Package: MSnbase
Authors: Laurent Gatto and Johannes Rainer
Modified: NA
Compiled: Wed Jan 04 19:08:09 2017

1 Introduction

In this vignette, we will document various timings and benchmarkings of the recent MSnbase development (aka MSnbase2), that focuses on on-disk data access (as opposed to in-memory). More details about the new implementation will be documented elsewhere.

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")
basename(f)

## [1] "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 FALSE.

library("MSnbase")
setMSnbaseVerbose(FALSE)

2 Reading data

We first read the data using the original readMSData function that 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,
                                centroided = TRUE))

##    user  system elapsed 
##   11.61    0.11   12.77

Next, we use the readMSData2 function to generate an on-disk representation of the same data.

system.time(ondisk <- readMSData2(f, msLevel = 2,
                                  centroided = TRUE))

##    user  system elapsed 
##    1.80    0.08    2.00

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:

all.equal(inmem, ondisk)

## [1] TRUE

3 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).

library("pryr")
object_size(inmem)

## 2.68 MB

object_size(ondisk)

## 115 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[[200]], full = TRUE)
plot(ondisk[[200]], full = TRUE)

Figure 1: Plotting in-memory and on-disk spectra

4 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).

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

## Unit: microseconds
##             expr         min          lq         mean       median
##   spectra(inmem)     137.119     213.153     256.5950     231.9480
##     inmem[[200]]      53.822      87.995     103.5437      96.5385
##  spectra(ondisk) 1577401.184 1600994.087 1819302.5074 1669543.2335
##    ondisk[[200]]  552788.651  598798.485  710468.2401  676850.8735
##           uq         max neval cld
##      304.138     431.859    10 a  
##      106.790     157.195    10 a  
##  2170828.985 2270449.112    10   c
##   815694.116  880959.042    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 readMSData2 call (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 
##    0.33    0.04    0.35

system.time(ondisk[i])

##    user  system elapsed 
##    0.09    0.00    0.09

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.

5 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 
##    0.41    0.01   11.69

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

##    user  system elapsed 
##     0.7     0.1     0.8

all.equal(eim, eod, check.attributes = FALSE)

## [1] TRUE

6 Conclusions

This document focuses on speed and size improvements of the new on-disk 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