---
title: "MSnbase benchmarking"
author:
- name: Laurent Gatto
  affiliation: de Duve Institute, UCLouvain, Belgium
- name: Johannes Rainer
  affiliation: Center for Biomedicine, EURAC, Bolzano, Italy
package: MSnbase
output:
  BiocStyle::html_document:
    toc_float: true
vignette: >
  %\VignetteIndexEntry{MSnbase benchmarking}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteKeyword{proteomics, mass spectrometry}
  %\VignetteEncoding{UTF-8}
---

```{r env, echo=FALSE, message=FALSE}
suppressPackageStartupMessages(library("BiocStyle"))
suppressPackageStartupMessages(library("MSnbase"))
suppressPackageStartupMessages(library("BiocParallel"))
```

# Introduction

In this vignette, we will document various timings and benchmarkings
of the `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
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 `r Biocexptpkg("msdata")` package

```{r msdata}
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 `FALSE`.

```{r verb}
library("MSnbase")
setMSnbaseVerbose(FALSE)
```

# Benchmarking

## 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.

```{r read1}
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"`.

```{r read2}
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:

```{r equal12}
all.equal(inmem, ondisk)
```

## Data size

To compare the size occupied in memory of these two objects, we are
going to use the `object_size` function from the `r CRANpkg("pryr")`
package, which accounts for the data (the spectra) in the `assayData`
environment (as opposed to the `object.size` function from the `utils`
package).

```{r}
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:

```{r plot0, eval=FALSE}
plot(inmem[[200]], full = TRUE)
plot(ondisk[[200]], full = TRUE)
```

```{r plot1, echo=FALSE, fig.wide=TRUE, fig.cap = "Plotting in-memory and on-disk spectra"}
suppressMessages(requireNamespace("gridExtra"))
gridExtra::grid.arrange(plot(inmem[[200]],  full = TRUE),
                        plot(ondisk[[200]], full = TRUE),
                        ncol = 2)

```

## 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 `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).

```{r mb, cache=TRUE}
library("microbenchmark")
mb <- microbenchmark(spectra(inmem),
                     inmem[[200]],
                     spectra(ondisk),
                     ondisk[[200]],
                     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:

```{r subset}
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.

## 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).

```{r qnt, cache=TRUE}
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)
```

# Notable differences *on-disk* and *in-memory* implementations

The `MSnExp` and `OnDiskMSnExp` documentation files and the *MSnbase
developement* vignette provide more information about implementation
details.

## 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.

## 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
`r Biocpkg("mzR")` to be able to implement serialisation for *on-disk*
data.

## Data processing

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.

## 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`).

# 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
`r Biocpkg("MSnbase")` in general, see other vignettes available with

```{r vigs, eval=FALSE}
vignette(package = "MSnbase")
```