MSnbase IO capabilities

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r Biocpkg("MSnbase")'s aims are to facilitate the reproducible analysis of mass spectrometry data within the R environment, from raw data import and processing, feature quantification, quantification and statistical analysis of the results [@Gatto2012]. Data import functions for several formats are provided and intermediate or final results can also be saved or exported. These capabilities are presented below.

Data input

Raw data {-}

Data stored in one of the published XML-based formats. i.e. mzXML [@Pedrioli2004], mzData [@Orchard2007] or mzML [@Martens2010], can be imported with the readMSData method, which makes use of the r Biocpkg("mzR") package to create MSnExp objects. The files can be in profile or centroided mode. See ?readMSData for details.

Data from mzML files containing chromatographic data (e.g. generated in SRM/MRM experiments) can be imported with the readSRMData function that returns the chromatographic data as a MChromatograms object. See ?readSRMData for more details.

Peak lists {-}

Peak lists in the mgf format^[] can be imported using the readMgfData. In this case, the peak data has generally been pre-processed by other software. See ?readMgfData for details.

Quantitation data {-}

Third party software can be used to generate quantitative data and exported as a spreadsheet (generally comma or tab separated format). This data as well as any additional meta-data can be imported with the readMSnSet function. See ?readMSnSet for details.

r Biocpkg("MSnbase") also supports the mzTab format^[], a light-weight, tab-delimited file format for proteomics data developed within the Proteomics Standards Initiative (PSI). mzTab files can be read into R with readMzTabData to create and MSnSet instance.

*MSnbase* input capabilities.  The white and red boxes represent R functions/methods and objects respectively.  The blue boxes represent different disk storage formats.

Data output

RData files {-}

R objects can most easily be stored on disk with the save function. It creates compressed binary images of the data representation that can later be read back from the file with the load function.

mzML/mzXML files {-}

MSnExp and OnDiskMSnExp files can be written to MS data files in mzML or mzXML files with the writeMSData method. See ?writeMSData for details.

Peak lists {-}

MSnExp instances as well as individual spectra can be written as mgf files with the writeMgfData method. Note that the meta-data in the original R object can not be included in the file. See ?writeMgfData for details.

Quantitation data {-}

Quantitation data can be exported to spreadsheet files with the write.exprs method. Feature meta-data can be appended to the feature intensity values. See ?writeMgfData for details.

Deprecated MSnSet instances can also be exported to mzTab files using the writeMzTabData function.

*MSnbase* output capabilities. The white and red boxes represent R functions/methods and objects respectively. The blue boxes represent different disk storage formats.

Creating MSnSet from text spread sheets

This section describes the generation of MSnSet objects using data available in a text-based spreadsheet. This entry point into R and r Biocpkg("MSnbase") allows to import data processed by any of the third party mass-spectrometry processing software available and proceed with data exploration, normalisation and statistical analysis using functions available in \R and the numerous Bioconductor packages.

A complete work flow

The following section describes a work flow that uses three input files to create the MSnSet. These files respectively describe the quantitative expression data, the sample meta-data and the feature meta-data. It is taken from the r Biocpkg("pRoloc") tutorial and uses example files from the r Biocpkg("pRolocdat") package.

We start by describing the csv to be used as input using the read.csv function.

## The original data for replicate 1, available
## from the pRolocdata package
f0 <- dir(system.file("extdata", package = "pRolocdata"),
          full.names = TRUE,
          pattern = "pr800866n_si_004-rep1.csv")
csv <- read.csv(f0)

The three first lines of the original spreadsheet, containing the data for replicate one, are illustrated below (using the function head). It contains r nrow(csv) rows (proteins) and r ncol(csv) columns, including protein identifiers, database accession numbers, gene symbols, reporter ion quantitation values, information related to protein identification, ...

head(csv, n=3)

Below read in turn the spread sheets that contain the quantitation data (exprsFile.csv), feature meta-data (fdataFile.csv) and sample meta-data (pdataFile.csv).

## The quantitation data, from the original data
f1 <- dir(system.file("extdata", package = "pRolocdata"),
          full.names = TRUE, pattern = "exprsFile.csv")
exprsCsv <- read.csv(f1)
## Feature meta-data, from the original data
f2 <- dir(system.file("extdata", package = "pRolocdata"),
          full.names = TRUE, pattern = "fdataFile.csv")
fdataCsv <- read.csv(f2)
## Sample meta-data, a new file
f3 <- dir(system.file("extdata", package = "pRolocdata"),
          full.names = TRUE, pattern = "pdataFile.csv")
pdataCsv <- read.csv(f3)

exprsFile.csv contains the quantitation (expression) data for the r nrow(exprsCsv) proteins and 4 reporter tags.

head(exprsCsv, n = 3)

fdataFile.csv contains meta-data for the r nrow(fdataCsv) features (here proteins).

head(fdataCsv, n = 3)

pdataFile.csv contains samples (here fractions) meta-data. This simple file has been created manually.


The self-contained MSnSet can now easily be generated using the readMSnSet constructor, providing the respective csv file names shown above and specifying that the data is comma-separated (with sep = ","). Below, we call that object res and display its content.

res <- readMSnSet(exprsFile = f1,
                  featureDataFile = f2,
                  phenoDataFile = f3,
                  sep = ",")

The MSnSet class

Although there are additional specific sub-containers for additional meta-data (for instance to make the object MIAPE compliant), the feature (the sub-container, or slot featureData) and sample (the phenoData slot) are the most important ones. They need to meet the following validity requirements (see figure below):

A detailed description of the MSnSet class is available by typing ?MSnSet in the R console.

Dimension requirements for the respective expression, feature and sample meta-data slots.

The individual parts of this data object can be accessed with their respective accessor methods:

A shorter work flow

The readMSnSet2 function provides a simplified import workforce. It takes a single spreadsheet as input (default is csv) and extract the columns identified by ecol to create the expression data, while the others are used as feature meta-data. ecol can be a character with the respective column labels or a numeric with their indices. In the former case, it is important to make sure that the names match exactly. Special characters like '-' or '(' will be transformed by R into '.' when the csv file is read in. Optionally, one can also specify a column to be used as feature names. Note that these must be unique to guarantee the final object validity.

ecol <- paste("area", 114:117, sep = ".")
fname <- "Protein.ID"
eset <- readMSnSet2(f0, ecol, fname)

The ecol columns can also be queried interactively from R using the getEcols and grepEcols function. The former return a character with all column names, given a splitting character, i.e. the separation value of the spreadsheet (typically "," for csv, "\t" for tsv, ...). The latter can be used to grep a pattern of interest to obtain the relevant column indices.

getEcols(f0, ",")
grepEcols(f0, "area", ",")
e <- grepEcols(f0, "area", ",")
readMSnSet2(f0, e)

The phenoData slot can now be updated accordingly using the replacement functions phenoData<- or pData<- (see ?MSnSet for details).

Session information


References {-}

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MSnbase documentation built on Jan. 23, 2021, 2 a.m.