MS/MS Spectra Matching with the `MetaboAnnotation` Package
Johannes Rainer1, Michael Witting2
Source:vignettes/Spectra-matching-with-MetaboAnnotation.Rmd
Spectra-matching-with-MetaboAnnotation.RmdLast modified: 2023-12-05 09:09:17.874995
Compiled: Tue Dec 5 09:14:26 2023
Overview
Introduction
The Spectra package provides all the functionality
required for annotation and identification workflows for untargeted
LC-MS/MS data, but, while being very flexible and customizable, it might
be too cumbersome for beginners or analysts not accustomed with R. To
fill this gap we developed the MetaboAnnotation
package that builds upon Spectra and provides functions for
annotation of LC-MS and LC-MS/MS data sets tailored towards the less
experienced R user (Rainer et al.
2022).
Convenient spectra matching using MetaboAnnotation
In this example use case we match experimental MS2 spectra from a DDA
experiment on a pesticide mix against reference spectra from MassBank.
Below we load the experimental data file which is distributed
via the msdata R package.
library(Spectra)
library(pander)
#' Load the pesticide mix data
fl <- system.file("TripleTOF-SWATH", "PestMix1_DDA.mzML", package = "msdata")
pest <- Spectra(fl)We next restrict the data set to MS2 spectra only and in addition clean these spectra by removing all peaks from a spectrum that have an intensity lower than 5% of the largest peak intensity of that spectrum. Finally, single-peak spectra are removed.
#' restrict to MS2 data and remove intensities with intensity lower 5%
pest <- filterMsLevel(pest, msLevel = 2L)
#' Remove peaks with an intensity below 5% or the spectra's BPC
low_int <- function(x, ...) {
x > max(x, na.rm = TRUE) * 0.05
}
pest <- filterIntensity(pest, intensity = low_int)
#' Remove peaks with a single peak
pest <- pest[lengths(pest) > 1]This leads to a data set consisting of 2451 spectra. We next connect
to a MassBank database (running within this docker image) and create a
Spectra object representing that data.
library(RMariaDB)
library(MsBackendMassbank)
#' Connect to the MassBank MySQL database
con <- dbConnect(MariaDB(), user = "massbank", dbname = "MassBank",
host = "localhost", pass = "massbank")
mbank <- Spectra(con, source = MsBackendMassbankSql())Alternatively, if no MySQL database system is available or if this
tutorial can not be run within docker, an SQLite database
version of the MassBank data (in the format required for
MsBackendMassbankSql is available here.
After downloading the database MassbankSql-2021-03.db to the
current R workspace, the resource can be loaded with:
## Alternative to the MySQL server - assuming the SQLite database was
## stored to the R working directory.
library(RSQLite)
library(MsBackendMassbank)
con <- dbConnect(SQLite(), "MassbankSql-2021-03.db")
mbank <- Spectra(con, source = MsBackendMassbankSql())We could now directly calculate similarities between the 2451
experimental (query) MS2 spectra and the 86576 MassBank reference
(target) spectra using the compareSpectra method, but this
would be computationally very intense because a similarity score would
be calculated between each query and each target spectrum. As
alternative we use here the matchSpectra function from the
MetaboAnnotation
package that allows to restrict similarity calculations between query
and target spectra with similar m/z of their precursor ion or have a
similar retention time.
Below we create a CompareSpectraParam object setting
parameter requirePrecursor = TRUE (to restrict similarity
calculations only to query and target spectra with a similar precursor
m/z) and ppm = 10 (m/z difference between the query and
target precursor has to be within 10 ppm). Parameter
THRESHFUN enables to define a threshold function
that defines which spectra are considered matching. With the function
used below only MS2 spectra with a similarity (calculated with the
default dotproduct function) larger or equal to 0.8 are
considered matching.
library(MetaboAnnotation)
prm <- CompareSpectraParam(ppm = 10, requirePrecursor = TRUE,
THRESHFUN = function(x) which(x >= 0.8))We next call matchSpectra with this parameter object and
pass pest and mbank as query and target
Spectra, respectively. This takes approximately 1 minute to
complete, which is not tremendously fast, but still much faster than a
pairwise comparison between all query and target spectra would be.
mtch <- matchSpectra(pest, mbank, param = prm)
mtch## Object of class MatchedSpectra
## Total number of matches: 78
## Number of query objects: 2451 (29 matched)
## Number of target objects: 86576 (47 matched)As a result we get a MatchedSpectra object that contains
the query and target spectra as well as the matching result (i.e. the
information which query spectrum matches with which target spectrum
based on what similarity score). We can use the query and
target functions to access the query and target
Spectra objects and matches to extract the
matching information. Below we display the first 6 rows of that
matrix.
## query_idx target_idx score
## 1 163 31941 0.9264249
## 2 163 32022 0.9264249
## 3 163 32214 0.9264249
## 4 163 32508 0.9264249
## 5 163 32667 0.9264249
## 6 163 32900 0.9264249Functions whichQuery and whichTarget return
the (unique) indices of the query and target spectra that could be
matched.
whichQuery(mtch)## [1] 163 320 420 433 493 496 497 571 682 685 686 805 806 809 810
## [16] 819 829 983 1095 1454 1457 1706 1830 1834 1839 1906 2047 2048 2050As we can see only few of the query spectra (29 of the 2451 spectra)
could be matched. This is in part because for a large proportion spectra
in MassBank no precursor m/z is available and with
requirePrecursor = TRUE these are not considered in the
similarity calculation. Setting requirePrecursor = FALSE
would calculate a similarity between all spectra (even those with
missing precursor information) but calculations can take up to several
hours.
sum(is.na(precursorMz(mbank)))## [1] 24498The MatchedSpectra object inherits much of the
functionality of a Spectra object.
spectraVariables returns for example all the available
spectra variables, from both the query as well as the target
Spectra. The variable names of the latter are prefixed with
target_ to discriminate them from the variable names of the
query.
spectraVariables(mtch)## [1] "msLevel" "rtime"
## [3] "acquisitionNum" "scanIndex"
## [5] "dataStorage" "dataOrigin"
## [7] "centroided" "smoothed"
## [9] "polarity" "precScanNum"
## [11] "precursorMz" "precursorIntensity"
## [13] "precursorCharge" "collisionEnergy"
## [15] "isolationWindowLowerMz" "isolationWindowTargetMz"
## [17] "isolationWindowUpperMz" "peaksCount"
## [19] "totIonCurrent" "basePeakMZ"
## [21] "basePeakIntensity" "ionisationEnergy"
## [23] "lowMZ" "highMZ"
## [25] "mergedScan" "mergedResultScanNum"
## [27] "mergedResultStartScanNum" "mergedResultEndScanNum"
## [29] "injectionTime" "filterString"
## [31] "spectrumId" "ionMobilityDriftTime"
## [33] "scanWindowLowerLimit" "scanWindowUpperLimit"
## [35] "target_msLevel" "target_rtime"
## [37] "target_acquisitionNum" "target_scanIndex"
## [39] "target_dataStorage" "target_dataOrigin"
## [41] "target_centroided" "target_smoothed"
## [43] "target_polarity" "target_precScanNum"
## [45] "target_precursorMz" "target_precursorIntensity"
## [47] "target_precursorCharge" "target_collisionEnergy"
## [49] "target_isolationWindowLowerMz" "target_isolationWindowTargetMz"
## [51] "target_isolationWindowUpperMz" "target_spectrum_id"
## [53] "target_spectrum_name" "target_date"
## [55] "target_authors" "target_license"
## [57] "target_copyright" "target_publication"
## [59] "target_splash" "target_compound_id"
## [61] "target_adduct" "target_ionization"
## [63] "target_ionization_voltage" "target_fragmentation_mode"
## [65] "target_collision_energy_text" "target_instrument"
## [67] "target_instrument_type" "target_formula"
## [69] "target_exactmass" "target_smiles"
## [71] "target_inchi" "target_inchikey"
## [73] "target_cas" "target_pubchem"
## [75] "target_synonym" "target_precursor_mz_text"
## [77] "target_compound_name" "score"We can access spectra individual variables using $ and
the variable name, or multiple variables with the
spectraData function. Below we extract the retention time,
the precursor m/z of the query spectrum, the precursor m/z of the target
spectrum as well as the similarity score from the object using the
spectraData function.
spectraData(mtch, c("rtime", "precursorMz", "target_precursorMz", "score"))## DataFrame with 2500 rows and 4 columns
## rtime precursorMz target_precursorMz score
## <numeric> <numeric> <numeric> <numeric>
## 1 7.216 137.9639 NA NA
## 2 13.146 56.9419 NA NA
## 3 13.556 89.9449 NA NA
## 4 23.085 207.0294 NA NA
## 5 27.385 121.0990 NA NA
## ... ... ... ... ...
## 2496 895.182 137.9850 NA NA
## 2497 895.472 56.0495 NA NA
## 2498 896.252 142.9611 NA NA
## 2499 896.662 53.0129 NA NA
## 2500 898.602 91.5022 NA NAThe returned DataFrame contains the matching information
for the full data set, i.e. of each query spectrum and hence, returns
NA values for query spectra that could not be matched with
a target spectrum. Note also that query spectra matching multiple target
spectra are represented by multiple rows (one for each matching target
spectrum).
Here we’re only interested in query spectra for which a match was
found and thus we subset the MatchedSpectra to query
spectra with a matching target spectrum.
mtch <- mtch[whichQuery(mtch)]Subsetting of MatchedSpectra is always relative to the
query, i.e. subsetting an object with an index 4 would
restrict the object to only the matching results for the 4th query
spectrum.
We now extract the matching information after subsetting:
spectraData(mtch, c("rtime", "precursorMz", "target_precursorMz", "score"))## DataFrame with 78 rows and 4 columns
## rtime precursorMz target_precursorMz score
## <numeric> <numeric> <numeric> <numeric>
## 1 173.184 305.156 305.157 0.926425
## 2 173.184 305.156 305.157 0.926425
## 3 173.184 305.156 305.157 0.926425
## 4 173.184 305.156 305.157 0.926425
## 5 173.184 305.156 305.157 0.926425
## ... ... ... ... ...
## 74 527.419 279.156 279.159 0.814331
## 75 527.419 279.156 279.159 0.822396
## 76 570.096 373.039 373.041 0.934031
## 77 570.506 373.042 373.041 0.888829
## 78 570.825 373.040 373.041 0.827892We can also return the compound names for the matching spectra.
pandoc.table(style = "rmarkdown",
as.data.frame(spectraData(mtch, c("rtime", "target_compound_name",
"score"))))| rtime | target_compound_name | score |
|---|---|---|
| 173.2 | Hexaethylene glycol | 0.9264 |
| 173.2 | Hexaethylene glycol | 0.9264 |
| 173.2 | Hexaethylene glycol | 0.9264 |
| 173.2 | Hexaethylene glycol | 0.9264 |
| 173.2 | Hexaethylene glycol | 0.9264 |
| 173.2 | Hexaethylene glycol | 0.9264 |
| 266.4 | Triacetin | 0.8046 |
| 320 | SPI_270.2429_14.9 | 0.9656 |
| 326.7 | Lenacil | 0.8067 |
| 326.7 | Lenacil | 0.8225 |
| 326.7 | Lenacil | 0.8205 |
| 338.5 | Azaconazole | 0.8943 |
| 338.9 | Azaconazole | 0.8179 |
| 338.9 | Azaconazole | 0.905 |
| 339.3 | Azaconazole | 0.9017 |
| 353.5 | Fosthiazate | 0.8962 |
| 353.5 | Fosthiazate | 0.8689 |
| 361.7 | Azaconazole | 0.9002 |
| 362.3 | Azaconazole | 0.8949 |
| 362.6 | Azaconazole | 0.8925 |
| 377.7 | triphenylphosphineoxide | 0.9025 |
| 377.7 | triphenylphosphineoxide | 0.8801 |
| 377.7 | Triphenylphosphine oxide | 0.8563 |
| 378.1 | N,N-Dimethyldodecylamine | 0.8195 |
| 378.9 | Dimethachlor | 0.8086 |
| 378.9 | Dimethachlor | 0.8623 |
| 378.9 | Dimethachlor | 0.8447 |
| 378.9 | Dimethachlor | 0.8627 |
| 378.9 | Dimethachlor | 0.8391 |
| 379 | Triphenylphosphine oxide | 0.8081 |
| 379 | triphenylphosphineoxide | 0.8743 |
| 379 | triphenylphosphineoxide | 0.8491 |
| 379 | Triphenylphosphine oxide | 0.8851 |
| 382 | Dimethachlor | 0.844 |
| 382 | Dimethachlor | 0.8923 |
| 382 | Dimethachlor | 0.8596 |
| 382 | Dimethachlor | 0.8922 |
| 382 | Dimethachlor | 0.8547 |
| 384.5 | Dimethachlor | 0.8457 |
| 384.5 | Dimethachlor | 0.8966 |
| 384.5 | Dimethachlor | 0.8625 |
| 384.5 | Dimethachlor | 0.8963 |
| 384.5 | Dimethachlor | 0.8573 |
| 405.1 | Cyproconazole | 0.8145 |
| 405.1 | Cyproconazole | 0.8357 |
| 405.1 | Cyproconazole (CP) | 0.8262 |
| 414.6 | Tris(1-chloro-2-propyl)phosphate | 0.8613 |
| 452.9 | Flufenacet | 0.8464 |
| 452.9 | Flufenacet | 0.9146 |
| 452.9 | Flufenacet | 0.8042 |
| 452.9 | Flufenacet | 0.9388 |
| 452.9 | Flufenacet | 0.8483 |
| 452.9 | Flufenacet | 0.8517 |
| 452.9 | Flufenacet | 0.865 |
| 452.9 | Flufenacet | 0.863 |
| 452.9 | Flufenacet | 0.8011 |
| 453.3 | Flufenacet | 0.9033 |
| 453.3 | Flufenacet | 0.9066 |
| 453.3 | Flufenacet | 0.8807 |
| 453.3 | Flufenacet | 0.8828 |
| 453.3 | Flufenacet | 0.9333 |
| 453.3 | Flufenacet | 0.9288 |
| 453.3 | Flufenacet | 0.8397 |
| 490.7 | Tri-isobutylphosphate | 0.8907 |
| 490.7 | Tri-isobutyl phosphate | 0.8871 |
| 506.8 | Diflufenican | 0.848 |
| 506.8 | Diflufenican | 0.8604 |
| 507.3 | Diflufenican | 0.8416 |
| 507.3 | Diflufenican | 0.8524 |
| 507.7 | Diflufenican | 0.8591 |
| 507.7 | Diflufenican | 0.8707 |
| 527.4 | Dibutyl phthalate | 0.818 |
| 527.4 | Dibutyl phthalate | 0.8847 |
| 527.4 | Di-n-butyl phthalate | 0.8143 |
| 527.4 | Di-n-butyl phthalate | 0.8224 |
| 570.1 | Proquinazid | 0.934 |
| 570.5 | Proquinazid | 0.8888 |
| 570.8 | Proquinazid | 0.8279 |
The matchSpectra enables thus to perform convenient
spectra matching between MS data represented as Spectra
objects. As a result, a MatchedSpectra object is returned
that, in addition to the matching results, contains also the query and
target spectra. Pre-filtering the spectra prior to the actual spectra
similarity calculation can reduce the running time of a
matchSpectra call but might also miss some potential
matches. Note that in addition to the precursor m/z-based pre-filter
also retention time pre-filtering would be available (see
?matchSpectra for more information). Also, a more advanced
matching approach would be available with the
MatchForwardReverseParam that calculates in addition to the
forward score also a reverse similarity for each
match.
MS2 spectra matching in an xcms workflow
In LC-MS/MS-based untargeted metabolomics (or small compound mass
spectrometry experiments in general) quantification of the compounds is
performed in MS1 while the MS2 data is used for identification of the
features. Quantification of MS1 data requires a chromatographic peak
detection step which can be performed using the functionality from the
xcms
package. Below we load thus the xcms package and import the
full MS data using the readMSData function.
library(xcms)
pest_all <- readMSData(fl, mode = "onDisk")We next perform the chromatographic peak detection using the
centWave algorithm (see the LC-MS/MS data analysis with
xcms vignette from the xcms package for details on the
chromatographic peak detection settings).
cwp <- CentWaveParam(snthresh = 5, noise = 100, ppm = 10,
peakwidth = c(3, 30))
pest_all <- findChromPeaks(pest_all, param = cwp)In total 99 chromatographic peaks have been identified. Below we display the first 6 of them.
head(chromPeaks(pest_all))## mz mzmin mzmax rt rtmin rtmax into intb
## CP01 142.9926 142.9921 142.9931 130.615 125.856 134.241 1113.8028 1106.229
## CP02 221.0918 221.0906 221.0925 240.897 236.657 246.984 756.6935 744.779
## CP03 220.0985 220.0978 220.0988 240.897 237.187 246.327 2060.5921 2052.549
## CP04 219.0957 219.0950 219.0962 241.018 236.253 246.327 15172.6662 15133.811
## CP05 153.0659 153.0655 153.0663 330.591 325.373 334.400 2148.7134 2141.943
## CP06 235.1447 235.1441 235.1454 330.591 326.431 334.400 2836.2675 2829.627
## maxo sn sample
## CP01 346.7006 102 1
## CP02 212.5239 21 1
## CP03 585.3036 151 1
## CP04 4877.1162 367 1
## CP05 784.9196 114 1
## CP06 1006.9720 110 1We can now extract all MS2 spectra for each chromatographic peak with
the chromPeakSpectra function. This function identifies all
MS2 spectra recorded by the instrument with a retention time within the
retention time and with a precursor m/z within the m/z boundaries of the
chromatographic peak. By setting return.type = "Spectra" we
ensure that the data is being returned in the newer Spectra
format hence enabling the simplified spectra matching with the
functionality presented here.
pest_ms2 <- chromPeakSpectra(pest_all, return.type = "Spectra")
pest_ms2## MSn data (Spectra) with 158 spectra in a MsBackendMzR backend:
## msLevel rtime scanIndex
## <integer> <numeric> <integer>
## F1.S1000 2 128.237 1000
## F1.S1008 2 128.737 1008
## F1.S1023 2 129.857 1023
## F1.S1812 2 237.869 1812
## F1.S1846 2 241.299 1846
## ... ... ... ...
## F1.S5115 2 575.255 5115
## F1.S5272 2 596.584 5272
## F1.S5236 2 592.424 5236
## F1.S5266 2 596.054 5266
## F1.S7344 2 873.714 7344
## ... 38 more variables/columns.
##
## file(s):
## PestMix1_DDA.mzMLSpectra variable peak_id contains the identified of the
chromatographic peak (i.e. its row name in chromPeaks).
pest_ms2$peak_id## [1] "CP01" "CP01" "CP01" "CP04" "CP04" "CP05" "CP05" "CP06" "CP06" "CP08"
## [11] "CP08" "CP11" "CP11" "CP12" "CP12" "CP13" "CP13" "CP13" "CP13" "CP14"
## [21] "CP14" "CP14" "CP14" "CP18" "CP22" "CP22" "CP22" "CP22" "CP22" "CP25"
## [31] "CP25" "CP25" "CP25" "CP25" "CP26" "CP26" "CP26" "CP26" "CP26" "CP26"
## [41] "CP33" "CP33" "CP34" "CP34" "CP34" "CP34" "CP34" "CP35" "CP35" "CP35"
## [51] "CP35" "CP35" "CP36" "CP41" "CP41" "CP41" "CP42" "CP42" "CP42" "CP42"
## [61] "CP42" "CP44" "CP44" "CP46" "CP46" "CP46" "CP46" "CP47" "CP47" "CP47"
## [71] "CP48" "CP48" "CP48" "CP50" "CP51" "CP51" "CP51" "CP52" "CP52" "CP52"
## [81] "CP53" "CP53" "CP53" "CP53" "CP53" "CP57" "CP57" "CP57" "CP57" "CP57"
## [91] "CP59" "CP59" "CP60" "CP60" "CP61" "CP61" "CP63" "CP63" "CP63" "CP63"
## [101] "CP64" "CP64" "CP64" "CP64" "CP64" "CP65" "CP66" "CP66" "CP66" "CP66"
## [111] "CP67" "CP67" "CP67" "CP69" "CP69" "CP69" "CP71" "CP71" "CP71" "CP71"
## [121] "CP72" "CP72" "CP72" "CP73" "CP81" "CP81" "CP81" "CP81" "CP82" "CP82"
## [131] "CP82" "CP85" "CP85" "CP88" "CP88" "CP89" "CP89" "CP89" "CP90" "CP90"
## [141] "CP90" "CP91" "CP91" "CP91" "CP93" "CP93" "CP93" "CP93" "CP93" "CP93"
## [151] "CP94" "CP94" "CP94" "CP94" "CP95" "CP98" "CP98" "CP99"We next, like in the previous section, clean up the spectra removing peaks with an intensity below 5% of the largest peak intensity per spectrum and removing spectra with a single peak.
#' Remove peaks with an intensity below 5%
pest_ms2 <- filterIntensity(pest_ms2, intensity = low_int)
#' Remove peaks with a single peak
pest_ms2 <- pest_ms2[lengths(pest_ms2) > 1]In addition we scale also the intensities within each MS2 spectrum by replacing them with intensities relative to the maximum peak intensity (see here for more information). In addition to the query spectra, we also normalize the MassBank spectra in the same way.
#' Define a function to *normalize* the intensities
norm_int <- function(x, ...) {
maxint <- max(x[, "intensity"], na.rm = TRUE)
x[, "intensity"] <- 100 * x[, "intensity"] / maxint
x
}
#' *Apply* the function to the data
pest_ms2 <- addProcessing(pest_ms2, norm_int)
mbank <- addProcessing(mbank, norm_int)Next we perform now the spectra matching with the same parameters as in the previous section.
pest_match <- matchSpectra(pest_ms2, mbank, param = prm)
pest_match## Object of class MatchedSpectra
## Total number of matches: 31
## Number of query objects: 155 (7 matched)
## Number of target objects: 86576 (19 matched)Again, we restrict the MatchedSpectra to query spectra
which could be matched.
pest_match <- pest_match[whichQuery(pest_match)]The table below lists the compound names of matching spectra for the chromatographic peaks.
pandoc.table(
style = "rmarkdown",
as.data.frame(spectraData(pest_match, c("peak_id", "rtime",
"target_compound_name"))))| peak_id | rtime | target_compound_name | |
|---|---|---|---|
| F1.S2860 | CP18 | 362.6 | Azaconazole |
| F1.S3061 | CP25 | 382 | Dimethachlor |
| F1.S3061.1 | CP25 | 382 | Dimethachlor |
| F1.S3061.2 | CP25 | 382 | Dimethachlor |
| F1.S3061.3 | CP25 | 382 | Dimethachlor |
| F1.S3061.4 | CP25 | 382 | Dimethachlor |
| F1.S3080 | CP25 | 384.5 | Dimethachlor |
| F1.S3080.1 | CP25 | 384.5 | Dimethachlor |
| F1.S3080.2 | CP25 | 384.5 | Dimethachlor |
| F1.S3080.3 | CP25 | 384.5 | Dimethachlor |
| F1.S3080.4 | CP25 | 384.5 | Dimethachlor |
| F1.S3314 | CP42 | 405.1 | Cyproconazole |
| F1.S3314.1 | CP42 | 405.1 | Cyproconazole |
| F1.S3314.2 | CP42 | 405.1 | Cyproconazole (CP) |
| F1.S3968 | CP67 | 452.9 | Flufenacet |
| F1.S3968.1 | CP67 | 452.9 | Flufenacet |
| F1.S3968.2 | CP67 | 452.9 | Flufenacet |
| F1.S3968.3 | CP67 | 452.9 | Flufenacet |
| F1.S3968.4 | CP67 | 452.9 | Flufenacet |
| F1.S3968.5 | CP67 | 452.9 | Flufenacet |
| F1.S3968.6 | CP67 | 452.9 | Flufenacet |
| F1.S3968.7 | CP67 | 452.9 | Flufenacet |
| F1.S3968.8 | CP67 | 452.9 | Flufenacet |
| F1.S3972 | CP67 | 453.3 | Flufenacet |
| F1.S3972.1 | CP67 | 453.3 | Flufenacet |
| F1.S3972.2 | CP67 | 453.3 | Flufenacet |
| F1.S3972.3 | CP67 | 453.3 | Flufenacet |
| F1.S3972.4 | CP67 | 453.3 | Flufenacet |
| F1.S3972.5 | CP67 | 453.3 | Flufenacet |
| F1.S3972.6 | CP67 | 453.3 | Flufenacet |
| F1.S5077 | CP94 | 570.5 | Proquinazid |
We can also directly plot matching (query and target) spectra against
each other using the plotSpectraMirror function subsetting
the MatchedSpectra object to the query spectrum of
interest. Below we plot the third query spectrum against all of its
matching target spectra.
plotSpectraMirror(pest_match[3])
Summarizing, with the chromPeakSpectra and the
featureSpectra functions, xcms allows to
return MS data as Spectra objects which enables, as shown
in this simple example, to perform MS2 spectra matching using the
Spectra as well as the MetaboAnnotation
packages hence simplifying MS/MS-based annotation of LC-MS features from
xcms.
Session information
## R version 4.3.2 (2023-10-31)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.3 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: Etc/UTC
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] xcms_4.0.0 MSnbase_2.28.1 mzR_2.36.0
## [4] Rcpp_1.0.11 Biobase_2.62.0 MetaboAnnotation_1.6.1
## [7] MsBackendMassbank_1.11.1 RMariaDB_1.3.1 pander_0.6.5
## [10] Spectra_1.12.0 ProtGenerics_1.34.0 BiocParallel_1.36.0
## [13] S4Vectors_0.40.2 BiocGenerics_0.48.1 BiocStyle_2.30.0
##
## loaded via a namespace (and not attached):
## [1] splines_4.3.2 later_1.3.1
## [3] bitops_1.0-7 filelock_1.0.2
## [5] tibble_3.2.1 preprocessCore_1.64.0
## [7] XML_3.99-0.16 lifecycle_1.0.4
## [9] doParallel_1.0.17 rprojroot_2.0.4
## [11] lattice_0.22-5 MASS_7.3-60
## [13] MultiAssayExperiment_1.28.0 magrittr_2.0.3
## [15] limma_3.58.1 sass_0.4.7
## [17] rmarkdown_2.25 jquerylib_0.1.4
## [19] yaml_2.3.7 httpuv_1.6.12
## [21] MsCoreUtils_1.14.1 DBI_1.1.3
## [23] RColorBrewer_1.1-3 lubridate_1.9.3
## [25] abind_1.4-5 zlibbioc_1.48.0
## [27] GenomicRanges_1.54.1 purrr_1.0.2
## [29] AnnotationFilter_1.26.0 RCurl_1.98-1.13
## [31] rappdirs_0.3.3 GenomeInfoDbData_1.2.11
## [33] IRanges_2.36.0 pkgdown_2.0.7
## [35] ncdf4_1.22 codetools_0.2-19
## [37] CompoundDb_1.6.0 DelayedArray_0.28.0
## [39] DT_0.30 xml2_1.3.6
## [41] tidyselect_1.2.0 matrixStats_1.1.0
## [43] BiocFileCache_2.10.1 base64enc_0.1-3
## [45] jsonlite_1.8.8 multtest_2.58.0
## [47] ellipsis_0.3.2 survival_3.5-7
## [49] iterators_1.0.14 systemfonts_1.0.5
## [51] foreach_1.5.2 tools_4.3.2
## [53] progress_1.2.2 ragg_1.2.6
## [55] glue_1.6.2 gridExtra_2.3
## [57] SparseArray_1.2.2 xfun_0.41
## [59] MatrixGenerics_1.14.0 GenomeInfoDb_1.38.1
## [61] dplyr_1.1.4 BiocManager_1.30.22
## [63] fastmap_1.1.1 fansi_1.0.5
## [65] digest_0.6.33 timechange_0.2.0
## [67] R6_2.5.1 mime_0.12
## [69] textshaping_0.3.7 colorspace_2.1-0
## [71] rsvg_2.6.0 RSQLite_2.3.3
## [73] utf8_1.2.4 generics_0.1.3
## [75] robustbase_0.99-1 prettyunits_1.2.0
## [77] httr_1.4.7 htmlwidgets_1.6.3
## [79] S4Arrays_1.2.0 pkgconfig_2.0.3
## [81] gtable_0.3.4 blob_1.2.4
## [83] impute_1.76.0 MassSpecWavelet_1.68.0
## [85] XVector_0.42.0 htmltools_0.5.7
## [87] MALDIquant_1.22.1 clue_0.3-65
## [89] scales_1.3.0 png_0.1-8
## [91] knitr_1.45 MetaboCoreUtils_1.10.0
## [93] rjson_0.2.21 curl_5.1.0
## [95] cachem_1.0.8 stringr_1.5.1
## [97] BiocVersion_3.18.1 parallel_4.3.2
## [99] AnnotationDbi_1.64.1 mzID_1.40.0
## [101] vsn_3.70.0 desc_1.4.2
## [103] pillar_1.9.0 grid_4.3.2
## [105] vctrs_0.6.5 MsFeatures_1.10.0
## [107] RANN_2.6.1 pcaMethods_1.94.0
## [109] promises_1.2.1 dbplyr_2.4.0
## [111] xtable_1.8-4 cluster_2.1.6
## [113] evaluate_0.23 cli_3.6.1
## [115] compiler_4.3.2 rlang_1.1.2
## [117] crayon_1.5.2 QFeatures_1.12.0
## [119] ChemmineR_3.54.0 affy_1.80.0
## [121] plyr_1.8.9 fs_1.6.3
## [123] stringi_1.8.2 munsell_0.5.0
## [125] Biostrings_2.70.1 lazyeval_0.2.2
## [127] Matrix_1.6-4 hms_1.1.3
## [129] MsExperiment_1.4.0 bit64_4.0.5
## [131] ggplot2_3.4.4 KEGGREST_1.42.0
## [133] statmod_1.5.0 shiny_1.8.0
## [135] highr_0.10 SummarizedExperiment_1.32.0
## [137] interactiveDisplayBase_1.40.0 AnnotationHub_3.10.0
## [139] igraph_1.5.1 memoise_2.0.1
## [141] affyio_1.72.0 bslib_0.6.1
## [143] DEoptimR_1.1-3 bit_4.0.5