 fiters are handled by a special MSnIDFilter class objects, where individual filters are set by name (that is present in names(msnid)) and comparison operator (>, <, = , …) defining if we should retain hits with higher or lower given the threshold and finally the threshold value itself.
filtObj <- MSnIDFilter(msnid)
filtObj$absParentMassErrorPPM <- list(comparison="<", threshold=10.0)
filtObj$msmsScore <- list(comparison=">", threshold=10.0)
show(filtObj)## MSnIDFilter object
## (absParentMassErrorPPM < 10) & (msmsScore > 10)We can then evaluate the filter on the identification data object, which return the false discovery rate and number of retained identifications for the filtering criteria at hand.
evaluate_filter(msnid, filtObj)## fdr n
## PSM 0 3807
## peptide 0 2455
## accession 0 1009Filter optimisation
Rather than setting filtering values by hand, as shown above, these can be set automativally to meet a specific false discovery rate.
filtObj.grid <- optimize_filter(filtObj, msnid, fdr.max=0.01,
method="Grid", level="peptide",
n.iter=500)
show(filtObj.grid)## MSnIDFilter object
## (absParentMassErrorPPM < 3) & (msmsScore > 7.4)evaluate_filter(msnid, filtObj.grid)## fdr n
## PSM 0.004097561 5146
## peptide 0.006447651 3278
## accession 0.021996616 1208Filters can eventually be applied (rather than just evaluated) using the apply_filter function.
msnid <- apply_filter(msnid, filtObj.grid)
show(msnid)## MSnID object
## Working directory: "."
## #Spectrum Files: 1
## #PSMs: 5146 at 0.41 % FDR
## #peptides: 3278 at 0.64 % FDR
## #accessions: 1208 at 2.2 % FDRAnd finally, identifications that matched decoy and contaminant protein sequences are removed
msnid <- apply_filter(msnid, "isDecoy == FALSE")
msnid <- apply_filter(msnid, "!grepl('Contaminant',accession)")
show(msnid)## MSnID object
## Working directory: "."
## #Spectrum Files: 1
## #PSMs: 5117 at 0 % FDR
## #peptides: 3251 at 0 % FDR
## #accessions: 1179 at 0 % FDRThe resulting filtered identification data can be exported to a data.frame or to a dedicated MSnSet data structure for quantitative MS data, described below, and further processed and analyses using appropriate statistical tests.
High-level data interface
The above sections introduced low-level interfaces to raw and identification results. The MSnbase package provides abstractions for raw data through the MSnExp class and containers for quantification data via the MSnSet class. Both store
- the actual assay data (spectra or quantitation matrix, see below), accessed with
spectra(or the[,[[operators) orexprs; - sample metadata, accessed as a
data.framewithpData; - feature metadata, accessed as a
data.framewithfData.
Another useful slot is processingData, accessed with processingData(.), that records all the processing that objects have undergone since their creation (see examples below).
The readMSData will parse the raw data, extract the MS2 spectra (by default) and construct an MS experiment object of class MSnExp.
(Note that while readMSData supports MS1 data, this is currently not convenient as all the data is read into memory.)
library("MSnbase")
rawFile <- dir(system.file(package = "MSnbase", dir = "extdata"),
full.name = TRUE, pattern = "mzXML$")
basename(rawFile)## [1] "dummyiTRAQ.mzXML"msexp <- readMSData(rawFile, verbose = FALSE, centroided = FALSE)
msexp## MSn experiment data ("MSnExp")
## Object size in memory: 0.18 Mb
## - - - Spectra data - - -
## MS level(s): 2
## Number of spectra: 5
## MSn retention times: 25:1 - 25:2 minutes
## - - - Processing information - - -
## Data loaded: Tue Jun 20 05:46:10 2017
## MSnbase version: 2.2.0
## - - - Meta data - - -
## phenoData
## rowNames: dummyiTRAQ.mzXML
## varLabels: sampleNames
## varMetadata: labelDescription
## Loaded from:
## dummyiTRAQ.mzXML
## protocolData: none
## featureData
## featureNames: X1.1 X2.1 ... X5.1 (5 total)
## fvarLabels: spectrum
## fvarMetadata: labelDescription
## experimentData: use 'experimentData(object)'MS2 spectra can be extracted as a list of Spectrum2 objects with the spectra accessor or as a subset of the original MSnExp data with the [ operator. Individual spectra can be accessed with [[.
length(msexp)## [1] 5msexp[1:2]## MSn experiment data ("MSnExp")
## Object size in memory: 0.07 Mb
## - - - Spectra data - - -
## MS level(s): 2
## Number of spectra: 2
## MSn retention times: 25:1 - 25:2 minutes
## - - - Processing information - - -
## Data loaded: Tue Jun 20 05:46:10 2017
## Data [numerically] subsetted 2 spectra: Tue Jun 20 05:46:10 2017
## MSnbase version: 2.2.0
## - - - Meta data - - -
## phenoData
## rowNames: dummyiTRAQ.mzXML
## varLabels: sampleNames
## varMetadata: labelDescription
## Loaded from:
## dummyiTRAQ.mzXML
## protocolData: none
## featureData
## featureNames: X1.1 X2.1
## fvarLabels: spectrum
## fvarMetadata: labelDescription
## experimentData: use 'experimentData(object)'msexp[[2]]## Object of class "Spectrum2"
## Precursor: 546.9586
## Retention time: 25:2
## Charge: 3
## MSn level: 2
## Peaks count: 1012
## Total ion count: 56758067The identification results stemming from the same raw data file can then be used to add PSM matches.
fData(msexp)## spectrum
## X1.1 1
## X2.1 2
## X3.1 3
## X4.1 4
## X5.1 5## find path to a mzIdentML file
identFile <- dir(system.file(package = "MSnbase", dir = "extdata"),
full.name = TRUE, pattern = "dummyiTRAQ.mzid")
basename(identFile)## [1] "dummyiTRAQ.mzid"msexp <- addIdentificationData(msexp, identFile)
fData(msexp)## spectrum scan number(s) passthreshold rank calculatedmasstocharge
## X1.1 1 1 TRUE 1 645.0375
## X2.1 2 2 TRUE 1 546.9633
## X3.1 3 NA NA NA NA
## X4.1 4 NA NA NA NA
## X5.1 5 5 TRUE 1 437.2997
## experimentalmasstocharge chargestate ms-gf:denovoscore ms-gf:evalue
## X1.1 645.3741 3 77 79.36958
## X2.1 546.9586 3 39 13.46615
## X3.1 NA NA NA NA
## X4.1 NA NA NA NA
## X5.1 437.8040 2 5 366.38422
## ms-gf:rawscore ms-gf:specevalue assumeddissociationmethod
## X1.1 -39 5.527468e-05 CID
## X2.1 -30 9.399048e-06 CID
## X3.1 NA NA <NA>
## X4.1 NA NA <NA>
## X5.1 -42 2.577830e-04 CID
## isotopeerror isdecoy post pre end start accession length
## X1.1 1 FALSE A R 186 170 ECA0984;ECA3829 231
## X2.1 0 FALSE A K 62 50 ECA1028 275
## X3.1 <NA> NA <NA> <NA> NA NA <NA> NA
## X4.1 <NA> NA <NA> <NA> NA NA <NA> NA
## X5.1 1 FALSE L K 28 22 ECA0510 166
## description
## X1.1 DNA mismatch repair protein;acetolactate synthase isozyme III large subunit
## X2.1 2,3,4,5-tetrahydropyridine-2,6-dicarboxylate N-succinyltransferase
## X3.1 <NA>
## X4.1 <NA>
## X5.1 putative capsular polysacharide biosynthesis transferase
## pepseq modified modification idFile
## X1.1 VESITARHGEVLQLRPK FALSE NA dummyiTRAQ.mzid
## X2.1 IDGQWVTHQWLKK FALSE NA dummyiTRAQ.mzid
## X3.1 <NA> NA NA <NA>
## X4.1 <NA> NA NA <NA>
## X5.1 LVILLFR FALSE NA dummyiTRAQ.mzid
## databaseFile nprot npep.prot npsm.prot npsm.pep
## X1.1 erwinia_carotovora.fasta 2 1 1 1
## X2.1 erwinia_carotovora.fasta 1 1 1 1
## X3.1 <NA> NA NA NA NA
## X4.1 <NA> NA NA NA NA
## X5.1 erwinia_carotovora.fasta 1 1 1 1The readMSData and addIdentificationData make use of mzR and mzID packages to access the raw and identification data.
Spectra and (parts of) experiments can be extracted and plotted.
msexp[[1]]## Object of class "Spectrum2"
## Precursor: 645.3741
## Retention time: 25:1
## Charge: 3
## MSn level: 2
## Peaks count: 2921
## Total ion count: 668170086plot(msexp[[1]], full=TRUE)
msexp[1:3]## MSn experiment data ("MSnExp")
## Object size in memory: 0.1 Mb
## - - - Spectra data - - -
## MS level(s): 2
## Number of spectra: 3
## MSn retention times: 25:1 - 25:2 minutes
## - - - Processing information - - -
## Data loaded: Tue Jun 20 05:46:10 2017
## Data [numerically] subsetted 3 spectra: Tue Jun 20 05:46:12 2017
## MSnbase version: 2.2.0
## - - - Meta data - - -
## phenoData
## rowNames: dummyiTRAQ.mzXML
## varLabels: sampleNames
## varMetadata: labelDescription
## Loaded from:
## dummyiTRAQ.mzXML
## protocolData: none
## featureData
## featureNames: X1.1 X2.1 X3.1
## fvarLabels: spectrum scan number(s) ... npsm.pep (30 total)
## fvarMetadata: labelDescription
## experimentData: use 'experimentData(object)'plot(msexp[1:3], full=TRUE)
Quantitative proteomics
There are a wide range of proteomics quantitation techniques that can broadly be classified as labelled vs. label-free, depending whether the features are labelled prior the MS acquisition and the MS level at which quantitation is inferred, namely MS1 or MS2.
| Label-free | Labelled | |
|---|---|---|
| MS1 | XIC | SILAC, 15N |
| MS2 | Counting | iTRAQ, TMT |
In terms of raw data quantitation, most efforts have been devoted to MS2-level quantitation. Label-free XIC quantitation has however been addressed in the frame of metabolomics data processing by the xcms infrastructure.
An MSnExp is converted to an MSnSet by the quantitation method. Below, we use the iTRAQ 4-plex isobaric tagging strategy (defined by the iTRAQ4 parameter; other tags are available) and the trapezoidation method to calculate the area under the isobaric reporter peaks.
plot(msexp[[1]], full=TRUE, reporters = iTRAQ4)
msset <- quantify(msexp, method = "trap", reporters = iTRAQ4, verbose=FALSE)
exprs(msset)## iTRAQ4.114 iTRAQ4.115 iTRAQ4.116 iTRAQ4.117
## X1.1 4483.320 4873.996 6743.441 4601.378
## X2.1 1918.082 1418.040 1117.601 1581.954
## X3.1 15210.979 15296.256 15592.760 16550.502
## X4.1 4133.103 5069.983 4724.845 4694.801
## X5.1 11947.881 13061.875 12809.491 12911.479processingData(msset)## - - - Processing information - - -
## Data loaded: Tue Jun 20 05:46:10 2017
## iTRAQ4 quantification by trapezoidation: Tue Jun 20 05:46:15 2017
## MSnbase version: 2.2.0Other MS2 quantitation methods available in quantify include the (normalised) spectral index SI and (normalised) spectral abundance factor SAF or simply a simple count method.
exprs(si <- quantify(msexp, method = "SIn")) ## dummyiTRAQ.mzXML
## ECA0510 0.003588641
## ECA1028 0.001470129exprs(saf <- quantify(msexp, method = "NSAF"))## dummyiTRAQ.mzXML
## ECA0510 0.6235828
## ECA1028 0.3764172Note that spectra that have not been assigned any peptide (NA) or that match non-unique peptides (npsm > 1) are discarded in the counting process.
See also The isobar package supports quantitation from centroided mgf peak lists or its own tab-separated files that can be generated from Mascot and Phenyx vendor files.
Importing third-party quantitation data
The PSI mzTab file format is aimed at providing a simpler (than XML formats) and more accessible file format to the wider community. It is composed of a key-value metadata section and peptide/protein/small molecule tabular sections.
Note that below, we specify version 0.9 (that generates the warning) to fit with the file. For recent files, the version argument would be ignored to use the recent importer.
mztf <- pxget(px, "F063721.dat-mztab.txt")## Downloading 1 file(mzt <- readMzTabData(mztf, what = "PEP", version = "0.9"))## Warning: Version 0.9 is deprecated. Please see '?readMzTabData' and '?
## MzTab' for details.## MSnSet (storageMode: lockedEnvironment)
## assayData: 1528 features, 6 samples
## element names: exprs
## protocolData: none
## phenoData
## sampleNames: sub[1] sub[2] ... sub[6] (6 total)
## varLabels: abundance
## varMetadata: labelDescription
## featureData
## featureNames: 1 2 ... 1528 (1528 total)
## fvarLabels: sequence accession ... uri (14 total)
## fvarMetadata: labelDescription
## experimentData: use 'experimentData(object)'
## Annotation:
## - - - Processing information - - -
## mzTab read: Tue Jun 20 05:46:20 2017
## MSnbase version: 2.2.0It is also possible to import arbitrary spreadsheets as MSnSet objects into R with the readMSnSet2 function. The main 2 arguments of the function are (1) a text-based spreadsheet and (2) column names of indices that identify the quantitation data. The latter can be queried with the getEcols function.
csv <- dir(system.file ("extdata" , package = "pRolocdata"),
full.names = TRUE, pattern = "pr800866n_si_004-rep1.csv")
getEcols(csv, split = ",")## [1] "\"Protein ID\"" "\"FBgn\""
## [3] "\"Flybase Symbol\"" "\"No. peptide IDs\""
## [5] "\"Mascot score\"" "\"No. peptides quantified\""
## [7] "\"area 114\"" "\"area 115\""
## [9] "\"area 116\"" "\"area 117\""
## [11] "\"PLS-DA classification\"" "\"Peptide sequence\""
## [13] "\"Precursor ion mass\"" "\"Precursor ion charge\""
## [15] "\"pd.2013\"" "\"pd.markers\""ecols <- 7:10
res <- readMSnSet2(csv, ecols)
head(exprs(res))## area.114 area.115 area.116 area.117
## 1 0.379000 0.281000 0.225000 0.114000
## 2 0.420000 0.209667 0.206111 0.163889
## 3 0.187333 0.167333 0.169667 0.476000
## 4 0.247500 0.253000 0.320000 0.179000
## 5 0.216000 0.183000 0.342000 0.259000
## 6 0.072000 0.212333 0.573000 0.142667head(fData(res))## Protein.ID FBgn Flybase.Symbol No..peptide.IDs Mascot.score
## 1 CG10060 FBgn0001104 G-ialpha65A 3 179.86
## 2 CG10067 FBgn0000044 Act57B 5 222.40
## 3 CG10077 FBgn0035720 CG10077 5 219.65
## 4 CG10079 FBgn0003731 Egfr 2 86.39
## 5 CG10106 FBgn0029506 Tsp42Ee 1 52.10
## 6 CG10130 FBgn0010638 Sec61beta 2 79.90
## No..peptides.quantified PLS.DA.classification Peptide.sequence
## 1 1 PM
## 2 9 PM
## 3 3
## 4 2 PM
## 5 1 GGVFDTIQK
## 6 3 ER/Golgi
## Precursor.ion.mass Precursor.ion.charge pd.2013 pd.markers
## 1 PM unknown
## 2 PM unknown
## 3 unknown unknown
## 4 PM unknown
## 5 626.887 2 Phenotype 1 unknown
## 6 ER/Golgi ERData processing and analysis
Raw data processing
For raw data processing look at MSnbase’s clean, smooth, pickPeaks, removePeaks and trimMz for MSnExp and spectra processing methods.
The MALDIquantand xcms packages also features a wide range of raw data processing methods on their own ad hoc data instance types.
Processing and normalisation
Each different types of quantitative data will require their own pre-processing and normalisation steps. Both isobar and MSnbase allow to correct for isobaric tag impurities normalise the quantitative data.
data(itraqdata)
qnt <- quantify(itraqdata, method = "trap",
reporters = iTRAQ4, verbose = FALSE)
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, byrow = TRUE)
## or, using makeImpuritiesMatrix()
## impurities <- makeImpuritiesMatrix(4)
qnt.crct <- purityCorrect(qnt, impurities)
processingData(qnt.crct)## - - - 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]
## iTRAQ4 quantification by trapezoidation: Tue Jun 20 05:46:22 2017
## Purity corrected: Tue Jun 20 05:46:22 2017
## MSnbase version: 1.1.22Various normalisation methods can be applied the MSnSet instances using the normalise method: variance stabilisation (vsn), quantile (quantiles), median or mean centring (center.media or center.mean), …
qnt.crct.nrm <- normalise(qnt.crct, "quantiles") The combineFeatures method combines spectra/peptides quantitation values into protein data. The grouping is defined by the groupBy parameter, which is generally taken from the feature metadata (protein accessions, for example).
## arbitraty grouping
g <- factor(c(rep(1, 25), rep(2, 15), rep(3, 15)))
g## [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2
## [36] 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## Levels: 1 2 3prt <- combineFeatures(qnt.crct.nrm, groupBy = g, fun = "sum")
processingData(prt)## - - - 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]
## iTRAQ4 quantification by trapezoidation: Tue Jun 20 05:46:22 2017
## Purity corrected: Tue Jun 20 05:46:22 2017
## Normalised (quantiles): Tue Jun 20 05:46:23 2017
## Combined 55 features into 3 using sum: Tue Jun 20 05:46:23 2017
## MSnbase version: 2.2.0Finally, proteomics data analysis is generally hampered by missing values. Missing data imputation is a sensitive operation whose success will be guided by many factors, such as degree and (non-)random nature of the missingness.
Below, missing values are randomly assigned to our test data and visualised on a heatmap.
set.seed(1)
qnt0 <- qnt
exprs(qnt0)[sample(prod(dim(qnt0)), 10)] <- NA
table(is.na(qnt0))##
## FALSE TRUE
## 209 11image(qnt0)
Missing value in MSnSet instances can be filtered out and imputed using the filterNA and impute functions.
## remove features with missing values
qnt00 <- filterNA(qnt0)
dim(qnt00)## [1] 44 4any(is.na(qnt00))## [1] FALSE## impute missing values using knn imputation
qnt.imp <- impute(qnt0, method = "knn")
dim(qnt.imp)## [1] 55 4any(is.na(qnt.imp))## [1] FALSEThere are various methods to perform data imputation, as described in ?impute.
Statistical analysis
R in general and Bioconductor in particular are well suited for the statistical analysis of data. Several packages provide dedicated resources for proteomics data:
MSstats: A set of tools for statistical relative protein significance analysis in DDA, SRM and DIA experiments. Data stored in
data.frameorMSnSetobjects can be used as input.msmsTests: Statistical tests for label-free LC-MS/MS data by spectral counts, to discover differentially expressed proteins between two biological conditions. Three tests are available: Poisson GLM regression, quasi-likelihood GLM regression, and the negative binomial of the edgeR package. All can be readily applied on
MSnSetinstances produced, for example byMSnID.isobar also provides dedicated infrastructure for the statistical analysis of isobaric data.
Machine learning
The MLInterfaces package provides a unified interface to a wide range of machine learning algorithms. Initially developed for microarray and ExpressionSet instances, the pRoloc package enables application of these algorithms to MSnSet data.
Classification
The example below uses knn with the 5 closest neighbours as an illustration to classify proteins of unknown sub-cellular localisation to one of 9 possible organelles.
library("MLInterfaces")
library("pRoloc")
library("pRolocdata")
data(dunkley2006)
traininds <- which(fData(dunkley2006)$markers != "unknown")
ans <- MLearn(markers ~ ., data = t(dunkley2006), knnI(k = 5), traininds)
ans## MLInterfaces classification output container
## The call was:
## MLearn(formula = markers ~ ., data = t(dunkley2006), .method = knnI(k = 5),
## trainInd = traininds)
## Predicted outcome distribution for test set:
##
## ER lumen ER membrane Golgi Mitochondrion PM
## 5 140 67 51 89
## Plastid Ribosome TGN vacuole
## 29 31 6 10
## Summary of scores on test set (use testScores() method for details):
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.4000 1.0000 1.0000 0.9332 1.0000 1.0000Clustering
kmeans
kcl <- MLearn( ~ ., data = dunkley2006, kmeansI, centers = 12)
kcl## clusteringOutput: partition table
##
## 1 2 3 4 5 6 7 8 9 10 11 12
## 55 28 29 18 60 30 44 145 78 116 33 53
## The call that created this object was:
## MLearn(formula = ~., data = dunkley2006, .method = kmeansI, centers = 12)plot(kcl, exprs(dunkley2006))
A wide range of classification and clustering algorithms are also available, as described in the ?MLearn documentation page. The pRoloc package also uses MSnSet instances as input and ,while being conceived with the analysis of spatial/organelle proteomics data in mind, is applicable many use cases.
Annotation
All the Bioconductor annotation infrastructure, such as biomaRt, GO.db, organism specific annotations, .. are directly relevant to the analysis of proteomics data. A total of 191 ontologies, including some proteomics-centred annotations such as the PSI Mass Spectrometry Ontology, Molecular Interaction (PSI MI 2.5) or Protein Modifications are available through the rols
library("rols")
res <- OlsSearch(q = "ESI", ontology = "MS", exact = TRUE)
res## Object of class 'OlsSearch':
## ontolgy: MS
## query: ESI
## requested: 20 (out of 1)
## response(s): 0There is a single exact match (default is to retrieve 20 results), that can be retrieved and coreced to a Terms or data.frame object with
res <- olsSearch(res)
as(res, "Terms")## Object of class 'Terms' with 1 entries
## From the MS ontology
## MS:1000073as(res, "data.frame")## id
## 1 ms:http://purl.obolibrary.org/obo/MS_1000073
## iri short_form obo_id
## 1 http://purl.obolibrary.org/obo/MS_1000073 MS_1000073 MS:1000073
## label
## 1 electrospray ionization
## description
## 1 A process in which ionized species in the gas phase are produced from an analyte-containing solution via highly charged fine droplets, by means of spraying the solution from a narrow-bore needle tip at atmospheric pressure in the presence of a high electric field. When a pressurized gas is used to aid in the formation of a stable spray, the term pneumatically assisted electrospray ionization is used. The term ion spray is not recommended.
## ontology_name ontology_prefix type is_defining_ontology
## 1 ms MS class TRUEData from the Human Protein Atlas is available via the hpar package.
Other relevant packages/pipelines
- Analysis of post translational modification with isobar.
- Analysis of label-free data from a Synapt G2 (including ion mobility) with synapter.
- Analysis of spatial proteomics data with pRoloc.
- Analysis of MALDI data with the MALDIquant package.
- Access to the Proteomics Standard Initiative Common QUery InterfaCe with the PSICQUIC package.
Additional relevant packages are described in the RforProteomics vignettes.
Session information
## R version 3.4.0 (2017-04-21)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server 2008 R2 x64 (build 7601) Service Pack 1
##
## Matrix products: default
##
## attached base packages:
## [1] stats4 parallel stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] hpar_1.18.0 rols_2.4.0 MSGFplus_1.10.0
## [4] pRolocdata_1.14.0 pRoloc_1.16.1 MLInterfaces_1.56.0
## [7] cluster_2.0.6 annotate_1.54.0 XML_3.98-1.8
## [10] AnnotationDbi_1.38.1 IRanges_2.10.2 S4Vectors_0.14.3
## [13] rpx_1.12.1 MSnID_1.10.0 mzID_1.14.0
## [16] nloptr_1.0.4 RforProteomics_1.14.0 MSnbase_2.2.0
## [19] ProtGenerics_1.8.0 BiocParallel_1.10.1 mzR_2.10.0
## [22] Rcpp_0.12.11 Biobase_2.36.2 BiocGenerics_0.22.0
## [25] BiocStyle_2.4.0 BiocInstaller_1.26.0 knitr_1.16
##
## loaded via a namespace (and not attached):
## [1] backports_1.1.0 plyr_1.8.4
## [3] lazyeval_0.2.0 GSEABase_1.38.0
## [5] splines_3.4.0 ggvis_0.4.3
## [7] ggplot2_2.2.1 digest_0.6.12
## [9] foreach_1.4.3 htmltools_0.3.6
## [11] viridis_0.4.0 gdata_2.18.0
## [13] magrittr_1.5 memoise_1.1.0
## [15] doParallel_1.0.10 sfsmisc_1.1-1
## [17] limma_3.32.2 rda_1.0.2-2
## [19] R.utils_2.5.0 prettyunits_1.0.2
## [21] lpSolve_5.6.13 colorspace_1.3-2
## [23] dplyr_0.7.0 RCurl_1.95-4.8
## [25] jsonlite_1.5 hexbin_1.27.1
## [27] graph_1.54.0 genefilter_1.58.1
## [29] lme4_1.1-13 impute_1.50.1
## [31] survival_2.41-3 iterators_1.0.8
## [33] glue_1.1.0 gtable_0.2.0
## [35] zlibbioc_1.22.0 MatrixModels_0.4-1
## [37] R.cache_0.12.0 car_2.1-4
## [39] kernlab_0.9-25 prabclus_2.2-6
## [41] DEoptimR_1.0-8 SparseM_1.77
## [43] scales_0.4.1 vsn_3.44.0
## [45] mvtnorm_1.0-6 DBI_0.7
## [47] progress_1.1.2 viridisLite_0.2.0
## [49] xtable_1.8-2 proxy_0.4-17
## [51] mclust_5.3 preprocessCore_1.38.1
## [53] httr_1.2.1 sampling_2.8
## [55] htmlwidgets_0.8 threejs_0.2.2
## [57] FNN_1.1 RColorBrewer_1.1-2
## [59] fpc_2.1-10 modeltools_0.2-21
## [61] R.methodsS3_1.7.1 flexmix_2.3-14
## [63] nnet_7.3-12 caret_6.0-76
## [65] labeling_0.3 rlang_0.1.1
## [67] reshape2_1.4.2 munsell_0.4.3
## [69] mlbench_2.1-1 biocViews_1.44.0
## [71] tools_3.4.0 RSQLite_1.1-2
## [73] pls_2.6-0 evaluate_0.10
## [75] stringr_1.2.0 yaml_2.1.14
## [77] ModelMetrics_1.1.0 robustbase_0.92-7
## [79] randomForest_4.6-12 dendextend_1.5.2
## [81] RBGL_1.52.0 nlme_3.1-131
## [83] quantreg_5.33 whisker_0.3-2
## [85] mime_0.5 R.oo_1.21.0
## [87] xml2_1.1.1 biomaRt_2.32.1
## [89] compiler_3.4.0 pbkrtest_0.4-7
## [91] curl_2.6 interactiveDisplayBase_1.14.0
## [93] e1071_1.6-8 affyio_1.46.0
## [95] tibble_1.3.3 stringi_1.1.5
## [97] highr_0.6 lattice_0.20-35
## [99] trimcluster_0.1-2 Matrix_1.2-10
## [101] gbm_2.1.3 RUnit_0.4.31
## [103] MALDIquant_1.16.2 data.table_1.10.4
## [105] bitops_1.0-6 httpuv_1.3.3
## [107] R6_2.2.2 pcaMethods_1.68.0
## [109] affy_1.54.0 hwriter_1.3.2
## [111] gridSVG_1.5-1 gridExtra_2.2.1
## [113] codetools_0.2-15 MASS_7.3-47
## [115] gtools_3.5.0 assertthat_0.2.0
## [117] interactiveDisplay_1.14.0 Category_2.42.0
## [119] rprojroot_1.2 diptest_0.75-7
## [121] mgcv_1.8-17 grid_3.4.0
## [123] rpart_4.1-11 class_7.3-14
## [125] minqa_1.2.4 rmarkdown_1.6
## [127] shiny_1.0.3 base64enc_0.1-3