The msPurity package can be used with XCMS as part of a data processing and annotation workflow for LC-MS/MS data
We first need to run XCMS so that we can later link the spectral matching result back to XCMS feature.
(Please use the appropriate settings for your data)
library(msPurity)
library(xcms)
mzMLpths <- list.files(system.file("extdata", "lcms", "mzML", package="msPurityData"), full.names = TRUE)
#read in the data
xset = xcms::xcmsSet(mzMLpths, method = 'centWave', mslevel=1, snthresh = 3, noise = 100, ppm = 10, peakwidth = c(3, 30))
#for this example we will subset the data to focus on retention time range 30-90 seconds and scan range 100-200 m/z
xset@peaks = xset@peaks[xset@peaks[,4] >= 30 & xset@peaks[,4] <= 90,] #retention time filter
xset@peaks = xset@peaks[xset@peaks[,1] >= 100 & xset@peaks[,1] <= 200,] #m/z filter
#group features across samples
xset = xcms::group(xset, minfrac = 0, bw = 5, mzwid = 0.017)
xcmsObj = xsetWe first need to run XCMS so that we can later link the spectral matching result back to XCMS feature.
(Please use the appropriate settings for your data)
library(msPurity)
library(magrittr)
library(xcms)
library(MSnbase)
mzMLpths <- list.files(system.file("extdata", "lcms", "mzML", package="msPurityData"), full.names = TRUE)
#read in data and subset to use data between 30 and 90 seconds and 100 and 200 m/z
msdata = readMSData(mzMLpths, mode = 'onDisk', msLevel. = 1)
rtr = c(30, 90)
mzr = c(100, 200)
msdata = msdata %>% MSnbase::filterRt(rt = rtr) %>% MSnbase::filterMz(mz = mzr)
#perform feature detection in individual files
cwp <- CentWaveParam(snthresh = 3, noise = 100, ppm = 10, peakwidth = c(3, 30))
xcmsObj <- xcms::findChromPeaks(msdata, param = cwp)
#update metadata
xcmsObj@phenoData@data$class = c('blank', 'blank', 'sample', 'sample')
xcmsObj@phenoData@varMetadata = data.frame('labelDescription' = c('sampleNames', 'class'))
#group chromatographic peaks across samples (correspondence analysis)
pdp <- PeakDensityParam(sampleGroups = xcmsObj@phenoData@data$class, minFraction = 0, bw = 5, binSize = 0.017)
xcmsObj <- groupChromPeaks(xcmsObj, param = pdp)The purityA function is then called to calculate the
precursor purity of the fragmentation results and the
frag4feature function will link the fragmentation data back
to the XCMS feature.
The fragmentation can be filtered prior to averaging using the “filterFragSpectra” function
Averaging of the fragmentation spectra can be done with either “averageAllFragSpectra” or with “averageIntraFragSpectra” and averageInterFragSpectra”. This will depend if the user wishes to treat the fragmentation spectra from within a file and between files. Another alternative is to ignore the averaging completely and just use the non-averaged fragmentation spectra for the spectral matching.
If the inter and intra fragmentation scans are to be treated differently the following should be followed:
pa <- averageIntraFragSpectra(pa = pa) # use parameters specific to intra spectra
pa <- averageInterFragSpectra(pa = pa) # use parameters specific to inter spectraIf the inter and intra fragmentation scans are to be treated the same the following workflow should be used.
An SQLite database is then created of the LC-MS/MS experiment. The SQLite schema of the spectral database can be detailed here.
The spectralMatching function allows users to perform spectral matching to be performed for Query SQLite spectral-database against a Library SQLite spectral-database.
The query spectral-database in most cases should contain be the “unknown” spectra database generated the msPurity function createDatabase as part of a msPurity-XCMS data processing workflow.
The library spectral-database in most cases should contain the “known” spectra from either public or user generated resources. The library SQLite database by default contains data from MoNA including Massbank, HMDB, LipidBlast and GNPS. A larger database can be downloaded from here. To create a user generated library SQLite database the following tool can be used to generate a SQLite database from a collection of MSP files: msp2db. It should be noted though, that as long as the schema of the spectral-database is as described here, then any database can be used for either the library or query - even allowing for the same database to be used.
The spectral matching functionality has four main components, spectral filtering, spectral alignment, spectral matching and finally summarising the results.
Spectral filtering is simply filtering both the library and query spectra to be search against (e.g. choosing the library source, instrument, retention time, precursor PPM tolerance, xcms features etc).
The spectral alignment stage involves aligning the query peaks to the library peaks. The approach used is similar to modified pMatch algorithm described in (Zhou et al. 2014).
The spectral matching of the aligned spectra is performed against a combined intensity and m/z weighted vector - created for both the query and library spectra (wq and wl). See below:
$$ \vec{w}=\vec{intensity}^x \cdot \vec{mz}^y$$
Where x and y represent weight factors, defaults to x = 0.5 and y = 2 as per MassBank recommendations for ESI based data (Horai, Arita, and Nishioka 2008). These can be adjusted by the user though.
The aligned weighted vectors are then matched using dot product cosine, reverse dot product cosine and the composite dot product. See below for dot product cosine equation.
$$ F_{dpc} = \frac{\sum \vec{w_{Q} }\cdot \vec{w_{L}}}{\sqrt{\sum \vec{w_{Q}^{2}}} \cdot \sqrt{\sum \vec{w_{L}^{2}}}} $$
The reverse dot product cosine (rpdc) uses the same algorithm as dpc but all peaks that do not match in the query spectra (based on the alignment) are omitted from the calculation. This will improve scores when the query spectra is noisy but should be used with caution as it might lead to more false positives.
The composite dot product cosine (cdpc) approach is also calculated - this approach is used in the NIST MS search tool and incorporates relative intensity of neighbouring peaks (see function Frel ), where N=number of peaks, Q=query, L=library, L&Q= matching library and query peaks, w is the weighted value and n is either 1 (if the abundance ratio of the library, i.e. $\frac{w_{L,i}}{w_{L,i-1}}$, is < than the abundance ratio of the query i.e. $\frac{w_{Q,i}}{w_{Q,i-1}}$) or -1 (if the abundance ratio of the library is > than the abundance ratio of the query). The approach was first described in (Stein and Scott 1994).
$$ F_{rel} = \Bigg( \frac{1}{N_{L\&Q}-1} \Bigg) \cdot \sum_{i=2}^{N_{L\&Q}} \Bigg( \frac{w_{L,i}}{w_{L,i-1}} \Bigg)_{}{^n} \cdot \Bigg( \frac{w_{Q,i}}{w_{Q,i-1}} \Bigg)_{}{^{-n}}$$
$$ F_{cpdc} = \frac{1000}{N_{Q} + N_{L\&Q}} \cdot (N_{Q} \cdot F_{dpc} + N_{L\&Q} \cdot F_{rel}) $$
The following example shows how to match one xcms group against library spectra filtered by their MoNA/MassBank accession id.
## Running msPurity spectral matching function for LC-MS(/MS) data## Filter query dataset## Filter library dataset## aligning and matching## Summarising LC feature annotations## $q_dbPth
## [1] "/tmp/RtmpNztsrf/lcmsms-processing.sqlite"
##
## $matchedResults
## lpid qpid mid dpc rdpc cdpc mcount allcount mpercent
## 1 14082 1700 1 0.939585 0.939585 0.9007655 4 15 0.2666667
## 2 14082 1701 2 0.939585 0.939585 0.9007655 4 15 0.2666667
## library_rt query_rt rtdiff library_precursor_mz query_precursor_mz
## 1 <NA> 59.83364 NA 150.058 150.0581
## 2 <NA> 59.83364 NA 150.058 150.0581
## library_precursor_ion_purity query_precursor_ion_purity library_accession
## 1 <NA> 1 CCMSLIB00003740033
## 2 <NA> 1 CCMSLIB00003740033
## library_precursor_type library_entry_name inchikey
## 1 M+H Methionine FFEARJCKVFRZRR-UHFFFAOYSA-N
## 2 M+H Methionine FFEARJCKVFRZRR-UHFFFAOYSA-N
## library_source_name library_compound_name
## 1 gnps Methionine
## 2 gnps Methionine
##
## $xcmsMatchedResults
## pid grpid mz mzmin mzmax rt rtmin rtmax npeaks blank
## 1 1700 432 150.0581 150.058 150.0582 59.83364 57.10389 63.07817 4 2
## 2 1701 432 150.0581 150.058 150.0582 59.83364 57.10389 63.07817 4 2
## sample peakidx ms_level LCMSMS_1_mzML LCMSMS_2_mzML LCMS_1_mzML
## 1 2 401, 777, 1846, 2958 1 455365992 461585449 642676169
## 2 2 401, 777, 1846, 2958 1 455365992 461585449 642676169
## LCMS_2_mzML grp_name lpid mid dpc rdpc cdpc mcount allcount
## 1 643826651 FT0432 14082 1 0.939585 0.939585 0.9007655 4 15
## 2 643826651 FT0432 14082 2 0.939585 0.939585 0.9007655 4 15
## mpercent library_rt query_rt rtdiff library_precursor_mz query_precursor_mz
## 1 0.2666667 <NA> 59.83364 NA 150.058 150.0581
## 2 0.2666667 <NA> 59.83364 NA 150.058 150.0581
## library_precursor_ion_purity query_precursor_ion_purity library_accession
## 1 <NA> 1 CCMSLIB00003740033
## 2 <NA> 1 CCMSLIB00003740033
## library_precursor_type library_entry_name inchikey
## 1 M+H Methionine FFEARJCKVFRZRR-UHFFFAOYSA-N
## 2 M+H Methionine FFEARJCKVFRZRR-UHFFFAOYSA-N
## library_source_name library_compound_name
## 1 gnps Methionine
## 2 gnps MethionineThe output of spectralMatching returns a list containing the following elements:
q_dbPth: Path of the query database (this will have been updated with the annotation results if updateDb argument used)
xcmsMatchedResults If the qeury spectra had XCMS based chromotographic peaks tables (e.g c_peak_groups, c_peaks) in the sqlite database - it will be possible to summarise the matches for each XCMS grouped feature. The dataframe contains the following columns
matchedResults
All matched results from the query spectra to the library spectra. Contains the same columns as above but without the XCMS details. This table is useful to observe spectral matching results for all MS/MS spectra irrespective of if they are linked to XCMS MS1 features.
It should be noted that in a typical Data Dependent Acquisition (DDA) experiment not all the fragmentation scans collected can be linked backed to an associated XCMS features and in some cases the percentage of XCMS features with fragmentation spectra can sometimes be quite small.