# CluMSID — Clustering of MS2 Spectra for Metabolite Identification

#### Tobias Depke

#### November 01, 2022

# Introduction

This tutorial shows how to use the `CluMSID` package to help annotate MS2 spectra from untargeted LC-MS/MS data. `CluMSID` works with MS2 data generated by data-dependent acquisition and requires an mzXML file (like in this example) or any other file that can be parsed by `mzR`, like mzML, mzTab or netCDF, as input. It can be used both stand-alone and together with the XCMS suite of preprocessing tools.

`CluMSID` extracts and merges MS2 spectra and generates neutral loss patterns for each feature. Additionally, it can make use of information from the `CAMERA` package to generate pseudospectra from MS1 level data. The tool uses cosine similarity to generate distance matrices from MS2 spectra, neutral loss patterns and pseudospectra.

These distance matrices are the basis for multivariate statistics methods such as multidimensional scaling, density-based clustering, hierarchical clustering and correlation networks. The `CluMSID` package provides functions for these methods including (interactive) visualisation but the distance/similarity data can also be analysed with other `R` functions.

For the demonstrations in this tutorial, we will mainly use data from pooled Pseudomonas aeruginosa cell extracts, measured in ESI-(+) mode with auto-MS/MS on a Bruker maxisHD qTOF after reversed phase separation by UPLC. For details, please refer to the Depke et al. 2017 publication (doi: 10.1016/j.jchromb.2017.06.002.).

To be able to access the example data, we also need the related package `CluMSIDdata`. The packages can be loaded as follows:

    if (!requireNamespace("BiocManager", quietly = TRUE))
        install.packages("BiocManager")
    BiocManager::install(c("CluMSIDdata", "CluMSID"))

    library(CluMSID)
    library(CluMSIDdata)

# `MS2spectrum` and `pseudospectrum` classes

`CluMSID` uses a custom S4 class named `MS2spectrum` to store spectral information in the following slots:

- `id`: a character string similar to the ID used by XCMSonline or the ID given in a predefined peak list
- `annotation`: a character string containing a user-defined annotation, defaults to empty
- `precursor`: (median) m/z of the spectrum’s precursor ion
- `rt`: (median) retention time of the spectrum’s precursor ion
- `polarity`: the polarity with which the spectrum was recorded, either `positive` or `negative`
- `spectrum`: the actual MS2 spectrum as two-column matrix (column 1 is (median) m/z, column 2 is (median) intensity of the product ions)
- `neutral_losses`: a neutral loss pattern generated by subtracting the product ion mass-to-charge ratios from the precursor m/z in a matrix format analogous to the `spectrum` slot

The `pseudospectrum` class is very similar but it contains no information on precursor m/z and therefore no neutral loss pattern, either. By default, the `id` slot contains the “pcgroup” number assigned by `CAMERA`.

The individual slots of `MS2spectrum` and `pseudospectrum` objects can be accessed via the standard S4 way using `object@slot`, e.g. `object@annotation` or by using an accessor function. These exist for all slots and are called `accessFoo()`, where `Foo` is the slot name (not exactly, though, because `Bioconductor` does not allow to mix `snake_case` and `camelCase` in function names):

- `accessID(object)`
- `accessAnnotation(object)`
- `accessPrecursor(object)`
- `accessRT(object)`
- `accessPolarity(object)`
- `accessSpectrum(object)`
- `accessNeutralLosses(object)`.

# Extract MS2 spectra from *.mzXML file

The first step in the `CluMSID` workflow is to extract MS2 spectra from the raw data file (in mzXML format). This is done by the `extractMS2spectra` function which internally uses several functions from the `mzR` package. The function offers the possibility to filter spectra that contain less a defined number of peaks and/or do not fall in a defined retention time window. Setting the `recalibrate_precursor` argument to `TRUE` activates a correction process for uncalibrated precursor m/z data that existed in older version of Bruker’s Compass Xport (cf. Depke et al. 2017). It is not necessary to use it with files generated by other software but does not corrupt the data, either.

Please be aware that `mzR` often throws warnings concerning the `Rcpp` version that can usually be ignored.

    ms2list <- extractMS2spectra(system.file("extdata",
                                                "PoolA_R_SE.mzXML",
                                                package = "CluMSIDdata"),
                                    min_peaks = 2,
                                    recalibrate_precursor = TRUE,
                                    RTlims = c(0,25))

This operation has now extracted all the MS2 spectra from the raw data file and stored them in a list. Each list entry is an object of class `MS2spectrum`. The list is quite long because it still contains a lot of spectra that derive from the same chromatographic peak.

    length(ms2list)
    #> [1] 2290

In our example, the first two spectra in the list derive from the same peak and thus have the same precursor ion and almost the same retention time.

    head(ms2list, 4)
    #> [[1]]
    #> An object of class "MS2spectrum"
    #>  id:
    #>  annotation:
    #>  precursor: 146.1652
    #>  retention time: 56.266
    #>  polarity: positive
    #>  MS2 spectrum with 2 fragment peaks
    #>  neutral loss pattern with 0 neutral losses
    #> [[2]]
    #> An object of class "MS2spectrum"
    #>  id:
    #>  annotation:
    #>  precursor: 146.1653
    #>  retention time: 57.292
    #>  polarity: positive
    #>  MS2 spectrum with 3 fragment peaks
    #>  neutral loss pattern with 0 neutral losses
    #> [[3]]
    #> An object of class "MS2spectrum"
    #>  id:
    #>  annotation:
    #>  precursor: 129.1387
    #>  retention time: 57.545
    #>  polarity: positive
    #>  MS2 spectrum with 2 fragment peaks
    #>  neutral loss pattern with 0 neutral losses
    #> [[4]]
    #> An object of class "MS2spectrum"
    #>  id:
    #>  annotation:
    #>  precursor: 112.1119
    #>  retention time: 57.797
    #>  polarity: positive
    #>  MS2 spectrum with 2 fragment peaks
    #>  neutral loss pattern with 0 neutral losses

From the output above, you also see that the `MS2spectrum` class has a `show()` generic that summarises the MS2 spectrum and neutral loss pattern data. To show the default output, use `showDefault()`. Be aware that neutral loss patterns have not been calculated in this step.

    showDefault(ms2list[[2]])
    #> An object of class "MS2spectrum"
    #> Slot "id":
    #> character(0)
    #>
    #> Slot "annotation":
    #> character(0)
    #>
    #> Slot "precursor":
    #> [1] 146.1653
    #>
    #> Slot "rt":
    #> [1] 57.292
    #>
    #> Slot "polarity":
    #> [1] "positive"
    #>
    #> Slot "spectrum":
    #>             mz intensity
    #> [1,]  72.08064      2448
    #> [2,]  84.08077       328
    #> [3,] 112.11228       843
    #>
    #> Slot "neutral_losses":
    #> <0 x 0 matrix>

# Merge MS2 spectra that derive from the same peak/feature

To reduce the amount of redundant MS2 spectra, the `mergeMS2spectra()` function is used to generate consensus spectra from the MS2 spectra that derive from the same precursor. `CluMSID` offers two possibilities to do so:

## Merge spectra without external peaktable

This possibility is the standard method for stand-alone use of `CluMSID` and is equivalent to what has been described in Depke et al. 2017. It does not need additional input and summarises consecutive spectra that have the same precursor m/z if their retention time fall within a defined threshold (`rt_tolerance`, defaults to 30s). A retention time difference between consecutive spectra larger than `rt_tolerance` is interpreted as chromatographic separation and respective spectra will be assigned to a new feature. The `mz_tolerance` argument should be set according to your instruments m/z precision, the default is 1 * 10-5 (10ppm, equivalent to ±5ppm instrument precision). The `peaktable` and `exclude_unmatched` arguments are not used in this method and are to be left at their default.

    featlist <- mergeMS2spectra(ms2list)

    length(featlist)
    #> [1] 518

    head(featlist, 4)
    #> [[1]]
    #> An object of class "MS2spectrum"
    #>  id: M146.17T59.35
    #>  annotation:
    #>  precursor: 146.1653
    #>  retention time: 59.35
    #>  polarity: positive
    #>  MS2 spectrum with 8 fragment peaks
    #>  neutral loss pattern with 7 neutral losses
    #> [[2]]
    #> An object of class "MS2spectrum"
    #>  id: M129.14T58.57
    #>  annotation:
    #>  precursor: 129.1387
    #>  retention time: 58.57
    #>  polarity: positive
    #>  MS2 spectrum with 4 fragment peaks
    #>  neutral loss pattern with 3 neutral losses
    #> [[3]]
    #> An object of class "MS2spectrum"
    #>  id: M112.11T57.8
    #>  annotation:
    #>  precursor: 112.1119
    #>  retention time: 57.8
    #>  polarity: positive
    #>  MS2 spectrum with 2 fragment peaks
    #>  neutral loss pattern with 1 neutral losses
    #> [[4]]
    #> An object of class "MS2spectrum"
    #>  id: M251.16T60.64
    #>  annotation:
    #>  precursor: 251.1603
    #>  retention time: 60.64
    #>  polarity: positive
    #>  MS2 spectrum with 9 fragment peaks
    #>  neutral loss pattern with 8 neutral losses

The total amount of spectra was reduced from 2290 to 518 and as many other, the redundant spectra #1 and #2 in the raw list are now merged to one consensus spectrum (#1 in the merged list).

In this step, neutral loss patterns have been generated that look like this:

    accessNeutralLosses(featlist[[1]])
    #>          [,1] [,2]
    #> [1,] 74.08475 6429
    #> [2,] 73.08163  262
    #> [3,] 71.07394  239
    #> [4,] 62.08476 1044
    #> [5,] 34.05341 2363
    #> [6,] 33.05024  144
    #> [7,] 17.02688  852

## Merge spectra with external peaktable, e.g. from XCMS

The second possibility is to supply a peaktable, i.e. a list of picked peaks with their mass-to-charge ratios and retention times. This is particularly useful if you want to annotate a complete metabolomics data set. In our example, we have a metabolomics dataset called “TD035” in which we have measured a range of samples in MS1 mode for relative quantification. Additionally, we have measured a pooled QC sample in MS2 mode for annotation. The MS1 data were analysed using XCMSonline and we want to group the MS2 spectra so that they match the XCMSonline peak picking.

The spectra are extracted as shown above:

    ms2list2 <- extractMS2spectra(system.file("extdata",
                                                "TD035-PoolMSMS2.mzXML",
                                                package = "CluMSIDdata"),
                                    min_peaks = 2,
                                    recalibrate_precursor = TRUE,
                                    RTlims = c(0,25))

The peaklist is imported from the XCMSonline output. The list has to contain at least 3 columns:

- column 1: name/identifier of the feature
- column 2: m/z
- column 3: retention time

Shown below is an easy way of getting from an XCMSonline annotated diffreport to a suitable peaktable using tidyverse functions. Of course, you can achieve the same goal with base R functions or even in Excel. Depending on the retention time format in your *.mzXML file, you might have to convert from minutes to seconds or vice versa. Here, we have minutes in the XCMSonline output but seconds in the MS2 file, so we multiply by 60.

    require(magrittr)
    ptable <-
        readr::read_delim(file = system.file("extdata",
                                            "TD035_XCMS.annotated.diffreport.tsv",
                                            package = "CluMSIDdata"),
                        delim = "\t") %>%
        dplyr::select(c(name, mzmed, rtmed)) %>%
        dplyr::mutate(rtmed = rtmed * 60)

    head(ptable)
    #> # A tibble: 6 × 3
    #>   name     mzmed  rtmed
    #>   <chr>    <dbl>  <dbl>
    #> 1 M245T2   245.   100.
    #> 2 M440T2_1 440.   107.
    #> 3 M578T2   578.   104.
    #> 4 M85T1     85.0   60.8
    #> 5 M126T1_1 126.    61.0
    #> 6 M688T24  688.  1468.

We can now use this peaktable as an argument for `mergeMS2spectra()`. You can choose whether you want to keep or exclude MS2 spectra that do not match any peak in the peaktable. These can occur in regions of the chromatogramm where there are no clear peaks but the auto-MS/MS still fragments the most abundant ions. These unmatched spectra are merged following the same rules as described above (method without peaktable). In this example, we keep the unmatched spectra. We use the default values for m/z and retention time tolerance and thus do not need to specify them.

    featlist2 <- mergeMS2spectra(ms2list2,
                                    peaktable = ptable,
                                    exclude_unmatched = FALSE)

    head(featlist2, 4)
    #> [[1]]
    #> An object of class "MS2spectrum"
    #>  id: M213T0
    #>  annotation:
    #>  precursor: 213.1462
    #>  retention time: 6.04
    #>  polarity: positive
    #>  MS2 spectrum with 5 fragment peaks
    #>  neutral loss pattern with 3 neutral losses
    #> [[2]]
    #> An object of class "MS2spectrum"
    #>  id: xM158T31.17
    #>  annotation:
    #>  precursor: 158.0027
    #>  retention time: 31.17
    #>  polarity: positive
    #>  MS2 spectrum with 3 fragment peaks
    #>  neutral loss pattern with 3 neutral losses
    #> [[3]]
    #> An object of class "MS2spectrum"
    #>  id: M146T1_3
    #>  annotation:
    #>  precursor: 146.1650
    #>  retention time: 61.15
    #>  polarity: positive
    #>  MS2 spectrum with 7 fragment peaks
    #>  neutral loss pattern with 6 neutral losses
    #> [[4]]
    #> An object of class "MS2spectrum"
    #>  id: M129T1_4
    #>  annotation:
    #>  precursor: 129.1384
    #>  retention time: 60.74
    #>  polarity: positive
    #>  MS2 spectrum with 2 fragment peaks
    #>  neutral loss pattern with 2 neutral losses

Note that the 2nd entry in `featlist2` is marked with an ‘x’ which means that it could not be assigned to a feature in the peaktable.

For the sake of simplicity, only the data generated from the stand-alone procedure will be used for the following examples. Be assured that all of them would also work with the data generated with the help of an external peaktable (`featlist2`).

# Add annotations

The next step is to add (external) annotations to the list of features, e.g. from a spectral library that you curate in-house or one that has been supplied by your instrument manufacturer. If you do not (want to) annotate your features at all, this step can be skipped completely, leaving the `annotation` slot of the `MS2spectrum` objects empty.

## Manual procedure

`CluMSID` offers several possibilities to add annotations to your feature list. The most basic one first generates a list of features and saves it as *.csv file. For that you use the `writeFeaturelist()` function and only have to specify your list of spectra and a file name for the output file (here: `pre_anno.csv`). You can then manually fill in your annotations in a new column in the table, save it (in this example under the name `post_anno.csv`) and reload it to `R`:

    writeFeaturelist(featlist, "pre_anno.csv")

    annotatedSpeclist <- addAnnotations(featlist, system.file("extdata",
                                                        "post_anno.csv",
                                                        package = "CluMSIDdata"))

`annotatedSpeclist` will then be equivalent to `featlist` with annotations added to the `annotation` slot of the list entries.

## Alternative procedures

You can add annotations without leaving the `R` environment, too. `addAnnotations()` also accepts objects of class `data.frame` as `annolist` argument. Be aware that `addAnnotations()` assigns the annotation based on the position in the feature list. I.e., if the order of the features in your list of features (`featlist`) and your list of annotations (`annolist`) is different, you will get nonsense results.

The savest ways to `addAnnotations()` with a `data.frame` is to use `featureList()` to generate a `data.frame` that is formatted in the same way as the file output from `writeFeaturelist()` and then match your identifications against this `data.frame` and use the result as argument for `addAnnotations()`.

Say you have an object called `annos` that contains feature IDs (the same as in `featlist`) and annotations in a two-column `data.frame` with “id” and “annotation” as column names. It could look like this:

    str(annos)
    #> 'data.frame':    154 obs. of  2 variables:
    #>  $ id        : chr  "M146.17T59.35" "M129.14T58.57" "M112.11T57.8" "M148.06T69.65" ...
    #>  $ annotation: chr  "spermidine" "spermidine (fragment)" "spermidine (fragment)" "glutamate" ...
    head(annos)
    #>              id            annotation
    #> 1 M146.17T59.35            spermidine
    #> 2 M129.14T58.57 spermidine (fragment)
    #> 3  M112.11T57.8 spermidine (fragment)
    #> 4 M148.06T69.65             glutamate
    #> 5 M130.05T69.64  glutamate (fragment)
    #> 6 M179.06T71.32        gluconolactone

`addAnnotations(featlist, annos, annotationColumn = 2)` will throw an error because `featlist` and `annos` are of different length. Instead, you need to do the following:

    fl <- featureList(featlist)

    fl_annos <- dplyr::left_join(fl, annos, by = "id")

Now, you can annotate your list of spectra using `addAnnotations(featlist, fl_annos, annotationColumn = 4)`.

An analogous procedure works if you have your annotations stored in a peaktable that you have used for `mergeMSspectra()`. As the order of spectra in the list will not be same as the order of features in your peaktable, you need to do a matching with the output of `featureList()` as well.

# Generate distance matrices

Once we have a list of `MS2spectrum` objects containing all the required information with or without annotation, we can generate distance matrices from (product ion) MS2 spectra as well as from neutral loss patterns. These distance matrices serve as the basis for further analysis of the data. Both for MS2 spectra and neutral loss patterns, cosine similarity is used as similarity metric:

\[ cos(\theta) = \frac{\sum_{i}a_i \cdot b_i}{\sqrt{\sum_{i}{a_{i}}^2 \cdot \sum_{i}{b_{i}}^2}} \]

## Distance matrix for product ion spectra

For most applications, analysing the similarity of product ion MS2 spectra will be most useful. The generation of the distance matrix is done by just one simple command but it can take some time to calculate.

    distmat <- distanceMatrix(annotatedSpeclist)

## Distance matrix for neutral loss patterns

Common neutral losses and neutral loss patterns can convey information about structural similarity, as well, e.g. with nucleotides or glykosylated secondary metabolites. `CluMSID` offers the possibility to study neutral loss patterns independently from product ion spectra. The generation of a distance matrix is analogous, you just need to set the ‘type’ argument to “neutral_losses”:

    nlmat <- distanceMatrix(annotatedSpeclist, type = "neutral_losses")

# Visualise distance/similarity data using multidimensional scaling (MDS)

One rather simple possibility to visually analyse the spectral similarity data is multidimensional scaling, a dimension reduction method that simplifies distances in n-dimensional space to those in two-dimensional space (n in this case being the number of consensus spectra or neutral loss patterns that were used to generate the distance matrix in the previous step). `CluMSID` offers a simple function to produce an MDS plot from the distance matrix with the option to highlight annotated metabolites and the possibility to generate an interactive plot using `plotly`.

Standard MDS plots are generated as follows:

For MS2 spectra:

    MDSplot(distmat, highlight_annotated = TRUE)

Figure 1: Multidimensional scaling plot as a visualisation of MS2 spectra similarities of the example data set. Red dots signify annotated spectra, black dots spectra from unknown metabolites.

For neutral loss patterns:

    MDSplot(nlmat, highlight_annotated = TRUE)

Figure 2: Multidimensional scaling plot as a visualisation of neutral loss similarities of the example data set. Red dots signify annotated spectra, black dots spectra from unknown metabolites.

Interactive plots are zoomable and show feature names upon mouse-over. They are generated like normal MDS plots and can be viewed within RStudio or—after saving as html file using `htmlwidgets`—displayed in a normal web browser.

    my_mds <- MDSplot(distmat, interactive = TRUE, highlight_annotated = TRUE)

    htmlwidgets::saveWidget(my_mds, "mds.html")

This is how it looks like if you open the html file in Firefox and mouse over a feature:

Figure 3: Screenshot of the interactive version of the Multidimensional scaling plot visualising MS2 spectra similarities of the example data set (cf Figure 1). Zoomed image section with tooltip displaying feature information upon mouse-over.

# Perform density-based clustering using the OPTICS algorithm

For density-based clustering with `CluMSID`, the ‘OPTICS’ algorithm and its implementation in the `dbscan` package is used. Density-based clustering is a useful clustering method that often yields different results than hierarchical clustering and can thus provide additional insight into the data. `CluMSID` has two functions to perform density-based clustering, one for the reachability plot which is the most useful visualisation of OPTICS results and one that outputs a `data.frame` containing the cluster assignations for every feature.

Both functions require as arguments a distance matrix as well as three parameters for the underlying functions `dbscan::optics` and `dbscan::extractDBSCAN`: `eps`, `minPts` and `eps_cl`. Lowering the `eps` parameter (default is 10000) limits the size of the epsilon neighbourhood which from experience has very little effect on the results. `minPts` defaults to 3 in `CluMSID`. It defines how many points are considered for reachability distance calculation between clusters. The `dbscan::optics` default for `minPts` is 5. Users are encourage to experiment with this parameter. `eps_cl` is the reachability threshold to identify clusters and can be varied based on your data. Lowering `eps_cl` leads to a larger number of smaller clusters and vice versa for raising the value. In general, it is advisable to chose a higher `eps_cl` for MS2 spectra than for neutral loss patterns, since the latter tend to show less similarity to each other. For details, please refer to the `dbscan` help for the `dbscan::optics` and `dbscan::extractDBSCAN` functions.

If the default parameters are used, the generation of an OPTICS reachability plots is very simple, shown here for MS2 spectra and neutral loss patterns:

    OPTICSplot(distmat)

Figure 4: Reachability distance plot resulting from OPTICS density based clustering of the MS2 spectra similarities of the example data set. Bars represent features in OPTICS order with heights corresponding to the reachability distance to the next feature. The dashed horizontal line marks the reachability threshold that separates clusters. The resulting clusters are colour-coded with black representing noise, i.e. features not assigned to any cluster.

    OPTICSplot(nlmat, eps_cl = 0.7)

Figure 5: Reachability distance plot resulting from OPTICS density based clustering of the neutral loss similarities of the example data set (cf Figure 4).

In the reachability plots, every line represents a feature and the height of the line is the reachability distance to the next feature in the OPTICS order. Thus, valleys represent groups of similar spectra or neutral loss patterns. The order and the cluster assignment can be studied using the `OPTICStbl` function that outputs a three-column `data.frame` with feature id, cluster assignment and OPTICS order. The order of features in the `data.frame` corresponds to the original order in the input distance matrix. Features that were not assigned to a cluster are black in the reachability plot and have the cluster ID 0. `OPTICStbl` takes the same arguments as `OPTICSplot`. The two functions have to be run with exactly the same parameters to assure compatibility of results.

    OPTICStbl <- OPTICStbl(distmat)

    head(OPTICStbl)
    #>                                 feature cluster_ID OPTICS_order
    #> 1            M146.17T59.35 - spermidine          1            1
    #> 2 M129.14T58.57 - spermidine (fragment)          1            3
    #> 3  M112.11T57.8 - spermidine (fragment)          1            4
    #> 4                         M251.16T60.64          0          185
    #> 5                         M212.85T65.02          0          518
    #> 6                         M290.85T64.76          0          517

# Perform hierarchical clustering

In Depke et al. 2017, hierarchical clustering proved the most useful method to unveil structural similarities between features. analogous to density-based clustering, `CluMSID` offers two functions, one for plots and one for a `data.frame` with cluster assignments, both taking a distance matrix as the only compulsory argument. The other two parameters are `h` (defaults to `0.95`), the height where the tree should be cut (see `stats::cutree` for details) and `type` that determines the type visualisation:

- `heatmap`: a heatmap displaying pairwise similarities/distances along with cluster dendrograms
- `dendrogram` (default): a circular dendrogram with colour code for cluster assignment

## Create a heatmap

Heatmaps of our example data for MS2 and neutral loss pattern similarity are created as follows (with reduced label font size by changing `cexRow` and `cexCol` as well as `margins` of the underlying `heatmap.2` function):

    HCplot(distmat, type = "heatmap",
                    cexRow = 0.1, cexCol = 0.1,
                    margins = c(6,6))

Figure 6: Symmetric heat map of the distance matrix displaying MS2 spectra similarities of the example data set along with dendrograms resulting from hierarchical clustering based on the distance matrix. The colour encoding is shown in the top-left insert.

    HCplot(nlmat, type = "heatmap",
                    cexRow = 0.1, cexCol = 0.1,
                    margins = c(6,6))

Figure 7: Symmetric heat map of the distance matrix displaying neutral loss similarities of the example data set along with dendrograms resulting from hierarchical clustering based on the distance matrix. The colour encoding is shown in the top-left insert.

Obviously, it makes sense to export the plots to larger pdf or png files (e.g. 2000 \(\times\) 2000 pixels) to examine them closely. If exported to pdf, the feature names remain searchable (`Ctrl+F` in Windows).

## Create a dendrogram

With the dendrogram, too, it is advisable to export is to pdf in a large format, e.g. as follows:

    pdf(file = "CluMSID_dendro.pdf", width = 20, height = 20)
    HCplot(distmat)
    dev.off()

The plot from our example data looks like this:

Figure 8: Circularised dendrogram as a result of agglomerative hierarchical clustering with average linkage as agglomeration criterion based on MS2 spectra similarities of the example data set. Each leaf represents one feature and colours encode cluster affiliation of the features. Leaf labels display feature IDs, along with feature annotations, if existent. Distance from the central point is indicative of the height of the dendrogram.

The clusters are colour-coded and if exported to pdf, the tip labels containing feature ID and annotation are searchable.The height of the dendrogram’s branching points serves as another piece of information when interpreting the clustered data as it signifies similarity of features.

For a detailed example of how to interpret, please refer to Depke et al. 2017, where `CluMSID` helped to identify new members of several classes of secondary metabolites in Pseudomonas aeruginosa.

Like with density-based clustering, it is also possible to generate a list of features with respective cluster assignments using `HCtbl`. As mentioned above for `CluMSID_OPTISplot` and `OPTICStbl`, it is crucial to run `HCplot` and `HCtbl` using the same parameters.

    HCtbl <- HCtbl(distmat)

    head(HCtbl)
    #>                                 feature cluster_ID
    #> 1            M146.17T59.35 - spermidine          1
    #> 2 M129.14T58.57 - spermidine (fragment)          1
    #> 3  M112.11T57.8 - spermidine (fragment)          1
    #> 4                         M251.16T60.64          1
    #> 5                         M212.85T65.02          2
    #> 6                         M290.85T64.76          3

# Generate a correlation network

As a new functionality, `CluMSID` offers the possibility to analyse the similarity data using weighted correlation networks. These networks offer some advantages with respect to standard clustering methods, most notably that they do not strictly assign every feature to a distinct cluster but also represent similarities between features that would fall into different clusters in hierarchical or density-based clustering. Thus, correlation networks potentially contain more useful information for data interpretation. On the downside, the interpretation is also complicated by this lack of concrete cluster assignments. E.g., we cannot simply look up which features belong to the same cluster in order to examine their spectra closely but we have to go back to the correlation network visualisation and search for connected features manually.

`networkplot` requires some arguments:

- `distmat`: matrix; a distance matrix like for all other functions described above
- `interactive`: logical; Similar to `MDSplotplot`, correlation network can be generate as interactive plots that are zoomable and display feature IDs on mouse-over. If that is desired, set `interactive` to `TRUE` (default is `FALSE`).
- `show_labels`: logical; whether to display feature IDs in the (non-interactive) plot (default is `FALSE`, ignored if `interacive = TRUE`)
- `label_size`: numeric; font size of feature ID labels (default is `1.5`, which is way smaller than the default in `GGally::ggnet2`, `4.5`)
- `highlight_annotated`: logical; whether to plot dots for features with annotation in a different colour (same as in `MDSplotplot`, default is `FALSE`)
- `min_similarity`: numeric; the minimum similarity (1 – distance) threshold (similarities below this threshold will be ignored, default is `0.1`)
- `exclude_singletons`: logical; whether to exclude features from the plot that do not have connections to other features, particularly useful with data sets containing very dissimilar spectra, e.g. neutral loss patterns or MS1 pseudospectra (default is `FALSE`)

A standard non-interactive correlation network for the MS2 example data can be plotted like this:

    networkplot(distmat, highlight_annotated = TRUE,
                    show_labels = TRUE, interactive = FALSE)

Figure 9: Correlation network plot based on MS2 spectra similarities of the example data set. Grey dots indicate non-identified features, orange dots identified ones. Labels display feature IDs, along with feature annotations, if existent. Edge widths are proportional to spectral similarity of the connected features.

As you can guess from this plot, it makes sense to use the interactive visualisation. Just like with `MDSplotplot`, you can view the interactive plot within RStudio or save it as html and view it in web browser.

    my_net <- networkplot(distmat, interactive = TRUE,
                                highlight_annotated = TRUE)

    htmlwidgets::saveWidget(my_net, "net.html")

This is how it looks like if you open the html file in Firefox, zoom in on a cluster and mouse over a feature:

Figure 10: Screenshot of the interactive version of the Correlation network plot based on MS2 spectra similarities of the example data set (cf Figure 9). Zoomed image section with tooltip displaying feature information upon mouse-over.

Please be aware that the spatial arrangement of the data points in the plot has a random component, i.e. while the relative position of the points (the distance to each other) is always the same, the absolute position varies and will not be the same even if the same command is executed twice.

The pairwise similarity of spectra or neutral loss patterns of features expressed by the cosine score is signified by the width of the line connecting the two features. All pairwise similarities greater than `min_similarity` result in a connecting line in the plot. The spatial proximity in which the features are mapped onto the plot is determined by the multivariate method underlying the network generation.

As we have already noticed after inspection of the heatmaps on p.13–14, the neutral loss patterns show much less similarity to each other than the MS2 spectra data. Thus, we expect quite a few neutral loss patterns that do not show any similarity to another neutral loss pattern. This expectation justifies the exclusion of these ‘singletons’ from the correlation network analysis. To do so, just set `exclude_singletons` to `TRUE`:

    networkplot(nlmat, highlight_annotated = TRUE,
                    show_labels = TRUE, exclude_singletons = TRUE)

Figure 11: Correlation network plot based on neutral loss similarities of the example data set (cf Figure 9).

# Additional functionalities

Multidimensional scaling, density-based clustering, hierarchical clustering and correlation network analysis are the main `CluMSID` tools to analyse MS2 spectra or neutral loss pattern similarity data, however, the package contains some additional functionalities that may facilitate data analysis in some cases and can also be used in other contexts with or without the above-mentioned unsupervised methods.

## Access individual spectra from a list of spectra by various slot entries

Accessing S4 objects within lists is not trivial. Therefore, `CluMSID` offers a function to access individual or several `MS2spectrum` objects by their slot entries. `getSpectrum()` requires the following arguments:

- `featlist`: a `list` that contains only objects of class `MS2spectrum`
- `slot`: the slot to be searched (invalid `slot` arguments will produce errors):
  - `id`
  - `annotation`
  - `precursor` (m/z of precursor ion)
  - `rt` (retention time of precursor)
- `what`: the search term or number, must be character for `id` and `annotation` and numeric for `precursor` and `rt`
- `mz.tol`: the tolerance used for precursor ion m/z searches, defaults to 1E-05 (10ppm)
- `rt.tol`: the tolerance used for precursor ion retention time searches, defaults to 30s; high values can be used to specify retention time ranges (see example)

Some examples will demonstrate the use of `getSpectrum()`:

1. Accessing a spectrum by its ID. For this, the exact feature ID must be known:

    getSpectrum(annotatedSpeclist, "id", "M244.17T796.4")
    #> An object of class "MS2spectrum"
    #>  id: M244.17T796.4
    #>  annotation: HHQ
    #>  precursor: 244.1700
    #>  retention time: 796.4
    #>  polarity: positive
    #>  MS2 spectrum with 98 fragment peaks
    #>  neutral loss pattern with 81 neutral losses

2. Accessing a spectrum by its annotation. For this, the exact annotation has to be known as well, other annotations will produce a message:

    getSpectrum(annotatedSpeclist, "annotation", "HHQ")
    #> An object of class "MS2spectrum"
    #>  id: M244.17T796.4
    #>  annotation: HHQ
    #>  precursor: 244.1700
    #>  retention time: 796.4
    #>  polarity: positive
    #>  MS2 spectrum with 98 fragment peaks
    #>  neutral loss pattern with 81 neutral losses

    getSpectrum(annotatedSpeclist, "annotation", "C7-HQ")
    #> No spectrum with that annotation.

3. Accessing spectra by their precursor ion m/z. If the list contains more than one spectrum with a precursor ion m/z within the tolerance, the output is again a list of `MS2spectrum` objects that meet the specified criterion:

    getSpectrum(annotatedSpeclist, "precursor", 286.18, mz.tol = 1E-03)
    #> [[1]]
    #> An object of class "MS2spectrum"
    #>  id: M286.18T728.73
    #>  annotation: C9:1-QNO
    #>  precursor: 286.1799
    #>  retention time: 728.73
    #>  polarity: positive
    #>  MS2 spectrum with 4 fragment peaks
    #>  neutral loss pattern with 2 neutral losses
    #> [[2]]
    #> An object of class "MS2spectrum"
    #>  id: M286.18T808.85
    #>  annotation: C9:1-QNO
    #>  precursor: 286.1804
    #>  retention time: 808.85
    #>  polarity: positive
    #>  MS2 spectrum with 7 fragment peaks
    #>  neutral loss pattern with 5 neutral losses
    #> [[3]]
    #> An object of class "MS2spectrum"
    #>  id: M286.18T864.04
    #>  annotation: C9:1-QNO
    #>  precursor: 286.1808
    #>  retention time: 864.04
    #>  polarity: positive
    #>  MS2 spectrum with 183 fragment peaks
    #>  neutral loss pattern with 167 neutral losses
    #> [[4]]
    #> An object of class "MS2spectrum"
    #>  id: M286.18T921.6
    #>  annotation: C9:1-PQS
    #>  precursor: 286.1808
    #>  retention time: 921.6
    #>  polarity: positive
    #>  MS2 spectrum with 3 fragment peaks
    #>  neutral loss pattern with 1 neutral losses

4. Accessing spectra by their precursor retention time. Here, too, we can extract several `MS2spectrum` objects by setting a larger retention time tolerance. If we want to extract the spectra of all compounds that elute from 6min (360s) to 8min (480s), we proceed as follows:

    six_eight <- getSpectrum(annotatedSpeclist, "rt", 420, rt.tol = 60)
    length(six_eight)
    #> [1] 75

## Find spectra that contain a specific fragment or neutral loss

Another pair of accessory functions is `findFragment()` and `findNL()` which are used to find spectra that contain a specific fragment ion or neutral loss. Analogous to `getSpectrum()`, they need as arguments a list of `MS2spectrum` objects, the m/z of the fragment or neutral loss of interest and the respective m/z tolerance in ppm (default is 10ppm).

The two functions can be useful in many situation, e.g. when working with lipid data where head groups and fatty acids often give characteristic fragments or neutral losses. In the world of P. aeruginosa secondary metabolites, alkylquinolones (AQs) play an important role and most of the AQ MS2 spectra contain a signature fragment with an m/z of 159.068. Based on this fragment m/z, we can create a list of putative AQs:

    putativeAQs <- findFragment(annotatedSpeclist, 159.068)
    #> 70 spectra were found that contain a fragment of m/z 159.068 +/- 10 ppm.

An example for common neutral losses are nucleoside monophospates that all loose ribose-5’-monophosphate, resulting in a neutral loss of 212.009 in ESI-(+). Using `findNL()` we find CMP, UMP, AMP and GMP.

    findNL(annotatedSpeclist, 212.009)
    #> 4 neutral loss patterns were found that contain a neutral loss of m/z 212.009 +/- 10 ppm.
    #> [[1]]
    #> An object of class "MS2spectrum"
    #>  id: M324.06T75.32
    #>  annotation: CMP
    #>  precursor: 324.0591
    #>  retention time: 75.32
    #>  polarity: positive
    #>  MS2 spectrum with 8 fragment peaks
    #>  neutral loss pattern with 8 neutral losses
    #> [[2]]
    #> An object of class "MS2spectrum"
    #>  id: M325.04T78.94
    #>  annotation: UMP
    #>  precursor: 325.0429
    #>  retention time: 78.94
    #>  polarity: positive
    #>  MS2 spectrum with 5 fragment peaks
    #>  neutral loss pattern with 5 neutral losses
    #> [[3]]
    #> An object of class "MS2spectrum"
    #>  id: M348.07T90.34
    #>  annotation: AMP
    #>  precursor: 348.0707
    #>  retention time: 90.34
    #>  polarity: positive
    #>  MS2 spectrum with 21 fragment peaks
    #>  neutral loss pattern with 19 neutral losses
    #> [[4]]
    #> An object of class "MS2spectrum"
    #>  id: M364.07T97.19
    #>  annotation: GMP
    #>  precursor: 364.0659
    #>  retention time: 97.19
    #>  polarity: positive
    #>  MS2 spectrum with 6 fragment peaks
    #>  neutral loss pattern with 6 neutral losses

## Match one spectrum against a set of spectra

If you are mainly interested in one or a few number of spectra or neutral loss patterns, it may be sufficient to match one feature at a time against a larger set of spectra. This set of spectra can be all spectra contained in one mzXML file like in all the examples in this tutorial or they could be a spectral library, as long as its format in `R` is a list of `MS2spectrum` objects.

The `getSimilarities()` function requires several arguments:

- `spec`: The spectrum to be compared to other spectra. Can be either an object of class `MS2spectrum` or a two-column numerical matrix that contains fragment mass-to-charge ratios in the first and intensities in the second column.
- `speclist`: The set of spectra to which `spec` is to be compared. Must be a list where every entry is an object of class `MS2spectrum`. Can be generated from an mzXML file as shown above or constructed using `new("MS2spectrum", ...)` for every list entry (see example).
- `type`: Specifies whether MS2 spectra or neutral loss patterns are to be compared. Must be either ‘spectrum’ (default) or ‘neutral_losses’.
- `hits_only`: Logical that indicates whether the result should contain only similarities greater than zero (see example).

In the first example, we want to find all MS2 spectra in our example data set that are similar to the spectrum of pyocyanin, an important secondary metabolite from Pseudomonas aeruginosa and therefore match the pyocyanin spectrum against our `annotatedSpeclist`. Because we have already identified pyocyanin in the data set, we can use `getSpectrum` to extract the `MS2spectrum` object from `annotatedSpeclist`. We do not want to search all 518 elements of the result vector, so we set `hits_only` to `TRUE` to exclude spectra that have 0 similarity to the pyocyanin spectrum.

    pyo <- getSpectrum(annotatedSpeclist, "annotation", "pyocyanin")

    sim_pyo <- getSimilarities(pyo, annotatedSpeclist, hits_only = TRUE)
    sim_pyo
    #>  M110.06T100.45  M123.06T103.31  M332.56T107.48  M332.08T113.21  M182.08T125.93
    #>    0.0235166588    0.0071763662    0.0032575891    0.0035153018    0.0414005385
    #>  M166.09T233.22  M120.08T233.48   M103.05T235.3  M174.06T277.59  M220.12T336.88
    #>    0.0394723541    0.0492390806    0.0826780036    0.0391004892    0.0205482303
    #>  M525.18T352.51   M243.08T362.4  M188.07T371.25   M205.1T370.99  M211.09T382.17
    #>    0.0060019991    0.0145904545    0.0176900909    0.0179895663    1.0000000000
    #>  M187.12T391.55  M188.12T399.72  M254.09T400.89  M160.08T433.66  M291.15T444.85
    #>    0.0210280136    0.0105392131    0.2071528536    0.0489638040    0.0106479317
    #>  M120.04T450.56  M138.06T451.33   M176.07T465.7  M491.29T496.41  M255.08T482.73
    #>    0.0287432023    0.0202198052    0.0275059908    0.0610208210    0.6451546287
    #>  M245.59T495.11  M145.08T508.11  M163.09T512.64  M188.11T535.78    M321.1T537.6
    #>    0.2583432230    0.0473127795    0.0034167239    0.0057005179    0.0293635312
    #>  M243.09T558.67  M136.08T584.09  M118.06T585.64  M215.12T626.24  M224.08T640.69
    #>    0.0116275804    0.0132716679    0.0203921366    0.3252546561    0.0325490977
    #>  M213.07T652.92  M216.14T670.01  M227.08T670.26  M264.18T675.46  M233.13T676.23
    #>    0.0083842257    0.0009299928    0.0034818309    0.0172023762    0.0143332573
    #>  M225.07T698.07   M207.06T699.1   M257.06T704.3  M226.18T703.76  M325.07T739.09
    #>    0.0253940205    0.0230298767    0.0028192053    0.0255571995    0.0010572974
    #>  M181.08T724.14   M330.19T724.4  M255.08T740.91  M446.19T745.32   M321.1T746.09
    #>    0.1283755709    0.5019236568    0.2030839014    0.0750793676    0.1017149711
    #>  M891.36T747.39   M231.1T763.28   M185.1T763.53   M288.2T765.88  M258.15T768.47
    #>    0.0714108632    0.0070294838    0.0094225379    0.0018840838    0.0168976240
    #>  M328.14T772.12   M242.15T789.6  M304.19T786.75  M260.16T796.66   M244.17T796.4
    #>    0.0009052790    0.0071606643    0.0066966285    0.0141834395    0.0106077162
    #>  M159.07T795.61  M314.21T825.99  M326.18T833.85  M286.18T864.04  M270.19T873.15
    #>    0.0124577125    0.0006167122    0.0023434969    0.0205024121    0.0089744139
    #>  M268.17T875.49  M178.05T867.16  M198.09T874.45    M170.1T885.9    M288.2T902.1
    #>    0.0011687551    0.0413446455    0.0064665757    0.0062571323    0.0100270721
    #>   M184.08T908.6   M272.2T910.42   M312.2T929.44  M314.21T944.87   M296.2T958.11
    #>    0.0036517997    0.0086043375    0.0085388744    0.0128215188    0.0054678320
    #>  M298.22T984.33  M500.22T974.72  M304.19T977.83  M303.19T979.65 M340.23T1007.62
    #>    0.0065812089    0.0396920510    0.0059045590    0.0049045093    0.0052002486
    #> M324.23T1025.79 M314.21T1034.15 M326.25T1043.73 M679.43T1051.39
    #>    0.0005826366    0.0030626495    0.0005424581    0.0008290325

We get 84 spectra that have a non-zero similarity to the pyocyanin spectrum, including pyocyanin itself with a similarity of `1`. Of course, we can further filter the data by subsetting the result vector in order to exclude spectra that have only minimal similarity, e.g. `M679.43T1051.39` with a cosine similarity of only `0.0008` (the last element in the vector).

In the second example, we generate a new `speclist`, e.g. from a spectral library. We look at the unknown feature that has most similarity to pyocyanin. As pyocyanin is contained in `annotatedSpeclist` itself, we have to look at the second highest similarity. Again, we use `getSpectrum()` to extract the object from `annotatedSpeclist`:

    highest_sim <- sort(sim_pyo, decreasing = TRUE)[2]

    sim_spec <- getSpectrum(annotatedSpeclist, "id", names(highest_sim))
    sim_spec
    #> An object of class "MS2spectrum"
    #>  id: M255.08T482.73
    #>  annotation:
    #>  precursor: 255.0761
    #>  retention time: 482.73
    #>  polarity: positive
    #>  MS2 spectrum with 5 fragment peaks
    #>  neutral loss pattern with 3 neutral losses

We see that the feature is not annotated. We are interested whether this feature also shows similarity to other members of the phenazine family of P. aeruginosa secondary metabolites. Some phenazines are contained in `annotatedSpeclist` but some are not, so we make a new `speclist` called `phenazines` and add the missing spectra manually from an in-house library:

    phenazines <- list()
    phenazines[[1]] <- getSpectrum(annotatedSpeclist,
                                    "annotation", "pyocyanin")
    phenazines[[2]] <- getSpectrum(annotatedSpeclist,
                                    "annotation", "phenazine-1-carboxamide")
    phenazines[[3]] <- getSpectrum(annotatedSpeclist,
                                    "annotation", "phenazine-1-carboxylic acid")
    phenazines[[4]] <- getSpectrum(annotatedSpeclist,
                                    "annotation", "phenazine-1,6-dicarboxylic acid")
    phenazines[[5]] <- new("MS2spectrum", id = "lib_entry_1",
                            annotation = "1-hydroxyphenazine",
                            spectrum = matrix(c(168.0632, 14,
                                                169.0711, 288,
                                                170.0743, 33,
                                                179.0551, 62,
                                                197.0653, 999),
                                            byrow = TRUE,
                                            ncol = 2))
    phenazines[[6]] <- new("MS2spectrum", id = "lib_entry_2",
                            annotation = "2-hydroxy-phenazine-1-carboxylic acid",
                            spectrum = matrix(c(167.0621, 43,
                                                179.0619, 93,
                                                180.0650, 12,
                                                195.0564, 40,
                                                223.0509, 999,
                                                224.0541, 142,
                                                241.0611, 60),
                                            byrow = TRUE,
                                            ncol = 2))
    phenazines[[7]] <- new("MS2spectrum", id = "lib_entry_3",
                            annotation = "pyocyanin (library spectrum)",
                            spectrum = matrix(c(168.0690, 58,
                                                183.0927, 152,
                                                184.0958, 19,
                                                196.0640, 118,
                                                197.0674, 15,
                                                211.0873, 999,
                                                212.0905, 145),
                                            byrow = TRUE,
                                            ncol = 2))

    getSimilarities(sim_spec, phenazines, hits_only = FALSE)
    #> M211.09T382.17 M224.08T640.69 M225.07T698.07 M269.06T708.74    lib_entry_1
    #>      0.6451546      0.0000000      0.0000000      0.0000000      0.0000000
    #>    lib_entry_2    lib_entry_3
    #>      0.0000000      0.6375061

As a result, we get the interesting information that the MS2 spectra similarity of our unknown feature seems to be specific to pyocyanin (both the experimental and the library spectrum).

## Convert `MSnbase` objects to class `MS2spectrum`

The `MSnbase` package—which is commonly used for proteomics applications and is also associated with XCMS3—has two classes for (MS2) spectra, `Spectrum` and `Spectrum2` which contain spectra along with metainformation. These metainformation differ from those contained in `MS2spectrum` objects and are not very well suited for metabolomics applications. Still, it is possible to use `CluMSID` functions with objects of those two classes by converting them to `MS2spectrum` objects using `as.MS2spectrum()`:

    CluMSID_object <- as.MS2spectrum(MSnbase_object)
    # or alternatively
    CluMSID_object <- as(MSnbase_object, "MS2spectrum")

## Split polarities from polarity-switching runs

As polarity-switching and similar methords are gaining importance in LC-MS/MS metabolomics, CluMSID offers the possibility to process LC-MS/MS data containing spectra of different polarities. As spectra from positive and negative ionisation show different fragmentation mechanisms and patterns, it does not appear to be useful to compare spectra of different polarity to each other. Therefore, CluMSID provides a function to separate positive and negative spectra from each other. This has to be done in the very beginning of the analysis to not interfere with spectral merging. Positive and negative spectra can than be processed independently from each other as shown above.

A schematic workflow would like like this:

    raw_list_mixedpolarities <- extractMS2spectra("raw_file_mixedpolarities.mzXML")

    raw_list_positive <- splitPolarities(raw_list_mixedpolarities, "positive")
    raw_list_negative <- splitPolarities(raw_list_mixedpolarities, "negative")

    speclist_positive <- mergeMS2spectra(raw_list_positive)
    speclist_negative <- mergeMS2spectra(raw_list_negative)

… and so on as described in this tutorial.

# Use MS1 pseudospectra instead of or in addition to MS2 data

MS1 pseudospectra are groups of peaks/ions that derive or are assumed to derive from the same compound. They consist of peaks for in-source fragment, adducts etc. Pseudospectra can contain structural information about analytes, e.g. about moieties that easily fragment even in MS1 mode without CID. Thus, it might sometimes be useful to study similarities between pseudospectra analogously to those between MS2 spectra. `CluMSID` makes use of the `CAMERA` package to assign peaks to pseudospectra. A custom S4 class named `pseudospectrum` is used which is very similar to the `MS2spectrum` class. For obvious reasons, it does not contain a precursor ion m/z slot and thus no neutral loss pattern, either. The `pcgroup` defined by `CAMERA` is used as ID, an annotation can be added if desired.

## Extract pseudospectra

To extract pseudospectra, you first have to process your data using the `CAMERA` package, either in R or via XCMSonline, where this is done automatically. There are two possibilities to use the `extractPseudospectra()` function in `CluMSID`: either with an `xsAnnotate` object which you generate with `CAMERA` in R or with a `data.frame` that contains data on m/z, retention time, intensity and `pcgroup`, e.g. the results table from XCMSonline.

The latter is demonstrated with the XCMSonline results table already used to generate a peak table. If the column names are not changed, the `data.frame` can be supplied as-is and `intensity_columns` does not have to be specified. We want to exclude pseudospectra that have only one peak, so we set `min_peaks = 2`.

    pstable <-
        readr::read_delim(file = system.file("extdata",
                                            "TD035_XCMS.annotated.diffreport.tsv",
                                            package = "CluMSIDdata"),
                        delim = "\t")

    pseudospeclist <- extractPseudospectra(pstable, min_peaks = 2)

As a result, we get a list with 198 pseudospectra that we can now process further.

## Create distance matrix for pseudospectra

The creation of a distance matrix is analogous to the procedure for MS2 spectra:

    pseudodistmat <- distanceMatrix(pseudospeclist)

## Generate a correlation network for pseudospectra

The distance matrix can now be used for MDS, clustering and correlation networks just like described above. For demonstration, we generate a correlation network:

    networkplot(pseudodistmat, show_labels = TRUE, exclude_singletons = TRUE)

Figure 12: Correlation network plot based on similarities of pseudospectra of the example data set (cf Figure 9).

With the exclusion of singletons, we get a much less busy plot than for MS2 data but we still find quite a few connections that may prove informative.

# Session Info

    sessionInfo()
    #> R version 4.2.1 (2022-06-23 ucrt)
    #> Platform: x86_64-w64-mingw32/x64 (64-bit)
    #> Running under: Windows Server 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
    #>
    #> attached base packages:
    #> [1] stats4    stats     graphics  grDevices utils     datasets  methods
    #> [8] base
    #>
    #> other attached packages:
    #>  [1] magrittr_2.0.3      metaMSdata_1.33.0   metaMS_1.34.0
    #>  [4] CAMERA_1.54.0       xcms_3.20.0         MSnbase_2.24.0
    #>  [7] ProtGenerics_1.30.0 S4Vectors_0.36.0    mzR_2.32.0
    #> [10] Rcpp_1.0.9          BiocParallel_1.32.0 Biobase_2.58.0
    #> [13] BiocGenerics_0.44.0 CluMSIDdata_1.13.0  CluMSID_1.14.0
    #>
    #> loaded via a namespace (and not attached):
    #>   [1] snow_0.4-4                  backports_1.4.1
    #>   [3] Hmisc_4.7-1                 plyr_1.8.7
    #>   [5] igraph_1.3.5                lazyeval_0.2.2
    #>   [7] splines_4.2.1               GenomeInfoDb_1.34.0
    #>   [9] ggplot2_3.3.6               digest_0.6.30
    #>  [11] foreach_1.5.2               htmltools_0.5.3
    #>  [13] fansi_1.0.3                 checkmate_2.1.0
    #>  [15] cluster_2.1.4               doParallel_1.0.17
    #>  [17] tzdb_0.3.0                  limma_3.54.0
    #>  [19] readr_2.1.3                 sna_2.7
    #>  [21] matrixStats_0.62.0          vroom_1.6.0
    #>  [23] MsFeatures_1.6.0            jpeg_0.1-9
    #>  [25] colorspace_2.0-3            xfun_0.34
    #>  [27] dplyr_1.0.10                crayon_1.5.2
    #>  [29] RCurl_1.98-1.9              jsonlite_1.8.3
    #>  [31] graph_1.76.0                impute_1.72.0
    #>  [33] survival_3.4-0              iterators_1.0.14
    #>  [35] ape_5.6-2                   glue_1.6.2
    #>  [37] gtable_0.3.1                zlibbioc_1.44.0
    #>  [39] XVector_0.38.0              DelayedArray_0.24.0
    #>  [41] DEoptimR_1.0-11             scales_1.2.1
    #>  [43] vsn_3.66.0                  DBI_1.1.3
    #>  [45] GGally_2.1.2                viridisLite_0.4.1
    #>  [47] htmlTable_2.4.1             clue_0.3-62
    #>  [49] archive_1.1.5               bit_4.0.4
    #>  [51] foreign_0.8-83              preprocessCore_1.60.0
    #>  [53] Formula_1.2-4               MsCoreUtils_1.10.0
    #>  [55] htmlwidgets_1.5.4           httr_1.4.4
    #>  [57] gplots_3.1.3                RColorBrewer_1.1-3
    #>  [59] ellipsis_0.3.2              farver_2.1.1
    #>  [61] pkgconfig_2.0.3             reshape_0.8.9
    #>  [63] XML_3.99-0.12               nnet_7.3-18
    #>  [65] sass_0.4.2                  deldir_1.0-6
    #>  [67] utf8_1.2.2                  labeling_0.4.2
    #>  [69] tidyselect_1.2.0            rlang_1.0.6
    #>  [71] munsell_0.5.0               tools_4.2.1
    #>  [73] cachem_1.0.6                cli_3.4.1
    #>  [75] dbscan_1.1-11               generics_0.1.3
    #>  [77] statnet.common_4.7.0        evaluate_0.17
    #>  [79] stringr_1.4.1               fastmap_1.1.0
    #>  [81] mzID_1.36.0                 yaml_2.3.6
    #>  [83] bit64_4.0.5                 knitr_1.40
    #>  [85] robustbase_0.95-0           caTools_1.18.2
    #>  [87] purrr_0.3.5                 RANN_2.6.1
    #>  [89] ncdf4_1.19                  RBGL_1.74.0
    #>  [91] nlme_3.1-160                compiler_4.2.1
    #>  [93] rstudioapi_0.14             plotly_4.10.0
    #>  [95] png_0.1-7                   affyio_1.68.0
    #>  [97] MassSpecWavelet_1.64.0      tibble_3.1.8
    #>  [99] bslib_0.4.0                 stringi_1.7.8
    #> [101] highr_0.9                   lattice_0.20-45
    #> [103] Matrix_1.5-1                vctrs_0.5.0
    #> [105] pillar_1.8.1                lifecycle_1.0.3
    #> [107] BiocManager_1.30.19         jquerylib_0.1.4
    #> [109] MALDIquant_1.21             data.table_1.14.4
    #> [111] bitops_1.0-7                GenomicRanges_1.50.0
    #> [113] R6_2.5.1                    latticeExtra_0.6-30
    #> [115] pcaMethods_1.90.0           affy_1.76.0
    #> [117] network_1.18.0              KernSmooth_2.23-20
    #> [119] gridExtra_2.3               IRanges_2.32.0
    #> [121] codetools_0.2-18            MASS_7.3-58.1
    #> [123] gtools_3.9.3                assertthat_0.2.1
    #> [125] SummarizedExperiment_1.28.0 withr_2.5.0
    #> [127] GenomeInfoDbData_1.2.9      hms_1.1.2
    #> [129] parallel_4.2.1              grid_4.2.1
    #> [131] rpart_4.1.19                tidyr_1.2.1
    #> [133] coda_0.19-4                 rmarkdown_2.17
    #> [135] MatrixGenerics_1.10.0       base64enc_0.1-3
    #> [137] interp_1.1-3