R / Bioconductor packages for Proteomics
Laurent Gatto
Abstract This workshop will give delegates the opportunity to discover and try some of the recent R / Bioconductor developments for proteomics. Topics covered will including support for open community-driven formats for raw data and identification results, packages for peptide-spectrum matching, quantitative proteomics, mass spectrometry (MS) and quantitation data processing, and visualisation. The workshop material will be a self-contained vignette/workflow including example data.
This short tutorial is part of the ProteomicsBioc2014Workshop package (version 0.2.0), available at https://github.com/ComputationalProteomicsUnit/ProteomicsBioc2014Workshop.
Introduction
In Bioconductor version 2.14, there are respectively 53 proteomics and 31 mass spectrometry software packages and 7 mass spectrometry experiment packages. These respective packages can be extracted with the proteomicsPackages(), massSpectrometryPackages() and massSpectrometryDataPackages() and explored interactively.
library("RforProteomics")
pp <- proteomicsPackages(biocv)
display(pp)Mass spectrometry data
| Type | Format | Package |
|---|---|---|
| raw | mzML, mzXML, netCDF, mzData | mzR (read) |
| identification | mzIdentML | mzID (read) |
| quantitation | mzQuantML | |
| peak lists | mgf | MSnbase (read/write) |
| other | mzTab | MSnbase (read/write) |
Getting data from proteomics repositories
Contemporary 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 and unified access to PX data.
library("rpx")
pxannounced()## 15 new ProteomeXchange annoucements## Data.Set Publication.Data Message
## 1 PXD000605 2014-09-16 13:44:11 New
## 2 PXD000333 2014-09-16 13:08:03 New
## 3 PXD001006 2014-09-16 10:42:25 New
## 4 PXD001000 2014-09-16 10:07:44 New
## 5 PXD000966 2014-09-16 10:01:46 New
## 6 PXD001310 2014-09-16 09:00:47 New
## 7 PXD001061 2014-09-15 08:21:59 Updated information
## 8 PXD001127 2014-09-15 07:53:25 New
## 9 PXD001318 2014-09-13 01:18:16 New
## 10 PXD001317 2014-09-13 00:44:18 New
## 11 PXD001316 2014-09-13 00:19:10 New
## 12 PXD001315 2014-09-13 00:11:14 New
## 13 PXD001314 2014-09-13 00:05:00 New
## 14 PXD001313 2014-09-12 23:13:44 New
## 15 PXD001312 2014-09-12 16:34:37 Newpx <- PXDataset("PXD000001")
px## Object of class "PXDataset"
## Id: PXD000001 with 8 files
## [1] 'F063721.dat' ... [8] '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.mzXML"
## [7] "TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01.raw"
## [8] "erwinia_carotovora.fasta"Other metadata for the px dataset:
pxtax(px)
pxurl(px)
pxref(px)Data files can then be downloaded with the pxget function as illustrated below. Alternatively, the file is available on the workshop’s Amazon virtual machine in /data/Proteomics/data/.
mzf <- "TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01.mzXML"
mzf <- file.path("/data/Proteomics/data", mzf)
if (!file.exists(mzf))
mzf <- pxget(px, pxfiles(px)[6])## Downloading 1 file
## TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01.mzXML already present.mzf## [1] "TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01.mzXML"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.mzXML
## Number of scans: 7534hd <- 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"Exercise
Extract the index of the MS2 spectrum with the highest base peak intensity and plot its spectrum. Is the data centroided or in profile mode?
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 = "ProteomicsBioc2014Workshop"),
pattern = "mzid", full.names=TRUE))## [1] "/home/lgatto/R/x86_64-unknown-linux-gnu-library/3.1/ProteomicsBioc2014Workshop/extdata/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.
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?
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. The shinyTANDEM provides a interactive interface to explore the search results.
library("rTANDEM")
?rtandem
library("shinyTANDEM")
?shinyTANDEMHigh-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$")
quantFile## [1] "/home/lg390/R/x86_64-unknown-linux-gnu-library/3.2/MSnbase/extdata/dummyiTRAQ.mzXML"msexp <- readMSData(quantFile, verbose=FALSE)
msexp## Object of class "MSnExp"
## Object size in memory: 0.2 Mb
## - - - Spectra data - - -
## MS level(s): 2
## Number of MS1 acquisitions: 1
## Number of MSn scans: 5
## Number of precursor ions: 5
## 4 unique MZs
## Precursor MZ's: 437.8 - 716.34
## MSn M/Z range: 100 2016.66
## MSn retention times: 25:1 - 25:2 minutes
## - - - Processing information - - -
## Data loaded: Wed Sep 17 01:23:39 2014
## MSnbase version: 1.13.15
## - - - Meta data - - -
## phenoData
## rowNames: 1
## varLabels: sampleNames
## varMetadata: labelDescription
## Loaded from:
## dummyiTRAQ.mzXML
## protocolData: none
## featureData
## featureNames: X1.1 X2.1 ... X5.1 (5 total)
## fvarLabels: spectrum
## fvarMetadata: labelDescription
## experimentData: use 'experimentData(object)'The identification results stemming from the same raw data file can then be used to add PSM matches.
## find path to a mzIdentML file
identFile <- dir(system.file(package = "MSnbase", dir = "extdata"),
full.name = TRUE, pattern = "mzid$")
identFile## [1] "/home/lg390/R/x86_64-unknown-linux-gnu-library/3.2/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 databaseFile
## X1.1 VESITARHGEVLQLRPK FALSE NA erwinia_carotovora.fasta
## X2.1 IDGQWVTHQWLKK FALSE NA erwinia_carotovora.fasta
## X3.1 <NA> NA NA <NA>
## X4.1 <NA> NA NA <NA>
## X5.1 LVILLFR FALSE NA erwinia_carotovora.fasta
## identFile nprot npep.prot npsm.prot npsm.pep
## X1.1 2 2 1 1 1
## X2.1 2 1 1 1 1
## X3.1 NA NA NA NA NA
## X4.1 NA NA NA NA NA
## X5.1 2 1 1 1 1msexp[[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)
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 4752.541Quantitative 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).
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: Wed Sep 17 01:23:39 2014
## iTRAQ4 quantification by trapezoidation: Wed Sep 17 01:23:40 2014
## MSnbase version: 1.13.15Other 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")) ## 1
## ECA0510 0.003588641
## ECA1028 0.001470129exprs(saf <- quantify(msexp, method = "NSAF"))## 1
## 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 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.
mztf <- pxget(px, pxfiles(px)[2])## Downloading 1 file
## F063721.dat-mztab.txt already present.(mzt <- readMzTabData(mztf, what = "PEP"))## Warning: Support for mzTab version 0.9 only. Support will be added soon.## 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 Sep 17 01:23:42 2014
## MSnbase version: 1.13.15It 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.
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
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
## iTRAQ4 quantification by trapezoidation: Wed Sep 17 01:23:44 2014
## Purity corrected: Wed Sep 17 01:23:44 2014
## MSnbase version: 1.1.22plot0 <- function(x, y, main = "") {
old.par <- par(no.readonly = TRUE)
on.exit(par(old.par))
par(mar = c(4, 4, 1, 1))
par(mfrow = c(2, 2))
sx <- sampleNames(x)
sy <- sampleNames(y)
for (i in seq_len(ncol(x))) {
plot(exprs(x)[, i], exprs(y)[, i], log = "xy",
xlab = sx[i], ylab = sy[i])
grid()
}
}
plot0(qnt, qnt.crct)
Various 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")
plot0(qnt, qnt.crct.nrm)
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)))
prt <- combineFeatures(qnt.crct.nrm, groupBy = g, fun = "sum")## Combined 55 features into 3 using sumprocessingData(prt)## - - - Processing information - - -
## Data loaded: Wed May 11 18:54:39 2011
## iTRAQ4 quantification by trapezoidation: Wed Sep 17 01:23:44 2014
## Purity corrected: Wed Sep 17 01:23:44 2014
## Normalised (quantiles): Wed Sep 17 01:23:44 2014
## Combined 55 features into 3 using sum: Wed Sep 17 01:23:44 2014
## MSnbase version: 1.1.22Finally, 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. Missing value in MSnSet instances can be filtered out and imputed using the filterNA and impute functions.
set.seed(1)
qnt0 <- qnt
exprs(qnt0)[sample(prod(dim(qnt0)), 10)] <- NA
table(is.na(qnt0))##
## FALSE TRUE
## 209 11qnt00 <- filterNA(qnt0)
dim(qnt00)## [1] 44 4qnt.imp <- impute(qnt0)
plot0(qnt, qnt.imp)
Exercise
The mzt instance created from the mzTab file has the following is a TMT 6-plex with the following design:
In this TMT 6-plex experiment, four exogenous proteins were spiked into an equimolar Erwinia carotovora lysate with varying proportions in each channel of quantitation; yeast enolase (ENO) at 10:5:2.5:1:2.5:10, bovine serum albumin (BSA) at 1:2.5:5:10:5:1, rabbit glycogen phosphorylase (PHO) at 2:2:2:2:1:1 and bovin cytochrome C (CYT) at 1:1:1:1:1:2. Proteins were then digested, differentially labelled with TMT reagents, fractionated by reverse phase nanoflow UPLC (nanoACQUITY, Waters), and analysed on an LTQ Orbitrap Velos mass spectrometer (Thermo Scientic).
Explore the mzt data using some of the illustrated functions. The heatmap and MAplot (see MAplot function), taken from the RforProteomics vignette, have been produced using the same data.
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.msmsTest: 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.
isobaralso 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.
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 Golgi mit/plastid PM vacuole
## 204 87 128 111 17
## 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 0.9653 1.0000 1.0000kcl <- MLearn( ~ ., data = dunkley2006, kmeansI, centers = 12)
kcl## clusteringOutput: partition table
##
## 1 2 3 4 5 6 7 8 9 10 11 12
## 78 48 29 118 43 38 116 94 9 32 49 35
## The call that created this object was:
## MLearn(formula = ~., data = dunkley2006, .method = kmeansI, centers = 12)plot(kcl, exprs(dunkley2006))
hcl <- MLearn( ~ ., data = t(dunkley2006), hclustI(distFun = dist, cutParm = list(k = 4)))
hcl## clusteringOutput: partition table
##
## 1 2 3 4
## 4 4 4 4
## The call that created this object was:
## MLearn(formula = ~., data = t(dunkley2006), .method = hclustI(distFun = dist,
## cutParm = list(k = 4)))plot(hcl, exprs(t(dunkley2006)))
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 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
MALDIquantpackage. - Access to the Proteomics Standard Initiative Common QUery InterfaCe with the
PSICQUICpackage.
Additional relevant packages are described in the RforProteomics vignette.
Session information
## R Under development (unstable) (2014-08-05 r66309)
## Platform: x86_64-unknown-linux-gnu (64-bit)
##
## attached base packages:
## [1] parallel methods stats graphics grDevices utils datasets
## [8] base
##
## other attached packages:
## [1] shinyTANDEM_1.3.0 xtable_1.7-3 mixtools_1.0.2
## [4] segmented_0.4-0.0 MASS_7.3-34 boot_1.3-11
## [7] shiny_0.10.1.9006 rTANDEM_1.5.1 data.table_1.9.2
## [10] BiocInstaller_1.15.5 pRolocdata_1.3.5 pRoloc_1.5.16
## [13] MLInterfaces_1.45.3 cluster_1.15.2 annotate_1.43.5
## [16] XML_3.98-1.1 AnnotationDbi_1.27.9 GenomeInfoDb_1.1.18
## [19] IRanges_1.99.25 S4Vectors_0.1.5 rpx_1.1.1
## [22] mzID_1.3.5 RforProteomics_1.3.5 MSnbase_1.13.15
## [25] BiocParallel_0.99.19 mzR_1.99.0 Rcpp_0.11.2
## [28] Biobase_2.25.0 BiocGenerics_0.11.4 knitr_1.6.12
##
## loaded via a namespace (and not attached):
## [1] affy_1.43.3 affyio_1.33.0
## [3] BatchJobs_1.3 BBmisc_1.7
## [5] biocViews_1.33.14 BradleyTerry2_1.0-5
## [7] brew_1.0-6 brglm_0.5-9
## [9] car_2.0-21 caret_6.0-35
## [11] Category_2.31.1 checkmate_1.3
## [13] class_7.3-11 codetools_0.2-9
## [15] colorspace_1.2-4 DBI_0.3.0
## [17] digest_0.6.4 doParallel_1.0.8
## [19] e1071_1.6-4 evaluate_0.5.5
## [21] fail_1.2 FNN_1.1
## [23] foreach_1.4.2 formatR_1.0
## [25] gdata_2.13.3 genefilter_1.47.6
## [27] ggplot2_1.0.0 graph_1.43.0
## [29] grid_3.2.0 gridSVG_1.4-0
## [31] GSEABase_1.27.1 gtable_0.1.2
## [33] gtools_3.4.1 htmltools_0.2.6
## [35] httpuv_1.3.0 impute_1.39.0
## [37] interactiveDisplay_1.3.9 iterators_1.0.7
## [39] kernlab_0.9-19 labeling_0.3
## [41] lattice_0.20-29 limma_3.21.14
## [43] lme4_1.1-7 lpSolve_5.6.10
## [45] MALDIquant_1.11 Matrix_1.1-4
## [47] mclust_4.3 minqa_1.2.3
## [49] munsell_0.4.2 mvtnorm_1.0-0
## [51] nlme_3.1-117 nloptr_1.0.4
## [53] nnet_7.3-8 pcaMethods_1.55.0
## [55] pls_2.4-3 plyr_1.8.1
## [57] preprocessCore_1.27.1 proto_0.3-10
## [59] proxy_0.4-12 R6_2.0
## [61] randomForest_4.6-10 RBGL_1.41.1
## [63] RColorBrewer_1.0-5 RCurl_1.95-4.3
## [65] rda_1.0.2-2 reshape2_1.4
## [67] RJSONIO_1.3-0 R.methodsS3_1.6.1
## [69] R.oo_1.18.0 rpart_4.1-8
## [71] RSQLite_0.11.4 RUnit_0.4.26
## [73] R.utils_1.33.0 sampling_2.6
## [75] scales_0.2.4 sendmailR_1.1-2
## [77] sfsmisc_1.0-26 splines_3.2.0
## [79] stats4_3.2.0 stringr_0.6.2
## [81] survival_2.37-7 tools_3.2.0
## [83] vsn_3.33.0 zlibbioc_1.11.1