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
library("msdata") f <- msdata::proteomics(full.names = TRUE, pattern = "TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01.mzML.gz") basename(f)
##  "TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01.mzML.gz"
We need to load the 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))
## user system elapsed ## 7.402 0.084 7.618
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))
## user system elapsed ## 2.102 0.096 2.188
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:
##  TRUE
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
environment (as opposed to the
object.size function from the
## Registered S3 method overwritten by 'pryr': ## method from ## print.bytes Rcpp
## 2.77 MB
## 238 kB
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)
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).
library("microbenchmark") mb <- microbenchmark(spectra(inmem), inmem[], spectra(ondisk), ondisk[], times = 10) mb
## Unit: microseconds ## expr min lq mean median uq ## spectra(inmem) 94.071 141.612 309.0229 250.1770 382.005 ## inmem[] 32.221 36.507 72.6566 81.6275 85.966 ## spectra(ondisk) 421345.186 422612.725 430923.8398 425661.4545 432366.749 ## ondisk[] 207794.566 211700.367 220903.3304 215646.1425 228550.324 ## max neval cld ## 938.152 10 a ## 120.397 10 a ## 467562.989 10 c ## 247389.610 10 b
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])
## user system elapsed ## 0.214 0.000 0.214
## user system elapsed ## 0.021 0.000 0.021
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"))
## user system elapsed ## 6.277 0.362 2.118
system.time(eod <- quantify(ondisk[1:100], reporters = TMT6, method = "max"))
## user system elapsed ## 0.361 0.020 0.378
all.equal(eim, eod, check.attributes = FALSE)
##  TRUE
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
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
MSnbase in general, see other vignettes available with
vignette(package = "MSnbase")