1 Introduction

source("https://bioconductor.org/biocLite.R")

library("BiocInstaller") 
biocLite("lgatto/ProteomicsBioc2016Workshop",
         dependencies = TRUE, build_vignettes=TRUE)

library("ProteomicsBioc2016Workshop")
bioc2016()

library("RforProteomics")
pp <- proteomicsPackages("3.3")
display(pp)

2 Getting data

library("AnnotationHub")
ah <- AnnotationHub()

## snapshotDate(): 2016-06-06

query(ah, "proteomics")

## AnnotationHub with 4 records
## # snapshotDate(): 2016-06-06 
## # $dataprovider: PRIDE
## # $species: Erwinia carotovora
## # $rdataclass: AAStringSet, MSnSet, mzRident, mzRpwiz
## # additional mcols(): taxonomyid, genome, description,
## #   preparerclass, tags, sourceurl, sourcetype 
## # retrieve records with, e.g., 'object[["AH49006"]]' 
## 
##             title                                                         
##   AH49006 | PXD000001: Erwinia carotovora and spiked-in protein fasta file
##   AH49007 | PXD000001: Peptide-level quantitation data                    
##   AH49008 | PXD000001: raw mass spectrometry data                         
##   AH49009 | PXD000001: MS-GF+ identiciation data

ms <- ah[["AH49008"]]

## Warning: Failed to parse headers:
## 220-
## 220-
## 220-ftp.pride.ebi.ac.uk FTP server
## 220-
## 220-
## 220 
## 331 Please specify the password.
## 230 Login successful.
## 257 "/"
## 250 Directory successfully changed.
## 250 Directory successfully changed.
## 250 Directory successfully changed.
## 250 Directory successfully changed.
## 250 Directory successfully changed.
## 250 Directory successfully changed.
## 213 20150116075122
## 229 Entering Extended Passive Mode (|||44630|)
## 200 Switching to Binary mode.
## 213 450032788
## 150 Opening BINARY mode data connection for TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01-20141210.mzML (450032788 bytes).
## 226 Transfer complete.

ms

## Mass Spectrometry file handle.
## Filename:  55314 
## Number of scans:  7534

library("rpx")
pxannounced()

## 15 new ProteomeXchange annoucements

##     Data.Set    Publication.Data             Message
## 1  PXD003861 2016-06-22 20:04:04                 New
## 2  PXD003326 2016-06-22 13:50:09                 New
## 3  PXD004081 2016-06-22 13:30:33                 New
## 4  PXD003943 2016-06-22 10:40:18                 New
## 5  PXD002823 2016-06-22 09:18:12                 New
## 6  PXD004411 2016-06-21 18:46:36 Updated information
## 7  PXD004411 2016-06-21 17:41:43                 New
## 8  PXD004410 2016-06-21 17:41:41                 New
## 9  PXD004408 2016-06-21 17:41:39                 New
## 10 PXD004406 2016-06-21 17:41:35                 New
## 11 PXD004405 2016-06-21 17:41:33                 New
## 12 PXD004404 2016-06-21 17:41:31                 New
## 13 PXD004403 2016-06-21 17:41:28                 New
## 14 PXD004402 2016-06-21 17:41:26                 New
## 15 PXD004401 2016-06-21 17:41:23                 New

px <- PXDataset("PXD000001")
px

## Object of class "PXDataset"
##  Id: PXD000001 with 10 files
##  [1] 'F063721.dat' ... [10] 'erwinia_carotovora.fasta'
##  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] "TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01-20141210.mzML" 
##  [7] "TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01-20141210.mzXML"
##  [8] "TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01.mzXML"         
##  [9] "TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01.raw"           
## [10] "erwinia_carotovora.fasta"

pxtax(px)
pxurl(px)
pxref(px)

mzf <- pxget(px, pxfiles(px)[6])

## Downloading 1 file

mzf

## [1] "TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01-20141210.mzML"

3 Handling raw MS data

The mzR package provides an interface to the proteowizard code base, the legacy RAMP is a non-sequential parser and other C/C++ code to access various raw data files, such as mzML, mzXML, netCDF, and mzData. The data is accessed on-disk, i.e it does not get loaded entirely in memory by default. 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.

library("mzR")
ms <- openMSfile(mzf)
ms

## Mass Spectrometry file handle.
## Filename:  TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01-20141210.mzML 
## Number of scans:  7534

hd <- header(ms)
dim(hd)

## [1] 7534   21

names(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"

head(peaks(ms, 234))

##          [,1]        [,2]
## [1,] 100.3852    93.86617
## [2,] 101.6572    83.94572
## [3,] 107.3893    91.63007
## [4,] 112.0505 15697.57129
## [5,] 113.0538   106.47455
## [6,] 113.3050    83.92274

str(peaks(ms, 1:5))

## List of 5
##  $ : num [1:25800, 1:2] 400 400 400 400 400 ...
##  $ : num [1:25934, 1:2] 400 400 400 400 400 ...
##  $ : num [1:26148, 1:2] 400 400 400 400 400 ...
##  $ : num [1:26330, 1:2] 400 400 400 400 400 ...
##  $ : num [1:26463, 1:2] 400 400 400 400 400 ...

Exercise Extract the index of the MS2 spectrum with the highest base peak intensity and plot its spectrum (using the plot method - we will see more about MS data visualisation in the next section). Is the data centroided or in profile mode?

4 Handling identification data

The ProteomicsBioc2016Workshop 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 = "ProteomicsBioc2016Workshop"),
         pattern = "mzid", full.names = TRUE))

## [1] "/tmp/RtmpDXbAgP/Rinst1f032df26d37/ProteomicsBioc2016Workshop/extdata/TMT_Erwinia.mzid.gz"

id <- mzID(f)

## reading TMT_Erwinia.mzid.gz... DONE!

software(id)

##         version   name          id
## 1 Beta (v10072) MS-GF+ ID_software

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: 5563

Various data can be extracted from the mzID object, using one the accessor functions such as database, sofware, scans, peptides, … The object can also be converted into a data.frame using the flatten function.

head(flatten(id))

##   spectrumid scan number(s) acquisitionnum passthreshold rank
## 1  scan=5782           5782           5782          TRUE    1
## 2  scan=6037           6037           6037          TRUE    1
## 3  scan=5235           5235           5235          TRUE    1
## 4  scan=5397           5397           5397          TRUE    1
## 5  scan=6075           6075           6075          TRUE    1
## 6  scan=5761           5761           5761          TRUE    1
##   calculatedmasstocharge experimentalmasstocharge chargestate
## 1              1080.2321                1080.2325           3
## 2              1002.2115                1002.2089           3
## 3              1189.2800                1189.2836           3
## 4               960.5365                 960.5365           3
## 5              1264.3419                1264.3409           3
## 6              1268.6501                1268.6429           2
##   ms-gf:denovoscore ms-gf:evalue ms-gf:rawscore ms-gf:specevalue
## 1               174 5.430080e-21            147     3.764831e-27
## 2               245 9.943751e-20            214     6.902626e-26
## 3               264 2.564787e-19            211     1.778789e-25
## 4               178 2.581753e-18            154     1.792541e-24
## 5               252 2.178423e-17            188     1.510364e-23
## 6               138 2.329453e-17            123     1.618941e-23
##   assumeddissociationmethod isotopeerror isdecoy post pre end start
## 1                       HCD            0   FALSE    S   R  84    50
## 2                       HCD            0   FALSE    R   K 315   288
## 3                       HCD            0   FALSE    A   R 224   192
## 4                       HCD            0   FALSE    -   R 290   264
## 5                       HCD            0   FALSE    F   R 153   119
## 6                       HCD            0   FALSE    Y   K 286   264
##   accession length                                       description
## 1   ECA1932    155                        outer membrane lipoprotein
## 2   ECA1147    434                                    trigger factor
## 3   ECA0013    295                ribose-binding periplasmic protein
## 4   ECA1731    290                                         flagellin
## 5   ECA1443    298      UTP--glucose-1-phosphate uridylyltransferase
## 6   ECA1444    468 6-phosphogluconate dehydrogenase, decarboxylating
##                                pepseq modified modification
## 1 PVQIQAGEDSNVIGALGGAVLGGFLGNTIGGGSGR    FALSE         <NA>
## 2        TQVLDGLINANDIEVPVALIDGEIDVLR    FALSE         <NA>
## 3   TKGLNVMQNLLTAHPDVQAVFAQNDEMALGALR    FALSE         <NA>
## 4         SQILQQAGTSVLSQANQVPQTVLSLLR    FALSE         <NA>
## 5 PIIGDNPFVVVLPDVVLDESTADQTQENLALLISR    FALSE         <NA>
## 6             WTSQSSLDLGEPLSLITESVFAR    FALSE         <NA>
##                idFile
## 1 TMT_Erwinia.mzid.gz
## 2 TMT_Erwinia.mzid.gz
## 3 TMT_Erwinia.mzid.gz
## 4 TMT_Erwinia.mzid.gz
## 5 TMT_Erwinia.mzid.gz
## 6 TMT_Erwinia.mzid.gz
##                                                  spectrumFile
## 1 TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01.mzXML
## 2 TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01.mzXML
## 3 TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01.mzXML
## 4 TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01.mzXML
## 5 TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01.mzXML
## 6 TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01.mzXML
##               databaseFile
## 1 erwinia_carotovora.fasta
## 2 erwinia_carotovora.fasta
## 3 erwinia_carotovora.fasta
## 4 erwinia_carotovora.fasta
## 5 erwinia_carotovora.fasta
## 6 erwinia_carotovora.fasta

The mzR interface provides a similar interface. It is however much faster as it does not read all the data into memory and only extracts relevant data on demand. It has also accessor functions such as softwareInfo, mzidInfo, … (use showMethods(classes = "mzRident", where = "package:mzR")) to see all available methods.

library("mzR")
id2 <- openIDfile(f)
id2

## Identification file handle.
## Filename:  TMT_Erwinia.mzid.gz 
## Number of psms:  5655

softwareInfo(id2)

## [1] "MS-GF+ Beta (v10072) "                       
## [2] "ProteoWizard MzIdentML 3.0.6239 ProteoWizard"

The identification data can be accessed as a data.frame with the psms accessor.

head(psms(id2))

##   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           5761

Exercise Is there a relation between the length of a protein and the number of identified peptides, conditioned by the (average) e-value of the identifications?

5 MS/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.

library("rTANDEM")
?rtandem

The MSGPplus package provides an interface to the very fast MSGF+ engine.

library("MSGFplus")
parameters <- msgfPar(database = 'milk-proteins.fasta',
                      tolerance = '20 ppm',
                      instrument = 'TOF')
runMSGF(parameters, 'msraw.mzML')

6 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) and sample and feature metadata, accessed with spectra (or the [, [[ operators) or exprs, pData and fData.

The figure below give a schematics of an MSnSet instance and the relation between the assay data and the respective feature and sample metadata.

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 and construct an MS experiment file.

library("MSnbase")
quantFile <- dir(system.file(package = "MSnbase", dir = "extdata"),
                 full.name = TRUE, pattern = "mzXML$")
quantFile

## [1] "/home/ubuntu/R-libs/MSnbase/extdata/dummyiTRAQ.mzXML"

msexp <- readMSData(quantFile, verbose=FALSE)
msexp

## Object of class "MSnExp"
##  Object size in memory: 0.2 Mb
## - - - Spectra data - - -
##  MS level(s): 2 
##  Number of MS1 acquisitions: 1 
##  Number of MSn scans: 5 
##  Number of precursor ions: 5 
##  4 unique MZs
##  Precursor MZ's: 437.8 - 716.34 
##  MSn M/Z range: 100 2016.66 
##  MSn retention times: 25:1 - 25:2 minutes
## - - - Processing information - - -
## Data loaded: Wed Jun 22 21:21:37 2016 
##  MSnbase version: 1.21.7 
## - - - 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)'

The identification results stemming from the same raw data file can then be used to add PSM matches.

## find path to a mzIdentML file
identFile <- dir(system.file(package = "MSnbase", dir = "extdata"),
                 full.name = TRUE, pattern = "mzid$")
identFile

## [1] "/home/ubuntu/R-libs/MSnbase/extdata/dummyiTRAQ.mzid"

msexp <- addIdentificationData(msexp, identFile)

## reading dummyiTRAQ.mzid... DONE!

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        1

msexp[[1]]

## Object of class "Spectrum2"
##  Precursor: 645.3741 
##  Retention time: 25:1 
##  Charge: 3 
##  MSn level: 2 
##  Peaks count: 2921 
##  Total ion count: 668170086

as(msexp[[1]], "data.frame")[100:105, ]

##           mz          i
## 100 141.0990 588594.812
## 101 141.1015 845401.250
## 102 141.1041 791352.125
## 103 141.1066 477623.000
## 104 141.1091 155376.312
## 105 141.1117   4752.541

MSnExp object load all MS data into memory. This is a viable solution for MS2 data, but does not scale to MS1 data, especially when multiple files are loaded together. With the help of Johannes Rainer, a new MSnExp class supporting on disk access is being developed.

7 Visualising raw data

chromatogram(ms)

## manual download
library("RforProteomics")
url <- "http://proteome.sysbiol.cam.ac.uk/lgatto/files/Thermo-HELA-PRT/"
f1 <- downloadData(file.path(url, "Thermo_Hela_PRTC_1.mzML"))
f2 <- downloadData(file.path(url, "Thermo_Hela_PRTC_2.mzML"))
f3 <- downloadData(file.path(url, "Thermo_Hela_PRTC_3.mzML"))

## plotting
c1 <- chromatogram(f1)
c2 <- chromatogram(f2, plot = FALSE)
lines(c2, col = "steelblue", lty = "dashed", lwd = 2)
c3 <- chromatogram(f3, plot = FALSE)
lines(c2, col = "orange", lty = "dotted")

par(mfrow = c(1, 2))
xic(ms, mz = 636.925, width = 0.01)
x <- xic(ms, mz = 636.925, width = 0.01, rtlim = c(2120, 2200))

7.4 Spectra

We first load a test iTRAQ data called itraqdata and distributed as part of the MSnbase package using the data function. This is a pre-packaged data that comes as a dedicated data structure called MSnExp. We then plot the 10th spectrum using specific code that recognises what to do with an element of an MSnExp.

data(itraqdata)
itraqdata

## Object of class "MSnExp"
##  Object size in memory: 1.88 Mb
## - - - Spectra data - - -
##  MS level(s): 2 
##  Number of MS1 acquisitions: 1 
##  Number of MSn scans: 55 
##  Number of precursor ions: 55 
##  55 unique MZs
##  Precursor MZ's: 401.74 - 1236.1 
##  MSn M/Z range: 100 2069.27 
##  MSn retention times: 19:9 - 50:18 minutes
## - - - Processing information - - -
## Data loaded: Wed May 11 18:54:39 2011 
##  MSnbase version: 1.1.22 
## - - - Meta data  - - -
## phenoData
##   rowNames: 1
##   varLabels: sampleNames sampleNumbers
##   varMetadata: labelDescription
## Loaded from:
##   dummyiTRAQ.mzXML 
## protocolData: none
## featureData
##   featureNames: X1 X10 ... X9 (55 total)
##   fvarLabels: spectrum ProteinAccession ProteinDescription
##     PeptideSequence
##   fvarMetadata: labelDescription
## experimentData: use 'experimentData(object)'

plot(itraqdata[[10]], reporters = iTRAQ4, full=TRUE) 

The ms data is not pre-packaged as an MSnExp data. It is a more bare-bone mzRramp object, a pointer to a raw data file (here TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01-20141210.mzML): we need first to extract a spectrum of interest (here the 3071st spectrum, an MS1 spectrum), and use the generic plot function to visualise the spectrum.

plot(peaks(ms, 3071), type = "h",
     xlab = "M/Z", ylab = "Intensity",     
     sub = formatRt(hd[3071, "retentionTime"]))

The importance of flexible access to specialised data becomes visible in the figure below (taken from the RforProteomics visualisation vignette). Not only can we access specific data and understand/visualise them, but we can transverse all the data and extracted/visualise/understand structured slices of data.

In this code chunks we start by selecting relevant spectra of interest. We will focus on the first MS1 spectrum acquired after 30 minutes of retention time.

## (1) Open raw data file
ms <- openMSfile(mzf)
## (2) Extract the header information
hd <- header(ms)
## (3) MS1 spectra indices
ms1 <- which(hd$msLevel == 1)
## (4) Select MS1 spectra with retention time between 30 and 35 minutes
rtsel <- hd$retentionTime[ms1] / 60 > 30 & hd$retentionTime[ms1] / 60 < 35
## (5) Indices of the 1st and 2nd MS1 spectra after 30 minutes
i <- ms1[which(rtsel)][1]
j <- ms1[which(rtsel)][2]
## (6) Interleaved MS2 spectra
ms2 <- (i+1):(j-1)

Now now extract and plot all relevant information:

1. The upper panel represents the chromatogram of the TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01-20141210.mzML raw data file, produced with chromatogram.

chromatogram(ms)

2. We concentrate at a specific retention time, 30:1 minutes (1800.6836 seconds)

chromatogram(ms)
abline(v = hd[i, "retentionTime"], col = "red")

3. This corresponds to the 2807th MS1 spectrum, shown on the second row of figures.

plot(peaks(ms, i), type = "l", xlim = c(400, 1000))
legend("topright", bty = "n",
       legend = paste0(
           "Acquisition ", hd[i, "acquisitionNum"],  "\n",
           "Retention time ", formatRt(hd[i, "retentionTime"])))

4. The ions that were selected for MS2 are highlighted by vertical lines. These are represented in the bottom part of the figure.

plot(peaks(ms, i), type = "l", xlim = c(400, 1000))
legend("topright", bty = "n",
       legend = paste0(
           "Acquisition ", hd[i, "acquisitionNum"],  "\n",
           "Retention time ", formatRt(hd[i, "retentionTime"])))
abline(v = hd[ms2, "precursorMZ"],
       col = c("#FF000080",
           rep("#12121280", 9)))

5. On the right, we zoom on the isotopic envelope of one peptide in particular (the one highlighted with a red line).

plot(peaks(ms, i), type = "l", xlim = c(521, 522.5))
abline(v = hd[ms2, "precursorMZ"], col = "#FF000080")

6. A final loop through the relevant MS2 spectra plots the length(ms2) MS2 spectra highlighted above.

par(mfrow = c(5, 2), mar = c(2, 2, 0, 1))
for (ii in ms2) {
    p <- peaks(ms, ii)
    plot(p, xlab = "", ylab = "", type = "h", cex.axis = .6)
    legend("topright", legend = paste0("Prec M/Z\n",
                           round(hd[ii, "precursorMZ"], 2)),
           bty = "n", cex = .8)
}

Putting it all together:

layout(lout)
par(mar=c(4,2,1,1))
chromatogram(ms)

abline(v = hd[i, "retentionTime"], col = "red")
par(mar = c(3, 2, 1, 0))
plot(peaks(ms, i), type = "l", xlim = c(400, 1000))
legend("topright", bty = "n",
       legend = paste0(
           "Acquisition ", hd[i, "acquisitionNum"],  "\n",
           "Retention time ", formatRt(hd[i, "retentionTime"])))
abline(h = 0)
abline(v = hd[ms2, "precursorMZ"],
       col = c("#FF000080",
           rep("#12121280", 9)))

par(mar = c(3, 0.5, 1, 1))
plot(peaks(ms, i), type = "l", xlim = c(521, 522.5),
     yaxt = "n")
abline(h = 0)
abline(v = hd[ms2, "precursorMZ"], col = "#FF000080")

par(mar = c(2, 2, 0, 1))
for (ii in ms2) {
    p <- peaks(ms, ii)
    plot(p, xlab = "", ylab = "", type = "h", cex.axis = .6)
    legend("topright", legend = paste0("Prec M/Z\n",
                           round(hd[ii, "precursorMZ"], 2)),
           bty = "n", cex = .8)
}

Below, we illustrate some additional visualisation and animations of raw MS data, also taken from the RforProteomics visualisation vignette. On the left, we have a heatmap visualisation of a MS map and a 3 dimensional representation of the same data. On the right, 2 MS1 spectra in blue and the set of interleaves 10 MS2 spectra.

## (1) MS space heaptmap
M <- MSmap(ms, ms1[rtsel], 521, 523, .005, hd)

## 1

ff <- colorRampPalette(c("yellow", "steelblue"))
trellis.par.set(regions=list(col=ff(100)))
m1 <- plot(M, aspect = 1, allTicks = FALSE)
## (2) Same data as (1), in 3 dimenstion
M@map[msMap(M) == 0] <- NA
m2 <- plot3D(M, rgl = FALSE)
## (3) The 2 MS1 and 10 interleaved MS2 spectra from above
i <- ms1[which(rtsel)][1]
j <- ms1[which(rtsel)][2]
M2 <- MSmap(ms, i:j, 100, 1000, 1, hd)

## 1

m3 <- plot3D(M2)
grid.arrange(m1, m2, m3, ncol = 3)

Below, we have animations build from extracting successive slices as above.

8 Identification data

Annotated spectra and comparing spectra.

par(mfrow = c(1, 2))
itraqdata2 <- pickPeaks(itraqdata, verbose = FALSE)
s <- "SIGFEGDSIGR"
plot(itraqdata2[[14]], s, main = s)
plot(itraqdata2[[25]], itraqdata2[[28]], sequences = rep("IMIDLDGTENK", 2))

The annotation of spectra is obtained by simulating fragmentation of a peptide and matching observed peaks to fragments:

calculateFragments("SIGFEGDSIGR")

## Modifications used: C=57.02146

##            mz  ion type pos z         seq
## 1    88.03931   b1    b   1 1           S
## 2   201.12337   b2    b   2 1          SI
## 3   258.14483   b3    b   3 1         SIG
## 4   405.21324   b4    b   4 1        SIGF
## 5   534.25583   b5    b   5 1       SIGFE
## 6   591.27729   b6    b   6 1      SIGFEG
## 7   706.30423   b7    b   7 1     SIGFEGD
## 8   793.33626   b8    b   8 1    SIGFEGDS
## 9   906.42032   b9    b   9 1   SIGFEGDSI
## 10  963.44178  b10    b  10 1  SIGFEGDSIG
## 11 1119.54289  b11    b  11 1 SIGFEGDSIGR
## 12  175.11895   y1    y   1 1           R
## 13  232.14041   y2    y   2 1          GR
## 14  345.22447   y3    y   3 1         IGR
## 15  432.25650   y4    y   4 1        SIGR
## 16  547.28344   y5    y   5 1       DSIGR
## 17  604.30490   y6    y   6 1      GDSIGR
## 18  733.34749   y7    y   7 1     EGDSIGR
## 19  880.41590   y8    y   8 1    FEGDSIGR
## 20  937.43736   y9    y   9 1   GFEGDSIGR
## 21 1050.52142  y10    y  10 1  IGFEGDSIGR
## 22 1137.55345  y11    y  11 1 SIGFEGDSIGR
## 23  873.42266  b9_   b_   9 1   SIGFEGDSI
## 24  930.44412 b10_   b_  10 1  SIGFEGDSIG
## 25 1086.54523 b11_   b_  11 1 SIGFEGDSIGR
## 26  514.28579  y5_   y_   5 1       DSIGR
## 27  571.30725  y6_   y_   6 1      GDSIGR
## 28  700.34984  y7_   y_   7 1     EGDSIGR
## 29  847.41825  y8_   y_   8 1    FEGDSIGR
## 30  904.43971  y9_   y_   9 1   GFEGDSIGR
## 31 1017.52377 y10_   y_  10 1  IGFEGDSIGR
## 32 1104.55580 y11_   y_  11 1 SIGFEGDSIGR
## 33  142.12130  y1_   y_   1 1           R
## 34  199.14276  y2_   y_   2 1          GR
## 35  312.22682  y3_   y_   3 1         IGR
## 36  399.25885  y4_   y_   4 1        SIGR

Visualising a pair of spectra means that we can access them, and that, in addition to plotting, we can manipulate them and perform computations. The two spectra corresponding to the IMIDLDGTENK peptide, for example have 22 common peaks, a correlation of 0.198 and a dot product of 0.21 (see ?compareSpectra for details).

There are 2 Bioconductor packages for peptide-spectrum matching directly in R, namely MSGFplus and rTANDEM, replying on MSGF+ and X!TANDEM respectively. See also the MSGFgui package for visualisation of identification data.

9 Quantitative proteomics data

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.

• Isobaric tagging (iTRAQ and TMT): MSnbase and isboar.
• Label-free: xcms (metabolomics).
• Counting: MSnbase and MSnID for peptide-spectrum matching confidence assessment.
• N14N15 for heavy Nitrogen-labelled data.

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  12911.479

processingData(msset)

## - - - Processing information - - -
## Data loaded: Wed Jun 22 21:21:37 2016 
## iTRAQ4 quantification by trapezoidation: Wed Jun 22 21:22:18 2016 
##  MSnbase version: 1.21.7

exprs(si <- quantify(msexp, method = "SIn"))

## Combined 2 features into 2 using sum

##         dummyiTRAQ.mzXML
## ECA0510      0.003588641
## ECA1028      0.001470129

exprs(saf <- quantify(msexp, method = "NSAF"))

## Combined 2 features into 2 using user-defined function

##         dummyiTRAQ.mzXML
## ECA0510        0.6235828
## ECA1028        0.3764172

mztf <- pxget(px, pxfiles(px)[2])

## Downloading 1 file

(mzt <- readMzTabData(mztf, what = "PEP", version = "0.9"))

## Warning: Version 0.9 is deprecated. Please see '?readMzTabData' and '?
## MzTab' for details.

## Detected a metadata section

## Detected a peptide section

## MSnSet (storageMode: lockedEnvironment)
## assayData: 1528 features, 6 samples 
##   element names: exprs 
## protocolData: none
## phenoData
##   rowNames: 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: Wed Jun 22 21:22:23 2016 
##  MSnbase version: 1.21.7

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 0.142667

head(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         ER

10 Quantitative data processing and analysis

library("msmsTests")
data(msms.dataset) ## an MSnSet
e <- pp.msms.data(msms.dataset)
head(exprs(e))

##         U2.2502.1 U2.2502.2 U2.2502.3 U2.2502.4 U6.2502.1 U6.2502.2
## YJR104C       156       176       201       203       194       208
## YKL060C       183       205       202       171       171       171
## YDR155C       180       184       198       185       161       160
## YGR192C       176       172       174       170       170       171
## YOL086C       171       182       137       103       159       164
## YLR150W        97       111       104       100        99       103
##         U6.2502.3 U6.2502.4 U2.0302.1 U2.0302.2 U2.0302.3 U6.0302.1
## YJR104C       215       217       246       266       261       249
## YKL060C       149       144       247       249       236       278
## YDR155C       166       168       193       175       155       179
## YGR192C       162       167       243       310       270       270
## YOL086C       116       113       409       384       364       395
## YLR150W        98       103        73        90        96        79
##         U6.0302.2 U6.0302.3
## YJR104C       240       265
## YKL060C       252       247
## YDR155C       165       154
## YGR192C       275       259
## YOL086C       358       355
## YLR150W        81        88

pData(e)

##           treat batch
## U2.2502.1  U200  2502
## U2.2502.2  U200  2502
## U2.2502.3  U200  2502
## U2.2502.4  U200  2502
## U6.2502.1  U600  2502
## U6.2502.2  U600  2502
## U6.2502.3  U600  2502
## U6.2502.4  U600  2502
## U2.0302.1  U200  0302
## U2.0302.2  U200  0302
## U2.0302.3  U200  0302
## U6.0302.1  U600  0302
## U6.0302.2  U600  0302
## U6.0302.3  U600  0302

null.f <- "y~batch"
alt.f <- "y~treat+batch"
div <- apply(exprs(e),2,sum)
res <- msms.edgeR(e,alt.f,null.f,div=div,fnm="treat")     
head(res)

##               LogFC         LR     p.value
## YJR104C  0.02689322  0.2692032 0.603866753
## YKL060C -0.12644850  5.5841459 0.018123769
## YDR155C -0.18781161 10.2706909 0.001351602
## YGR192C -0.08495735  2.5941288 0.107260402
## YOL086C -0.11853002  5.7587712 0.016406542
## YLR150W -0.09299164  1.3766332 0.240675471

11 Some differences with RNASeq and transcriptomics in general

data(naset)
naplot(naset, col = "black")

## features.na
##   0   1   2   3   4   8   9  10 
## 301 247  91  13   2  23  10   2 
## samples.na
## 34 39 41 42 43 45 47 49 51 52 53 55 56 57 61 
##  1  1  1  1  1  2  1  1  1  1  1  1  1  1  1

flt <- filterNA(naset)
processingData(flt)

## - - - Processing information - - -
## Subset [689,16][301,16] Wed Jun 22 21:22:25 2016 
## Removed features with more than 0 NAs: Wed Jun 22 21:22:25 2016 
## Dropped featureData's levels Wed Jun 22 21:22:25 2016 
##  MSnbase version: 1.15.6

data(cvg)
hist(cvg$coverage, breaks = 100, xlab = "coverage", main = "")

data(upens)
data(upenst)

12 Annotation

All the Bioconductor annotation infrastructure, such as biomaRt, GO.db, organism specific annotations, .. are directly relevant to the analysis of proteomics data. 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. Data from the Human Protein Atlas (hpar) and UniProt (UniProt.ws) are also available.

13 More information

14 Session information

The source used to generate this document is available here.

## R version 3.3.0 (2016-05-03)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 14.04.4 LTS
## 
## attached base packages:
## [1] parallel  stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] rpx_1.9.2                        gplots_3.0.1                    
##  [3] msmsTests_1.11.0                 msmsEDA_1.11.0                  
##  [5] mzID_1.11.2                      gridExtra_2.2.1                 
##  [7] lattice_0.20-33                  BiocInstaller_1.23.4            
##  [9] AnnotationHub_2.5.4              RforProteomics_1.11.2           
## [11] MSnbase_1.21.7                   ProtGenerics_1.5.0              
## [13] BiocParallel_1.7.4               mzR_2.7.3                       
## [15] Rcpp_0.12.5                      Biobase_2.33.0                  
## [17] BiocGenerics_0.19.1              ProteomicsBioc2016Workshop_0.2.1
## [19] BiocStyle_2.1.10                
## 
## loaded via a namespace (and not attached):
##  [1] httr_1.2.0                    edgeR_3.15.0                 
##  [3] vsn_3.41.0                    interactiveDisplay_1.11.2    
##  [5] splines_3.3.0                 foreach_1.4.3                
##  [7] R.utils_2.3.0                 gtools_3.5.0                 
##  [9] shiny_0.13.2                  interactiveDisplayBase_1.11.3
## [11] highr_0.6                     affy_1.51.0                  
## [13] stats4_3.3.0                  RBGL_1.49.1                  
## [15] yaml_2.1.13                   Category_2.39.0              
## [17] impute_1.47.0                 RSQLite_1.0.0                
## [19] limma_3.29.10                 RUnit_0.4.31                 
## [21] digest_0.6.9                  RColorBrewer_1.1-2           
## [23] qvalue_2.5.2                  colorspace_1.2-6             
## [25] htmltools_0.3.5               httpuv_1.3.3                 
## [27] preprocessCore_1.35.0         Matrix_1.2-6                 
## [29] R.oo_1.20.0                   plyr_1.8.4                   
## [31] GSEABase_1.35.0               MALDIquant_1.14              
## [33] XML_3.98-1.4                  genefilter_1.55.2            
## [35] zlibbioc_1.19.0               xtable_1.8-2                 
## [37] scales_0.4.0                  gdata_2.17.0                 
## [39] affyio_1.43.0                 annotate_1.51.0              
## [41] biocViews_1.41.5              IRanges_2.7.11               
## [43] ggplot2_2.1.0                 survival_2.39-4              
## [45] RJSONIO_1.3-0                 magrittr_1.5                 
## [47] mime_0.4                      evaluate_0.9                 
## [49] R.methodsS3_1.7.1             doParallel_1.0.10            
## [51] MASS_7.3-45                   graph_1.51.0                 
## [53] tools_3.3.0                   formatR_1.4                  
## [55] stringr_1.0.0                 S4Vectors_0.11.7             
## [57] munsell_0.4.3                 AnnotationDbi_1.35.3         
## [59] pcaMethods_1.65.0             caTools_1.17.1               
## [61] grid_3.3.0                    RCurl_1.95-4.8               
## [63] iterators_1.0.8               labeling_0.3                 
## [65] bitops_1.0-6                  rmarkdown_0.9.6              
## [67] gtable_0.2.0                  codetools_0.2-14             
## [69] curl_0.9.7                    DBI_0.4-1                    
## [71] reshape2_1.4.1                R6_2.1.2                     
## [73] knitr_1.13                    KernSmooth_2.23-15           
## [75] gridSVG_1.5-0                 stringi_1.1.1