MSnbase provides infrastructure for manipulation, processing and visualisation of mass spectrometry and proteomics data, ranging from raw to quantitative and annotated data.
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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.
Package-intrinsic requirements from the Bioconductor landing page — reproduce in any R environment.
BiocManager::install("MSnbase")Alternatives: Spectra (modern successor), openms (Python/C++), msqrob2 (DE protein)
| Requirement | Details |
|---|---|
| Input Format | mzML, mzXML, mzData (open MS formats; convert with ProteoWizard) |
| Notes | Use mode='onDisk' for files > 1GB to avoid memory issues |
if (!require("BiocManager", quietly=TRUE))
install.packages("BiocManager")
BiocManager::install("MSnbase")
library(MSnbase)
Load mzML files, filter spectra, and perform isobaric quantification
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
# 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))
# Smooth then pick peaks
ms2_smooth <- smooth(ms2, method="SavitzkyGolay", halfWindowSize=3)
ms2_peaks <- pickPeaks(ms2_smooth, snr=3, method="MAD")
# 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
readMSData()()Read MS data files into an MSnExp object
| Parameter | Description |
|---|---|
files | character vector of mzML/mzXML file paths |
mode | 'onDisk' (memory-efficient, recommended) | 'inMemory' |
msLevel. | MS levels to load (e.g. 1L, 2L, c(1L,2L)) |
centroided. | logical: is data already centroided |
verbose | print progress |
filterMsLevel()()Keep only spectra of specified MS level
| Parameter | Description |
|---|---|
object | MSnExp |
msLevel. | integer MS level (1L or 2L) |
pickPeaks()()Peak picking (centroiding) raw profile spectra
smooth()()Smooth spectra with moving average or Savitzky-Golay
| Parameter | Description |
|---|---|
x | MSnExp |
method | 'SavitzkyGolay' | 'MovingAverage' |
halfWindowSize | window half-width (default 2) |
combineSpectra()()Aggregate spectra within a file/run
quantify()()Quantify isobaric tags (TMT/iTRAQ)
| Parameter | Description |
|---|---|
object | MSnExp with MS2 spectra |
method | 'trap' | 'max' | 'sum' | 'reporter' |
reporters | ReporterIons object (e.g. TMT10, iTRAQ4) |
The method assumes the following about your data and experiment:
exprs(): rows = features (peptides/proteins), columns = samples; values = quantified intensities.impute() function (MSnbase::impute); choose method based on missingness pattern.mode='onDisk' for large files — it avoids loading all spectra into memory.filterMsLevel(msdata, 2L).smooth() then pickPeaks() in that order.MSnbase + msqrob2 or DEP for statistical analysis after quantification.Spectra for new projects.| Alternative | Prefer this tool when | Prefer alternative when |
|---|---|---|
Spectra | New projects; you need pluggable backends for large data; metabolomics spectral matching. | You have existing MSnbase-based workflows or need MSnSet quantification. |
Key studies that have validated or benchmarked this tool:
Error reading mzML: corrupted or non-standard fileCause: Vendor format not fully converted to standard mzML
Fix: Re-convert with ProteoWizard/msconvert using standard parameters
Extracted from the MSnbase Bioconductor vignette(s)
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"
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
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
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
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
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.
In-memory objects can be save() ed and load() ed, while on-disk canât. 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.
The on-disk validObject method doesnât verify the validity on the spectra (as there arenât any to check). The validateOnDiskMSnExp function, on the other hand, instantiates all spectra and checks their validity (in addition to calling validObject ).
This skill is the knowledge layer — when, why, and how to use msnbase. To run this analysis on your own data with managed compute, automated QC, and reproducible outputs, use BioMate — free to start.
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