# LC-MS/MS data analysis with xcms

#### 21 August 2024

#### Package

xcms 4.2.3

Package: xcms
Authors: Johannes Rainer, Michael Witting
Modified: 2024-08-21 14:00:31.389692
Compiled: Wed Aug 21 22:26:04 2024

# 1 Introduction

Metabolite identification is an important step in non-targeted metabolomics and requires different steps. One involves the use of tandem mass spectrometry to generate fragmentation spectra of detected metabolites (LC-MS/MS), which are then compared to fragmentation spectra of known metabolites. Different approaches exist for the generation of these fragmentation spectra, whereas the most used is data dependent acquisition (DDA) also known as the top-n method. In this method the top N most intense ions (m/z values) from a MS1 scan are selected for fragmentation in the next N scans before the cycle starts again. This method allows to generate clean MS2 fragmentation spectra on the fly during acquisition without the need for further experiments, but suffers from poor coverage of the detected metabolites (since only a limited number of ions are fragmented) and less accurate quantification of the compounds (since fewer MS1 scans are generated).

Data independent approaches (DIA) like Bruker bbCID, Agilent AllIons or Waters MSe don’t use such a preselection, but rather fragment all detected molecules at once. They are using alternating schemes with scan of low and high collision energy to collect MS1 and MS2 data. Using this approach, there is no problem in coverage, but the relation between the precursor and fragment masses is lost leading to chimeric spectra. Sequential Window Acquisition of all Theoretical Mass Spectra (or SWATH [1]) combines both approaches through a middle-way approach. There is no precursor selection and acquisition is independent of acquired data, but rather than isolating all precusors at once, defined windows (i.e. ranges of m/z values) are used and scanned. This reduces the overlap of fragment spectra while still keeping a high coverage.

This document showcases the analysis of two small LC-MS/MS data sets using xcms. The data files used are reversed-phase LC-MS/MS runs from the Agilent Pesticide mix obtained from a Sciex 6600 Triple ToF operated in SWATH acquisition mode. For comparison a DDA file from the same sample is included.

# 2 Analysis of DDA data

Below we load the example DDA data set and create a total ion chromatogram of its MS1 data.

    library(xcms)
    library(MsExperiment)

    dda_file <- system.file("TripleTOF-SWATH", "PestMix1_DDA.mzML",
                            package = "msdata")
    dda_data <- readMsExperiment(dda_file)
    chr <- chromatogram(dda_data, aggregationFun = "sum", msLevel = 1L)

According to the TIC most of the signal is measured between ~ 200 and 600 seconds (see plot below). We thus filter the DDA data to this retention time range.

    plot(chr)
    abline(v = c(230, 610))

    ## filter the data
    dda_data <- filterRt(dda_data, rt = c(230, 610))

    ## Filter spectra

The variable `dda_data` contains now all MS1 and MS2 spectra from the specified mzML file. The number of spectra for each MS level is listed below. Note that we subset the experiment to the first data file (using `[1]`) and then access directly the spectra within this sample with the `spectra()` function (which returns a `Spectra` object from the Spectra package). Note that we use the pipe operator `|>` for better readability.

    dda_data[1] |>
    spectra() |>
    msLevel() |>
    table()

    ##
    ##    1    2
    ## 1389 2214

For the MS2 spectra we can get the m/z of the precursor ion with the `precursorMz()` function. Below we first subset the data set again to a single sample and filter to spectra from MS level 2 extracting then their precursor m/z values.

    dda_data[1] |>
    spectra() |>
    filterMsLevel(2) |>
    precursorMz() |>
    head()

    ## [1] 182.18777 182.18893  55.00579 182.19032 237.12296 237.11987

With the `precursorIntensity()` function it is also possible to extract the intensity of the precursor ion.

    dda_data[1] |>
    spectra() |>
    filterMsLevel(2) |>
    precursorIntensity() |>
    head()

    ## [1] 0 0 0 0 0 0

Some manufacturers (like Sciex) don’t define/export the precursor intensity and thus either `NA` or `0` is reported. We can however use the `estimatePrecursorIntensity()` function from the Spectra package to determine the precursor intensity for a MS 2 spectrum based on the intensity of the respective ion in the previous MS1 scan (note that with `method = "interpolation"` the precursor intensity would be defined based on interpolation between the intensity in the previous and subsequent MS1 scan). Below we estimate the precursor intensities, on the full data (for MS1 spectra a `NA` value is reported).

    prec_int <- estimatePrecursorIntensity(spectra(dda_data))

We next set the precursor intensity in the spectrum metadata of `dda_data`. So that it can be extracted later with the `precursorIntensity()` function.

    spectra(dda_data)$precursorIntensity <- prec_int

    dda_data[1] |>
    spectra() |>
    filterMsLevel(2) |>
    precursorIntensity() |>
    head()

    ## [1]        NA  9.198155  2.773988 27.590797  3.443145  7.621923

Next we perform the chromatographic peak detection on the MS level 1 data with the `findChromPeaks()` method. Below we define the settings for a centWave-based peak detection and perform the analysis.

    cwp <- CentWaveParam(snthresh = 5, noise = 100, ppm = 10,
                         peakwidth = c(3, 30))
    dda_data <- findChromPeaks(dda_data, param = cwp, msLevel = 1L)

In total 114 peaks were identified in the present data set.

The advantage of LC-MS/MS data is that (MS1) ions are fragmented and the corresponding MS2 spectra measured. Thus, for some of the ions (identified as MS1 chromatographic peaks) MS2 spectra are available. These can facilitate the annotation of the respective MS1 chromatographic peaks (or MS1 features after a correspondence analysis). Spectra for identified chromatographic peaks can be extracted with the `chromPeakSpectra()` method. MS2 spectra with their precursor m/z and retention time within the rt and m/z range of the chromatographic peak are returned.

    library(Spectra)
    dda_spectra <- chromPeakSpectra(dda_data, msLevel = 2L)
    dda_spectra

    ## MSn data (Spectra) with 142 spectra in a MsBackendMzR backend:
    ##       msLevel     rtime scanIndex
    ##     <integer> <numeric> <integer>
    ## 1           2   237.869      1812
    ## 2           2   241.299      1846
    ## 3           2   325.763      2439
    ## 4           2   326.583      2446
    ## 5           2   327.113      2450
    ## ...       ...       ...       ...
    ## 138         2   574.725      5110
    ## 139         2   575.255      5115
    ## 140         2   596.584      5272
    ## 141         2   592.424      5236
    ## 142         2   596.054      5266
    ##  ... 34 more variables/columns.
    ##
    ## file(s):
    ## PestMix1_DDA.mzML
    ## Processing:
    ##  Filter: select retention time [230..610] on MS level(s) 1 2 [Wed Aug 21 22:26:06 2024]
    ##  Filter: select MS level(s) 2 [Wed Aug 21 22:26:17 2024]
    ##  Merge 1 Spectra into one [Wed Aug 21 22:26:17 2024]

By default `chromPeakSpectra()` returns all spectra associated with a MS1 chromatographic peak, but parameter `method` allows to choose and return only one spectrum per peak (have a look at the `?chromPeakSpectra` help page for more details). Also, it would be possible to extract MS1 spectra for each peak by specifying `msLevel = 1L` in the call above (e.g. to evaluate the full MS1 signal at the peak’s apex position).

The returned `Spectra` contains also the reference to the respective chromatographic peak as additional spectra variable `"peak_id"` that contains the identifier for the chromatographic peak (i.e. its row name in the `chromPeaks` matrix).

    dda_spectra$peak_id

    ##   [1] "CP004" "CP004" "CP005" "CP005" "CP006" "CP006" "CP008" "CP008" "CP011"
    ##  [10] "CP011" "CP012" "CP012" "CP013" "CP013" "CP013" "CP013" "CP014" "CP014"
    ##  [19] "CP014" "CP014" "CP018" "CP022" "CP022" "CP022" "CP025" "CP025" "CP025"
    ##  [28] "CP025" "CP026" "CP026" "CP026" "CP026" "CP033" "CP033" "CP034" "CP034"
    ##  [37] "CP034" "CP034" "CP034" "CP035" "CP035" "CP035" "CP041" "CP041" "CP041"
    ##  [46] "CP042" "CP042" "CP042" "CP043" "CP047" "CP047" "CP049" "CP049" "CP049"
    ##  [55] "CP049" "CP050" "CP050" "CP050" "CP051" "CP051" "CP051" "CP054" "CP055"
    ##  [64] "CP055" "CP055" "CP056" "CP056" "CP056" "CP056" "CP056" "CP060" "CP060"
    ##  [73] "CP060" "CP060" "CP064" "CP064" "CP065" "CP065" "CP066" "CP066" "CP069"
    ##  [82] "CP069" "CP069" "CP070" "CP070" "CP070" "CP072" "CP072" "CP072" "CP073"
    ##  [91] "CP074" "CP074" "CP074" "CP074" "CP075" "CP075" "CP075" "CP077" "CP077"
    ## [100] "CP077" "CP079" "CP079" "CP079" "CP079" "CP080" "CP080" "CP080" "CP081"
    ## [109] "CP086" "CP086" "CP086" "CP086" "CP086" "CP088" "CP088" "CP088" "CP089"
    ## [118] "CP089" "CP091" "CP091" "CP093" "CP093" "CP094" "CP094" "CP094" "CP095"
    ## [127] "CP095" "CP095" "CP096" "CP096" "CP096" "CP098" "CP098" "CP098" "CP098"
    ## [136] "CP098" "CP099" "CP099" "CP099" "CP100" "CP101" "CP101"

Note also that with `return.type = "List"` a list parallel to the `chromPeaks` matrix would be returned, i.e. each element in that list would contain the spectra for the chromatographic peak with the same index. Such data representation might eventually simplify further processing.

We next use the MS2 information to aid in the annotation of a chromatographic peak. As an example we use a chromatographic peak of an ion with an m/z of 304.1131 which we extract in the code block below.

    ex_mz <- 304.1131
    chromPeaks(dda_data, mz = ex_mz, ppm = 20)

    ##             mz    mzmin    mzmax      rt   rtmin   rtmax    into     intb
    ## CP056 304.1133 304.1126 304.1143 425.024 417.985 441.773 13040.8 13007.79
    ##           maxo  sn sample
    ## CP056 3978.987 232      1

A search of potential ions with a similar m/z in a reference database (e.g. Metlin) returned a large list of potential hits, most with a very small ppm. For two of the hits, Flumazenil (Metlin ID 2724) and Fenamiphos (Metlin ID 72445) experimental MS2 spectra are available. Thus, we could match the MS2 spectrum for the identified chromatographic peak against these to annotate our ion. Below we extract all MS2 spectra that were associated with the candidate chromatographic peak using the ID of the peak in the present data set.

    ex_id <- rownames(chromPeaks(dda_data, mz = ex_mz, ppm = 20))
    ex_spectra <- dda_spectra[dda_spectra$peak_id == ex_id]
    ex_spectra

    ## MSn data (Spectra) with 5 spectra in a MsBackendMzR backend:
    ##     msLevel     rtime scanIndex
    ##   <integer> <numeric> <integer>
    ## 1         2   418.926      3505
    ## 2         2   419.306      3510
    ## 3         2   423.036      3582
    ## 4         2   423.966      3603
    ## 5         2   424.296      3609
    ##  ... 34 more variables/columns.
    ##
    ## file(s):
    ## PestMix1_DDA.mzML
    ## Processing:
    ##  Filter: select retention time [230..610] on MS level(s) 1 2 [Wed Aug 21 22:26:06 2024]
    ##  Filter: select MS level(s) 2 [Wed Aug 21 22:26:17 2024]
    ##  Merge 1 Spectra into one [Wed Aug 21 22:26:17 2024]

There are 5 MS2 spectra representing fragmentation of the ion(s) measured in our candidate chromatographic peak. We next reduce this to a single MS2 spectrum using the `combineSpectra()` method employing the `combinePeaks()` function to determine which peaks to keep in the resulting spectrum (have a look at the `?combinePeaks` help page for details). Parameter `f` allows to specify which spectra in the input object should be combined into one. Note that this combination of multiple fragment spectra into a single spectrum might not be generally the best approach or suggested for all types of data.

    ex_spectrum <- combineSpectra(ex_spectra, FUN = combinePeaks, ppm = 20,
                                  peaks = "intersect", minProp = 0.8,
                                  intensityFun = median, mzFun = median,
                                  f = ex_spectra$peak_id)

    ## Warning in FUN(X[[i]], ...): 'combinePeaks' for lists of peak matrices is
    ## deprecated; please use 'combinePeaksData' instead.

    ex_spectrum

    ## MSn data (Spectra) with 1 spectra in a MsBackendMemory backend:
    ##     msLevel     rtime scanIndex
    ##   <integer> <numeric> <integer>
    ## 1         2   418.926      3505
    ##  ... 34 more variables/columns.
    ## Processing:
    ##  Filter: select retention time [230..610] on MS level(s) 1 2 [Wed Aug 21 22:26:06 2024]
    ##  Filter: select MS level(s) 2 [Wed Aug 21 22:26:17 2024]
    ##  Merge 1 Spectra into one [Wed Aug 21 22:26:17 2024]
    ##  ...1 more processings. Use 'processingLog' to list all.

Mass peaks from all input spectra with a difference in m/z smaller 20 ppm (parameter `ppm`) were combined into one peak and the median m/z and intensity is reported for these. Due to parameter `minProp = 0.8`, the resulting MS2 spectrum contains only peaks that were present in 80% of the input spectra.

A plot of this consensus spectrum is shown below.

    plotSpectra(ex_spectrum)

Figure 1: Consensus MS2 spectrum created from all measured MS2 spectra for ions of chromatographic peak CP53

We could now match the consensus spectrum against a database of MS2 spectra. In our example we simply load MS2 spectra for the two compounds with matching m/z exported from Metlin. For each of the compounds MS2 spectra created with collision energies of 0V, 10V, 20V and 40V are available. Below we import the respective data and plot our candidate spectrum against the MS2 spectra of Flumanezil and Fenamiphos (from a collision energy of 20V). To import files in MGF format we have to load the MsBackendMgf Bioconductor package which adds MGF file support to the Spectra package.

Prior plotting we scale our experimental spectra to replace all peak intensities with values relative to the maximum peak intensity (which is set to a value of 100).

    scale_fun <- function(z, ...) {
        z[, "intensity"] <- z[, "intensity"] /
            max(z[, "intensity"], na.rm = TRUE) * 100
        z
    }
    ex_spectrum <- addProcessing(ex_spectrum, FUN = scale_fun)

    library(MsBackendMgf)
    flumanezil <- Spectra(
        system.file("mgf", "metlin-2724.mgf", package = "xcms"),
        source = MsBackendMgf())

    ## Start data import from 1 files ... done

    fenamiphos <- Spectra(
        system.file("mgf", "metlin-72445.mgf", package = "xcms"),
        source = MsBackendMgf())

    ## Start data import from 1 files ... done

    par(mfrow = c(1, 2))
    plotSpectraMirror(ex_spectrum, flumanezil[3], main = "against Flumanezil",
                      ppm = 40)
    plotSpectraMirror(ex_spectrum, fenamiphos[3], main = "against Fenamiphos",
                      ppm = 40)

Figure 2: Mirror plots for the candidate MS2 spectrum against Flumanezil (left) and Fenamiphos (right)
The upper panel represents the candidate MS2 spectrum, the lower the target MS2 spectrum. Matching peaks are indicated with a dot.

Our candidate spectrum matches Fenamiphos, thus, our example chromatographic peak represents signal measured for this compound. In addition to plotting the spectra, we can also calculate similarities between them with the `compareSpectra()` method (which uses by default the normalized dot-product to calculate the similarity).

    compareSpectra(ex_spectrum, flumanezil, ppm = 40)

    ## [1] 4.520957e-02 3.283806e-02 2.049379e-03 3.374354e-05

    compareSpectra(ex_spectrum, fenamiphos, ppm = 40)

    ## [1] 0.1326234432 0.4879399946 0.7198406271 0.3997922658 0.0004876129
    ## [6] 0.0028408885 0.0071030051 0.0053809736

Clearly, the candidate spectrum does not match Flumanezil, while it has a high similarity to Fenamiphos. While we performed here the MS2-based annotation on a single chromatographic peak, this could be easily extended to the full list of MS2 spectra (returned by `chromPeakSpectra()`) for all chromatographic peaks in an experiment. See also here or here for alternative tutorials on matching experimental fragment spectra against a reference.

In the present example we used only a single data file and we did thus not need to perform a sample alignment and correspondence analysis. These tasks could however be performed similarly to plain LC-MS data, retention times of recorded MS2 spectra would however also be adjusted during alignment based on the MS1 data. After correspondence analysis (peak grouping) MS2 spectra for features can be extracted with the `featureSpectra()` function which returns all MS2 spectra associated with any chromatographic peak of a feature.

Note also that this workflow can be included into the Feature-Based Molecular Networking FBMN to match MS2 spectra against GNPS. See here for more details and examples.

# 3 DIA (SWATH) data analysis

In this section we analyze a small SWATH data set consisting of a single mzML file with data from the same sample analyzed in the previous section but recorded in SWATH mode. We again read the data with the `readMsExperiment()` function. The resulting object will contain all recorded MS1 and MS2 spectra in the specified file. Similar to the previous data file, we filter the file to signal between 230 and 610 seconds.

    swath_file <- system.file("TripleTOF-SWATH",
                              "PestMix1_SWATH.mzML",
                              package = "msdata")

    swath_data <- readMsExperiment(swath_file)
    swath_data <- filterRt(swath_data, rt = c(230, 610))

Below we determine the number of MS level 1 and 2 spectra in the present data set.

    spectra(swath_data) |>
    msLevel() |>
    table()

    ##
    ##    1    2
    ##  422 3378

As described in the introduction, in SWATH mode all ions within pre-defined isolation windows are fragmented and MS2 spectra measured. The definition of these isolation windows (SWATH pockets) is imported from the mzML files and available as additional spectra variables. Below we inspect the respective information for the first few spectra. The upper and lower isolation window m/z is available with spectra variables `"isolationWindowLowerMz"` and `"isolationWindowUpperMz"` respectively and the target m/z of the isolation window with `"isolationWindowTargetMz"`. We can use the `spectraData()` function to extract this information from the spectra within our `swath_data` object.

    spectra(swath_data) |>
    spectraData(c("isolationWindowTargetMz", "isolationWindowLowerMz",
                  "isolationWindowUpperMz", "msLevel", "rtime")) |>
    head()

    ## DataFrame with 6 rows and 5 columns
    ##   isolationWindowTargetMz isolationWindowLowerMz isolationWindowUpperMz
    ##                 <numeric>              <numeric>              <numeric>
    ## 1                  299.10                  283.5                  314.7
    ## 2                  329.80                  313.7                  345.9
    ## 3                  367.35                  344.9                  389.8
    ## 4                  601.85                  388.8                  814.9
    ## 5                      NA                     NA                     NA
    ## 6                  163.75                  139.5                  188.0
    ##     msLevel     rtime
    ##   <integer> <numeric>
    ## 1         2   230.073
    ## 2         2   230.170
    ## 3         2   230.267
    ## 4         2   230.364
    ## 5         1   230.459
    ## 6         2   230.585

We could also access these variables directly with the dedicated `isolationWindowLowerMz()` and `isolationWindowUpperMz()` functions.

    head(isolationWindowLowerMz(spectra(swath_data)))

    ## [1] 283.5 313.7 344.9 388.8    NA 139.5

    head(isolationWindowUpperMz(spectra(swath_data)))

    ## [1] 314.7 345.9 389.8 814.9    NA 188.0

In the present data set we use the value of the isolation window target m/z to define the individual SWATH pockets. Below we list the number of spectra that are recorded in each pocket/isolation window.

    table(isolationWindowTargetMz(spectra(swath_data)))

    ##
    ## 163.75 208.95 244.05 270.85  299.1  329.8 367.35 601.85
    ##    422    422    422    422    423    423    422    422

We have thus between 422 and 423 MS2 spectra measured in each isolation window.

To inspect the data we can also extract chromatograms from both the measured MS1 as well as MS2 data. For MS2 data we have to set parameter `msLevel = 2L` and, for SWATH data, in addition also specify the isolation window from which we want to extract the data. Below we extract the TIC of the MS1 data and of one of the isolation windows (isolation window target m/z of 270.85) and plot these.

    tic_ms1 <- chromatogram(swath_data, msLevel = 1L, aggregationFun = "sum")
    tic_ms2 <- chromatogram(swath_data, msLevel = 2L, aggregationFun = "sum",
                            isolationWindowTargetMz = 270.85)
    par(mfrow = c(2, 1))
    plot(tic_ms1, main = "MS1")
    plot(tic_ms2, main = "MS2, isolation window m/z 270.85")

Figure 3: TIC for MS1 (upper panel) and MS2 data from the isolation window with target m/z 270.85 (lower panel)

Without specifying the `isolationWindowTargetMz` parameter, all MS2 spectra would be considered in the chromatogram extraction which would result in a chimeric chromatogram such as the one shown below:

    tic_all_ms2 <- chromatogram(swath_data, msLevel = 2L, aggregationFun = "sum")
    plot(tic_all_ms2, main = "MS2, all isolation windows")

Figure 4: TIC considering all MS2 spectra (from all isolation windows)

For MS2 data without specific, different, m/z isolation windows (such as e.g. Waters MSe data) parameter `isolationWindowTargetMz` can be omitted in the `chromatograms()` call in which case, as already stated above, all MS2 spectra are considered in the chromatogram calculation. Alternatively, if the isolation window is not provided or specified in the original data files, it would be possible to manually define a value for this spectra variable, such as in the example below (from which the code is however not evaluated) were we assign the value of the precursor m/z to the spectra’s isolation window target m/z.

    spectra(swath_data)$isolationWindowTargetMz <- precursorMz(spectra(swath_data))

## 3.1 Chromatographic peak detection in MS1 and MS2 data

Similar to a conventional LC-MS analysis, we perform first a chromatographic peak detection (on the MS level 1 data) with the `findChromPeaks()` method. Below we define the settings for a centWave-based peak detection and perform the analysis.

    cwp <- CentWaveParam(snthresh = 5, noise = 100, ppm = 10,
                         peakwidth = c(3, 30))
    swath_data <- findChromPeaks(swath_data, param = cwp)
    swath_data

    ## Object of class XcmsExperiment
    ##  Spectra: MS1 (422) MS2 (3378)
    ##  Experiment data: 1 sample(s)
    ##  Sample data links:
    ##   - spectra: 1 sample(s) to 3800 element(s).
    ##  xcms results:
    ##   - chromatographic peaks: 62 in MS level(s): 1

Next we perform a chromatographic peak detection in MS level 2 data separately for each individual isolation window. We use the `findChromPeaksIsolationWindow()` function employing the same peak detection algorithm reducing however the required signal-to-noise ratio. The `isolationWindow` parameter allows to specify which MS2 spectra belong to which isolation window and hence defines in which set of MS2 spectra chromatographic peak detection should be performed. As a default the `"isolationWindowTargetMz"` variable of the object’s spectra is used.

    cwp <- CentWaveParam(snthresh = 3, noise = 10, ppm = 10,
                         peakwidth = c(3, 30))
    swath_data <- findChromPeaksIsolationWindow(swath_data, param = cwp)
    swath_data

    ## Object of class XcmsExperiment
    ##  Spectra: MS1 (422) MS2 (3378)
    ##  Experiment data: 1 sample(s)
    ##  Sample data links:
    ##   - spectra: 1 sample(s) to 3800 element(s).
    ##  xcms results:
    ##   - chromatographic peaks: 370 in MS level(s): 1, 2

The `findChromPeaksIsolationWindow()` function added all peaks identified in the individual isolation windows to the `chromPeaks` matrix containing already the MS1 chromatographic peaks. These newly added peaks can be identified through the `"isolationWindow"` column in the object’s `chromPeakData`.

    chromPeakData(swath_data)

    ## DataFrame with 370 rows and 6 columns
    ##        ms_level is_filled isolationWindow isolationWindowTargetMZ
    ##       <integer> <logical>       <numeric>               <numeric>
    ## CP01          1     FALSE              NA                      NA
    ## CP02          1     FALSE              NA                      NA
    ## CP03          1     FALSE              NA                      NA
    ## CP04          1     FALSE              NA                      NA
    ## CP05          1     FALSE              NA                      NA
    ## ...         ...       ...             ...                     ...
    ## CP366         2     FALSE          601.85                  601.85
    ## CP367         2     FALSE          601.85                  601.85
    ## CP368         2     FALSE          601.85                  601.85
    ## CP369         2     FALSE          601.85                  601.85
    ## CP370         2     FALSE          601.85                  601.85
    ##       isolationWindowLowerMz isolationWindowUpperMz
    ##                    <numeric>              <numeric>
    ## CP01                      NA                     NA
    ## CP02                      NA                     NA
    ## CP03                      NA                     NA
    ## CP04                      NA                     NA
    ## CP05                      NA                     NA
    ## ...                      ...                    ...
    ## CP366                  388.8                  814.9
    ## CP367                  388.8                  814.9
    ## CP368                  388.8                  814.9
    ## CP369                  388.8                  814.9
    ## CP370                  388.8                  814.9

Below we count the number of chromatographic peaks identified within each isolation window (the number of chromatographic peaks identified in MS1 is 62).

    table(chromPeakData(swath_data)$isolationWindow)

    ##
    ## 163.75 208.95 244.05 270.85  299.1  329.8 367.35 601.85
    ##      2     38     32     14    105     23     62     32

We thus successfully identified chromatographic peaks in the different MS levels and isolation windows. As a next step we have to identify which of the measured signals represents data from the same original compound to reconstruct fragment spectra for each MS1 signal (chromatographic peak).

## 3.2 Reconstruction of MS2 spectra

Identifying the signal of the fragment ions for the precursor measured by each MS1 chromatographic peak is a non-trivial task. The MS2 spectrum of the fragment ion for each MS1 chromatographic peak has to be reconstructed from the available MS2 signal (i.e. the chromatographic peaks identified in MS level 2). For SWATH data, fragment ion signal should be present in the same isolation window that contains the m/z of the precursor ion and the chromatographic peak shape of the MS2 chromatographic peaks of fragment ions of a specific precursor should have a similar retention time and peak shape than the precursor’s MS1 chromatographic peak.

After detection of MS1 and MS2 chromatographic peaks has been performed, we can reconstruct the MS2 spectra using the `reconstructChromPeakSpectra()` function. This function defines an MS2 spectrum for each MS1 chromatographic peak based on the following approach:

- Identify MS2 chromatographic peaks in the isolation window containing the m/z of the ion (the MS1 chromatographic peak) that have approximately the same retention time than the MS1 chromatographic peak (the accepted difference in retention time can be defined with the `diffRt` parameter).
- Extract the MS1 chromatographic peak and all MS2 chromatographic peaks identified by the previous step and correlate the peak shapes of the candidate MS2 chromatographic peaks with the shape of the MS1 peak. MS2 chromatographic peaks with a correlation coefficient larger than `minCor` are retained.
- Reconstruct the MS2 spectrum using the m/z of all above selected MS2 chromatographic peaks and their intensity; each MS2 chromatographic peak selected for an MS1 peak will thus represent one mass peak in the reconstructed spectrum.

To illustrate this process we perform the individual steps on the example of fenamiphos (exact mass 303.105800777 and m/z of [M+H]+ adduct 304.113077). As a first step we extract the chromatographic peak for this ion.

    fenamiphos_mz <- 304.113077
    fenamiphos_ms1_peak <- chromPeaks(swath_data, mz = fenamiphos_mz, ppm = 2)
    fenamiphos_ms1_peak

    ##            mz    mzmin    mzmax      rt   rtmin   rtmax     into     intb
    ## CP34 304.1124 304.1121 304.1126 423.945 419.445 428.444 10697.34 10688.34
    ##          maxo  sn sample
    ## CP34 2401.849 618      1

Next we identify all MS2 chromatographic peaks that were identified in the isolation window containing the m/z of fenamiphos. The information on the isolation window in which a chromatographic peak was identified is available in the `chromPeakData`.

    keep <- chromPeakData(swath_data)$isolationWindowLowerMz < fenamiphos_mz &
            chromPeakData(swath_data)$isolationWindowUpperMz > fenamiphos_mz

We also require the retention time of the MS2 chromatographic peaks to be similar to the retention time of the MS1 peak and extract the corresponding peak information. We thus below select all chromatographic peaks for which the retention time range contains the retention time of the apex position of the MS1 chromatographic peak.

    keep <- keep &
        chromPeaks(swath_data)[, "rtmin"] < fenamiphos_ms1_peak[, "rt"] &
        chromPeaks(swath_data)[, "rtmax"] > fenamiphos_ms1_peak[, "rt"]

    fenamiphos_ms2_peak <- chromPeaks(swath_data)[which(keep), ]

In total 24 MS2 chromatographic peaks match all the above conditions. Next we extract the ion chromatogram of the MS1 peak and of all selected candidate MS2 signals. To ensure chromatograms are extracted from spectra in the correct isolation window we need to specify the respective isolation window by passing its isolation window target m/z to the `chromatogram()` function (in addition to setting `msLevel = 2`). This can be done by either getting the `isolationWindowTargetMz` of the spectra after the data was subset using `filterIsolationWindow()` (as done below) or by selecting the `isolationWindowTargetMz` closest to the m/z of the compound of interest.

    rtr <- fenamiphos_ms1_peak[, c("rtmin", "rtmax")]
    mzr <- fenamiphos_ms1_peak[, c("mzmin", "mzmax")]
    fenamiphos_ms1_chr <- chromatogram(swath_data, rt = rtr, mz = mzr)

    ## Processing chromatographic peaks

    rtr <- fenamiphos_ms2_peak[, c("rtmin", "rtmax")]
    mzr <- fenamiphos_ms2_peak[, c("mzmin", "mzmax")]
    ## Get the isolationWindowTargetMz for spectra containing the m/z of the
    ## compound of interest
    swath_data |>
    filterIsolationWindow(mz = fenamiphos_mz) |>
    spectra() |>
    isolationWindowTargetMz() |>
    table()

    ##
    ## 299.1
    ##   423

The target m/z of the isolation window containing the m/z of interest is thus 299.1 and we can use this in the `chromatogram()` call below to extract the data from the correct (MS2) spectra.

    fenamiphos_ms2_chr <- chromatogram(
        swath_data, rt = rtr, mz = mzr, msLevel = 2L,
        isolationWindowTargetMz = rep(299.1, nrow(rtr)))

    ## Processing chromatographic peaks

We can now plot the extracted ion chromatogram of the MS1 and the extracted MS2 data.

    plot(rtime(fenamiphos_ms1_chr[1, 1]),
         intensity(fenamiphos_ms1_chr[1, 1]),
         xlab = "retention time [s]", ylab = "intensity", pch = 16,
         ylim = c(0, 5000), col = "blue", type = "b", lwd = 2)
    #' Add data from all MS2 peaks
    tmp <- lapply(fenamiphos_ms2_chr@.Data,
                  function(z) points(rtime(z), intensity(z),
                                     col = "#00000080",
                                     type = "b", pch = 16))

Figure 5: Extracted ion chromatograms for Fenamiphos from MS1 (blue) and potentially related signal in MS2 (grey)

Next we can calculate correlations between the peak shapes of each MS2 chromatogram with the MS1 peak. We illustrate this process on the example of one MS2 chromatographic peaks. Note that, because MS1 and MS2 spectra are recorded consecutively, the retention times of the individual data points will differ between the MS2 and MS1 chromatographic data and data points have thus to be matched (aligned) before performing the correlation analysis. This is done automatically by the `correlate()` function. See the help for the `align()` method for more information on alignment options.

    compareChromatograms(fenamiphos_ms2_chr[1, 1],
                   fenamiphos_ms1_chr[1, 1],
                   ALIGNFUNARGS = list(method = "approx"))

    ## [1] 0.9997871

After identifying the MS2 chromatographic peaks with shapes of enough high similarity to the MS1 chromatographic peaks, an MS2 spectrum could be reconstructed based on the m/z and intensities of the MS2 chromatographic peaks (i.e., using their `"mz"` and `"maxo"` or `"into"` values).

Instead of performing this assignment of MS2 signal to MS1 chromatographic peaks manually as above, we can use the `reconstructChromPeakSpectra()` function that performs the exact same steps for all MS1 chromatographic peaks in a DIA data set. Below we use this function to reconstruct MS2 spectra for our example data requiring a peak shape correlation higher than `0.9` between the candidate MS2 chromatographic peak and the target MS1 chromatographic peak.

    swath_spectra <- reconstructChromPeakSpectra(swath_data, minCor = 0.9)
    swath_spectra

    ## MSn data (Spectra) with 62 spectra in a MsBackendMemory backend:
    ##        msLevel     rtime scanIndex
    ##      <integer> <numeric> <integer>
    ## CP01         2   239.458        NA
    ## CP02         2   240.358        NA
    ## CP03         2   329.577        NA
    ## CP04         2   329.771        NA
    ## CP05         2   346.164        NA
    ## ...        ...       ...       ...
    ## CP58         2   551.735        NA
    ## CP59         2   551.735        NA
    ## CP60         2   575.134        NA
    ## CP61         2   575.134        NA
    ## CP62         2   574.942        NA
    ##  ... 20 more variables/columns.
    ## Processing:
    ##  Merge 1 Spectra into one [Wed Aug 21 22:26:32 2024]

As a result we got a `Spectra` object of length equal to the number of MS1 peaks in our data. The length of a spectrum represents the number of peaks it contains. Thus, a length of 0 indicates that no matching peak (MS2 signal) could be found for the respective MS1 chromatographic peak.

    lengths(swath_spectra)

    ##  [1]  0  0  1  1  1  0  0  0  0  0  0  0  3  0  3  4  0  3  0  1  0  9 14  1  0
    ## [26]  0 15  4  1  1  2  4  6 15 12 11  2  4 13  0  0  0  0  1  2  0  1  0  0  0
    ## [51]  3  0  2  1  7  7  0  0  0  0  0  2

For reconstructed spectra additional annotations are available such as the IDs of the MS2 chromatographic peaks from which the spectrum was reconstructed (`"ms2_peak_id"`) as well as the correlation coefficient of their chromatographic peak shape with the precursor’s shape (`"ms2_peak_cor"`). Metadata column `"peak_id"` contains the ID of the MS1 chromatographic peak:

    spectraData(swath_spectra, c("peak_id", "ms2_peak_id", "ms2_peak_cor"))

    ## DataFrame with 62 rows and 3 columns
    ##          peak_id ms2_peak_id      ms2_peak_cor
    ##      <character>      <list>            <list>
    ## CP01        CP01
    ## CP02        CP02
    ## CP03        CP03       CP063          0.950582
    ## CP04        CP04       CP105           0.95157
    ## CP05        CP05       CP153          0.924545
    ## ...          ...         ...               ...
    ## CP58        CP58
    ## CP59        CP59
    ## CP60        CP60
    ## CP61        CP61
    ## CP62        CP62 CP334,CP329 0.918915,0.911944

We next extract the MS2 spectrum for our example peak most likely representing [M+H]+ ions of Fenamiphos using its chromatographic peak ID:

    fenamiphos_swath_spectrum <- swath_spectra[
        swath_spectra$peak_id == rownames(fenamiphos_ms1_peak)]

We can now compare the reconstructed spectrum to the example consensus spectrum from the DDA experiment in the previous section (variable `ex_spectrum`) as well as to the MS2 spectrum for Fenamiphos from Metlin (with a collision energy of 10V). For better visualization we normalize also the peak intensities of the reconstructed SWATH spectrum with the same function we used for the experimental DDA spectrum.

    fenamiphos_swath_spectrum <- addProcessing(fenamiphos_swath_spectrum,
                                               scale_fun)

    par(mfrow = c(1, 2))
    plotSpectraMirror(fenamiphos_swath_spectrum, ex_spectrum,
         ppm = 50, main = "against DDA")
    plotSpectraMirror(fenamiphos_swath_spectrum, fenamiphos[2],
         ppm = 50, main = "against Metlin")

Figure 6: Mirror plot comparing the reconstructed MS2 spectrum for Fenamiphos (upper panel) against the measured spectrum from the DDA data and the Fenamiphhos spectrum from Metlin

If we wanted to get the EICs for the MS2 chromatographic peaks used to generate this MS2 spectrum we can use the IDs of these peaks which are provided with `$ms2_peak_id` of the result spectrum.

    pk_ids <- fenamiphos_swath_spectrum$ms2_peak_id[[1]]
    pk_ids

    ##  [1] "CP199" "CP201" "CP211" "CP208" "CP200" "CP202" "CP217" "CP215" "CP205"
    ## [10] "CP212" "CP221" "CP223" "CP213" "CP207" "CP220"

With these peak IDs available we can extract their retention time window and m/z ranges from the `chromPeaks` matrix and use the `chromatogram()` function to extract their EIC. Note however that for SWATH data we have MS2 signal from different isolation windows. Thus we have to first filter the `swath_data` object by the isolation window containing the precursor m/z with the `filterIsolationWindow()` to subset the data to MS2 spectra related to the ion of interest. In addition, we have to use `msLevel = 2L` in the `chromatogram()` call because `chromatogram()` extracts by default only data from MS1 spectra and we need to specify the target m/z of the isolation window containing the fragment data from the compound of interest.

    rt_range <- chromPeaks(swath_data)[pk_ids, c("rtmin", "rtmax")]
    mz_range <- chromPeaks(swath_data)[pk_ids, c("mzmin", "mzmax")]

    pmz <- precursorMz(fenamiphos_swath_spectrum)[1]
    ## Determine the isolation window target m/z
    tmz <- swath_data |>
    filterIsolationWindow(mz = pmz) |>
    spectra() |>
    isolationWindowTargetMz() |>
    unique()

    ms2_eics <- chromatogram(
        swath_data, rt = rt_range, mz = mz_range, msLevel = 2L,
        isolationWindowTargetMz = rep(tmz, nrow(rt_range)))

    ## Processing chromatographic peaks

Each row of this `ms2_eics` contains now the EIC of one of the MS2 chromatographic peaks. We can also plot these in an overlay plot.

    plotChromatogramsOverlay(ms2_eics)

Figure 7: Overlay of EICs of chromatographic peaks used to reconstruct the MS2 spectrum for fenamiphos

As a second example we analyze the signal from an [M+H]+ ion with an m/z of 376.0381 (which would match Prochloraz). We first identify the MS1 chromatographic peak for that m/z and retrieve the reconstructed MS2 spectrum for that peak.

    prochloraz_mz <- 376.0381

    prochloraz_ms1_peak <- chromPeaks(swath_data, msLevel = 1L,
                                      mz = prochloraz_mz, ppm = 5)
    prochloraz_ms1_peak

    ##            mz   mzmin    mzmax      rt   rtmin   rtmax     into     intb
    ## CP22 376.0373 376.037 376.0374 405.046 401.446 409.546 3664.051 3655.951
    ##          maxo  sn sample
    ## CP22 897.3923 278      1

    prochloraz_swath_spectrum <- swath_spectra[
        swath_spectra$peak_id == rownames(prochloraz_ms1_peak)]
    lengths(prochloraz_swath_spectrum)

    ## [1] 9

The MS2 spectrum for the (tentative) MS1 signal for prochloraz reconstructed from the SWATH MS2 data has thus 9 peaks.

In addition we identify the corresponding MS1 peak in the DDA data set, extract all measured MS2 chromatographic peaks and build the consensus spectrum from these.

    prochloraz_dda_peak <- chromPeaks(dda_data, msLevel = 1L,
                                      mz = prochloraz_mz, ppm = 5)
    prochloraz_dda_peak

    ##             mz    mzmin    mzmax      rt   rtmin   rtmax     into    intb
    ## CP034 376.0385 376.0378 376.0391 405.295 401.166 410.145 5082.157 5072.77
    ##           maxo  sn sample
    ## CP034 1350.633 310      1

The retention times for the chromatographic peaks from the DDA and SWATH data match almost perfectly. Next we get the MS2 spectra for this peak.

    prochloraz_dda_spectra <- dda_spectra[
        dda_spectra$peak_id == rownames(prochloraz_dda_peak)]
    prochloraz_dda_spectra

    ## MSn data (Spectra) with 5 spectra in a MsBackendMzR backend:
    ##     msLevel     rtime scanIndex
    ##   <integer> <numeric> <integer>
    ## 1         2   401.438      3253
    ## 2         2   402.198      3259
    ## 3         2   404.677      3306
    ## 4         2   405.127      3316
    ## 5         2   405.877      3325
    ##  ... 34 more variables/columns.
    ##
    ## file(s):
    ## PestMix1_DDA.mzML
    ## Processing:
    ##  Filter: select retention time [230..610] on MS level(s) 1 2 [Wed Aug 21 22:26:06 2024]
    ##  Filter: select MS level(s) 2 [Wed Aug 21 22:26:17 2024]
    ##  Merge 1 Spectra into one [Wed Aug 21 22:26:17 2024]

In total 5 spectra were measured, some with a relatively high number of peaks. Next we combine them into a consensus spectrum.

    prochloraz_dda_spectrum <- combineSpectra(
        prochloraz_dda_spectra, FUN = combinePeaks, ppm = 20,
        peaks = "intersect", minProp = 0.8, intensityFun = median, mzFun = median,
        f = prochloraz_dda_spectra$peak_id)

    ## Backend of the input object is read-only, will change that to an 'MsBackendMemory'

    ## Warning in FUN(X[[i]], ...): 'combinePeaks' for lists of peak matrices is
    ## deprecated; please use 'combinePeaksData' instead.

At last we load also the Prochloraz MS2 spectra (for different collision energies) from Metlin.

    prochloraz <- Spectra(
        system.file("mgf", "metlin-68898.mgf", package = "xcms"),
        source = MsBackendMgf())

    ## Start data import from 1 files ... done

To validate the reconstructed spectrum we plot it against the corresponding DDA spectrum and the MS2 spectrum for Prochloraz (for a collision energy of 10V) from Metlin.

    prochloraz_swath_spectrum <- addProcessing(prochloraz_swath_spectrum, scale_fun)
    prochloraz_dda_spectrum <- addProcessing(prochloraz_dda_spectrum, scale_fun)

    par(mfrow = c(1, 2))
    plotSpectraMirror(prochloraz_swath_spectrum, prochloraz_dda_spectrum,
                      ppm = 40, main = "against DDA")
    plotSpectraMirror(prochloraz_swath_spectrum, prochloraz[2],
                      ppm = 40, main = "against Metlin")

Figure 8: Mirror plot comparing the reconstructed MS2 spectrum for Prochloraz (upper panel) against the measured spectrum from the DDA data and the Prochloraz spectrum from Metlin

The spectra fit relatively well. Interestingly, the peak representing the precursor (the right-most peak) seems to have a slightly shifted m/z value in the reconstructed spectrum. Also, by closer inspecting the spectrum two groups of peaks with small differences in m/z can be observed (see plot below).

    plotSpectra(prochloraz_swath_spectrum)

Figure 9: SWATH-derived MS2 spectrum for prochloraz

These could represent fragments from isotopes of the original compound. DIA MS2 data, since all ions at a given retention time are fragmented, can contain fragments from isotopes. We thus below use the `isotopologues()` function from the MetaboCoreUtils package to check for presence of potential isotope peaks in the reconstructed MS2 spectrum for prochloraz.

    library(MetaboCoreUtils)
    isotopologues(peaksData(prochloraz_swath_spectrum)[[1]])

    ## [[1]]
    ## [1] 3 4 5
    ##
    ## [[2]]
    ## [1] 6 7

Indeed, peaks 3, 4 and 5 as well as 6 and 7 have been assigned to a group of potential isotope peaks. While this is no proof that the peaks are indeed fragment isotopes of prochloraz it is highly likely (given their difference in m/z and relative intensity differences). Below we thus define a function that keeps only the monoisotopic peak for each isotope group in a spectrum.

    ## Function to keep only the first (monoisotopic) peak for potential
    ## isotopologue peak groups.
    rem_iso <- function(x, ...) {
        idx <- isotopologues(x)
        idx <- unlist(lapply(idx, function(z) z[-1]), use.names = FALSE)
        if (length(idx))
            x[-idx, , drop = FALSE]
        else x
    }
    prochloraz_swath_spectrum2 <- addProcessing(prochloraz_swath_spectrum,
                                                rem_iso)

    par(mfrow = c(1, 2))
    plotSpectra(prochloraz_swath_spectrum)
    plotSpectra(prochloraz_swath_spectrum2)

Figure 10: SWATH MS2 spectrum for prochloraz before (left) and after deisotoping (right)

Removing the isotope peaks from the SWATH MS2 spectrum increases also the spectra similarity score (since reference spectra generally will contain only fragments of the ion of interest, but not of any of its isotopes).

    compareSpectra(prochloraz_swath_spectrum, prochloraz_dda_spectrum)

    ## [1] 0.4623719

    compareSpectra(prochloraz_swath_spectrum2, prochloraz_dda_spectrum)

    ## [1] 0.5932303

Similar to the DDA data, the reconstructed MS2 spectra from SWATH data could be used in the annotation of the MS1 chromatographic peaks.

# 4 Outlook

Currently, spectra data representation, handling and processing is being re-implemented as part of the RforMassSpectrometry initiative aiming at increasing the performance of methods and simplifying their use. Thus, parts of the workflow described here will be changed (improved) in future.

Along with these developments, improved matching strategies for larger data sets will be implemented as well as functionality to compare `Spectra` directly to reference MS2 spectra from public annotation resources (e.g. Massbank or HMDB). See for example here for more information.

Regarding SWATH data analysis, future development will involve improved selection of the correct MS2 chromatographic peaks considering also correlation with intensity values across several samples.

# 5 Session information

    sessionInfo()

    ## R version 4.4.1 (2024-06-14 ucrt)
    ## Platform: x86_64-w64-mingw32/x64
    ## Running under: Windows Server 2022 x64 (build 20348)
    ##
    ## Matrix products: default
    ##
    ##
    ## locale:
    ## [1] LC_COLLATE=C
    ## [2] LC_CTYPE=English_United States.utf8
    ## [3] LC_MONETARY=English_United States.utf8
    ## [4] LC_NUMERIC=C
    ## [5] LC_TIME=English_United States.utf8
    ##
    ## time zone: America/New_York
    ## tzcode source: internal
    ##
    ## attached base packages:
    ## [1] stats4    stats     graphics  grDevices utils     datasets  methods
    ## [8] base
    ##
    ## other attached packages:
    ##  [1] MetaboCoreUtils_1.12.0 MsBackendMgf_1.12.0    MsExperiment_1.6.0
    ##  [4] pander_0.6.5           Spectra_1.14.1         MassSpecWavelet_1.70.0
    ##  [7] pheatmap_1.0.12        faahKO_1.44.0          MSnbase_2.30.1
    ## [10] ProtGenerics_1.36.0    S4Vectors_0.42.1       mzR_2.38.0
    ## [13] Rcpp_1.0.13            Biobase_2.64.0         BiocGenerics_0.50.0
    ## [16] MsFeatures_1.12.0      xcms_4.2.3             BiocParallel_1.38.0
    ## [19] BiocStyle_2.32.1
    ##
    ## loaded via a namespace (and not attached):
    ##   [1] DBI_1.2.3                   rlang_1.1.4
    ##   [3] magrittr_2.0.3              clue_0.3-65
    ##   [5] matrixStats_1.3.0           compiler_4.4.1
    ##   [7] vctrs_0.6.5                 reshape2_1.4.4
    ##   [9] stringr_1.5.1               pkgconfig_2.0.3
    ##  [11] crayon_1.5.3                fastmap_1.2.0
    ##  [13] magick_2.8.4                XVector_0.44.0
    ##  [15] utf8_1.2.4                  rmarkdown_2.28
    ##  [17] UCSC.utils_1.0.0            preprocessCore_1.66.0
    ##  [19] tinytex_0.52                purrr_1.0.2
    ##  [21] xfun_0.47                   MultiAssayExperiment_1.30.3
    ##  [23] zlibbioc_1.50.0             cachem_1.1.0
    ##  [25] GenomeInfoDb_1.40.1         jsonlite_1.8.8
    ##  [27] progress_1.2.3              highr_0.11
    ##  [29] DelayedArray_0.30.1         prettyunits_1.2.0
    ##  [31] parallel_4.4.1              cluster_2.1.6
    ##  [33] R6_2.5.1                    bslib_0.8.0
    ##  [35] stringi_1.8.4               RColorBrewer_1.1-3
    ##  [37] limma_3.60.4                GenomicRanges_1.56.1
    ##  [39] jquerylib_0.1.4             bookdown_0.40
    ##  [41] SummarizedExperiment_1.34.0 iterators_1.0.14
    ##  [43] knitr_1.48                  IRanges_2.38.1
    ##  [45] Matrix_1.7-0                igraph_2.0.3
    ##  [47] tidyselect_1.2.1            abind_1.4-5
    ##  [49] yaml_2.3.10                 doParallel_1.0.17
    ##  [51] codetools_0.2-20            affy_1.82.0
    ##  [53] lattice_0.22-6              tibble_3.2.1
    ##  [55] plyr_1.8.9                  signal_1.8-1
    ##  [57] evaluate_0.24.0             pillar_1.9.0
    ##  [59] affyio_1.74.0               BiocManager_1.30.24
    ##  [61] MatrixGenerics_1.16.0       foreach_1.5.2
    ##  [63] MALDIquant_1.22.3           ncdf4_1.23
    ##  [65] generics_0.1.3              hms_1.1.3
    ##  [67] ggplot2_3.5.1               munsell_0.5.1
    ##  [69] scales_1.3.0                glue_1.7.0
    ##  [71] lazyeval_0.2.2              tools_4.4.1
    ##  [73] mzID_1.42.0                 QFeatures_1.14.2
    ##  [75] vsn_3.72.0                  fs_1.6.4
    ##  [77] XML_3.99-0.17               grid_4.4.1
    ##  [79] impute_1.78.0               tidyr_1.3.1
    ##  [81] MsCoreUtils_1.16.1          colorspace_2.1-1
    ##  [83] GenomeInfoDbData_1.2.12     PSMatch_1.8.0
    ##  [85] cli_3.6.3                   fansi_1.0.6
    ##  [87] S4Arrays_1.4.1              dplyr_1.1.4
    ##  [89] AnnotationFilter_1.28.0     pcaMethods_1.96.0
    ##  [91] gtable_0.3.5                sass_0.4.9
    ##  [93] digest_0.6.37               SparseArray_1.4.8
    ##  [95] farver_2.1.2                htmltools_0.5.8.1
    ##  [97] lifecycle_1.0.4             httr_1.4.7
    ##  [99] statmod_1.5.0               MASS_7.3-61

# References

1. Ludwig C, Gillet L, Rosenberger G, Amon S, Collins BC, Aebersold R: Data-independent acquisition-based SWATH-MS for quantitative proteomics: a tutorial. Molecular systems biology 2018, 14:e8126.