Introduction
As described in the GC-EI-MS tutorial, CluMSID can also be used to
analyse low resolution data – although using low resolution data comes
at a cost.
In this example, we will use a similar sample (1uL Pseudomonas
aeruginosa PA14 cell extract) as in the General Tutorial, measured
with similar chromatography but on a different mass spectrometer, a
Bruker amaZon ion trap instrument operated in ESI-(+) mode with
auto-MS/MS. In addition to introducing a workflow for low resolution
LC-MS/MS data, this example also demonstrates that CluMSID can work with
data from different types of mass spectrometers.
Data import
We load the file from the CluMSIDdata package:
library(CluMSID)
library(CluMSIDdata)
lowresfile <- system.file("extdata",
"PA14_amazon_lowres.mzXML",
package = "CluMSIDdata")
Data preprocessing
The extraction of spectra works the same way as with high resolution
LC-MS/MS data:
ms2list <- extractMS2spectra(lowresfile)
length(ms2list)
#> [1] 1989
Like in the GC-EI-MS example, we have to adjust
mz_tolerance to a much higher value compared to high
resolution data, while the retention time tolerance can remain
unchanged.
featlist <- mergeMS2spectra(ms2list, mz_tolerance = 0.02)
length(featlist)
#> [1] 525
We see that we have similar numbers of spectra as in the General
Tutorial, because we tried to keep all parameters except for the mass
spectrometer type comparable.
Generation of distance matrix
As we do not have low resolution spectral libraries at hand, we skip
the integration of previous knowledge on feature identities in this
example and generate a distance matrix right away:
distmat <- distanceMatrix(featlist)
Data exploration
Starting from this distance matrix, we can use all the data
exploration functions that CluMSID offers.
When we now make an MDS plot, we learn that the similarity data is
very different from the comparable high resolution data:
Figure 1: Multidimensional
scaling plot as a visualisation of MS2 spectra similarities
of the low resolution LC-MS/MS example data set. Black dots signify
spectra from unknown metabolites.
To get a better overview of the data and the general similarity
behaviour, we create a heat map of the distance matrix:
HCplot(distmat, type = "heatmap",
cexRow = 0.1, cexCol = 0.1,
margins = c(6,6))
Figure 2: Symmetric heat map of
the distance matrix displaying MS2 spectra similarities of
the low resolution LC-MS/MS 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.
We clearly see that the heat map is generally a lot “warmer” than in
the General Tutorial (an intuition that is supported by the histogram in
the top-left corner), i.e. we have a higher general degree of similarity
between spectra. That is not surprising as the m/z information
has much fewer levels than in high resolution data and fragments of
different sum formula are more likely to have indistinguishable
mass-to-charge ratios.
We also see that some more or less compact clusters can be
identified. This is easier to inspect in the dendrogram
visualisation:
HCplot(distmat, h = 0.8, cex = 0.3)
Figure 3: Circularised
dendrogram as a result of agglomerative hierarchical clustering with
average linkage as agglomeration criterion based on MS2
spectra similarities of the low resolution LC-MS/MS example data set.
Each leaf represents one feature and colours encode cluster affiliation
of the features. Leaf labels display feature IDs. Distance from the
central point is indicative of the height of the dendrogram.
In conclusion, CluMSID is capable of processing low resolution
LC-MS/MS data and if high resolution data is not available, it can be
very useful to provide an overview of spectral similarities in low
resolution data, thereby helping metabolite annotation if some
individual metabolites can be identified by comparison to authentic
standards. However, concerning feature annotation, high resolution
methods should always be favoured for the many benefits they
provide.