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
classesCluMSID
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, 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
slotThe 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)
.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 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>
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:
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 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:
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:
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
).
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.
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.
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:
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.
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:
\[ cos(\theta) = \frac{\sum_{i}a_i \cdot b_i}{\sqrt{\sum_{i}{a_{i}}^2 \cdot \sum_{i}{b_{i}}^2}} \]
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.
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”:
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:
For neutral loss patterns:
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.
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:
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.
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 assignmentHeatmaps 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):
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).