CluMSID — Clustering of MS2 Spectra for Metabolite Identification
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:
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 listannotation: a character string containing a user-defined annotation, defaults to emptyprecursor: (median) m/z of the spectrum’s precursor ionrt: (median) retention time of the spectrum’s precursor ionpolarity: the polarity with which the spectrum was recorded, eitherpositiveornegativespectrum: 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 thespectrumslot
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.
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 lossesFrom 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.
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 lossesThe 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:
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 lossesNote 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:
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 gluconolactoneaddAnnotations(featlist, annos, annotationColumn = 2)
will throw an error because featlist and annos
are of different length. Instead, you need to do the following:
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.
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”:
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:
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:
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:
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.
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.
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 dendrogramsdendrogram(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):
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.
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 × 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:
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.
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 aboveinteractive: logical; Similar toMDSplotplot, correlation network can be generate as interactive plots that are zoomable and display feature IDs on mouse-over. If that is desired, setinteractivetoTRUE(default isFALSE).show_labels: logical; whether to display feature IDs in the (non-interactive) plot (default isFALSE, ignored ifinteracive = TRUE)label_size: numeric; font size of feature ID labels (default is1.5, which is way smaller than the default inGGally::ggnet2,4.5)highlight_annotated: logical; whether to plot dots for features with annotation in a different colour (same as inMDSplotplot, default isFALSE)min_similarity: numeric; the minimum similarity (1 – distance) threshold (similarities below this threshold will be ignored, default is0.1)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 isFALSE)
A standard non-interactive correlation network for the MS2 example data can be plotted like this:
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:
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: alistthat contains only objects of classMS2spectrumslot: the slot to be searched (invalidslotarguments will produce errors):idannotationprecursor(m/z of precursor ion)rt(retention time of precursor)
what: the search term or number, must be character foridandannotationand numeric forprecursorandrtmz.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 losses2. 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 losses3. 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 losses4. 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:
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 lossesMatch 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 classMS2spectrumor 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 whichspecis to be compared. Must be a list where every entry is an object of classMS2spectrum. Can be generated from an mzXML file as shown above or constructed usingnew("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 0.0008290325We 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 lossesWe 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 0.6375061As 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():
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:
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:
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 4.4.1 (2024-06-14)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.1 LTS
#>
#> Matrix products: default
#> BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so; LAPACK version 3.12.0
#>
#> locale:
#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=en_US.UTF-8 LC_COLLATE=C
#> [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
#> [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
#> [9] LC_ADDRESS=C LC_TELEPHONE=C
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
#>
#> time zone: Etc/UTC
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] magrittr_2.0.3 metaMSdata_1.41.0 metaMS_1.42.0
#> [4] CAMERA_1.63.0 xcms_4.4.0 BiocParallel_1.41.0
#> [7] Biobase_2.67.0 BiocGenerics_0.53.0 CluMSIDdata_1.21.0
#> [10] CluMSID_1.23.0 rmarkdown_2.28
#>
#> loaded via a namespace (and not attached):
#> [1] bitops_1.0-9 tibble_3.2.1
#> [3] preprocessCore_1.68.0 graph_1.85.0
#> [5] XML_3.99-0.17 rpart_4.1.23
#> [7] lifecycle_1.0.4 doParallel_1.0.17
#> [9] lattice_0.22-6 vroom_1.6.5
#> [11] MASS_7.3-61 MultiAssayExperiment_1.33.0
#> [13] backports_1.5.0 limma_3.63.0
#> [15] Hmisc_5.2-0 plotly_4.10.4
#> [17] sass_0.4.9 jquerylib_0.1.4
#> [19] yaml_2.3.10 MsCoreUtils_1.19.0
#> [21] DBI_1.2.3 buildtools_1.0.0
#> [23] RColorBrewer_1.1-3 abind_1.4-8
#> [25] zlibbioc_1.52.0 GenomicRanges_1.59.0
#> [27] purrr_1.0.2 AnnotationFilter_1.31.0
#> [29] nnet_7.3-19 GenomeInfoDbData_1.2.13
#> [31] IRanges_2.41.0 S4Vectors_0.44.0
#> [33] maketools_1.3.1 MSnbase_2.33.0
#> [35] ncdf4_1.23 codetools_0.2-20
#> [37] DelayedArray_0.33.1 tidyselect_1.2.1
#> [39] Spectra_1.16.0 UCSC.utils_1.2.0
#> [41] farver_2.1.2 matrixStats_1.4.1
#> [43] stats4_4.4.1 base64enc_0.1-3
#> [45] jsonlite_1.8.9 Formula_1.2-5
#> [47] iterators_1.0.14 foreach_1.5.2
#> [49] dbscan_1.2-0 tools_4.4.1
#> [51] progress_1.2.3 sna_2.8
#> [53] Rcpp_1.0.13 glue_1.8.0
#> [55] gridExtra_2.3 SparseArray_1.6.0
#> [57] xfun_0.48 MatrixGenerics_1.19.0
#> [59] GenomeInfoDb_1.43.0 dplyr_1.1.4
#> [61] withr_3.0.2 BiocManager_1.30.25
#> [63] fastmap_1.2.0 GGally_2.2.1
#> [65] fansi_1.0.6 caTools_1.18.3
#> [67] digest_0.6.37 R6_2.5.1
#> [69] colorspace_2.1-1 gtools_3.9.5
#> [71] utf8_1.2.4 tidyr_1.3.1
#> [73] generics_0.1.3 data.table_1.16.2
#> [75] robustbase_0.99-4-1 prettyunits_1.2.0
#> [77] PSMatch_1.10.0 httr_1.4.7
#> [79] htmlwidgets_1.6.4 S4Arrays_1.6.0
#> [81] ggstats_0.7.0 pkgconfig_2.0.3
#> [83] gtable_0.3.6 impute_1.81.0
#> [85] MassSpecWavelet_1.73.0 XVector_0.46.0
#> [87] sys_3.4.3 htmltools_0.5.8.1
#> [89] RBGL_1.82.0 MALDIquant_1.22.3
#> [91] ProtGenerics_1.38.0 clue_0.3-65
#> [93] scales_1.3.0 knitr_1.48
#> [95] MetaboCoreUtils_1.15.0 rstudioapi_0.17.1
#> [97] tzdb_0.4.0 reshape2_1.4.4
#> [99] coda_0.19-4.1 statnet.common_4.10.0
#> [101] checkmate_2.3.2 nlme_3.1-166
#> [103] cachem_1.1.0 stringr_1.5.1
#> [105] KernSmooth_2.23-24 parallel_4.4.1
#> [107] foreign_0.8-87 mzID_1.45.0
#> [109] vsn_3.74.0 pillar_1.9.0
#> [111] grid_4.4.1 vctrs_0.6.5
#> [113] MsFeatures_1.15.0 gplots_3.2.0
#> [115] pcaMethods_1.99.0 cluster_2.1.6
#> [117] htmlTable_2.4.3 evaluate_1.0.1
#> [119] readr_2.1.5 cli_3.6.3
#> [121] compiler_4.4.1 rlang_1.1.4
#> [123] crayon_1.5.3 labeling_0.4.3
#> [125] QFeatures_1.16.0 affy_1.85.0
#> [127] plyr_1.8.9 fs_1.6.5
#> [129] stringi_1.8.4 viridisLite_0.4.2
#> [131] network_1.18.2 munsell_0.5.1
#> [133] lazyeval_0.2.2 Matrix_1.7-1
#> [135] hms_1.1.3 MsExperiment_1.9.0
#> [137] bit64_4.5.2 ggplot2_3.5.1
#> [139] statmod_1.5.0 SummarizedExperiment_1.36.0
#> [141] highr_0.11 mzR_2.41.0
#> [143] igraph_2.1.1 affyio_1.77.0
#> [145] bslib_0.8.0 DEoptimR_1.1-3
#> [147] bit_4.5.0 ape_5.8- Introduction
MS2spectrumandpseudospectrumclasses- Extract MS2 spectra from *.mzXML file
- Merge MS2 spectra that derive from the same peak/feature
- Add annotations
- Generate distance matrices
- Visualise distance/similarity data using multidimensional scaling (MDS)
- Perform density-based clustering using the OPTICS algorithm
- Perform hierarchical clustering
- Generate a correlation network
- Additional functionalities
- Use MS1 pseudospectra instead of or in addition to MS2 data
- Session Info