# LCMS data preprocessing and analysis with xcms

#### 18 September 2020

#### Package

xcms 3.10.2

Package: xcms
Authors: Johannes Rainer
Modified: 2020-09-18 14:51:01
Compiled: Fri Sep 18 20:04:53 2020

# 1 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.

# 2 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 mzR is used. For demonstration purpose we will analyze a subset of the data from [1] 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.

    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 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).

    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.

    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.

# 3 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.

    head(rtime(raw_data))

    ## F1.S0001 F1.S0002 F1.S0003 F1.S0004 F1.S0005 F1.S0006
    ## 2501.378 2502.943 2504.508 2506.073 2507.638 2509.203

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.

    mzs <- mz(raw_data)

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

    length(mzs_by_file)

    ## [1] 8

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`.

    ## 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.

    bpi_1 <- bpis[1, 1]
    head(rtime(bpi_1))

    ## F1.S0001 F1.S0002 F1.S0003 F1.S0004 F1.S0005 F1.S0006
    ## 2501.378 2502.943 2504.508 2506.073 2507.638 2509.203

    head(intensity(bpi_1))

    ## F1.S0001 F1.S0002 F1.S0003 F1.S0004 F1.S0005 F1.S0006
    ##    43888    43960    43392    42632    42200    42288

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.

    ## 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")

Figure 1: Distribution of total ion currents per file

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.

    ## 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))

Figure 2: Grouping of samples based on similarity of their base peak chromatogram

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

# 4 Chromatographic peak detection

Next we perform the chromatographic peak detection using the centWave algorithm [2]. 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.

    ## 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])

Figure 3: Extracted ion chromatogram for one peak

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.

    raw_data %>%
        filterRt(rt = rtr) %>%
        filterMz(mz = mzr) %>%
        plot(type = "XIC")

Figure 4: 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.

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`.

    xchr <- findChromPeaks(chr_raw, param = CentWaveParam(snthresh = 2))

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

    head(chromPeaks(xchr))

    ##            rt    rtmin    rtmax       into       intb  maxo  sn row column
    ## [1,] 2781.505 2761.160 2809.674  412134.25  355516.37 16856  13   1      1
    ## [2,] 2786.199 2764.290 2812.803 1496244.21 1391821.33 58736  20   1      2
    ## [3,] 2734.556 2714.211 2765.855   21579.37   18449.43   899   4   1      3
    ## [4,] 2797.154 2775.245 2815.933  159058.78  150289.31  6295  12   1      3
    ## [5,] 2784.635 2761.160 2808.109   54947.54   37923.53  2715   2   1      4
    ## [6,] 2859.752 2845.668 2878.532   13895.21   13874.87   905 904   1      4

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.

    chromPeakData(xchr)

    ## DataFrame with 12 rows and 4 columns
    ##      ms_level is_filled       row    column
    ##     <integer> <logical> <integer> <integer>
    ## 1           1     FALSE         1         1
    ## 2           1     FALSE         1         2
    ## 3           1     FALSE         1         3
    ## 4           1     FALSE         1         3
    ## 5           1     FALSE         1         4
    ## ...       ...       ...       ...       ...
    ## 8           1     FALSE         1         4
    ## 9           1     FALSE         1         5
    ## 10          1     FALSE         1         6
    ## 11          1     FALSE         1         7
    ## 12          1     FALSE         1         8

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.

    sample_colors <- group_colors[xchr$sample_group]
    plot(xchr, col = sample_colors,
         peakBg = sample_colors[chromPeaks(xchr)[, "column"]])

Figure 5: 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.

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.

    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.

    head(chromPeaks(xdata))

    ##           mz mzmin mzmax       rt    rtmin    rtmax      into      intb  maxo
    ## CP0001 453.2 453.2 453.2 2509.203 2501.378 2527.982 1007409.0 1007380.8 38152
    ## CP0002 236.1 236.1 236.1 2518.593 2501.378 2537.372  253501.0  226896.3 12957
    ## CP0003 594.0 594.0 594.0 2601.535 2581.191 2637.529  161042.2  149297.3  7850
    ## CP0004 577.0 577.0 577.0 2604.665 2581.191 2626.574  136105.2  129195.5  6215
    ## CP0005 369.2 369.2 369.2 2587.451 2556.151 2631.269  483852.3  483777.1  7215
    ## CP0006 369.2 369.2 369.2 2568.671 2557.716 2578.061  144624.8  144602.9  7033
    ##           sn sample
    ## CP0001 38151      1
    ## CP0002    11      1
    ## CP0003    13      1
    ## CP0004    13      1
    ## CP0005  7214      1
    ## CP0006  7032      1

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.

    chromPeakData(xdata)

    ## DataFrame with 1707 rows and 2 columns
    ##         ms_level is_filled
    ##        <integer> <logical>
    ## CP0001         1     FALSE
    ## CP0002         1     FALSE
    ## CP0003         1     FALSE
    ## CP0004         1     FALSE
    ## CP0005         1     FALSE
    ## ...          ...       ...
    ## CP1703         1     FALSE
    ## CP1704         1     FALSE
    ## CP1705         1     FALSE
    ## CP1706         1     FALSE
    ## CP1707         1     FALSE

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.

    mpp <- MergeNeighboringPeaksParam(expandRt = 4)
    xdata_pp <- refineChromPeaks(xdata, mpp)

An example for a merged peak is given below.

    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)

Figure 6: Result from the peak refinement step
Left: data before processing, right: after refinement. The splitted peak was merged into one.

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).

    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)

Figure 7: Result from the peak refinement step
Left: data before processing, right: after refinement. The peaks were not merged.

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.

    res <- refineChromPeaks(chr_1, MergeNeighboringPeaksParam(minProp = 0.05))
    chromPeaks(res)

    ##         mz mzmin mzmax       rt    rtmin    rtmax     into intb    maxo   sn
    ## CPM1 496.2 496.2 496.2 3384.012 3294.809 3412.181 45940118   NA 1128960 1255
    ##      sample row column
    ## CPM1      1   1      1

    plot(res)

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

    xdata <- xdata_pp

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

    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."))

Summary statistics on identified chromatographic peaks. Shown are number of identified peaks per sample and widths/duration of chromatographic peaks.
| peak_count | rt.0% | rt.25% | rt.50% | rt.75% | rt.100%
--- | --- | --- | --- | --- | --- | ---
ko15.CDF | 224 | 10.95 | 29.73 | 40.69 | 51.64 | 319.2
ko16.CDF | 280 | 9.39 | 32.86 | 44.6 | 54.77 | 150.2
ko21.CDF | 132 | 10.95 | 42.25 | 50.08 | 64.55 | 164.3
ko22.CDF | 149 | 10.95 | 28.17 | 45.38 | 57.9 | 118.9
wt15.CDF | 260 | 10.95 | 27.78 | 42.25 | 51.64 | 161.2
wt16.CDF | 180 | 10.95 | 34.43 | 45.38 | 54.77 | 142.4
wt21.CDF | 147 | 9.389 | 32.08 | 46.95 | 63.38 | 133
wt22.CDF | 204 | 10.95 | 34.43 | 43.82 | 56.34 | 137.7

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.

    plotChromPeaks(xdata, file = 3)

Figure 8: Identified chromatographic peaks in the m/z by retention time space for one sample

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.

    plotChromPeakImage(xdata)

Figure 9: 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.

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.

    chr_ex <- chromatogram(xdata, mz = mzr, rt = rtr)
    chromPeaks(chr_ex)

    ##         mz mzmin mzmax       rt    rtmin    rtmax      into      intb  maxo sn
    ## CP0045 335   335   335 2781.505 2761.160 2809.674  412134.3  383167.4 16856 23
    ## CP0309 335   335   335 2786.199 2764.290 2812.803 1496244.2 1461187.3 58736 72
    ## CP0587 335   335   335 2797.154 2775.245 2815.933  159058.8  149229.6  6295 13
    ## CP1194 335   335   335 2786.199 2764.290 2812.803  932645.2  915333.8 35856 66
    ## CP1378 335   335   335 2792.461 2768.987 2823.760  876585.5  848569.1 27200 36
    ##        sample row column
    ## CP0045      1   1      1
    ## CP0309      2   1      2
    ## CP0587      3   1      3
    ## CP1194      6   1      6
    ## CP1378      7   1      7

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.

    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)

Figure 10: 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.

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.

    plot(chr_ex, col = group_colors[chr_raw$sample_group], lwd = 2,
         peakBg = sample_colors[chromPeaks(chr_ex)[, "sample"]])

Figure 11: 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.

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.

    pander(chromPeaks(xdata, mz = mzr, rt = rtr),
           caption = paste("Identified chromatographic peaks in a selected ",
                           "m/z and retention time range."))

Identified chromatographic peaks in a selected m/z and retention time range. (continued below)
| mz | mzmin | mzmax | rt | rtmin | rtmax | into | intb
--- | --- | --- | --- | --- | --- | --- | --- | ---
CP0045 | 335 | 335 | 335 | 2782 | 2761 | 2810 | 412134 | 383167
CP0309 | 335 | 335 | 335 | 2786 | 2764 | 2813 | 1496244 | 1461187
CP0587 | 335 | 335 | 335 | 2797 | 2775 | 2816 | 159059 | 149230
CP1194 | 335 | 335 | 335 | 2786 | 2764 | 2813 | 932645 | 915334
CP1378 | 335 | 335 | 335 | 2792 | 2769 | 2824 | 876586 | 848569

| maxo | sn | sample
--- | --- | --- | ---
CP0045 | 16856 | 23 | 1
CP0309 | 58736 | 72 | 2
CP0587 | 6295 | 13 | 3
CP1194 | 35856 | 66 | 6
CP1378 | 27200 | 36 | 7

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.

    ## 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)

Figure 12: Peak intensity distribution per sample

# 5 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 [3]), 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 [4] 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.

    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.

    ## Extract adjusted retention times
    head(adjustedRtime(xdata))

    ## F1.S0001 F1.S0002 F1.S0003 F1.S0004 F1.S0005 F1.S0006
    ## 2501.378 2502.958 2504.538 2506.118 2507.699 2509.280

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

    ## F1.S0001 F1.S0002 F1.S0003 F1.S0004 F1.S0005 F1.S0006
    ## 2501.378 2502.958 2504.538 2506.118 2507.699 2509.280

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.

    ## 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])

Figure 13: Obiwarp aligned data
Base peak chromatogram after alignment (top) and difference between adjusted and raw retention times along the retention time axis (bottom).

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.

    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")

Figure 14: Example extracted ion chromatogram before (top) and after alignment (bottom)

## 5.1 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.

    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))`).

    ## 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).

    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")

Figure 15: 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.

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.

# 6 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 [5] 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.

    ## 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)

Figure 16: 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.

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.

    ## 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 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.

    res <- quantify(xdata, value = "into")

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

    colData(res)

    ## DataFrame with 8 rows and 3 columns
    ##          sample_name sample_group sample_type
    ##          <character>  <character> <character>
    ## ko15.CDF        ko15           KO          QC
    ## ko16.CDF        ko16           KO       study
    ## ko21.CDF        ko21           KO       study
    ## ko22.CDF        ko22           KO          QC
    ## wt15.CDF        wt15           WT       study
    ## wt16.CDF        wt16           WT       study
    ## wt21.CDF        wt21           WT          QC
    ## wt22.CDF        wt22           WT       study

Feature annotations with `rowData`:

    rowData(res)

    ## DataFrame with 225 rows and 11 columns
    ##           mzmed     mzmin     mzmax     rtmed     rtmin     rtmax    npeaks
    ##       <numeric> <numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
    ## FT001     200.1     200.1     200.1   2901.63   2880.73   2922.53         2
    ## FT002     205.0     205.0     205.0   2789.39   2782.30   2795.36         8
    ## FT003     206.0     206.0     206.0   2788.73   2780.73   2792.86         7
    ## FT004     207.1     207.1     207.1   2718.12   2713.21   2726.70         7
    ## FT005     219.1     219.1     219.1   2518.82   2517.40   2520.81         3
    ## ...         ...       ...       ...       ...       ...       ...       ...
    ## FT221    591.30     591.3     591.3   3005.03   2992.87   3006.05         5
    ## FT222    592.15     592.1     592.3   3022.11   2981.91   3107.59         6
    ## FT223    594.20     594.2     594.2   3418.16   3359.10   3427.90         3
    ## FT224    595.25     595.2     595.3   3010.15   2992.87   3013.77         6
    ## FT225    596.20     596.2     596.2   2997.91   2992.87   3002.95         2
    ##              KO        WT         peakidx  ms_level
    ##       <numeric> <numeric>          <list> <integer>
    ## FT001         2         0         287,679         1
    ## FT002         4         4  47,272,542,...         1
    ## FT003         3         4  32,259,663,...         1
    ## FT004         4         3  19,249,525,...         1
    ## FT005         1         2   639, 788,1376         1
    ## ...         ...       ...             ...       ...
    ## FT221         2         3 349,684,880,...         1
    ## FT222         1         3  86,861,862,...         1
    ## FT223         1         2   604, 985,1543         1
    ## FT224         2         3  67,353,876,...         1
    ## FT225         0         2        866,1447         1

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

    head(assay(res))

    ##        ko15.CDF  ko16.CDF  ko21.CDF  ko22.CDF  wt15.CDF  wt16.CDF  wt21.CDF
    ## FT001        NA  506848.9        NA  169955.6        NA        NA        NA
    ## FT002 1924712.0 1757151.0 1383416.7 1180288.2 2129885.1 1634342.0 1623589.2
    ## FT003  213659.3  289500.7        NA  178285.7  253825.6  241844.4  240606.0
    ## FT004  349011.5  451863.7  343897.8  208002.8  364609.8  360908.9        NA
    ## FT005        NA        NA        NA  107348.5  223951.8        NA        NA
    ## FT006  286221.4        NA  164009.0  149097.6  255697.7  311296.8  366441.5
    ##         wt22.CDF
    ## FT001         NA
    ## FT002 1354004.93
    ## FT003  185399.47
    ## FT004  221937.53
    ## FT005   84772.92
    ## FT006  271128.02

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.

    ## Extract the feature definitions
    featureDefinitions(xdata)

    ## DataFrame with 225 rows and 11 columns
    ##           mzmed     mzmin     mzmax     rtmed     rtmin     rtmax    npeaks
    ##       <numeric> <numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
    ## FT001     200.1     200.1     200.1   2901.63   2880.73   2922.53         2
    ## FT002     205.0     205.0     205.0   2789.39   2782.30   2795.36         8
    ## FT003     206.0     206.0     206.0   2788.73   2780.73   2792.86         7
    ## FT004     207.1     207.1     207.1   2718.12   2713.21   2726.70         7
    ## FT005     219.1     219.1     219.1   2518.82   2517.40   2520.81         3
    ## ...         ...       ...       ...       ...       ...       ...       ...
    ## FT221    591.30     591.3     591.3   3005.03   2992.87   3006.05         5
    ## FT222    592.15     592.1     592.3   3022.11   2981.91   3107.59         6
    ## FT223    594.20     594.2     594.2   3418.16   3359.10   3427.90         3
    ## FT224    595.25     595.2     595.3   3010.15   2992.87   3013.77         6
    ## FT225    596.20     596.2     596.2   2997.91   2992.87   3002.95         2
    ##              KO        WT         peakidx  ms_level
    ##       <numeric> <numeric>          <list> <integer>
    ## FT001         2         0         287,679         1
    ## FT002         4         4  47,272,542,...         1
    ## FT003         3         4  32,259,663,...         1
    ## FT004         4         3  19,249,525,...         1
    ## FT005         1         2   639, 788,1376         1
    ## ...         ...       ...             ...       ...
    ## FT221         2         3 349,684,880,...         1
    ## FT222         1         3  86,861,862,...         1
    ## FT223         1         2   604, 985,1543         1
    ## FT224         2         3  67,353,876,...         1
    ## FT225         0         2        866,1447         1

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.

    ## Extract the into column for each feature.
    head(featureValues(xdata, value = "into"))

    ##        ko15.CDF  ko16.CDF  ko21.CDF  ko22.CDF  wt15.CDF  wt16.CDF  wt21.CDF
    ## FT001        NA  506848.9        NA  169955.6        NA        NA        NA
    ## FT002 1924712.0 1757151.0 1383416.7 1180288.2 2129885.1 1634342.0 1623589.2
    ## FT003  213659.3  289500.7        NA  178285.7  253825.6  241844.4  240606.0
    ## FT004  349011.5  451863.7  343897.8  208002.8  364609.8  360908.9        NA
    ## FT005        NA        NA        NA  107348.5  223951.8        NA        NA
    ## FT006  286221.4        NA  164009.0  149097.6  255697.7  311296.8  366441.5
    ##         wt22.CDF
    ## FT001         NA
    ## FT002 1354004.93
    ## FT003  185399.47
    ## FT004  221937.53
    ## FT005   84772.92
    ## FT006  271128.02

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.

    ## Filling missing peaks using default settings. Alternatively we could
    ## pass a FillChromPeaksParam object to the method.
    xdata <- fillChromPeaks(xdata)

    head(featureValues(xdata))

    ##         ko15.CDF   ko16.CDF   ko21.CDF  ko22.CDF  wt15.CDF   wt16.CDF  wt21.CDF
    ## FT001   81148.04  506848.88   50550.78  169955.6  110866.4   52550.07  153240.9
    ## FT002 1924712.02 1757150.96 1383416.72 1180288.2 2129885.1 1634341.99 1623589.2
    ## FT003  213659.29  289500.67  150602.21  178285.7  253825.6  241844.44  240606.0
    ## FT004  349011.46  451863.66  343897.76  208002.8  364609.8  360908.93  208429.4
    ## FT005  115281.92   12561.99   57376.27  107348.5  223951.8  121975.54  185422.8
    ## FT006  286221.45  284160.61  164008.97  149097.6  255697.7  311296.82  366441.5
    ##         wt22.CDF
    ## FT001   88512.74
    ## FT002 1354004.93
    ## FT003  185399.47
    ## FT004  221937.53
    ## FT005   84772.92
    ## FT006  271128.02

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.

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

    ## ko15.CDF ko16.CDF ko21.CDF ko22.CDF wt15.CDF wt16.CDF wt21.CDF wt22.CDF
    ##       71       69      114      102       54       89      104       75

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

    ## ko15.CDF ko16.CDF ko21.CDF ko22.CDF wt15.CDF wt16.CDF wt21.CDF wt22.CDF
    ##        3        8       10       10        4        7       11        4

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.

    head(featureSummary(xdata, group = xdata$sample_group))

    ##       count  perc multi_count multi_perc       rsd KO_count KO_perc
    ## FT001     2  25.0           0          0 0.7039537        2      50
    ## FT002     8 100.0           0          0 0.1936518        4     100
    ## FT003     7  87.5           0          0 0.1717662        3      75
    ## FT004     7  87.5           0          0 0.2609145        4     100
    ## FT005     3  37.5           0          0 0.5385767        1      25
    ## FT006     7  87.5           0          0 0.3016973        3      75
    ##       KO_multi_count KO_multi_perc    KO_rsd WT_count WT_perc WT_multi_count
    ## FT001              0             0 0.7039537        0       0              0
    ## FT002              0             0 0.2178920        4     100              0
    ## FT003              0             0 0.2501505        4     100              0
    ## FT004              0             0 0.2957873        3      75              0
    ## FT005              0             0        NA        2      50              0
    ## FT006              0             0 0.3765933        4     100              0
    ##       WT_multi_perc    WT_rsd
    ## FT001             0        NA
    ## FT002             0 0.1918936
    ## FT003             0 0.1327983
    ## FT004             0 0.2575039
    ## FT005             0 0.6375539
    ## FT006             0 0.1641781

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:

    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.

    assayNames(res)

    ## [1] "raw"        "raw_filled"

    head(assay(res, "raw"))

    ##        ko15.CDF  ko16.CDF  ko21.CDF  ko22.CDF  wt15.CDF  wt16.CDF  wt21.CDF
    ## FT001        NA  506848.9        NA  169955.6        NA        NA        NA
    ## FT002 1924712.0 1757151.0 1383416.7 1180288.2 2129885.1 1634342.0 1623589.2
    ## FT003  213659.3  289500.7        NA  178285.7  253825.6  241844.4  240606.0
    ## FT004  349011.5  451863.7  343897.8  208002.8  364609.8  360908.9        NA
    ## FT005        NA        NA        NA  107348.5  223951.8        NA        NA
    ## FT006  286221.4        NA  164009.0  149097.6  255697.7  311296.8  366441.5
    ##         wt22.CDF
    ## FT001         NA
    ## FT002 1354004.93
    ## FT003  185399.47
    ## FT004  221937.53
    ## FT005   84772.92
    ## FT006  271128.02

    head(assay(res, "raw_filled"))

    ##         ko15.CDF   ko16.CDF   ko21.CDF  ko22.CDF  wt15.CDF   wt16.CDF  wt21.CDF
    ## FT001   81148.04  506848.88   50550.78  169955.6  110866.4   52550.07  153240.9
    ## FT002 1924712.02 1757150.96 1383416.72 1180288.2 2129885.1 1634341.99 1623589.2
    ## FT003  213659.29  289500.67  150602.21  178285.7  253825.6  241844.44  240606.0
    ## FT004  349011.46  451863.66  343897.76  208002.8  364609.8  360908.93  208429.4
    ## FT005  115281.92   12561.99   57376.27  107348.5  223951.8  121975.54  185422.8
    ## FT006  286221.45  284160.61  164008.97  149097.6  255697.7  311296.82  366441.5
    ##         wt22.CDF
    ## FT001   88512.74
    ## FT002 1354004.93
    ## FT003  185399.47
    ## FT004  221937.53
    ## FT005   84772.92
    ## FT006  271128.02

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.

    feature_chroms <- featureChromatograms(xdata, features = 1:4)

    feature_chroms

    ## XChromatograms with 4 rows and 8 columns
    ##                    1               2               3               4
    ##      <XChromatogram> <XChromatogram> <XChromatogram> <XChromatogram>
    ## [1,]        peaks: 0        peaks: 1        peaks: 0        peaks: 1
    ## [2,]        peaks: 1        peaks: 1        peaks: 1        peaks: 1
    ## [3,]        peaks: 1        peaks: 1        peaks: 0        peaks: 1
    ## [4,]        peaks: 1        peaks: 1        peaks: 1        peaks: 1
    ##                    5               6               7               8
    ##      <XChromatogram> <XChromatogram> <XChromatogram> <XChromatogram>
    ## [1,]        peaks: 0        peaks: 0        peaks: 0        peaks: 0
    ## [2,]        peaks: 1        peaks: 1        peaks: 1        peaks: 1
    ## [3,]        peaks: 1        peaks: 1        peaks: 1        peaks: 1
    ## [4,]        peaks: 1        peaks: 1        peaks: 0        peaks: 1
    ## phenoData with 3 variables
    ## featureData with 5 variables
    ## - - - xcms preprocessing - - -
    ## Chromatographic peak detection:
    ##  method: centWave
    ## Correspondence:
    ##  method: chromatographic peak density
    ##  4 feature(s) identified.

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

    plot(feature_chroms, col = sample_colors,
         peakBg = sample_colors[chromPeaks(feature_chroms)[, "sample"]])

Figure 17: Extracted ion chromatograms for features 1 to 4

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

    eic_2 <- feature_chroms[2, ]
    chromPeaks(eic_2)

    ##         mz mzmin mzmax       rt    rtmin    rtmax    into    intb  maxo sn
    ## CP0055 205   205   205 2790.427 2770.441 2813.596 1924712 1850331 84280 64
    ## CP0310 205   205   205 2794.406 2772.731 2819.327 1757151 1711473 68384 69
    ## CP0595 205   205   205 2795.358 2773.524 2820.417 1383417 1334570 47384 54
    ## CP0736 205   205   205 2788.495 2768.076 2812.080 1180288 1126958 48336 32
    ## CP0921 205   205   205 2782.296 2761.887 2805.849 2129885 2054677 93312 44
    ## CP1197 205   205   205 2787.083 2766.688 2812.188 1634342 1566379 67984 53
    ## CP1379 205   205   205 2790.294 2763.639 2821.635 1623589 1531573 49208 28
    ## CP1542 205   205   205 2787.159 2766.777 2812.235 1354005 1299188 55712 35
    ##        sample row column
    ## CP0055      1   1      1
    ## CP0310      2   1      2
    ## CP0595      3   1      3
    ## CP0736      4   1      4
    ## CP0921      5   1      5
    ## CP1197      6   1      6
    ## CP1379      7   1      7
    ## CP1542      8   1      8

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.

    ## 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)

Figure 18: PCA for the faahKO data set, un-normalized intensities

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).

# 7 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.

# 8 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.

## 8.1 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.

    processHistory(xdata)

    ## [[1]]
    ## Object of class "XProcessHistory"
    ##  type: Peak detection
    ##  date: Fri Sep 18 20:05:37 2020
    ##  info:
    ##  fileIndex: 1,2,3,4,5,6,7,8
    ##  Parameter class: CentWaveParam
    ##  MS level(s) 1
    ##
    ## [[2]]
    ## Object of class "XProcessHistory"
    ##  type: Peak refinement
    ##  date: Fri Sep 18 20:05:49 2020
    ##  info:
    ##  fileIndex: 1,2,3,4,5,6,7,8
    ##  Parameter class: MergeNeighboringPeaksParam
    ##  MS level(s) 1
    ##
    ## [[3]]
    ## Object of class "XProcessHistory"
    ##  type: Peak grouping
    ##  date: Fri Sep 18 20:06:15 2020
    ##  info:
    ##  fileIndex: 1,2,3,4,5,6,7,8
    ##  Parameter class: PeakDensityParam
    ##  MS level(s) 1
    ##
    ## [[4]]
    ## Object of class "XProcessHistory"
    ##  type: Retention time correction
    ##  date: Fri Sep 18 20:06:16 2020
    ##  info:
    ##  fileIndex: 1,2,3,4,5,6,7,8
    ##  Parameter class: PeakGroupsParam
    ##  MS level(s) 1
    ##
    ## [[5]]
    ## Object of class "XProcessHistory"
    ##  type: Peak grouping
    ##  date: Fri Sep 18 20:06:31 2020
    ##  info:
    ##  fileIndex: 1,2,3,4,5,6,7,8
    ##  Parameter class: PeakDensityParam
    ##  MS level(s) 1
    ##
    ## [[6]]
    ## Object of class "XProcessHistory"
    ##  type: Missing peak filling
    ##  date: Fri Sep 18 20:06:33 2020
    ##  info:
    ##  fileIndex: 1,2,3,4,5,6,7,8
    ##  Parameter class: FillChromPeaksParam
    ##  MS level(s) 1

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.

    ph <- processHistory(xdata, type = "Retention time correction")

    ph

    ## [[1]]
    ## Object of class "XProcessHistory"
    ##  type: Retention time correction
    ##  date: Fri Sep 18 20:06:16 2020
    ##  info:
    ##  fileIndex: 1,2,3,4,5,6,7,8
    ##  Parameter class: PeakGroupsParam
    ##  MS level(s) 1

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

    ## Access the parameter
    processParam(ph[[1]])

    ## Object of class:  PeakGroupsParam
    ## Parameters:
    ##  minFraction: 0.85
    ##  extraPeaks: 1
    ##  smooth: loess
    ##  span: 0.4
    ##  family: gaussian
    ##  subset: 1 4 7
    ##  number of peak groups: 62

## 8.2 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.

    subs <- filterFile(xdata, file = c(2, 4))

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

    ## [1] TRUE

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).

    ## Do we still have features?
    hasFeatures(subs)

    ## [1] FALSE

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

    ## [1] FALSE

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

    subs <- filterFile(xdata, keepAdjustedRtime = TRUE)

    hasAdjustedRtime(subs)

    ## [1] TRUE

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

    subs <- filterRt(xdata, rt = c(3000, 3500))

    range(rtime(subs))

    ## [1] 3000.186 3499.956

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).

    hasAdjustedRtime(subs)

    ## [1] TRUE

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

    hasChromPeaks(subs)

    ## [1] TRUE

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

    ## [1] 3000.712 3499.399

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.

    ## 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]])

    ## [1] TRUE

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 `[`.

    ## Subsetting with filterFile preserves chromatographic peaks
    head(chromPeaks(one_file))

    ##         mz mzmin mzmax       rt    rtmin    rtmax      into      intb   maxo sn
    ## CP0555 231   231   231 2507.638 2501.378 2513.897  184167.0  165087.9  15818 11
    ## CP0556 334   334   334 2512.332 2502.943 2518.592  107281.7  104197.1   7976 14
    ## CP0557 337   337   337 2513.897 2501.378 2551.456 4381724.8 4040088.7 123448 21
    ## CP0558 333   333   333 2513.897 2501.378 2543.632  934386.2  877441.9  27944 15
    ## CP0559 316   316   316 2518.592 2501.378 2554.586  863947.7  832855.2  24496 29
    ## CP0560 332   332   332 2517.027 2501.378 2551.456 4835730.4 4644120.0 131520 41
    ##        sample
    ## CP0555      1
    ## CP0556      1
    ## CP0557      1
    ## CP0558      1
    ## CP0559      1
    ## CP0560      1

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

    ## Warning in .local(object, ...): No chromatographic peaks available.

    ## NULL

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

    subs <- xdata[20:30, keepAdjustedRtime = TRUE]

    ## Warning in xdata[20:30, keepAdjustedRtime = TRUE]: Removed preprocessing results

    hasAdjustedRtime(subs)

    ## [1] TRUE

    ## Access adjusted retention times:
    rtime(subs)

    ## F1.S0020 F1.S0021 F1.S0022 F1.S0023 F1.S0024 F1.S0025 F1.S0026 F1.S0027
    ## 2539.356 2540.921 2542.486 2544.051 2545.616 2547.181 2548.746 2550.311
    ## F1.S0028 F1.S0029 F1.S0030
    ## 2551.876 2553.441 2555.006

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

    ## F1.S0020 F1.S0021 F1.S0022 F1.S0023 F1.S0024 F1.S0025 F1.S0026 F1.S0027
    ## 2531.112 2532.677 2534.242 2535.807 2537.372 2538.937 2540.502 2542.067
    ## F1.S0028 F1.S0029 F1.S0030
    ## 2543.632 2545.197 2546.762

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.

    ## Extract a single spectrum
    xdata[[14]]

    ## Object of class "Spectrum1"
    ##  Retention time: 42:10
    ##  MSn level: 1
    ##  Total ion count: 445
    ##  Polarity: NA

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.

    x_list <- split(xdata, f = fromFile(xdata), keepAdjustedRtime = TRUE)

    lengths(x_list)

    ##   1   2   3   4   5   6   7   8
    ## 639 639 639 639 639 639 639 639

    lapply(x_list, hasAdjustedRtime)

    ## $`1`
    ## [1] TRUE
    ##
    ## $`2`
    ## [1] TRUE
    ##
    ## $`3`
    ## [1] TRUE
    ##
    ## $`4`
    ## [1] TRUE
    ##
    ## $`5`
    ## [1] TRUE
    ##
    ## $`6`
    ## [1] TRUE
    ##
    ## $`7`
    ## [1] TRUE
    ##
    ## $`8`
    ## [1] TRUE

## 8.3 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.

    register(bpstart(MulticoreParam(2)))

Windows supports only socket-based parallel processing:

    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

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2. Tautenhahn R, Böttcher C, Neumann S: Highly sensitive feature detection for high resolution LC/MS. BMC Bioinformatics 2008, 9:504.

3. Smith R, Ventura D, Prince JT: LC-MS alignment in theory and practice: a comprehensive algorithmic review. Briefings in bioinformatics 2013, 16:bbt080–117.

4. Prince JT, Marcotte EM: Chromatographic alignment of ESI-LC-MS proteomics data sets by ordered bijective interpolated warping. Analytical chemistry 2006, 78:6140–6152.

5. Smith CA, Want EJ, O’Maille G, Abagyan R, Siuzdak G: XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Analytical chemistry 2006, 78:779–787.