suppressPackageStartupMessages(library("BiocStyle")) suppressPackageStartupMessages(library("MSnbase")) suppressPackageStartupMessages(library("BiocParallel"))
In this vignette, we will document various timings and benchmarkings
r Biocpkg("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
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
r Biocexptpkg("msdata") package
library("msdata") f <- msdata::proteomics(full.names = TRUE, pattern = "TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01.mzML.gz") basename(f)
We need to load the
r Biocpkg("MSnbase") package and set the
session-wide verbosity flag to
We first read the data using the original behaviour
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.
system.time(inmem <- readMSData(f, msLevel = 2, mode = "inMemory", centroided = TRUE))
Next, we use the
readMSData function to generate an on-disk
representation of the same data by setting
mode = "onDisk".
system.time(ondisk <- readMSData(f, msLevel = 2, mode = "onDisk", centroided = TRUE))
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:
To compare the size occupied in memory of these two objects, we are
going to use the
object_size function from the
package, which accounts for the data (the spectra) in the
environment (as opposed to the
object.size function from the
library("pryr") object_size(inmem) object_size(ondisk)
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:
plot(inmem[], full = TRUE) plot(ondisk[], full = TRUE)
suppressMessages(requireNamespace("gridExtra")) gridExtra::grid.arrange(plot(inmem[], full = TRUE), plot(ondisk[], full = TRUE), ncol = 2)
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
r CRANpkg("microbenchmark") and repeat access 10 times to
compare access to all
r length(inmem) and a single spectrum
in-memory (i.e. pre-loaded and constructed) and on-disk
(i.e. on-the-fly access).
library("microbenchmark") mb <- microbenchmark(spectra(inmem), inmem[], spectra(ondisk), ondisk[], times = 10) mb
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 showed
only small differences for the
onDisk mode (1.07 vs 1.36 seconds),
while no difference were observed for accessing individual or all
spectra. Thus, for this particular setup, performance was about the
same for SSD and HDD. This might however not apply to setting in which
data import is performed in parallel from multiple files.
Data access does not prohibit interactive usage, such as plotting, for example, as it is about 1/2 seconds, which is an operation that is relatively rare, compared to subsetting and filtering, which are faster for on-disk data:
i <- sample(length(inmem), 100) system.time(inmem[i]) system.time(ondisk[i])
Operations on the spectra data, such as peak picking, smoothing, cleaning, ... are cleverly cached and only applied when the data is accessed, to minimise file access overhead. Finally, specific operations such as for example quantitation (see next section) are optimised for speed.
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")) system.time(eod <- quantify(ondisk[1:100], reporters = TMT6, method = "max")) all.equal(eim, eod, check.attributes = FALSE)
OnDiskMSnExp documentation files and the MSnbase
developement vignette provide more information about implementation
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
load()ed, while on-disk
can't. As a workaround, the latter can be coerced to in-memory
as(, "MSnExp"). We would need
mzML write support in
r Biocpkg("mzR") to be able to implement serialisation for on-disk
Whenever possible, accessing and processing on-disk data is delayed (lazy processing). These operations are stored in a processing queue until the spectra are effectively instantiated.
validObject method doesn't verify the validity on the
spectra (as there aren't any to check). The
function, on the other hand, instantiates all spectra and checks their
validity (in addition to calling
This document focuses on speed and size improvements of the new
MSnExp representation. The extend of these improvements will
substantially increase for larger data.
For general functionality about the on-disk
MSnExp data class and
r Biocpkg("MSnbase") in general, see other vignettes available with
vignette(package = "MSnbase")
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