---
title: "A parser for raw and identification mass-spectrometry data"
author:
- name: Bernd Fischer
- name: Steffen Neumann
- name: Laurent Gatto
- name: Qiang Kou
package: mzR
output:
  BiocStyle::html_document:
    toc_float: true
bibliography: mzR.bib
vignette: >
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteIndexEntry{Accessin raw mass spectrometry and identification data}
  %\VignetteKeywords{mzXML, netCDF, mzML, mzIdentML, mass spectrometry, proteomics, metabolomics}
  %\VignetteEncoding{UTF-8}
  %\VignettePackage{mzR}
---

# Introduction

The `r BiocStyle::Biocpkg("mzR")` package aims at providing a common,
low-level interface to several mass spectrometry data formats, namely,
`mzXML` [@Pedrioli2004], `mzML` [@Martens2010] for raw data, and
`mzIdentML` [@Jones2012], somewhat similar to the Bioconductor package
affyio for affymetrix raw data. No processing is done in `r
BiocStyle::Biocpkg("mzR")`, which is left to packages such as `r
BiocStyle::Biocpkg("xcms")` [@Smith:2006, Tautenhahn:2008] or `r
BiocStyle::Biocpkg("MSnbase")` [@Gatto:2012]. These packages also
provide more convenient, high-level interfaces to raw and
identification. data

Most importantly, access to the data should be fast and memory
efficient. This is made possible by allowing on-disk random file
access, i.e. retrieving specific data of interest without having to
sequentially browser the full content nor loading the entire data into
memory.

The actual work of reading and parsing the data files is handled by the included
C/C++ libraries or *backends*. The C++ reference implementation for the `mzML`
is the proteowizard library [@Kessner08] (pwiz in short), which in turn makes
use of the boost C++ (<http://www.boost.org/>) library. More recently, the
proteowizard (http://proteowizard.sourceforge.net/) [@Chambers2012] has been
fully integrated using the `mzRpwiz` backend for raw data, and is not the
default option. The `mzRnetCDF` backend provides support to `CDF`-based
formats. Finally, the `mzRident` backend is available to access identification
data (`mzIdentML`) through pwiz.

The `r BiocStyle::Biocpkg("mzR")` package is in essence a collection of wrappers
to the C++ code, and benefits from the C++ interface provided through
the Rcpp package [@Rcpp11].

**IMPORTANT** New developers that need to access and manipulate raw
mass spectrometry data are advised against using this infrastucture
directly. They are invited to use the corresponding `MSnExp` (with *on
disk* mode) from the`r BiocStyle::Biocpkg("MSnbase")` package instead. The
latter supports reading multiple files at once and offers access to
the spectra data (m/z and intensity) as well as all the spectra
metadata using a coherent interface. The MSnbase infrastructure itself
used the low level classes in mzR, thus offering fast and efficient
access.


# Mass spectrometry raw data

All the mass spectrometry file formats are organized similarly, where
a set of metadata nodes about the run is followed by a list of spectra
with the actual masses and intensities. In addition, each of these
spectra has its own set of metadata, such as the retention time and
acquisition parameters.

## Spectral data access

Access to the spectral data is done via the `peaks` function. The
return value is a list of two-column mass-to-charge and intensity
matrices or a single matrix if one spectrum is queried.

## Chromatogram access

Access to the chromatogram(s) is done using the `chromatogram` (or
`chromatograms`) function, that return one (or a list of)
data.frames. See `?chromatogram` for details. This functionality is
only available with the `pwiz` backend.

## Identification result access

The main access to identification result is done via `psms`, `score`
and `modifications`.  `psms` and `score` will return the detailed
information on each psm and scores.  `modifications` will return the
details on each modification found in peptide.

## Metadata access

**Run metadata** is available via several functions such as
`instrumentInfo()` or `runInfo()`. The individual fields can be
accessed via e.g. `detector()` etc.

**Spectrum metadata** is available via `header()`, which will return a
list (for single scans) or a dataframe with information such as the
`basePeakMZ`, `peaksCount`, ... or, for higher-order MS the `msLevel`
and precursor information.

**Identification metadata**is available via `mzidInfo()`, which will
return a list with information such as the `software`,
`ModificationSearched`, `enzymes`, `SpectraSource` and other
information for this identification result.

The availability of this metadata can not always be guaranteed, and
depends on the MS software which converted the data.

# Example

## `mzXML`/`mzML` files

A short example sequence to read data from a mass spectrometer.
First open the file.

```{r openraw}
library(mzR)
library(msdata)

mzxml <- system.file("threonine/threonine_i2_e35_pH_tree.mzXML",
                     package = "msdata")
aa <- openMSfile(mzxml)
```

We can obtain different kind of header information.

```{r get header information}
runInfo(aa)
instrumentInfo(aa)
header(aa,1)
```

Read a single spectrum from the file.

```{r plotspectrum}
pl <- peaks(aa,10)
peaksCount(aa,10)
head(pl)
plot(pl[,1], pl[,2], type="h", lwd=1)
```

One should always close the file when not needed any more. This will
release the memory of cached content.

```{r close the file}
close(aa)
```

## `mzIdentML` files

You can use `openIDfile` to read a `mzIdentML` file (version 1.1),
which use the pwiz backend.

```{r openid}
library(mzR)
library(msdata)

file <- system.file("mzid", "Tandem.mzid.gz", package="msdata")
x <- openIDfile(file)
```

`mzidInfo` function will return general information about this
identification result.

```{r metadata}
mzidInfo(x)
```

`psms` will return the detailed information on each
peptide-spectrum-match, include `spectrumID`, `chargeState`,
`sequence`. `modNum` and others.

```{r psms0}
p <- psms(x)
colnames(p)
```

The modifications information can be accessed using `modifications`,
which will return the `spectrumID`, `sequence`, `name`, `mass` and
`location`.

```{r psms1}
m <- modifications(x)
head(m)
```

Since different software will use different scoring function, we
provide a `score` to extract the scores for each psm. It will return a
data.frame with different columns depending on software generating
this file.

```{r psms2}
scr <- score(x)
colnames(scr)
```

# Future plans

Other file formats provided by HUPO, such as `mzQuantML` for
quantitative data [@Walzer:2013] are also possible in the future.

# Session information {#sec:sessionInfo}

```{r label=sessioninfo, results='asis', echo=FALSE}
toLatex(sessionInfo())
```
