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 appropiate settings for your data)
library(msPurity)
mzMLpths <- list.files(system.file("extdata", "lcms", "mzML", package="msPurityData"), full.names = TRUE)
xset <- xcms::xcmsSet(mzMLpths)
xset <- xcms::group(xset)
xset <- xcms::retcor(xset)
xset <- xcms::group(xset)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.
pa <- purityA(mzMLpths)
pa <- frag4feature(pa, xset)The fragmentation can be filtered prior to averaging using the “filterFragSpectra” function
pa <- filterFragSpectra(pa)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) # use parameters specific to intra spectra
pa <- averageInterFragSpectra(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.
pa <- averageAllFragSpectra(pa) An SQLite database is then created of the LC-MS/MS experiment. The SQLite schema of the spectral database can be detailed here.
td <- tempdir()
q_dbPth <- createDatabase(pa, xset, outDir = td, dbName = 'lcmsms-processing.sqlite')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 appraoch is used in the NIST MS search tool and incorporates relative intensity of neighbouring peaks (see function \(M_{rel}\) ), 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 two xcms groups against two of the library spectral filtered by their MoNA/MassBank accession ids.
result <- spectralMatching(q_dbPth, q_xcmsGroups = c(17, 41), l_accessions=c('CCMSLIB00000577898','CE000616'))## Running msPurity spectral matching function for LC-MS(/MS) data## Filter query dataset## Filter library dataset## aligning and matching## Summarising LC feature annotationsprint(result)## $q_dbPth
## [1] "/tmp/RtmpQmaQ9y/lcmsms-processing.sqlite"
##
## $matchedResults
## dpc rdpc cdpc mcount allcount mpercent accession
## 1 0.8739497 1 0.8359519 1 22 0.04545455 CCMSLIB00000577898
## 2 0.9408905 1 0.8960862 1 20 0.05000000 CE000616
## name inchikey lpid qpid mid
## 1 L-PROLINE ONIBWKKTOPOVIA-UHFFFAOYSA-N 5325 1662 1
## 2 Isoleucine AGPKZVBTJJNPAG-UHFFFAOYSA-N 53807 1665 2
##
## $xcmsMatchedResults
## pid grpid mz mzmin mzmax rt rtmin rtmax npeaks
## 1 1662 17 116.0705 116.0703 116.0706 44.45066 43.95639 44.90363 4
## 2 1665 41 132.1018 132.1017 132.1018 70.39686 69.81528 70.75930 4
## mzML LCMSMS_1 LCMSMS_2 LCMS_1 LCMS_2 grp_name dpc rdpc
## 1 4 130337063 124086404 230819937 234755462 M116T44 0.8739497 1
## 2 4 2879676651 2794252073 3455044747 3379259900 M132T70 0.9408905 1
## cdpc mcount allcount mpercent accession name
## 1 0.8359519 1 22 0.04545455 CCMSLIB00000577898 L-PROLINE
## 2 0.8960862 1 20 0.05000000 CE000616 Isoleucine
## inchikey lpid mid
## 1 ONIBWKKTOPOVIA-UHFFFAOYSA-N 5325 1
## 2 AGPKZVBTJJNPAG-UHFFFAOYSA-N 53807 2The 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)
matchedResults: All matched results from the query spectra to the library spectra. Contains the following columns
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
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.
Horai, Hisayuki, Masanori Arita, and Takaaki Nishioka. 2008. “Comparison of ESI-MS spectra in MassBank database.” BioMedical Engineering and Informatics: New Development and the Future - Proceedings of the 1st International Conference on BioMedical Engineering and Informatics, BMEI 2008 2:853–57. https://doi.org/10.1109/BMEI.2008.339.
Stein, Stephen E, and Donald R Scott. 1994. “Optimization and testing of mass spectral library search algorithms for compound identification.” Journal of the American Society for Mass Spectrometry 5 (9). Elsevier:859–66.
Zhou, Jiarui, Ralf J M Weber, J William Allwood, Robert Mistrik, Zexuan Zhu, Zhen Ji, Siping Chen, Warwick B Dunn, Shan He, and Mark R Viant. 2014. “HAMMER: automated operation of mass frontier to construct in silico mass spectral fragmentation libraries.” Bioinformatics (Oxford, England) 30 (4):581–3. https://doi.org/10.1093/bioinformatics/btt711.