---
title: "LCMS data preprocessing and analysis with xcms"
package: xcms
output:
  BiocStyle::html_document:
    toc_float: true
    includes:
      in_header: xcms.bioschemas.html
vignette: >
  %\VignetteIndexEntry{LCMS data preprocessing and analysis with xcms}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
  %\VignetteDepends{xcms,RColorBrewer,faahKO,pander,magrittr,BiocStyle,pheatmap,SummarizedExperiment}
  %\VignettePackage{xcms}
  %\VignetteKeywords{mass spectrometry, metabolomics}
bibliography: references.bib
csl: biomed-central.csl
references:
- id: dummy
  title: no title
  author:
  - family: noname
    given: noname
---

```{r biocstyle, echo = FALSE, results = "asis" }
BiocStyle::markdown()
```

**Package**: `r Biocpkg("xcms")`<br />
**Authors**: Johannes Rainer<br />
**Modified**: `r file.info("xcms.Rmd")$mtime`<br />
**Compiled**: `r date()`

```{r init, message = FALSE, echo = FALSE, results = "hide"}
## Silently loading all packages
library(BiocStyle)
library(xcms)
library(faahKO)
library(pander)
## Use socket based parallel processing on Windows systems
if (.Platform$OS.type == "unix") {
    register(bpstart(MulticoreParam(3)))
} else {
    register(bpstart(SnowParam(3)))
}
## register(SerialParam())

```


# Introduction

This documents describes data import, exploration, preprocessing and analysis of
LCMS experiments with `xcms` version >= 3. The examples and basic workflow was
adapted from the original *LC/MS Preprocessing and Analysis with xcms* vignette
from Colin A. Smith.

The new user interface and methods use the `XCMSnExp` object (instead of the
*old* `xcmsSet` object) as a container for the pre-processing results. To
support packages and pipelines relying on the `xcmsSet` object, it is however
possible to convert an `XCMSnExp` into a `xcmsSet` object using the `as` method
(i.e.`xset <- as(x, "xcmsSet")`, with `x` being an `XCMSnExp` object.


# Data import

`xcms` supports analysis of LC/MS data from files in (AIA/ANDI) NetCDF,
mzML/mzXML and mzData format. For the actual data import Bioconductor's
`r Biocpkg("mzR")` is used. For demonstration purpose we will analyze a
subset of the data from [@Saghatelian04] in which the metabolic consequences of
knocking out the fatty acid amide hydrolase (FAAH) gene in mice was
investigated. The raw data files (in NetCDF format) are provided with the
`faahKO` data package. The data set consists of samples from the spinal cords of
4 knock-out and 4 wild-type mice. Each file contains data in centroid mode
acquired in positive ion mode form 200-600 m/z and 2500-4500 seconds. To speed
up processing of this vignette we will restrict the analysis to only 8 files and
to the retention time range from 2500 to 3500 seconds.

Below we load all required packages, locate the raw CDF files within the `faahKO`
package and build a *phenodata* data frame describing the experimental setup.

```{r load-libs-pheno, message = FALSE }
library(xcms)
library(faahKO)
library(RColorBrewer)
library(pander)
library(magrittr)
library(pheatmap)
library(SummarizedExperiment)

## Get the full path to the CDF files
cdfs <- dir(system.file("cdf", package = "faahKO"), full.names = TRUE,
            recursive = TRUE)[c(1, 2, 5, 6, 7, 8, 11, 12)]
## Create a phenodata data.frame
pd <- data.frame(sample_name = sub(basename(cdfs), pattern = ".CDF",
                                   replacement = "", fixed = TRUE),
                 sample_group = c(rep("KO", 4), rep("WT", 4)),
                 stringsAsFactors = FALSE)
```

Subsequently we load the raw data as an `OnDiskMSnExp` object using the
`readMSData` method from the `r Biocpkg("MSnbase")` package. The `MSnbase`
provides based structures and infrastructure for the processing of mass
spectrometry data. Also, `MSnbase` can be used to *centroid* profile-mode MS
data (see the corresponding vignette in the `MSnbase` package).

```{r load-with-msnbase, message = FALSE }
raw_data <- readMSData(files = cdfs, pdata = new("NAnnotatedDataFrame", pd),
                       mode = "onDisk")
```

We next restrict the data set to the retention time range from 2500 to 3500
seconds. This is merely to reduce the processing time of this vignette.

```{r subsetting, message = FALSE, echo = TRUE}
raw_data <- filterRt(raw_data, c(2500, 3500))
```

The resulting `OnDiskMSnExp` object contains general information about the
number of spectra, retention times, the measured total ion current etc, but does
not contain the full raw data (i.e. the m/z and intensity values from each
measured spectrum). Its memory footprint is thus rather small making it an ideal
object to represent large metabolomics experiments while allowing to perform
simple quality controls, data inspection and exploration as well as data
sub-setting operations. The m/z and intensity values are imported from the raw
data files on demand, hence the location of the raw data files should not be
changed after initial data import.


# Initial data inspection

The `OnDiskMSnExp` organizes the MS data by spectrum and provides the methods
`intensity`, `mz` and `rtime` to access the raw data from the files (the measured
intensity values, the corresponding m/z and retention time values). In addition,
the `spectra` method could be used to return all data encapsulated in `Spectrum`
objects. Below we extract the retention time values from the object.

```{r data-inspection-rtime, message = FALSE }
head(rtime(raw_data))
```

All data is returned as one-dimensional vectors (a numeric vector for `rtime`
and a `list` of numeric vectors for `mz` and `intensity`, each containing the
values from one spectrum), even if the experiment consists of multiple
files/samples. The `fromFile` function returns an integer vector providing
the mapping of the values to the originating file. Below we use the `fromFile`
indices to organize the `mz` values by file.

```{r data-inspection-mz, message = FALSE }
mzs <- mz(raw_data)

## Split the list by file
mzs_by_file <- split(mzs, f = fromFile(raw_data))

length(mzs_by_file)
```

As a first evaluation of the data we plot below the base peak chromatogram (BPC)
for each file in our experiment. We use the `chromatogram` method and set the
`aggregationFun` to `"max"` to return for each spectrum the maximal intensity
and hence create the BPC from the raw data. To create a total ion chromatogram
we could set `aggregationFun` to `sum`.

```{r data-inspection-bpc, message = FALSE, fig.align = "center", fig.width = 12, fig.height = 6 }
## Get the base peak chromatograms. This reads data from the files.
bpis <- chromatogram(raw_data, aggregationFun = "max")
## Define colors for the two groups
group_colors <- paste0(brewer.pal(3, "Set1")[1:2], "60")
names(group_colors) <- c("KO", "WT")

## Plot all chromatograms.
plot(bpis, col = group_colors[raw_data$sample_group])
```

The `chromatogram` method returned a `MChromatograms` object that organizes
individual `Chromatogram` objects (which in fact contain the chromatographic
data) in a two-dimensional array: columns represent samples and rows
(optionally) m/z and/or retention time ranges. Below we extract the chromatogram
of the first sample and access its retention time and intensity values.

```{r data-inspection-chromatogram, message = FALSE }
bpi_1 <- bpis[1, 1]
head(rtime(bpi_1))
head(intensity(bpi_1))
```

The `chromatogram` method supports also extraction of chromatographic data from
a m/z-rt slice of the MS data. In the next section we will use this method to
create an extracted ion chromatogram (EIC) for a selected peak.

Note that `chromatogram` reads the raw data from each file to calculate the
chromatogram. The `bpi` and `tic` methods on the other hand do not read any data
from the raw files but use the respective information that was provided in the
header definition of the input files (which might be different from the actual
data).

Below we create boxplots representing the distribution of total ion currents per
file. Such plots can be very useful to spot problematic or failing MS runs.

```{r data-inspection-tic-boxplot, message = FALSE, fig.align = "center", fig.width = 8, fig.height = 4, fig.cap = "Distribution of total ion currents per file." }
## Get the total ion current by file
tc <- split(tic(raw_data), f = fromFile(raw_data))
boxplot(tc, col = group_colors[raw_data$sample_group],
        ylab = "intensity", main = "Total ion current")
```

Also, we can cluster the samples based on similarity of their base peak
chromatogram. This can also be helpful to spot potentially problematic samples
in an experiment or generally get an initial overview of the sample grouping in
the experiment. Since the retention times between samples are not exactly
identical, we use the `bin` function to group intensities in fixed time ranges
(bins) along the retention time axis. In the present example we use a bin size
of 1 second, the default is 0.5 seconds. The clustering is performed using
complete linkage hierarchical clustering on the pairwise correlations of the
binned base peak chromatograms.

```{r data-inspection-bpc-heatmap, message = FALSE, fig.align = "center", fig.width = 7, fig.height = 6, fig.cap = "Grouping of samples based on similarity of their base peak chromatogram."}
## Bin the BPC
bpis_bin <- bin(bpis, binSize = 2)

## Calculate correlation on the log2 transformed base peak intensities
cormat <- cor(log2(do.call(cbind, lapply(bpis_bin, intensity))))
colnames(cormat) <- rownames(cormat) <- raw_data$sample_name

## Define which phenodata columns should be highlighted in the plot
ann <- data.frame(group = raw_data$sample_group)
rownames(ann) <- raw_data$sample_name

## Perform the cluster analysis
pheatmap(cormat, annotation = ann,
         annotation_color = list(group = group_colors))
```

The samples cluster in a pairwise manner, the KO and WT samples for the sample
index having the most similar BPC.

# Chromatographic peak detection

Next we perform the chromatographic peak detection using the *centWave*
algorithm [@Tautenhahn:2008fx]. Before running the peak detection it is however
strongly suggested to visually inspect e.g. the extracted ion chromatogram of
internal standards or known compounds to evaluate and adapt the peak detection
settings since the default settings will not be appropriate for most LCMS
experiments. The two most critical parameters for *centWave* are the `peakwidth`
(expected range of chromatographic peak widths) and `ppm` (maximum expected
deviation of m/z values of centroids corresponding to one chromatographic peak;
this is usually much larger than the ppm specified by the manufacturer)
parameters.  To evaluate the typical chromatographic peak width we plot the EIC
for one peak.

```{r peak-detection-plot-eic, message = FALSE, fig.align = "center", fig.width = 8, fig.height = 5, fig.cap = "Extracted ion chromatogram for one peak." }
## Define the rt and m/z range of the peak area
rtr <- c(2700, 2900)
mzr <- c(334.9, 335.1)
## extract the chromatogram
chr_raw <- chromatogram(raw_data, mz = mzr, rt = rtr)
plot(chr_raw, col = group_colors[chr_raw$sample_group])
```

Note that `Chromatogram` objects extracted by the `chromatogram` method contain
an `NA` value if in a certain scan (i.e. for a specific retention time) no
signal was measured in the respective mz range. This is reflected by the lines
not being drawn as continuous lines in the plot above.

The peak above has a width of about 50 seconds. The `peakwidth` parameter should
be set to accommodate the expected widths of peak in the data set. We set it to
`20,80` for the present example data set.

For the `ppm` parameter we extract the full MS data (intensity, retention time
and m/z values) corresponding to the above peak. To this end we first filter the
raw object by retention time, then by m/z and finally plot the object with `type
= "XIC"` to produce the plot below. We use the *pipe* (`%>%`) command better
illustrate the corresponding workflow. Note also that in this type of plot
identified chromatographic peaks would be indicated by default if present.

```{r peak-detection-plot-ms-data, message = FALSE, warning = FALSE, fig.aligh = "center", fig.width = 14, fig.height = 14, fig.cap = "Visualization of the raw MS data for one peak. For each plot: upper panel: chromatogram plotting the intensity values against the retention time, lower panel m/z against retention time plot. The individual data points are colored according to the intensity." }
raw_data %>%
    filterRt(rt = rtr) %>%
    filterMz(mz = mzr) %>%
    plot(type = "XIC")
```

In the present data there is actually no variation in the m/z values. Usually
one would see the m/z values (lower panel) scatter around the *real* m/z value
of the compound. The first step of the *centWave* algorithm defines so called
regions of interest (ROI) based on the difference of m/z values from consecutive
scans. In detail, m/z values from consecutive scans are included into a ROI if
the difference between the m/z and the mean m/z of the ROI is smaller than the
user defined `ppm` parameter. A reasonable choice for the `ppm` could thus be
the maximal m/z difference of data points from neighboring scans/spectra that
are part of the chromatographic peak. It is suggested to inspect the ranges of
m/z values for many compounds (either internal standards or compounds known to
be present in the sample) and define the `ppm` parameter for *centWave*
according to these.

Note that we can also perform the peak detection on the extracted ion
chromatogram. This can help to evaluate different peak detection settings. Only
be aware that peak detection on an extracted ion chromatogram will not consider
the `ppm` parameter and that the estimation of the background signal is
different to the peak detection on the full data set; values for the `snthresh`
will hence have different consequences. Below we perform the peak detection with
the `findChromPeaks` function on the extracted ion chromatogram. The submitted
*parameter* object defines which algorithm will be used and allows to define the
settings for this algorithm. We use the *centWave* algorithm with default
settings, except for `snthresh`.

```{r peak-detection-eic, message = FALSE}
xchr <- findChromPeaks(chr_raw, param = CentWaveParam(snthresh = 2))
```

We can access the identified chromatographic peaks with the `chromPeaks`
function.

```{r peak-detection-eic-chromPeaks}
head(chromPeaks(xchr))
```

Parallel to the `chromPeaks` matrix there is also a data frame `chromPeakData`
that allows to add arbitrary annotations to each chromatographic peak. Below we
extract this data frame that by default contains only the MS level in which the
peak was identified.

```{r peak-detection-chromatogram-chromPeakData}
chromPeakData(xchr)
```

Next we plot the identified chromatographic peaks in the extracted ion chromatogram. We use the `col`
parameter to color the individual chromatogram lines. Colors can also be
specified for the identified peaks, `peakCol` for the foreground/border color,
`peakBg` for the background/fill color. One color has to be provided for each
chromatographic peak listed by `chromPeaks`. Below we define a color to indicate
the sample group from which the sample is and use the sample information in the
peaks' `"sample"` column to assign the correct color to each chromatographic
peak. More peak highlighting options are described further below.

```{r peak-detection-eic-plot, message = FALSE, fig.align = "center", fig.width = 10, fig.height = 8, fig.cap = "Signal for an example peak. Red and blue colors represent KO and wild type samples, respectively. Peak area of identified chromatographic peaks are highlighted in the sample group color."}
sample_colors <- group_colors[xchr$sample_group]
plot(xchr, col = sample_colors,
     peakBg = sample_colors[chromPeaks(xchr)[, "column"]])

```

Finally we perform the chromatographic peak detection on the full data set. Note
that we set the argument `prefilter` to `c(6, 5000)` and `noise` to `5000` to
reduce the run time of this vignette. With this setting we consider only signals
with a value larger than 5000 in the peak detection step.

```{r peak-detection-centwave, message = FALSE, results = "hide" }
cwp <- CentWaveParam(peakwidth = c(20, 80), noise = 5000,
                     prefilter = c(6, 5000))
xdata <- findChromPeaks(raw_data, param = cwp)
```

The results are returned as an `XCMSnExp` object which extends the
`OnDiskMSnExp` object by storing also LC/GC-MS preprocessing results. This means
also that all methods to sub-set and filter the data or to access the (raw) data
are inherited from the `OnDiskMSnExp` object and can thus be re-used. Note also
that it is possible to perform additional rounds of peak detection (e.g. on MS
level > 1 data) on the `xdata` object by calling `findChromPeaks` with the
parameter `add = TRUE`.

The results from the chromatographic peak detection can be accessed with the
`chromPeaks` method.

```{r peak-detection-chromPeaks, message = FALSE }
head(chromPeaks(xdata))
```

The returned `matrix` provides the m/z and retention time range for each
identified chromatographic peak as well as the integrated signal intensity
("into") and the maximal peak intensitity ("maxo"). Columns "sample" contains
the index of the sample in the object/experiment in which the peak was
identified.

Annotations for each individual peak can be extracted with the `chromPeakData`
function. This data frame could also be used to add/store arbitrary annotations
for each detected peak.

```{r peak-detection-chromPeakData}
chromPeakData(xdata)
```

Peak detection will not always work perfectly leading to peak detection
artifacts, such as overlapping peaks or artificially split peaks. The
`refineChromPeaks` function allows to *refine* peak detection results by either
removing identified peaks not passing a certain criteria or by merging
artificially split chromatographic peaks. With parameter objects
`CleanPeaksParam` and `FilterIntensityParam` it is possible to remove peaks with
a retention time range or intensities below a threshold, respectively (see their
respective help pages for more details and examples). With
`MergeNeighboringPeaksParam` it is possible to merge chromatographic
peaks. Below we post-process the peak detection results merging peaks
overlapping in a 4 second window per file if the signal between in between them
is lower than 75% of the smaller peak's maximal intensity. See the
`MergeNeighboringPeaksParam` help page for a detailed description of the
settings and the approach.

```{r peak-postprocessing, message = FALSE}
mpp <- MergeNeighboringPeaksParam(expandRt = 4)
xdata_pp <- refineChromPeaks(xdata, mpp)
```

An example for a merged peak is given below.

```{r peak-postprocessing-merged, fig.widht = 10, fig.height = 5, fig.cap = "Result from the peak refinement step. Left: data before processing, right: after refinement. The splitted peak was merged into one."}
mzr_1 <- 305.1 + c(-0.01, 0.01)
chr_1 <- chromatogram(filterFile(xdata, 1), mz = mzr_1)
chr_2 <- chromatogram(filterFile(xdata_pp, 1), mz = mzr_1)
par(mfrow = c(1, 2))
plot(chr_1)
plot(chr_2)
```

For the first trace in the chromatogram above centWave detected 3 peaks (1 for
the full area and two smaller ones, see left panel in the plot above). The peak
refinement with `MergeNeighboringPeaksParam` reduced them to a single peak
(right panel in the figure above). Note that this refinement does not merge
neighboring peaks for which the signal in between them is lower than a certain
proportion (see figure below).

```{r peak-postprocessing-not-merged, fig.widht = 10, fig.height = 5, fig.cap = "Result from the peak refinement step. Left: data before processing, right: after refinement. The peaks were not merged."}
mzr_1 <- 496.2 + c(-0.01, 0.01)
chr_1 <- chromatogram(filterFile(xdata, 1), mz = mzr_1)
chr_2 <- chromatogram(filterFile(xdata_pp, 1), mz = mzr_1)
par(mfrow = c(1, 2))
plot(chr_1)
plot(chr_2)
```

Note also that it is possible to perform the peak refinement on extracted ion
chromatograms. This could e.g. be used to fine-tune the settings for the
parameter. To illustrate this we perform below a peak refinement on the
extracted ion chromatogram `chr_1` reducing the `minProp` parameter to force
joining the two peaks.

```{r peak-postprocessing-chr, fig..width = 5, fig.height = 5}
res <- refineChromPeaks(chr_1, MergeNeighboringPeaksParam(minProp = 0.05))
chromPeaks(res)
plot(res)
```

Before proceeding we replace the `xdata` object with the results from the peak
refinement.

```{r}
xdata <- xdata_pp
```

Below we use the data from the `chromPeaks` matrix to calculate some per-file
summaries.

```{r peak-detection-peaks-per-sample, message = FALSE, results = "asis" }
summary_fun <- function(z)
    c(peak_count = nrow(z), rt = quantile(z[, "rtmax"] - z[, "rtmin"]))

T <- lapply(split.data.frame(
    chromPeaks(xdata), f = chromPeaks(xdata)[, "sample"]),
    FUN = summary_fun)
T <- do.call(rbind, T)
rownames(T) <- basename(fileNames(xdata))
pandoc.table(
    T,
    caption = paste0("Summary statistics on identified chromatographic",
                     " peaks. Shown are number of identified peaks per",
                     " sample and widths/duration of chromatographic ",
                     "peaks."))
```

We can also plot the location of the identified chromatographic peaks in the
m/z - retention time space for one file using the `plotChromPeaks`
function. Below we plot the chromatographic peaks for the 3rd sample.

```{r peak-detection-chrom-peaks-plot, message = FALSE, fig.align = "center", fig.width = 8, fig.height = 8, fig.cap = "Identified chromatographic peaks in the m/z by retention time space for one sample." }
plotChromPeaks(xdata, file = 3)
```

To get a global overview of the peak detection we can plot the frequency of
identified peaks per file along the retention time axis. This allows to identify
time periods along the MS run in which a higher number of peaks was identified
and evaluate whether this is consistent across files.

```{r peak-detection-chrom-peak-image, message = FALSE, fig.align = "center", fig.width = 10, fig.height = 8, fig.cap = "Frequency of identified chromatographic peaks along the retention time axis. The frequency is color coded with higher frequency being represented by yellow-white. Each line shows the peak frequency for one file." }
plotChromPeakImage(xdata)
```

Next we highlight the identified chromatographic peaks for the example peak from
before. Evaluating such plots on a list of peaks corresponding to known peaks or
internal standards helps to ensure that peak detection settings were appropriate
and correctly identified the expected peaks. We extract the ion chromatogram
from the peak detection result object, which contains then also the identified
chromatographic peaks for that ion that we can extract with the `chromPeaks`
function.

```{r peak-detection-eic-example-peak, message = FALSE}
chr_ex <- chromatogram(xdata, mz = mzr, rt = rtr)
chromPeaks(chr_ex)
```

We can also plot the extracted ion chromatogram. Identified chromatographic
peaks will be automatically highlighted in the plot. Below we highlight
chromatographic peaks with a rectangle from the peak's minimal to maximal rt and
from an intensity of 0 to the maximal signal of the peak.

```{r peak-detection-highlight-chrom-peaks-plot, message = FALSE, fig.align = "center", fig.width = 10, fig.height = 8, fig.cap = "Signal for an example peak. Red and blue colors represent KO and wild type samples, respectively. The rectangles indicate the identified chromatographic peaks per sample." }
sample_colors <- group_colors[chr_ex$sample_group]
plot(chr_ex, col = sample_colors, peakType = "rectangle",
     peakCol = sample_colors[chromPeaks(chr_ex)[, "sample"]],
     peakBg = NA)
```

Alternatively to the rectangle visualization above, it is possible to represent
the apex position of each peak with a single point (passing argument
`type = "point"` to the function), or draw the actually identified peak by
specifying `type = "polygon"`. To completely omit highlighting the identified
peaks (e.g. to plot base peak chromatograms or similar) `type = "none"` can be
used. Below we use `type = "polygon"` to fill the peak area
for each identified chromatographic peak in each sample. Whether individual
peaks can be still identified in such a plot depends however on the number of
samples from which peaks are drawn.

```{r peak-detection-highlight-chrom-peaks-plot-polygon, message = FALSE, fig.align = "center", fig.width = 10, fig.height = 8, fig.cap = "Signal for an example peak. Red and blue colors represent KO and wild type samples, respectively. The signal area of identified chromatographic peaks are filled with a color." }
plot(chr_ex, col = group_colors[chr_raw$sample_group], lwd = 2,
     peakBg = sample_colors[chromPeaks(chr_ex)[, "sample"]])
```

Note that we can also specifically extract identified chromatographic peaks for
a selected region by providing the respective m/z and retention time ranges with
the `mz` and `rt` arguments in the `chromPeaks` method.

```{r peak-detection-chrom-peak-table-selected, message = FALSE, results = "asis" }
pander(chromPeaks(xdata, mz = mzr, rt = rtr),
       caption = paste("Identified chromatographic peaks in a selected ",
                       "m/z and retention time range."))
```

Finally we plot also the distribution of peak intensity per sample. This allows
to investigate whether systematic differences in peak signals between samples
are present.

```{r peak-detection-chrom-peak-intensity-boxplot, message = FALSE, fig.align = "center", fig.width = 10, fig.height = 8, fig.cap = "Peak intensity distribution per sample." }
## Extract a list of per-sample peak intensities (in log2 scale)
ints <- split(log2(chromPeaks(xdata)[, "into"]),
              f = chromPeaks(xdata)[, "sample"])
boxplot(ints, varwidth = TRUE, col = group_colors[xdata$sample_group],
        ylab = expression(log[2]~intensity), main = "Peak intensities")
grid(nx = NA, ny = NULL)
```


# Alignment

The time at which analytes elute in the chromatography can vary between samples
(and even compounds). Such a difference was already observable in the extracted
ion chromatogram plot shown as an example in the previous section. The alignment
step, also referred to as retention time correction, aims at adjusting this by
shifting signals along the retention time axis to align the signals between
different samples within an experiment.

A plethora of alignment algorithms exist (see [@Smith:2013gr]), with some of
them being implemented also in `xcms`. The method to perform the
alignment/retention time correction in `xcms` is `adjustRtime` which uses
different alignment algorithms depending on the provided parameter class.

In the example below we use the *obiwarp* method [@Prince:2006jj] to align the
samples. We use a `binSize = 0.6` which creates warping functions in mz bins of
0.6. Also here it is advisable to modify the settings for each experiment and
evaluate if retention time correction did align internal controls or known
compounds properly.

```{r alignment-obiwarp, message = FALSE, results = "hide" }
xdata <- adjustRtime(xdata, param = ObiwarpParam(binSize = 0.6))
```

`adjustRtime`, besides calculating adjusted retention times for each spectrum,
does also adjust the reported retention times of the identified chromatographic
peaks.

To extract the adjusted retention times we can use the `adjustedRtime` method,
or simply the `rtime` method that, if present, returns by default adjusted
retention times from an `XCMSnExp` object.

```{r alignment-rtime, message = FALSE }
## Extract adjusted retention times
head(adjustedRtime(xdata))

## Or simply use the rtime method
head(rtime(xdata))
```

*Raw* retention times can be extracted from an `XCMSnExp` containing
aligned data with `rtime(xdata, adjusted = FALSE)`.

To evaluate the impact of the alignment we plot the BPC on the adjusted data. In
addition we plot the differences of the adjusted- to the raw retention times per
sample using the `plotAdjustedRtime` function. For a base peak chromatogram it
makes no sense to also extract identified chromatographic peaks from the result
object. We thus use parameter `include = "none"` in the `chromatogram` call to
not include chromatographic peaks in the returned object. Note that
alternatively it would also be possible to simply avoid plotting them by setting
`peakType = "none"` in the `plot` call.

```{r alignment-obiwarp-plot, message = FALSE, fig.align = "center", fig.width = 10, fig.height = 10, fig.cap = "Obiwarp aligned data. Base peak chromatogram after alignment (top) and difference between adjusted and raw retention times along the retention time axis (bottom)." }
## Get the base peak chromatograms.
bpis_adj <- chromatogram(xdata, aggregationFun = "max", include = "none")
par(mfrow = c(2, 1), mar = c(4.5, 4.2, 1, 0.5))
plot(bpis_adj, col = group_colors[bpis_adj$sample_group])
## Plot also the difference of adjusted to raw retention time.
plotAdjustedRtime(xdata, col = group_colors[xdata$sample_group])
```

Too large differences between adjusted and raw retention times could indicate
poorly performing samples or alignment.

**Note**: `XCMSnExp` objects hold the raw along with the adjusted retention
times and subsetting will in most cases drop the adjusted retention
times. Sometimes it might thus be useful to **replace** the raw retention times
with the adjusted retention times. This can be done with the
`applyAdjustedRtime`.

At last we evaluate the impact of the alignment on the test peak.

```{r alignment-peak-groups-example-peak, message = FALSE, fig.align = "center", fig.width = 10, fig.height = 10, fig.cap = "Example extracted ion chromatogram before (top) and after alignment (bottom)." }
par(mfrow = c(2, 1))
## Plot the raw data
plot(chr_raw, col = group_colors[chr_raw$sample_group])

## Extract the chromatogram from the adjusted object
chr_adj <- chromatogram(xdata, rt = rtr, mz = mzr)
plot(chr_adj, col = group_colors[chr_raw$sample_group], peakType = "none")
```

## Subset-based alignment

In some experiments it might be helpful to perform the alignment based on only a
subset of the samples, e.g. if QC samples were injected at regular intervals or
if the experiment contains blanks. Alignment method in `xcms` allow to
estimate retention time drifts on a subset of samples (either all samples
excluding blanks or QC samples injected at regular intervals during a
measurement run) and use these to adjust the full data set.

Parameters `subset` (of the `PeakGroupsParam` or `ObiwarpParam` object) can be
used to define the subset of samples on which the alignment of the full data set
will be based (e.g. `subset` being the index of QC samples), and parameter
`subsetAdjust` allows to specify the method by which the *left-out* samples will
be adjusted. There are currently two options available:

- `subsetAdjust = "previous"`: adjust the retention times of a non-subset
  sample based on the alignment results of the previous subset sample (e.g. a
  QC sample). If samples are e.g. in the order *A1*, *B1*, *B2*, *A2*, *B3*,
  *B4* with *A* representing QC samples and *B* study samples, using
  `subset = c(1, 4)` and `subsetAdjust = "previous"` would result in all *A*
  samples to be aligned with each other and non-subset samples *B1* and *B2*
  being adjusted based on the alignment result of subset samples *A1* and *B3*
  and *B4* on those of *A2*.

- `subsetAdjust = "average"`: adjust retention times of non-subset samples based
  on an interpolation of the alignment results of the previous and subsequent
  subset sample. In the example above, *B1* would be adjusted based on the
  average of adjusted retention times between subset (QC) samples *A1* and
  *A2*. Since there is no subset sample after non-subset samples *B3* and *B4*
  these will be adjusted based on the alignment results of *A2* alone. Note
  that a weighted average is used to calculate the adjusted retention time
  averages, which uses the inverse of the difference of the index of the
  non-subset sample to the subset samples as weights. Thus, if we have a
  setup like *A1*, *B1*, *B2*, *A2* the adjusted retention times of *A1*
  would get a larger weight than those of *A2* in the adjustment of
  non-subset sample *B1* causing it's adjusted retention times to be closer
  to those of *A1* than to *A2*. See below for examples.

Both cases require a meaningful/correct ordering of the samples within the
object (e.g. ordering by injection index).

The examples below aim to illustrate the effect of these alignment options.  We
assume that samples 1, 4 and 7 in the *faahKO* data set are QC samples (sample
pools). We thus want to perform the alignment based on these samples and
subsequently adjust the retention times of the left-out samples (2, 3, 5, 6 and
8) based on interpolation of the results from the neighboring *subset* (QC)
samples. After initial peak grouping we perform below the alignment with the
*peak groups* method passing the indices of the samples on which we want the
alignment to be based on with the `subset` argument and specify `subsetAdjust =
"average"` to adjust the study samples based on interpolation of the alignment
results from neighboring subset/QC samples.

Note that for any subset-alignment all parameters such as `minFraction` are
relative to the `subset`, not the full experiment!

To re-perform an alignment we can first remove previous alignment results with
the `dropAdjustedRtime` function.

```{r subset-define, message = FALSE, warning = FALSE}
xdata <- dropAdjustedRtime(xdata)

## Define the experimental layout
xdata$sample_type <- "study"
xdata$sample_type[c(1, 4, 7)] <- "QC"
```

We next have to perform an initial correspondence analysis because the *peak
groups* alignment method adjusts the retention time by aligning previously
identified *hook peaks* (chromatographic peaks present in most/all samples;
details about the algorithm used are presented in the next section). We use here
the default settings, but it is strongly advised to adapt the parameters for
each data set. The definition of the sample groups (i.e. assignment of
individual samples to the sample groups in the experiment) is mandatory for the
`PeakDensityParam`. If there are no sample groups in the experiment
`sampleGroups` should be set to a single value for each file (e.g. `rep(1,
length(fileNames(xdata))`).

```{r alignment-subset, message = FALSE, warning = FALSE}
## Initial peak grouping. Use sample_type as grouping variable
pdp_subs <- PeakDensityParam(sampleGroups = xdata$sample_type,
                             minFraction = 0.9)
xdata <- groupChromPeaks(xdata, param = pdp_subs)

## Define subset-alignment options and perform the alignment
pgp_subs <- PeakGroupsParam(minFraction = 0.85,
                            subset = which(xdata$sample_type == "QC"),
                            subsetAdjust = "average", span = 0.4)
xdata <- adjustRtime(xdata, param = pgp_subs)
```

Below we plot the results of the alignment labeling the samples being part of
the *subset* in green and the others in grey. This nicely shows how the
interpolation of the `subsetAdjust = "average"` works: retention times of sample
2 are adjusted based on those from subset sample 1 and 4, giving however more
weight to the closer subset sample 1 which results in the adjusted retention
times of 2 being more similar to those of sample 1. Sample 3 on the other hand
gets adjusted giving more weight to the second subset sample (4).

```{r alignment-subset-plot-2, message = FALSE, warning = FALSE, fig.align = "center", fig.width = 10, fig.height = 10, fig.cap = "Subset-alignment results with option average. Difference between adjusted and raw retention times along the retention time axis. Samples on which the alignment models were estimated are shown in green, study samples in grey." }
clrs <- rep("#00000040", 8)
clrs[xdata$sample_type == "QC"] <- c("#00ce0080")
par(mfrow = c(2, 1), mar = c(4, 4.5, 1, 0.5))
plot(chromatogram(xdata, aggregationFun = "sum"),
     col = clrs, peakType = "none")
plotAdjustedRtime(xdata, col = clrs, peakGroupsPch = 1,
                  peakGroupsCol = "#00ce0040")
```

Option `subsetAdjust = "previous"` adjusts the retention times of a non-subset
sample based on a single subset sample (the previous), which results in most
cases in the adjusted retention times of the non-subset sample being highly
similar to those of the subset sample which was used for adjustment.


# Correspondence

The final step in the metabolomics preprocessing is the correspondence that
matches detected chromatographic peaks between samples (and depending on the
settings, also within samples if they are adjacent). The method to perform the
correspondence in `xcms` is `groupChromPeaks`. We will use the *peak density*
method [@Smith:2006ic] to group chromatographic peaks. The algorithm combines
chromatographic peaks depending on the density of peaks along the retention time
axis within small slices along the mz dimension. To illustrate this we plot
below the chromatogram for an mz slice with multiple chromatographic peaks
within each sample. We use below a value of 0.4 for the `minFraction` parameter
hence only chromatographic peaks present in at least 40% of the samples per
sample group are grouped into a feature. The sample group assignment is
specified with the `sampleGroups` argument.

```{r correspondence-example-slice, message = FALSE, fig.align = "center", fig.width = 10, fig.height = 10, fig.cap = "Example for peak density correspondence. Upper panel: chromatogram for an mz slice with multiple chromatographic peaks. lower panel: identified chromatographic peaks at their retention time (x-axis) and index within samples of the experiments (y-axis) for different values of the bw parameter. The black line represents the peak density estimate. Grouping of peaks (based on the provided settings) is indicated by grey rectangles." }
## Define the mz slice.
mzr <- c(305.05, 305.15)

## Extract and plot the chromatograms
chr_mzr <- chromatogram(xdata, mz = mzr)
## Define the parameters for the peak density method
pdp <- PeakDensityParam(sampleGroups = xdata$sample_group,
                        minFraction = 0.4, bw = 30)
plotChromPeakDensity(chr_mzr, col = sample_colors, param = pdp,
                     peakBg = sample_colors[chromPeaks(chr_mzr)[, "sample"]],
                     peakCol = sample_colors[chromPeaks(chr_mzr)[, "sample"]],
                     peakPch = 16)

```

The upper panel in the plot above shows the extracted ion chromatogram for each
sample with the detected peaks highlighted. The middle and lower plot shows the
retention time for each detected peak within the different samples. The black
solid line represents the density distribution of detected peaks along the
retention times. Peaks combined into *features* (peak groups) are indicated with
grey rectangles. This type of visualization is ideal to test correspondence
settings on example m/z slices before applying them to the full data set.

Below we perform the correspondence analysis with the defined settings on the
full data set.

```{r correspondence, message = FALSE }
## Perform the correspondence
pdp <- PeakDensityParam(sampleGroups = xdata$sample_group,
                        minFraction = 0.4, bw = 30)
xdata <- groupChromPeaks(xdata, param = pdp)
```

Results from the xcms-based preprocessing can be summarized into a
`SummarizedExperiment` object from the `r Biocpkg("SummarizedExperiment")`
package with the `quantify` method. This object will contain the feature
abundances as the *assay matrix*, the feature definition (their m/z, retention
time and other metadata) as `rowData` (i.e. row annotations) and the
sample/phenotype information as `colData` (i.e. column annotations). All the
processing history will be put into the object's `metadata`. This object can
then be used for any further (`xcms`-independent) processing and analysis.

Below we use `quantify` to generate the result object for the present
analysis. The parameters `value` and any other additional parameters are passed
along to the `featureValues` method that is used internally to create the
feature abundance matrix.

```{r quantify}
res <- quantify(xdata, value = "into")
```

Sample annotations can be accessed with the `colData` method.

```{r quantify-colData}
colData(res)
```

Feature annotations with `rowData`:

```{r quantify-rowData}
rowData(res)
```

The feature abundances can be accessed with the `assay` method. Note also that a
`SummarizedExperiment` supports multiple such assay matrices.

```{r}
head(assay(res))
```

In addition it is possible to extract the results from the correspondence
analysis individually using the `featureDefinitions` and `featureValues`
methods, the former returning a `DataFrame` with the definition of the features
(i.e. the mz and rt ranges and, in column `peakidx`, the index of the
chromatographic peaks in the `chromPeaks` matrix for each feature), the latter
the feature abundances.

```{r correspondence-featureDefs, message = FALSE }
## Extract the feature definitions
featureDefinitions(xdata)
```

The `featureValues` method returns a `matrix` with rows being features and
columns samples. The content of this matrix can be defined using the `value`
argument. The default `value = "into"` returns a matrix with the integrated
signal of the peaks corresponding to a feature in a sample. Any column name of
the `chromPeaks` matrix can be passed to the argument `value`. Below we extract
the integrated peak intensity per feature/sample.

```{r correspondence-feature-values, message = FALSE }
## Extract the into column for each feature.
head(featureValues(xdata, value = "into"))
```

This feature matrix contains `NA` for samples in which no chromatographic peak
was detected in the feature's m/z-rt region. While in many cases there might
indeed be no peak signal in the respective region, it might also be that there
is signal, but the peak detection algorithm failed to detect a chromatographic
peak. `xcms` provides the `fillChromPeaks` method to *fill in* intensity data
for such missing values from the original files. The *filled in* peaks are added
to the `chromPeaks` matrix and get a `TRUE` value in the `"is_filled"` column of
the `chromPeakData` data frame. Below we perform such a filling-in of missing
peaks.


```{r fill-chrom-peaks, message = FALSE }
## Filling missing peaks using default settings. Alternatively we could
## pass a FillChromPeaksParam object to the method.
xdata <- fillChromPeaks(xdata)

head(featureValues(xdata))
```

For features without detected peaks in a sample, the method extracts all
intensities in the mz-rt region of the feature, integrates the signal and adds a
*filled-in* peak to the `chromPeaks` matrix. No peak is added if no signal is
measured/available for the mz-rt region of the feature. For these, even after
filling in missing peak data, a `NA` is reported in the `featureValues` matrix.

It should be mentioned that `fillChromPeaks` uses columns `"rtmin"`, `"rtmax"`,
`"mzmin"` and `"mzmax"` from the `featureDefinitions` table to define the region
from which the signal should be integrated to generate the filled-in peak
signal. These values correspond however to the positions of the peak apex not
the peak boundaries of all chromatographic peaks assigned to a feature. It might
be advisable to increase this area in retention time dimension by a constant
value appropriate to the average peak width in the experiment. Such a value can
be specified with `fixedRt` of the `FillChromPeaksParam`. If the average peak
width in the experiment is 20 seconds, specifying`fixedRt = 10` ensures that the area
from which peaks are integrated is at least 20 seconds wide.

Below we compare the number of missing values before and after filling in
missing values. We can use the parameter `filled` of the `featureValues` method
to define whether or not filled-in peak values should be returned too.

```{r  fill-chrom-peaks-compare, message = FALSE }
## Missing values before filling in peaks
apply(featureValues(xdata, filled = FALSE), MARGIN = 2,
      FUN = function(z) sum(is.na(z)))

## Missing values after filling in peaks
apply(featureValues(xdata), MARGIN = 2,
      FUN = function(z) sum(is.na(z)))

```

```{r export-result, eval = FALSE, echo = FALSE}
save(xdata, file = "xdata.RData")
```

Next we use the `featureSummary` function to get a general per-feature summary
that includes the number of samples in which a peak was found or the number of
samples in which more than one peak was assigned to the feature. Specifying also
sample groups breaks down these summary statistics for each individual sample
group.

```{r featureSummary, message = FALSE }
head(featureSummary(xdata, group = xdata$sample_group))
```

We can add the feature value matrix with the filled-in data for missing peaks
also to our `SummarizedExperiment` object `res` as an additional *assay*:

```{r}
assays(res)$raw_filled <- featureValues(xdata, filled = TRUE)
```

We have now two matrices (assays) available, the matrix with the detected and
the matrix with the detected and filled-in values, each can be accessed by their
name.

```{r}
assayNames(res)

head(assay(res, "raw"))
head(assay(res, "raw_filled"))
```

The performance of peak detection, alignment and correspondence should always be
evaluated by inspecting extracted ion chromatograms e.g. of known compounds,
internal standards or identified features in general. The `featureChromatograms`
function allows to extract chromatograms for each feature present in
`featureDefinitions`. The returned `MChromatograms` object contains an ion
chromatogram for each feature (each row containing the data for one feature) and
sample (each column representing containing data for one sample). Below we
extract the chromatograms for the first 4 features.

```{r featureChromatograms, message = FALSE }
feature_chroms <- featureChromatograms(xdata, features = 1:4)

feature_chroms
```

And plot the extracted ion chromatograms. We again use the group color for each
identified peak to fill the area.

```{r feature-eic, message = FALSE, fig.align = "center", fig.width = 8, fig.height = 8, fig.cap = "Extracted ion chromatograms for features 1 to 4." }
plot(feature_chroms, col = sample_colors,
     peakBg = sample_colors[chromPeaks(feature_chroms)[, "sample"]])

```

To access the EICs of the second feature we can simply subset the
`feature_chroms` object.

```{r}
eic_2 <- feature_chroms[2, ]
chromPeaks(eic_2)
```

At last we perform a principal component analysis to evaluate the grouping of
the samples in this experiment. Note that we did not perform any data
normalization hence the grouping might (and will) also be influenced by
technical biases.

```{r final-pca, message = FALSE, fig.align = "center", fig.width = 8, fig.height = 8, fig.cap = "PCA for the faahKO data set, un-normalized intensities." }
## Extract the features and log2 transform them
ft_ints <- log2(assay(res, "raw_filled"))

## Perform the PCA omitting all features with an NA in any of the
## samples. Also, the intensities are mean centered.
pc <- prcomp(t(na.omit(ft_ints)), center = TRUE)

## Plot the PCA
cols <- group_colors[xdata$sample_group]
pcSummary <- summary(pc)
plot(pc$x[, 1], pc$x[,2], pch = 21, main = "",
     xlab = paste0("PC1: ", format(pcSummary$importance[2, 1] * 100,
                                   digits = 3), " % variance"),
     ylab = paste0("PC2: ", format(pcSummary$importance[2, 2] * 100,
                                   digits = 3), " % variance"),
     col = "darkgrey", bg = cols, cex = 2)
grid()
text(pc$x[, 1], pc$x[,2], labels = xdata$sample_name, col = "darkgrey",
     pos = 3, cex = 2)

```

We can see the expected separation between the KO and WT samples on PC2. On PC1
samples separate based on their ID, samples with an ID <= 18 from samples with
an ID > 18. This separation might be caused by a technical bias
(e.g. measurements performed on different days/weeks) or due to biological
properties of the mice analyzed (sex, age, litter mates etc).


# Further data processing and analysis

Normalizing features' signal intensities is required, but at present not (yet)
supported in `xcms` (some methods might be added in near future). It is advised
to use the `SummarizedExperiment` returned by the `quantify` method for any
further data processing, as this type of object stores feature definitions,
sample annotations as well as feature abundances in the same object. For the
identification of e.g. features with significant different
intensities/abundances it is suggested to use functionality provided in other R
packages, such as Bioconductor's excellent `limma` package. To enable support
also for other packages that rely on the *old* `xcmsSet` result object, it is
possible to coerce the new `XCMSnExp` object to an `xcmsSet` object using `xset
<- as(x, "xcmsSet")`, with `x` being an `XCMSnExp` object.


# Additional details and notes

For a detailed description of the new data objects and changes/improvements
compared to the original user interface see the *new\_functionality* vignette.


## Evaluating the process history

`XCMSnExp` objects allow to capture all performed pre-processing steps along
with the used parameter class within the `@processHistory` slot. Storing also
the parameter class ensures the highest possible degree of analysis
documentation and in future might enable to *replay* analyses or parts of it.
The list of all performed preprocessings can be extracted using the
`processHistory` method.

```{r processhistory, message = FALSE }
processHistory(xdata)
```

It is also possible to extract specific processing steps by specifying its
type. Available *types* can be listed with the `processHistoryTypes`
function. Below we extract the parameter class for the alignment/retention time
adjustment step.

```{r processhistory-select, message = FALSE }
ph <- processHistory(xdata, type = "Retention time correction")

ph
```

And we can also extract the parameter class used in this preprocessing step.

```{r processhistory-param, message = FALSE }
## Access the parameter
processParam(ph[[1]])

```


## Subsetting and filtering

`XCMSnEx` objects can be subsetted/filtered using the `[` method, or one of the
many `filter*` methods. All these methods aim to ensure that the data in the
returned object is consistent. This means for example that if the object is
subsetted by selecting specific spectra (by using the `[` method) all identified
chromatographic peaks are removed. Correspondence results (i.e. identified
features) are removed if the object is subsetted to contain only data from
selected files (using the `filterFile` method). This is because the
correspondence results depend on the files on which the analysis was performed -
running a correspondence on a subset of the files would lead to different
results. Note that with `keepFeatures = TRUE` it would be possible to overwrite
this and keep also correspondence results for the specified files.

As an exception, it is possible to force keeping adjusted retention times in the
subsetted object setting the `keepAdjustedRtime` argument to `TRUE` in any of
the subsetting methods.

Below we subset our results object the data for the files 2 and 4.

```{r subset-filterFile, message = FALSE }
subs <- filterFile(xdata, file = c(2, 4))

## Do we have identified chromatographic peaks?
hasChromPeaks(subs)
```

Peak detection is performed separately on each file, thus the subsetted object
contains all identified chromatographic peaks from the two files. However, we
used a retention time adjustment (alignment) that was based on available
features. All features have however been removed and also the adjusted retention
times (since the alignment based on features that were identified on
chromatographic peaks on all files).

```{r subset-filterFile-2, message = FALSE }
## Do we still have features?
hasFeatures(subs)

## Do we still have adjusted retention times?
hasAdjustedRtime(subs)
```

We can however use the `keepAdjustedRtime` argument to force keeping the
adjusted retention times, `keepFeatures` would even keep correspondence results.

```{r subset-filterFile-3, message = FALSE }
subs <- filterFile(xdata, keepAdjustedRtime = TRUE)

hasAdjustedRtime(subs)
```

The `filterRt` method can be used to subset the object to spectra within a
certain retention time range.

```{r subset-filterRt, message = FALSE }
subs <- filterRt(xdata, rt = c(3000, 3500))

range(rtime(subs))
```

Filtering by retention time does not change/affect adjusted retention times
(also, if adjusted retention times are present, the filtering is performed
**on** the adjusted retention times).

```{r subset-filterRt-2, message = FALSE }
hasAdjustedRtime(subs)
```

Also, we have all identified chromatographic peaks within the specified
retention time range:

```{r subset-filterRt-3, message = FALSE }
hasChromPeaks(subs)

range(chromPeaks(subs)[, "rt"])
```

The most natural way to subset any object in R is with `[`. Using `[` on an
`XCMSnExp` object subsets it keeping only the selected spectra. The index `i`
used in `[` has thus to be an integer between 1 and the total number of spectra
(across all files). Below we subset `xdata` using both `[` and `filterFile` to
keep all spectra from one file.

```{r subset-bracket, message = FALSE, warning = FALSE }
## Extract all data from the 3rd file.
one_file <- filterFile(xdata, file = 3)

one_file_2 <- xdata[fromFile(xdata) == 3]

## Is the content the same?
all.equal(one_file[[2]], one_file_2[[2]])
```

While the spectra-content is the same in both objects, `one_file` contains also
the identified chromatographic peaks while `one_file_2` does not. Thus, in most
situations subsetting using one of the filter functions is preferred over the
use of `[`.

```{r subset-bracket-peaks, message = FALSE }
## Subsetting with filterFile preserves chromatographic peaks
head(chromPeaks(one_file))

## Subsetting with [ not
head(chromPeaks(one_file_2))
```

Note however that also `[` does support the `keepAdjustedRtime` argument. Below
we subset the object to spectra 20:30.

```{r subset-bracket-keepAdjustedRtime, message = FALSE, warnings = FALSE }
subs <- xdata[20:30, keepAdjustedRtime = TRUE]

hasAdjustedRtime(subs)

## Access adjusted retention times:
rtime(subs)

## Access raw retention times:
rtime(subs, adjusted = FALSE)
```

As with `MSnExp` and `OnDiskMSnExp` objects, `[[` can be used to extract a
single spectrum object from an `XCMSnExp` object. The retention time of the
spectrum corresponds to the adjusted retention time if present.

```{r subset-double-bracket, message = FALSE }
## Extract a single spectrum
xdata[[14]]
```

At last we can also use the `split` method that allows to split an `XCMSnExp`
based on a provided factor `f`. Below we split `xdata` per file. Using
`keepAdjustedRtime = TRUE` ensures that adjusted retention times are not
removed.

```{r subset-split, message = FALSE }
x_list <- split(xdata, f = fromFile(xdata), keepAdjustedRtime = TRUE)

lengths(x_list)

lapply(x_list, hasAdjustedRtime)
```


## Parallel processing

Most methods in `xcms` support parallel processing. Parallel processing is
handled and configured by the `BiocParallel` Bioconductor package and can be
globally defined for an R session.

Unix-based systems (Linux, macOS) support `multicore`-based parallel
processing. To configure it globally we `register` the parameter class. Note also
that `bpstart` is used below to initialize the parallel processes.

```{r multicore, message = FALSE, eval = FALSE }
register(bpstart(MulticoreParam(2)))
```

Windows supports only socket-based parallel processing:

```{r snow, message = FALSE, eval = FALSE }
register(bpstart(SnowParam(2)))
```

Note that `multicore`-based parallel processing might be buggy or failing on
 macOS. If so, the `DoparParam` could be used instead (requiring the `foreach`
package).

For other options and details see the vignettes from the `BiocParallel` package.


# References
