A short introduction to *MSnbase* development

cat(readLines("./Foreword.md"), sep = "\n")
cat(readLines("./Bugs.md"), sep = "\n")

NB This document is going to be updated based on current major development plans in MSnbase.


This document is not a replacement for the individual manual pages, that document the slots of the r Biocpkg("MSnbase") classes. It is a centralised high-level description of the package design.

r Biocpkg("MSnbase") aims at being compatible with the r Biocpkg("Biobase") infrastructure [@Gentleman2004]. Many meta data structures that are used in eSet and associated classes are also used here. As such, knowledge of the Biobase development and the new eSet vignette would be beneficial; the vignette can directly be accessed with vignette("BiobaseDevelopment", package="Biobase").

The initial goal is to use the r Biocpkg("MSnbase") infrastructure for MS2 labelled (iTRAQ [@Ross2004] and TMT [@Thompson2003]) and label-free (spectral counting, index and abundance) quantitation - see the documentation for the quantify function for details. The infrastructure is currently extended to support a wider range of technologies, including metabolomics.

Coding style

MSnbase follows the Bioconductor style guide. In particular

# no wrap at 80
someVeryLongVariableName <- someVeryLongFunctionName(withSomeEvenLongerFunctionArgumentA = 1, withSomeEvenLongerFunctionArgumentB = 2)

and should be wrapped as shown below:

# alternative 1
someVeryLongVariableName <-
    someVeryLongFunctionName(withSomeEvenLongerFunctionArgumentA = 1,
                             withSomeEvenLongerFunctionArgumentB = 2)

# alternative 2
someVeryLongVariableName <- someVeryLongFunctionName(
    withSomeEvenLongerFunctionArgumentA = 1,
    withSomeEvenLongerFunctionArgumentB = 2)

r Biocpkg("MSnbase") classes

All classes have a .__classVersion__ slot, of class Versioned from the r Biocpkg("Biobase") package. This slot documents the class version for any instance to be used for debugging and object update purposes. Any change in a class implementation should trigger a version change.

pSet: a virtual class for raw mass spectrometry data and meta data

This virtual class is the main container for mass spectrometry data, i.e spectra, and meta data. It is based on the eSet implementation for genomic data. The main difference with eSet is that the assayData slot is an environment containing any number of Spectrum instances (see the Spectrum section).

One new slot is introduced, namely processingData, that contains one MSnProcess instance (see the MSnProcess section). and the experimentData slot is now expected to contain MIAPE data. The annotation slot has not been implemented, as no prior feature annotation is known in shotgun proteomics.


MSnExp: a class for MS experiments

MSnExp extends pSet to store MS experiments. It does not add any new slots to pSet. Accessors and setters are all inherited from pSet and new ones should be implemented for pSet. Methods that manipulate actual data in experiments are implemented for MSnExp objects.


OnDiskMSnExp: a on-disk implementation of the MSnExp class

The OnDiskMSnExp class extends MSnExp and inherits all of its functionality but is aimed to use as little memory as possible based on a balance between memory demand and performance. Most of the spectrum-specific data, like retention time, polarity, total ion current are stored within the object's featureData slot. The actual M/Z and intensity values from the individual spectra are, in contrast to MSnExp objects, not kept in memory (in the assayData slot), but are fetched from the original files on-demand. Because mzML files are indexed, using the r Biocpkg("mzR") package to read the relevant spectrum data is fast and only moderately slower than for in-memory MSnExp^[The benchmarking vignette compares data size and operation speed of the two implementations.].

To keep track of data manipulation steps that are applied to spectrum data (such as performed by methods removePeaks or clean) a lazy execution framework was implemented. Methods that manipulate or subset a spectrum's M/Z or intensity values can not be applied directly to a OnDiskMSnExp object, since the relevant data is not kept in memory. Thus, any call to a processing method that changes or subset M/Z or intensity values are added as ProcessingStep items to the object's spectraProcessingQueue. When the spectrum data is then queried from an OnDiskMSnExp, the spectra are read in from the file and all these processing steps are applied on-the-fly to the spectrum data before being returned to the user.

The operations involving extracting or manipulating spectrum data are applied on a per-file basis, which enables parallel processing. Thus, all corresponding method implementations for OnDiskMSnExp objects have an argument BPPARAM and users can set a PARALLEL_THRESH option flag^[see ?MSnbaseOptions for details.] that enables to define how and when parallel processing should be performed (using the r Biocpkg("BiocParallel") package).

Note that all data manipulations that are not applied to M/Z or intensity values of a spectrum (e.g. sub-setting by retention time etc) are very fast as they operate directly to the object's featureData slot.


The distinction between MSnExp and OnDiskMSnExp is often not explicitly stated as it should not matter, from a user's perspective, which data structure they are working with, as both behave in equivalent ways. Often, they are referred to as in-memory and on-disk MSnExp implementations.

MSnSet: a class for quantitative proteomics data

This class stores quantitation data and meta data after running quantify on an MSnExp object or by creating an MSnSet instance from an external file, as described in the MSnbase-io vignette and in ?readMSnSet, readMzTabData, etc. The quantitative data is in form of a n by p matrix, where n is the number of features/spectra originally in the MSnExp used as parameter in quantify and p is the number of reporter ions. If read from an external file, n corresponds to the number of features (protein groups, proteins, peptides, spectra) in the file and $p$ is the number of columns with quantitative data (samples) in the file.

This prompted to keep a similar implementation as the ExpressionSet class, while adding the proteomics-specific annotation slot introduced in the pSet class, namely processingData for objects of class MSnProcess.


The MSnSet class extends the virtual eSet class to provide compatibility for ExpressionSet-like behaviour. The experiment meta-data in experimentData is also of class MIAPE . The annotation slot, inherited from eSet is not used. As a result, it is easy to convert ExpressionSet data from/to MSnSet objects with the coersion method as.

class(as(msnset, "ExpressionSet"))

class(as(sample.ExpressionSet, "MSnSet"))

MSnProcess: a class for logging processing meta data {#MSnProcess}

This class aims at recording specific manipulations applied to MSnExp or MSnSet instances. The processing slot is a character vector that describes major processing. Most other slots are of class logical that indicate whether the data has been centroided, smoothed, \ldots although many of the functionality is not implemented yet. Any new processing that is implemented should be documented and logged here.

It also documents the raw data file from which the data originates (files slot) and the r Biocpkg("MSnbase") version that was in use when the MSnProcess instance, and hence the MSnExp/MSnSet objects, were originally created.


MIAPE: Minimum Information About a Proteomics Experiment

The Minimum Information About a Proteomics Experiment [@Taylor2007; @Taylor2008] MIAPE class describes the experiment, including contact details, information about the mass spectrometer and control and analysis software.


Spectrum et al.: classes for MS spectra {#Spectum}

Spectrum is a virtual class that defines common attributes to all types of spectra. MS1 and MS2 specific attributes are defined in the Spectrum1 and Spectrum2 classes, that directly extend Spectrum.


ReporterIons: a class for isobaric tags

The iTRAQ and TMT (or any other peak of interest) are implemented ReporterIons instances, that essentially defines an expected MZ position for the peak and a width around this value as well a names for the reporters.


Chromatogram and MChromatograms: classes to handle chromatographic data

The Chromatogram class represents chromatographic MS data, i.e. retention time and intensity duplets for one file/sample. The MChromatograms class (Matrix of Chromatograms) allows to arrange multiple Chromatogram instances in a two-dimensional grid, with columns supposed to represent different samples and rows two-dimensional areas in the plane spanned by the m/z and retention time dimensions from which the intensities are extracted (e.g. an extracted ion chromatogram for a specific ion). The MChromatograms class extends the base matrix class. MChromatograms objects can be extracted from an MSnExp or OnDiskMSnExp object using the chromatogram method.


Other classes

Lists of MSnSet instances {-}

When several MSnSet instances are related to each other and should be stored together as different objects, they can be grouped as a list into and MSnSetList object. In addition to the actual list slot, this class also has basic logging functionality and enables iteration over the MSnSet instances using a dedicated lapply methods.



Unit tests {-}

r Biocpkg("MSnbase") implements unit tests with the r CRANpkg("testthat") package.

Processing methods {-}

Methods that process raw data, i.e. spectra should be implemented for Spectrum objects first and then eapplyed (or similar) to the assayData slot of an MSnExp instance in the specific method.

Session information



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