---
title: "CluMSID --- Clustering of MS^2^ Spectra for Metabolite Identification"
author: "Tobias Depke"
date: '`r format(Sys.Date(), "%B %d, %Y")`'
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{CluMSID Tutorial}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
%\VignetteDepends{CluMSIDdata, magrittr, dplyr, readr}
---
```{r setup, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
echo = TRUE
)
```
<style>
p.caption {
font-size: 80%;
text-align: left;
}
</style>
```{r captions, include=FALSE}
fig1 <- paste("**Figure 1:**",
"Multidimensional scaling plot as a visualisation of",
"MS^2^ spectra similarities",
"of the example data set.",
"Red dots signify annotated spectra,",
"black dots spectra from unknown metabolites.")
fig2 <- paste("**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.")
fig3 <- paste("**Figure 3:**",
"Screenshot of the interactive version of the",
"Multidimensional scaling plot visualising",
"MS^2^ spectra similarities",
"of the example data set (cf Figure 1).",
"Zoomed image section with tooltip displaying",
"feature information upon mouse-over.")
fig4 <- paste("**Figure 4:**",
"Reachability distance plot resulting from",
"OPTICS density based clustering of the",
"MS^2^ 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.")
fig5 <- paste("**Figure 5:**",
"Reachability distance plot resulting from",
"OPTICS density based clustering of the",
"neutral loss similarities",
"of the example data set",
"(cf Figure 4).")
fig6 <- paste("**Figure 6:**",
"Symmetric heat map of the distance matrix displaying",
"MS^2^ 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.")
fig7 <- paste("**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.")
fig8 <- paste("**Figure 8:**",
"Circularised dendrogram as a result of",
"agglomerative hierarchical clustering with average linkage",
"as agglomeration criterion based on",
"MS^2^ 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.")
fig9 <- paste("**Figure 9:**",
"Correlation network plot based on",
"MS^2^ 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.")
fig10 <- paste("**Figure 10:**",
"Screenshot of the interactive version of the",
"Correlation network plot based on",
"MS^2^ spectra similarities",
"of the example data set (cf Figure 9).",
"Zoomed image section with tooltip displaying",
"feature information upon mouse-over.")
fig11 <- paste("**Figure 11:**",
"Correlation network plot based on",
"neutral loss similarities",
"of the example data set (cf Figure 9).")
fig12 <- paste("**Figure 12:**",
"Correlation network plot based on",
"similarities of pseudospectra",
"of the example data set (cf Figure 9).")
```
# Introduction
This tutorial shows how to use the `CluMSID` package to help annotate
MS^2^ spectra from untargeted LC-MS/MS data. `CluMSID` works with MS^2^
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 MS^2^ spectra and generates neutral loss
patterns for each feature. Additionally, it can make use of information
from the `CAMERA` package to generate pseudospectra from MS^1^ level data.
The tool uses cosine similarity to generate distance matrices from MS^2^
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 maxis^HD^ 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:
```{r load_package_0, eval=FALSE}
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install(c("CluMSIDdata", "CluMSID"))
```
```{r load_package_2, eval=TRUE}
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 MS^2^ 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 MS^2^ spectra from *.mzXML file
The first step in the `CluMSID` workflow is to extract MS^2^ 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.*
```{r extractMS2spectra_1, warning=FALSE}
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 MS^2^ 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.
```{r extractMS2spectra_2}
length(ms2list)
```
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.
```{r extractMS2spectra_3}
head(ms2list, 4)
```
From the output above, you also see that the `MS2spectrum` class has a `show()`
generic that summarises the MS^2^ 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.
```{r extractMS2spectra_4}
showDefault(ms2list[[2]])
```
# Merge MS^2^ spectra that derive from the same peak/feature
To reduce the amount of redundant MS^2^ spectra, the `mergeMS2spectra()`
function is used to generate consensus spectra from the MS^2^ 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.
```{r mergeMS2spectra_1}
featlist <- mergeMS2spectra(ms2list)
```
```{r mergeMS2spectra_2}
length(featlist)
```
```{r mergeMS2spectra_3}
head(featlist, 4)
```
The total amount of spectra was reduced from `r length(ms2list)` to
`r length(featlist)` 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:
```{r mergeMS2spectra_4}
accessNeutralLosses(featlist[[1]])
```
## 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 MS^1^ mode for relative quantification.
Additionally, we have measured a pooled QC sample in MS^2^ mode for annotation.
The MS^1^ data were analysed using XCMSonline and we want to group the MS^2^
spectra so that they match the XCMSonline peak picking.
The spectra are extracted as shown above:
```{r mergeMS2spectra_5}
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 MS^2^ file, so we multiply by 60.
```{r mergeMS2spectra_6, message=FALSE, warning=FALSE}
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)
```
We can now use this peaktable as an argument for `mergeMS2spectra()`.
You can choose whether you want to keep or exclude MS^2^ 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.
```{r mergeMS2spectra_7, warning=FALSE}
featlist2 <- mergeMS2spectra(ms2list2,
peaktable = ptable,
exclude_unmatched = FALSE)
head(featlist2, 4)
```
Note that the 2^nd^ 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`:
```{r addAnnotations_0, eval=FALSE}
writeFeaturelist(featlist, "pre_anno.csv")
```
```{r addAnnotations_1}
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()`.
```{r addAnnotations_2, include=FALSE}
require(magrittr)
annos <- read.csv(system.file("extdata",
"post_anno.csv",
package = "CluMSIDdata"),
stringsAsFactors = FALSE) %>%
dplyr::filter(nchar(annotation) > 1) %>%
dplyr::select(id, annotation)
```
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:
```{r addAnnotations_3}
str(annos)
head(annos)
```
`addAnnotations(featlist, annos, annotationColumn = 2)` will throw an error
because `featlist` and `annos` are of different length.
Instead, you need to do the following:
```{r addAnnotations_4}
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) MS^2^ spectra as well as from
neutral loss patterns. These distance matrices serve as the basis for
further analysis of the data. Both for MS^2^ spectra and neutral loss
patterns, cosine similarity is used as similarity metric:
$$
cos(\theta) =
\frac{\sum_{i}a_i \cdot b_i}{\sqrt{\sum_{i}{a_{i}}^2 \cdot \sum_{i}{b_{i}}^2}}
$$
```{r distanceMatrix_0, include=FALSE}
load(file = system.file("extdata", "distmat.RData", package = "CluMSIDdata"))
load(file = system.file("extdata", "nlmat.RData", package = "CluMSIDdata"))
```
## Distance matrix for product ion spectra
For most applications, analysing the similarity of product ion MS^2^ 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.
```{r distanceMatrix_1, eval=FALSE}
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":
```{r distanceMatrix_2, eval=FALSE}
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 MS^2^ spectra:
```{r MDSplotplot_1, fig.width=5, fig.asp=1, fig.cap=fig1, message=FALSE}
MDSplot(distmat, highlight_annotated = TRUE)
```
For neutral loss patterns:
```{r MDSplotplot_2, fig.width=5, fig.asp=1, fig.cap=fig2, message=FALSE}
MDSplot(nlmat, highlight_annotated = TRUE)
```
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.
```{r MDSplotplot_3, eval=FALSE}
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:
```{r MDSplotplot_4, echo=FALSE, out.width="100%", fig.cap=fig3}
knitr::include_graphics(system.file("extdata",
"interactive_mds.png",
package = "CluMSIDdata"))
```
# 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 MS^2^ 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 MS^2^ spectra and neutral loss patterns:
```{r OPTICSplot, fig.width=7, fig.asp=0.6, fig.cap=c(fig4, fig5)}
OPTICSplot(distmat)
OPTICSplot(nlmat, eps_cl = 0.7)
```
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.
```{r OPTICStbl}
OPTICStbl <- OPTICStbl(distmat)
head(OPTICStbl)
```
# 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 MS^2^ 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):
```{r HCplot_1, fig.width=7, fig.asp=1, fig.cap=c(fig6, fig7)}
HCplot(distmat, type = "heatmap",
cexRow = 0.1, cexCol = 0.1,
margins = c(6,6))
HCplot(nlmat, type = "heatmap",
cexRow = 0.1, cexCol = 0.1,
margins = c(6,6))
```
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:
```{r HCplot_2, eval=FALSE}
pdf(file = "CluMSID_dendro.pdf", width = 20, height = 20)
HCplot(distmat)
dev.off()
```
The plot from our example data looks like this:
```{r HCplot_3, echo=FALSE, out.width="100%", fig.asp=1, fig.cap=fig8}
knitr::include_graphics(system.file("extdata",
"CluMSID_dendro2.png",
package = "CluMSIDdata"))
```
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.
```{r HCtbl}
HCtbl <- HCtbl(distmat)
head(HCtbl)
```
# 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 MS^1^ pseudospectra (default is `FALSE`)
A standard non-interactive correlation network for the MS^2^ example data
can be plotted like this:
```{r networkplot_1, message=FALSE, fig.width=7, fig.asp=1, fig.cap=fig9}
networkplot(distmat, highlight_annotated = TRUE,
show_labels = TRUE, interactive = FALSE)
```
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.
```{r networkplot_2, eval=FALSE}
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:
```{r networkplot_3, echo=FALSE, out.width="95%", fig.cap=fig10}
knitr::include_graphics(system.file("extdata",
"interactive_net.png",
package = "CluMSIDdata"))
```
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 MS^2^ 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`:
```{r networkplot_4, message=FALSE, fig.width=7, fig.asp=1, fig.cap=fig11}
networkplot(nlmat, highlight_annotated = TRUE,
show_labels = TRUE, exclude_singletons = TRUE)
```
# Additional functionalities
Multidimensional scaling, density-based clustering,
hierarchical clustering and correlation network analysis are the main
`CluMSID` tools to analyse MS^2^ 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:
```{r getSpectrum_1}
getSpectrum(annotatedSpeclist, "id", "M244.17T796.4")
```
**2. Accessing a spectrum by its annotation.** For this,
the exact annotation has to be known as well,
other annotations will produce a message:
```{r getSpectrum_2}
getSpectrum(annotatedSpeclist, "annotation", "HHQ")
```
```{r getSpectrum_3}
getSpectrum(annotatedSpeclist, "annotation", "C7-HQ")
```
**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:
```{r getSpectrum_4}
getSpectrum(annotatedSpeclist, "precursor", 286.18, mz.tol = 1E-03)
```
**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:
```{r getSpectrum_5}
six_eight <- getSpectrum(annotatedSpeclist, "rt", 420, rt.tol = 60)
length(six_eight)
```
## 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 MS^2^ 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:
```{r findFragment_1}
putativeAQs <- findFragment(annotatedSpeclist, 159.068)
```
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.
```{r findNeutralLoss_1}
findNL(annotatedSpeclist, 212.009)
```
## 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 MS^2^ 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 MS^2^ 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
`r length(annotatedSpeclist)` elements of the result vector, so we set
`hits_only` to `TRUE` to exclude spectra that have 0 similarity to the
pyocyanin spectrum.
```{r getSimilarities_1}
pyo <- getSpectrum(annotatedSpeclist, "annotation", "pyocyanin")
sim_pyo <- getSimilarities(pyo, annotatedSpeclist, hits_only = TRUE)
sim_pyo
```
We get `r length(getSimilarities(pyo, annotatedSpeclist, hits_only = TRUE))`
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`:
```{r getSimilarities_2}
highest_sim <- sort(sim_pyo, decreasing = TRUE)[2]
sim_spec <- getSpectrum(annotatedSpeclist, "id", names(highest_sim))
sim_spec
```
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:
```{r getSimilarities_3}
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)
```
As a result, we get the interesting information that the MS^2^ 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 (MS^2^) 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()`:
```{r convertSpectrum_1, eval=FALSE}
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:
```{r polarities_1, eval=FALSE}
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 MS^1^ pseudospectra instead of or in addition to MS^2^ data
MS^1^ 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 MS^1^ mode without CID.
Thus, it might sometimes be useful to study similarities between pseudospectra
analogously to those between MS^2^ 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`.
```{r Pseudospectra_1, message=FALSE, warning=FALSE}
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 `r length(pseudospeclist)`
pseudospectra that we can now process further.
## Create distance matrix for pseudospectra
The creation of a distance matrix is analogous to the procedure for
MS^2^ spectra:
```{r Pseudospectra_2, eval=FALSE}
pseudodistmat <- distanceMatrix(pseudospeclist)
```
```{r Pseudospectra_3, include=FALSE}
load(file = system.file("extdata",
"pseudodistmat.RData",
package = "CluMSIDdata"))
```
## 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:
```{r Pseudospectra_4, , fig.width=5, fig.asp=1, fig.cap=fig12}
networkplot(pseudodistmat, show_labels = TRUE, exclude_singletons = TRUE)
```
With the exclusion of singletons, we get a much less busy plot than for
MS^2^ data but we still find quite a few connections that may prove
informative.
# Session Info
```{r session}
sessionInfo()
```