Bioconductor Msnbase

MSnbase — Comprehensive Skill Guide

Domain: Proteomics / Mass spectrometry Bioconductor: MSnbase Paper: Gatto L, Lilley KS (2012). Bioinformatics, 28(2):288-289.

Infrastructure for mass spectrometry data handling in R: reading mzML/mzXML/mzData files, spectral processing, quantification, and proteomics/metabolomics workflows.

Dependencies & Environment

Package-intrinsic requirements from the Bioconductor landing page — reproduce in any R environment.

• Version: 2.37.0 · Bioconductor: 3.23 · R: ≥ 4.6
• Depends: BiocGenerics, Biobase, mzR, S4Vectors, ProtGenerics
• Imports: MsCoreUtils, PSMatch, BiocParallel, IRanges, plyr, vsn, affy, impute, pcaMethods, MALDIquant, mzID, digest, lattice, ggplot2, scales, MASS, Rcpp
• Install: BiocManager::install("MSnbase")

When to Use

• Read mzML/mzXML files into R for proteomics or metabolomics workflows
• Process MS2 spectra: deisotoping, centroiding, filtering
• TMT/iTRAQ isobaric quantification
• Feature quantification from LC-MS data

Alternatives: Spectra (modern successor), openms (Python/C++), msqrob2 (DE protein)

Do NOT Use When

• Large-scale DIA proteomics — use PyProphet/OpenSWATH or Spectronaut.
• Metabolomics spectral matching — use Spectra + CompoundDb instead.
• New projects — consider Spectra as the modern successor.

Data Requirements

Installation

if (!require("BiocManager", quietly=TRUE))
    install.packages("BiocManager")
BiocManager::install("MSnbase")
library(MSnbase)

Workflows

Read and Process LC-MS Data for Proteomics

Load mzML files, filter spectra, and perform isobaric quantification

Read mzML files (on-disk mode)

library(MSnbase)
# on-disk mode: memory-efficient; reads spectra lazily
msdata <- readMSData(files=c("sample1.mzML","sample2.mzML"),
                     mode="onDisk",
                     msLevel.=c(1L, 2L))
msdata

Basic filtering

# Filter to MS2 spectra only
ms2 <- filterMsLevel(msdata, msLevel.=2L)

# Filter by retention time (RT in seconds)
ms2_rt <- filterRt(ms2, rt=c(600, 3600))

# Filter by m/z
ms2_mz <- filterMz(ms2, mz=c(400, 1400))

Centroiding profile data

# Smooth then pick peaks
ms2_smooth <- smooth(ms2, method="SavitzkyGolay", halfWindowSize=3)
ms2_peaks  <- pickPeaks(ms2_smooth, snr=3, method="MAD")

iTRAQ/TMT quantification

# Define reporter ions
reporters <- iTRAQ4  # or TMT6, TMT10, TMT11

# Quantify
msnset <- quantify(ms2_peaks,
                   method="trap",
                   reporters=reporters,
                   verbose=TRUE)
exprs(msnset)[1:5, ]  # reporter ion intensities

Key Functions & Parameters

readMSData()()

Read MS data files into an MSnExp object

filterMsLevel()()

Keep only spectra of specified MS level

pickPeaks()()

Peak picking (centroiding) raw profile spectra

smooth()()

Smooth spectra with moving average or Savitzky-Golay

combineSpectra()()

Aggregate spectra within a file/run

quantify()()

Quantify isobaric tags (TMT/iTRAQ)

Scientific Assumptions

The method assumes the following about your data and experiment:

• Reporter ion intensities (TMT/iTRAQ) are proportional to protein abundance.
• Missing values in quantification are missing-at-random or missing-not-at-random — treat accordingly.

Result Interpretation

• Reporter ion intensities: normalize across samples (median or total intensity normalization).
• MSnSet exprs(): rows = features (peptides/proteins), columns = samples; values = quantified intensities.
• Missing values: impute with impute() function (MSnbase::impute); choose method based on missingness pattern.

Best Practices

• Always use mode='onDisk' for large files — it avoids loading all spectra into memory.
• Filter to required MS levels early: filterMsLevel(msdata, 2L).
• For centroiding: smooth() then pickPeaks() in that order.
• Use MSnbase + msqrob2 or DEP for statistical analysis after quantification.
• MSnbase is the older package; consider Spectra for new projects.

When to Choose This vs Alternatives

Benchmark Evidence

Key studies that have validated or benchmarked this tool:

• Gatto L, Lilley KS (2012). Bioinformatics 28:288. PMID:22113085
  MSnbase provides unified infrastructure for MS-based proteomics and metabolomics in R.

Common Errors & Troubleshooting

Error reading mzML: corrupted or non-standard file

Cause: Vendor format not fully converted to standard mzML

Fix: Re-convert with ProteoWizard/msconvert using standard parameters

Additional Notes from Official Documentation

Extracted from the MSnbase Bioconductor vignette(s)

1 Introduction

In this vignette, we will document various timings and benchmarkings of the MSnbase version 2, that focuses on on-disk data access (as opposed to in-memory ). More details about the new implementation are documented in the respective classes manual pages and in MSnbase , efficient and elegant R-based processing and visualisation of raw mass spectrometry data . Laurent Gatto, Sebastian Gibb, Johannes Rainer. bioRxiv 2020.04.29.067868; doi: https://doi.org/10.1101/2020.04.29.067868 As a benchmarking dataset, we are going to use a subset of an TMT 6-plex experiment acquired on an LTQ Orbitrap Velos, that is distributed with the msdata package We need to load the MSnbase package and set the session-wide verbosity flag to FALSE .

library("msdata")
f <- msdata::proteomics(full.names = TRUE,
                        pattern = "TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01.mzML.gz")
basename(f)

## [1] "TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01.mzML.gz"

2.1 Reading data

We first read the data using the original behaviour readMSData function by setting the mode argument to "inMemory" to generates an in-memory representation of the MS2-level raw data and measure the time needed for this operation. Next, we use the readMSData function to generate an on-disk representation of the same data by setting mode = "onDisk" . Creating the on-disk experiment is considerable faster and scales to much bigger, multi-file data, both in terms of object creation time, but also in terms of object size (see next section). We must of course make sure that these two datasets are equivalent:

system.time(inmem <- readMSData(f, msLevel = 2,
                                mode = "inMemory",
                                centroided = TRUE))

##    user  system elapsed 
##   5.867   0.370   6.246

2.2 Data size

To compare the size occupied in memory of these two objects, we are going to use the object_size function from the pryr package, which accounts for the data (the spectra) in the assayData environment (as opposed to the object.size function from the utils package). The difference is explained by the fact that for ondisk , the spectra are not created and stored in memory; they are access on disk when needed, such as for example for plotting: Figure 1: Plotting in-memory and on-disk spectra

library("pryr")
object_size(inmem)

## 2.77 MB

2.3 Accessing spectra

The drawback of the on-disk representation is when the spectrum data has to actually be accessed. To compare access time, we are going to use the microbenchmark and repeat access 10 times to compare access to all 451 and a single spectrum in-memory (i.e. pre-loaded and constructed) and on-disk (i.e. on-the-fly access). While it takes order or magnitudes more time to access the data on-the-fly rather than a pre-generated spectrum, accessing all spectra is only marginally slower than accessing all spectra, as most of the time is spent preparing the file for access, which is done only once. On-disk access performance will depend on the read throughput of the disk. A comparison of the data import of the above file from an internal solid state drive and from an USB3 connected hard disk showe

library("microbenchmark")
mb <- microbenchmark(spectra(inmem),
                     inmem[[200]],
                     spectra(ondisk),
                     ondisk[[200]],
                     times = 10)
mb

## Unit: microseconds
##             expr        min         lq        mean      median         uq
##   spectra(inmem)     76.694     87.322   1312.3126    211.3235    270.330
##     inmem[[200]]     30.221     32.500     68.5462     80.4705     88.825
##  spectra(ondisk) 453102.954 456584.668 461123.4430 459971.6310 463125.542
##    ondisk[[200]] 261970.948 262110.568 269558.7601 262512.2775 267099.225
##         max neval cld
##   11023.685    10 a  
##      94.427    10 a  
##  473925.730    10  b 
##  318789.700    10   c

2.4 MS2 quantitation

Below, we perform TMT 6-plex reporter ions quantitation on the first 100 spectra and verify that the results are identical (ignoring feature names).

system.time(eim <- quantify(inmem[1:100], reporters = TMT6,
                            method = "max"))

##    user  system elapsed 
##   0.160   0.175   1.340

3.1 MS levels

On-disk support multiple MS levels in one object, while in-memory only supports a single level. While support for multiple MS levels could be added to the in-memory back-end, memory constrains make this pretty-much useless and will most likely never happen.

3.2 Serialisation

In-memory objects can be save() ed and load() ed, while on-disk can’t. As a workaround, the latter can be coerced to in-memory instances with as(, "MSnExp") . We would need mzML write support in mzR to be able to implement serialisation for on-disk data.

3.4 Validity

The on-disk validObject method doesn’t verify the validity on the spectra (as there aren’t any to check). The validateOnDiskMSnExp function, on the other hand, instantiates all spectra and checks their validity (in addition to calling validObject ).

Resources

• Bioconductor page: https://bioconductor.org/packages/release/bioc/html/MSnbase.html
• Vignette: https://bioconductor.org/packages/release/bioc/html/MSnbase.html
• GitHub:
• Bioconductor support forum: https://support.bioconductor.org/
• Paper: Gatto L, Lilley KS (2012). Bioinformatics, 28(2):288-289.

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