Package: MetaboAnnotation
Authors: Michael Witting [aut] (https://orcid.org/0000-0002-1462-4426), Johannes Rainer [aut, cre] (https://orcid.org/0000-0002-6977-7147), Andrea Vicini [aut] (https://orcid.org/0000-0001-9438-6909), Carolin Huber [aut] (https://orcid.org/0000-0002-9355-8948)
Compiled: Thu Jul 28 16:52:37 2022

1 Introduction

The MetaboAnnotation package defines high-level user functionality to support and facilitate annotation of MS-based metabolomics data (Rainer et al. 2022).

2 Installation

The package can be installed with the BiocManager package. To install BiocManager use install.packages("BiocManager") and, after that, BiocManager::install("MetaboAnnotation") to install this package.

3 General description

MetaboAnnotation provides a set of matching functions that allow comparison (and matching) between query and target entities. These entities can be chemical formulas, numeric values (e.g. m/z or retention times) or fragment spectra. The available matching functions are:

  • matchFormula: to match chemical formulas.
  • matchSpectra: to match fragment spectra.
  • matchValues (formerly matchMz): to match numerical values (m/z, masses, retention times etc).

For each of these matching functions parameter objects are available that allow different types or matching algorithms. Refer to the help pages for a detailed listing of these (e.g. ?matchFormula, ?matchSpectra or ?matchValues). As a result, a Matched (or MatchedSpectra) object is returned which streamlines and simplifies handling of the potential one-to-many (or one-to-none) matching.

4 Example use cases

The following sections illustrate example use cases of the functionality provided by the MetaboAnnotation package.

library(MetaboAnnotation)

4.1 Matching of m/z values

In this section a simple matching of feature m/z values against theoretical m/z values is performed. This is the lowest level of confidence in metabolite annotation. However, it gives ideas about potential metabolites that can be analyzed in further downstream experiments and analyses.

The following example loads the feature table from a lipidomics experiments and matches the measured m/z values against reference masses from LipidMaps. Below we use a data.frame as reference database, but a CompDb compound database instance would also be supported.

ms1_features <- read.table(system.file("extdata", "MS1_example.txt",
                                       package = "MetaboAnnotation"),
                           header = TRUE, sep = "\t")
head(ms1_features)
##     feature_id       mz    rtime
## 1 Cluster_0001 102.1281 1.560147
## 2 Cluster_0002 102.1279 2.153590
## 3 Cluster_0003 102.1281 2.925570
## 4 Cluster_0004 102.1281 3.419617
## 5 Cluster_0005 102.1270 5.801039
## 6 Cluster_0006 102.1230 8.137535
target_df <- read.table(system.file("extdata", "LipidMaps_CompDB.txt",
                                    package = "MetaboAnnotation"),
                        header = TRUE, sep = "\t")
head(target_df)
##   headgroup        name exactmass    formula chain_type
## 1       NAE  NAE 20:4;O  363.2773  C22H37NO3       even
## 2       NAT  NAT 20:4;O  427.2392 C22H37NO5S       even
## 3       NAE NAE 20:3;O2  381.2879  C22H39NO4       even
## 4       NAE    NAE 20:4  347.2824  C22H37NO2       even
## 5       NAE    NAE 18:2  323.2824  C20H37NO2       even
## 6       NAE    NAE 18:3  321.2668  C20H35NO2       even

For reference (target) compounds we have only the mass available. We need to convert this mass to m/z values in order to match the m/z values from the features (i.e. the query m/z values) against them. For this we need to define the most likely ions/adducts that would be generated from the compounds based on the ionization used in the experiment. We assume the most abundant adducts from the compounds being "[M+H]+" and "[M+Na]+. We next perform the matching with the matchValues function providing the query and target data as well as a parameter object (in our case a Mass2MzParam) with the settings for the matching. With the Mass2MzParam, the mass or target compounds get first converted to m/z values, based on the defined adducts, and these are then matched against the query m/z values (i.e. the m/z values for the features). To get a full list of supported adducts the MetaboCoreUtils::adductNames(polarity = "positive") or MetaboCoreUtils::adductNames(polarity = "negative") can be used). Note also, to keep the runtime of this vignette short, we match only the first 100 features.

parm <- Mass2MzParam(adducts = c("[M+H]+", "[M+Na]+"),
                           tolerance = 0.005, ppm = 0)

matched_features <- matchValues(ms1_features[1:100, ], target_df, parm)
matched_features
## Object of class Matched 
## Total number of matches: 55 
## Number of query objects: 100 (55 matched)
## Number of target objects: 57599 (1 matched)

From the tested 100 features 55 were matched against at least one target compound (all matches are against a single compound). The result object (of type Matched) contains the full query data frame and target data frames as well as the matching information. We can access the original query data with query and the original target data with target function:

head(query(matched_features))
##     feature_id       mz    rtime
## 1 Cluster_0001 102.1281 1.560147
## 2 Cluster_0002 102.1279 2.153590
## 3 Cluster_0003 102.1281 2.925570
## 4 Cluster_0004 102.1281 3.419617
## 5 Cluster_0005 102.1270 5.801039
## 6 Cluster_0006 102.1230 8.137535
head(target(matched_features))
##   headgroup        name exactmass    formula chain_type
## 1       NAE  NAE 20:4;O  363.2773  C22H37NO3       even
## 2       NAT  NAT 20:4;O  427.2392 C22H37NO5S       even
## 3       NAE NAE 20:3;O2  381.2879  C22H39NO4       even
## 4       NAE    NAE 20:4  347.2824  C22H37NO2       even
## 5       NAE    NAE 18:2  323.2824  C20H37NO2       even
## 6       NAE    NAE 18:3  321.2668  C20H35NO2       even

Functions whichQuery and whichTarget can be used to identify the rows in the query and target data that could be matched:

whichQuery(matched_features)
##  [1]  46  47  48  49  50  51  52  53  54  55  56  57  58  59  60  61  62  63  64
## [20]  65  66  67  68  69  70  71  72  73  74  75  76  77  78  79  80  81  82  83
## [39]  84  85  86  87  88  89  90  91  92  93  94  95  96  97  98  99 100
whichTarget(matched_features)
## [1] 3149

The colnames function can be used to evaluate which variables/columns are available in the Matched object.

colnames(matched_features)
##  [1] "feature_id"        "mz"                "rtime"            
##  [4] "target_headgroup"  "target_name"       "target_exactmass" 
##  [7] "target_formula"    "target_chain_type" "adduct"           
## [10] "score"             "ppm_error"

These are all columns from the query, all columns from the target (the prefix "target_" is added to the original column names in target) and information on the matching result (in this case columns "adduct", "score" and "ppm_error").

We can extract the full matching table with matchedData. This returns a DataFrame with all rows in query the corresponding matches in target along with the matching adduct (column "adduct") and the difference in m/z (column "score" for absolute differences and "ppm_error" for the m/z relative differences). Note that if a row in query matches multiple elements in target, this row will be duplicated in the DataFrame returned by data. For rows that can not be matched NA values are reported.

matchedData(matched_features)
## DataFrame with 100 rows and 11 columns
##        feature_id        mz     rtime target_headgroup target_name
##       <character> <numeric> <numeric>      <character> <character>
## 1   Cluster_00...   102.128   1.56015               NA          NA
## 2   Cluster_00...   102.128   2.15359               NA          NA
## 3   Cluster_00...   102.128   2.92557               NA          NA
## 4   Cluster_00...   102.128   3.41962               NA          NA
## 5   Cluster_00...   102.127   5.80104               NA          NA
## ...           ...       ...       ...              ...         ...
## 96  Cluster_00...   201.113   11.2722               FA  FA 10:2;O2
## 97  Cluster_00...   201.113   11.4081               FA  FA 10:2;O2
## 98  Cluster_00...   201.113   11.4760               FA  FA 10:2;O2
## 99  Cluster_00...   201.114   11.5652               FA  FA 10:2;O2
## 100 Cluster_01...   201.114   11.7752               FA  FA 10:2;O2
##     target_exactmass target_formula target_chain_type      adduct     score
##            <numeric>    <character>       <character> <character> <numeric>
## 1                 NA             NA                NA          NA        NA
## 2                 NA             NA                NA          NA        NA
## 3                 NA             NA                NA          NA        NA
## 4                 NA             NA                NA          NA        NA
## 5                 NA             NA                NA          NA        NA
## ...              ...            ...               ...         ...       ...
## 96           200.105       C10H16O4              even      [M+H]+ 0.0007312
## 97           200.105       C10H16O4              even      [M+H]+ 0.0005444
## 98           200.105       C10H16O4              even      [M+H]+ 0.0005328
## 99           200.105       C10H16O4              even      [M+H]+ 0.0014619
## 100          200.105       C10H16O4              even      [M+H]+ 0.0020342
##     ppm_error
##     <numeric>
## 1          NA
## 2          NA
## 3          NA
## 4          NA
## 5          NA
## ...       ...
## 96    3.63578
## 97    2.70695
## 98    2.64927
## 99    7.26908
## 100  10.11476

Individual columns can be simply extracted with the $ operator:

matched_features$target_name
##   [1] NA           NA           NA           NA           NA          
##   [6] NA           NA           NA           NA           NA          
##  [11] NA           NA           NA           NA           NA          
##  [16] NA           NA           NA           NA           NA          
##  [21] NA           NA           NA           NA           NA          
##  [26] NA           NA           NA           NA           NA          
##  [31] NA           NA           NA           NA           NA          
##  [36] NA           NA           NA           NA           NA          
##  [41] NA           NA           NA           NA           NA          
##  [46] "FA 10:2;O2" "FA 10:2;O2" "FA 10:2;O2" "FA 10:2;O2" "FA 10:2;O2"
##  [51] "FA 10:2;O2" "FA 10:2;O2" "FA 10:2;O2" "FA 10:2;O2" "FA 10:2;O2"
##  [56] "FA 10:2;O2" "FA 10:2;O2" "FA 10:2;O2" "FA 10:2;O2" "FA 10:2;O2"
##  [61] "FA 10:2;O2" "FA 10:2;O2" "FA 10:2;O2" "FA 10:2;O2" "FA 10:2;O2"
##  [66] "FA 10:2;O2" "FA 10:2;O2" "FA 10:2;O2" "FA 10:2;O2" "FA 10:2;O2"
##  [71] "FA 10:2;O2" "FA 10:2;O2" "FA 10:2;O2" "FA 10:2;O2" "FA 10:2;O2"
##  [76] "FA 10:2;O2" "FA 10:2;O2" "FA 10:2;O2" "FA 10:2;O2" "FA 10:2;O2"
##  [81] "FA 10:2;O2" "FA 10:2;O2" "FA 10:2;O2" "FA 10:2;O2" "FA 10:2;O2"
##  [86] "FA 10:2;O2" "FA 10:2;O2" "FA 10:2;O2" "FA 10:2;O2" "FA 10:2;O2"
##  [91] "FA 10:2;O2" "FA 10:2;O2" "FA 10:2;O2" "FA 10:2;O2" "FA 10:2;O2"
##  [96] "FA 10:2;O2" "FA 10:2;O2" "FA 10:2;O2" "FA 10:2;O2" "FA 10:2;O2"

NA is reported for query entries for which no match was found. See also the help page for ?Matched for more details and information. In addition to the matching of query m/z against target exact masses as described above it would also be possible to match directly query m/z against target m/z values by using the MzParam instead of the Mass2MzParam.

4.2 Matching of m/z and retention time values

If expected retention time values were available for the target compounds, an annotation with higher confidence could be performed with matchValues and a Mass2MzRtParam parameter object. To illustrate this we randomly assign retention times from query features to the target compounds adding also 2 seconds difference. In a real use case the target data.frame would contain masses (or m/z values) for standards along with the retention times when ions of these standards were measured on the same LC-MS setup from which the query data derives.

Below we subset our data table with the MS1 features to the first 100 rows (to keep the runtime of the vignette short).

ms1_subset <- ms1_features[1:100, ]
head(ms1_subset)
##     feature_id       mz    rtime
## 1 Cluster_0001 102.1281 1.560147
## 2 Cluster_0002 102.1279 2.153590
## 3 Cluster_0003 102.1281 2.925570
## 4 Cluster_0004 102.1281 3.419617
## 5 Cluster_0005 102.1270 5.801039
## 6 Cluster_0006 102.1230 8.137535

The table contains thus retention times of the features in a column named "rtime".

Next we randomly assign retention times of the features to compounds in our target data adding a deviation of 2 seconds. As described above, in a real use case retention times are supposed to be determined by measuring the compounds with the same LC-MS setup.

set.seed(123)
target_df$rtime <- sample(ms1_subset$rtime,
                          nrow(target_df), replace = TRUE) + 2

We have now retention times available for both the query and the target data and can thus perform a matching based on m/z and retention times. We use the Mass2MzRtParam which allows us to specify (as for the Mass2MzParam) the expected adducts, the maximal acceptable m/z relative and absolute deviation as well as the maximal acceptable (absolute) difference in retention times. We use the settings from the previous section and allow a difference of 10 seconds in retention times. The retention times are provided in columns named "rtime" which is different from the default ("rt"). We thus specify the name of the column containing the retention times with parameter rtColname.

parm <- Mass2MzRtParam(adducts = c("[M+H]+", "[M+Na]+"),
                       tolerance = 0.005, ppm = 0,
                       toleranceRt = 10)
matched_features <- matchValues(ms1_subset, target_df, param = parm,
                                rtColname = "rtime")
matched_features
## Object of class Matched 
## Total number of matches: 31 
## Number of query objects: 100 (31 matched)
## Number of target objects: 57599 (1 matched)

Less features were matched based on m/z and retention times.

matchedData(matched_features)[whichQuery(matched_features), ]
## DataFrame with 31 rows and 13 columns
##        feature_id        mz     rtime target_headgroup target_name
##       <character> <numeric> <numeric>      <character> <character>
## 1   Cluster_00...   201.113   5.87206               FA  FA 10:2;O2
## 2   Cluster_00...   201.113   5.93346               FA  FA 10:2;O2
## 3   Cluster_00...   201.113   6.03653               FA  FA 10:2;O2
## 4   Cluster_00...   201.114   6.16709               FA  FA 10:2;O2
## 5   Cluster_00...   201.113   6.31781               FA  FA 10:2;O2
## ...           ...       ...       ...              ...         ...
## 27  Cluster_00...   201.113   11.2722               FA  FA 10:2;O2
## 28  Cluster_00...   201.113   11.4081               FA  FA 10:2;O2
## 29  Cluster_00...   201.113   11.4760               FA  FA 10:2;O2
## 30  Cluster_00...   201.114   11.5652               FA  FA 10:2;O2
## 31  Cluster_01...   201.114   11.7752               FA  FA 10:2;O2
##     target_exactmass target_formula target_chain_type target_rtime      adduct
##            <numeric>    <character>       <character>    <numeric> <character>
## 1            200.105       C10H16O4              even      15.8624      [M+H]+
## 2            200.105       C10H16O4              even      15.8624      [M+H]+
## 3            200.105       C10H16O4              even      15.8624      [M+H]+
## 4            200.105       C10H16O4              even      15.8624      [M+H]+
## 5            200.105       C10H16O4              even      15.8624      [M+H]+
## ...              ...            ...               ...          ...         ...
## 27           200.105       C10H16O4              even      15.8624      [M+H]+
## 28           200.105       C10H16O4              even      15.8624      [M+H]+
## 29           200.105       C10H16O4              even      15.8624      [M+H]+
## 30           200.105       C10H16O4              even      15.8624      [M+H]+
## 31           200.105       C10H16O4              even      15.8624      [M+H]+
##         score ppm_error  score_rt
##     <numeric> <numeric> <numeric>
## 1   0.0004538   2.25645  -9.99030
## 2   0.0004407   2.19131  -9.92890
## 3   0.0005655   2.81186  -9.82583
## 4   0.0015560   7.73698  -9.69527
## 5   0.0006845   3.40357  -9.54455
## ...       ...       ...       ...
## 27  0.0007312   3.63578  -4.59014
## 28  0.0005444   2.70695  -4.45431
## 29  0.0005328   2.64927  -4.38634
## 30  0.0014619   7.26908  -4.29719
## 31  0.0020342  10.11476  -4.08719

4.3 Matching of SummarizedExperiment or QFeatures objects

Results from LC-MS preprocessing (e.g. by the xcms package) or generally metabolomics results might be best represented and bundled as SummarizedExperiment or QFeatures objects (from the same-named Bioconductor packages). A XCMSnExp preprocessing result from xcms can for example be converted to a SummarizedExperiment using the quantify method from the xcms package. The feature definitions (i.e. their m/z and retention time values) will then be stored in the object’s rowData while the assay (the numerical matrix) will contain the feature abundances across all samples. Such SummarizedExperiment objects can be simply passed as query objects to the matchValues method. To illustrate this, we create below a simple SummarizedExperiment using the ms1_features data frame from the example above as rowData and adding a matrix with random values as assay.

library(SummarizedExperiment)
## Loading required package: MatrixGenerics
## Loading required package: matrixStats
## 
## Attaching package: 'MatrixGenerics'
## The following objects are masked from 'package:matrixStats':
## 
##     colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse,
##     colCounts, colCummaxs, colCummins, colCumprods, colCumsums,
##     colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs,
##     colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
##     colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,
##     colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,
##     colWeightedMeans, colWeightedMedians, colWeightedSds,
##     colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,
##     rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods,
##     rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps,
##     rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins,
##     rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,
##     rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars,
##     rowWeightedMads, rowWeightedMeans, rowWeightedMedians,
##     rowWeightedSds, rowWeightedVars
## Loading required package: GenomicRanges
## Loading required package: stats4
## Loading required package: BiocGenerics
## 
## Attaching package: 'BiocGenerics'
## The following objects are masked from 'package:stats':
## 
##     IQR, mad, sd, var, xtabs
## The following objects are masked from 'package:base':
## 
##     Filter, Find, Map, Position, Reduce, anyDuplicated, append,
##     as.data.frame, basename, cbind, colnames, dirname, do.call,
##     duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,
##     lapply, mapply, match, mget, order, paste, pmax, pmax.int, pmin,
##     pmin.int, rank, rbind, rownames, sapply, setdiff, sort, table,
##     tapply, union, unique, unsplit, which.max, which.min
## Loading required package: S4Vectors
## 
## Attaching package: 'S4Vectors'
## The following object is masked from 'package:MetaboAnnotation':
## 
##     endoapply
## The following objects are masked from 'package:base':
## 
##     I, expand.grid, unname
## Loading required package: IRanges
## Loading required package: GenomeInfoDb
## Loading required package: Biobase
## Welcome to Bioconductor
## 
##     Vignettes contain introductory material; view with
##     'browseVignettes()'. To cite Bioconductor, see
##     'citation("Biobase")', and for packages 'citation("pkgname")'.
## 
## Attaching package: 'Biobase'
## The following object is masked from 'package:MatrixGenerics':
## 
##     rowMedians
## The following objects are masked from 'package:matrixStats':
## 
##     anyMissing, rowMedians
se <- SummarizedExperiment(
    assays = matrix(nrow = nrow(ms1_features), ncol = 4),
    rowData = ms1_features)

We can now use the same matchValues call as before to perform the matching. Matching will be performed on the object’s rowData, i.e. each row/element of the SummarizedExperiment will be matched against the target using e.g. m/z values available in columns of the object’s rowData:

parm <- Mass2MzParam(adducts = c("[M+H]+", "[M+Na]+"),
                     tolerance = 0.005, ppm = 0)
matched_features <- matchValues(se, target_df, param = parm)
matched_features
## Object of class Matched 
## Total number of matches: 9173 
## Number of query objects: 2842 (1969 matched)
## Number of target objects: 57599 (3296 matched)

As query, the result contains the full SummarizedExperiment, but colnames and matchedData will access the respective information from the rowData of this SummarizedExperiment:

colnames(matched_features)
##  [1] "feature_id"        "mz"                "rtime"            
##  [4] "target_headgroup"  "target_name"       "target_exactmass" 
##  [7] "target_formula"    "target_chain_type" "target_rtime"     
## [10] "adduct"            "score"             "ppm_error"
matchedData(matched_features)
## DataFrame with 10046 rows and 12 columns
##          feature_id        mz     rtime target_headgroup   target_name
##         <character> <numeric> <numeric>      <character>   <character>
## 1     Cluster_00...   102.128   1.56015               NA            NA
## 2     Cluster_00...   102.128   2.15359               NA            NA
## 3     Cluster_00...   102.128   2.92557               NA            NA
## 4     Cluster_00...   102.128   3.41962               NA            NA
## 5     Cluster_00...   102.127   5.80104               NA            NA
## ...             ...       ...       ...              ...           ...
## 10042 Cluster_28...   957.771   20.2705               TG    TG 54:2;O3
## 10043 Cluster_28...   960.791   20.8865           HexCer HexCer 52:...
## 10044 Cluster_28...   961.361   13.0214               NA            NA
## 10045 Cluster_28...   970.873   22.0981             ACer ACer 60:1;...
## 10046 Cluster_28...   972.734   15.6914          Hex2Cer Hex2Cer 42...
##       target_exactmass target_formula target_chain_type target_rtime
##              <numeric>    <character>       <character>    <numeric>
## 1                   NA             NA                NA           NA
## 2                   NA             NA                NA           NA
## 3                   NA             NA                NA           NA
## 4                   NA             NA                NA           NA
## 5                   NA             NA                NA           NA
## ...                ...            ...               ...          ...
## 10042          934.784      C57H106O9              even      15.9950
## 10043          959.779     C58H105NO9              even      10.5076
## 10044               NA             NA                NA           NA
## 10045          947.888     C60H117NO6              even       4.2806
## 10046          971.727  C54H101NO1...              even      19.7329
##            adduct      score ppm_error
##       <character>  <numeric> <numeric>
## 1              NA         NA        NA
## 2              NA         NA        NA
## 3              NA         NA        NA
## 4              NA         NA        NA
## 5              NA         NA        NA
## ...           ...        ...       ...
## 10042     [M+Na]+ -0.0021897  2.286241
## 10043      [M+H]+  0.0045398  4.725089
## 10044          NA         NA        NA
## 10045     [M+Na]+ -0.0045054  4.640545
## 10046      [M+H]+ -0.0004240  0.435885

Subsetting the result object, to e.g. just matched elements will also subset the SummarizedExperiment.

matched_sub <- matched_features[whichQuery(matched_features)]
query(matched_sub)
## class: SummarizedExperiment 
## dim: 1969 4 
## metadata(0):
## assays(1): ''
## rownames: NULL
## rowData names(3): feature_id mz rtime
## colnames: NULL
## colData names(0):

A QFeatures object is essentially a container for several SummarizedExperiment objects which rows (features) are related with each other. Such an object could thus for example contain the full feature data from an LC-MS experiment as one assay and a compounded feature data in which data from ions of the same compound are aggregated as an additional assay. Below we create such an object using our SummarizedExperiment as an assay of name "features". For now we don’t add any additional assay to that QFeatures, thus, the object contains only this single data set.

library(QFeatures)
## Loading required package: MultiAssayExperiment
## 
## Attaching package: 'QFeatures'
## The following object is masked from 'package:MultiAssayExperiment':
## 
##     longFormat
## The following object is masked from 'package:base':
## 
##     sweep
qf <- QFeatures(list(features = se))
qf
## An instance of class QFeatures containing 1 assays:
##  [1] features: SummarizedExperiment with 2842 rows and 4 columns

matchValues supports also matching of QFeatures objects but the user needs to define the assay which should be used for the matching with the queryAssay parameter.

matched_qf <- matchValues(qf, target_df, param = parm, queryAssay = "features")
matched_qf
## Object of class Matched 
## Total number of matches: 9173 
## Number of query objects: 2842 (1969 matched)
## Number of target objects: 57599 (3296 matched)

colnames and matchedData allow to access the rowData of the SummarizedExperiment stored in the QFeatures"features" assay:

colnames(matched_qf)
##  [1] "feature_id"        "mz"                "rtime"            
##  [4] "target_headgroup"  "target_name"       "target_exactmass" 
##  [7] "target_formula"    "target_chain_type" "target_rtime"     
## [10] "adduct"            "score"             "ppm_error"
matchedData(matched_qf)
## DataFrame with 10046 rows and 12 columns
##          feature_id        mz     rtime target_headgroup   target_name
##         <character> <numeric> <numeric>      <character>   <character>
## 1     Cluster_00...   102.128   1.56015               NA            NA
## 2     Cluster_00...   102.128   2.15359               NA            NA
## 3     Cluster_00...   102.128   2.92557               NA            NA
## 4     Cluster_00...   102.128   3.41962               NA            NA
## 5     Cluster_00...   102.127   5.80104               NA            NA
## ...             ...       ...       ...              ...           ...
## 10042 Cluster_28...   957.771   20.2705               TG    TG 54:2;O3
## 10043 Cluster_28...   960.791   20.8865           HexCer HexCer 52:...
## 10044 Cluster_28...   961.361   13.0214               NA            NA
## 10045 Cluster_28...   970.873   22.0981             ACer ACer 60:1;...
## 10046 Cluster_28...   972.734   15.6914          Hex2Cer Hex2Cer 42...
##       target_exactmass target_formula target_chain_type target_rtime
##              <numeric>    <character>       <character>    <numeric>
## 1                   NA             NA                NA           NA
## 2                   NA             NA                NA           NA
## 3                   NA             NA                NA           NA
## 4                   NA             NA                NA           NA
## 5                   NA             NA                NA           NA
## ...                ...            ...               ...          ...
## 10042          934.784      C57H106O9              even      15.9950
## 10043          959.779     C58H105NO9              even      10.5076
## 10044               NA             NA                NA           NA
## 10045          947.888     C60H117NO6              even       4.2806
## 10046          971.727  C54H101NO1...              even      19.7329
##            adduct      score ppm_error
##       <character>  <numeric> <numeric>
## 1              NA         NA        NA
## 2              NA         NA        NA
## 3              NA         NA        NA
## 4              NA         NA        NA
## 5              NA         NA        NA
## ...           ...        ...       ...
## 10042     [M+Na]+ -0.0021897  2.286241
## 10043      [M+H]+  0.0045398  4.725089
## 10044          NA         NA        NA
## 10045     [M+Na]+ -0.0045054  4.640545
## 10046      [M+H]+ -0.0004240  0.435885

4.4 Matching of MS/MS spectra

In this section we match experimental MS/MS spectra against reference spectra. This can also be performed with functions from the Spectra package (see SpectraTutorials, but the functions and concepts used here are more suitable to the end user as they simplify the handling of the spectra matching results.

Below we load spectra from a file from a reversed-phase (DDA) LC-MS/MS run of the Agilent Pesticide mix. With filterMsLevel we subset the data set to only MS2 spectra. To reduce processing time of the example we further subset the Spectra to a small set of selected MS2 spectra. In addition we assign feature identifiers to each spectrum (again, for this example these are arbitrary IDs, but in a real data analysis such identifiers could indicate to which LC-MS feature these spectra belong).

library(Spectra)
library(msdata)
fl <- system.file("TripleTOF-SWATH", "PestMix1_DDA.mzML", package = "msdata")
pest_ms2 <- filterMsLevel(Spectra(fl), 2L)
## subset to selected spectra.
pest_ms2 <- pest_ms2[c(808, 809, 945:955)]
## assign arbitrary *feature IDs* to each spectrum.
pest_ms2$feature_id <- c("FT001", "FT001", "FT002", "FT003", "FT003", "FT003",
                         "FT004", "FT004", "FT004", "FT005", "FT005", "FT006",
                         "FT006")
## assign also *spectra IDs* to each
pest_ms2$spectrum_id <- paste0("sp_", seq_along(pest_ms2))
pest_ms2
## MSn data (Spectra) with 13 spectra in a MsBackendMzR backend:
##       msLevel     rtime scanIndex
##     <integer> <numeric> <integer>
## 1           2   361.651      2853
## 2           2   361.741      2854
## 3           2   377.609      3030
## 4           2   377.699      3031
## 5           2   378.120      3033
## ...       ...       ...       ...
## 9           2   378.959      3039
## 10          2   379.379      3041
## 11          2   380.059      3045
## 12          2   380.609      3048
## 13          2   381.029      3050
##  ... 35 more variables/columns.
## 
## file(s):
## PestMix1_DDA.mzML
## Processing:
##  Filter: select MS level(s) 2 [Thu Jul 28 16:53:01 2022]

This Spectra should now represent MS2 spectra associated with LC-MS features from an untargeted LC-MS/MS experiment that we would like to annotate by matching them against a spectral reference library.

We thus load below a Spectra object that represents MS2 data from a very small subset of MassBank release 2021.03. This small Spectra object is provided within this package but it would be possible to use any other Spectra object instead (see the SpectraTutorials for different ways and options to provide access to spectral libraries/databases via Spectra).

load(system.file("extdata", "minimb.RData", package = "MetaboAnnotation"))
minimb
## MSn data (Spectra) with 100 spectra in a MsBackendDataFrame backend:
##       msLevel     rtime scanIndex
##     <integer> <numeric> <integer>
## 1           2        NA        NA
## 2           2        NA        NA
## 3           2        NA        NA
## 4           2        NA        NA
## 5           2        NA        NA
## ...       ...       ...       ...
## 96         NA        NA        NA
## 97          2        NA        NA
## 98          2        NA        NA
## 99          2        NA        NA
## 100         2        NA        NA
##  ... 42 more variables/columns.
## Processing:
##  Filter: select spectra with polarity 1 [Wed Mar 31 10:06:28 2021]
##  Switch backend from MsBackendMassbankSql to MsBackendDataFrame [Wed Mar 31 10:07:59 2021]

We can now use the matchSpectra function to match each of our experimental query spectra against the target (reference) spectra. Settings for this matching can be defined with a dedicated param object. We use below the CompareSpectraParam that uses the compareSpectra function from the Spectra package to calculate similarities between each query spectrum and all target spectra. CompareSpectraParam allows to set all individual settings for the compareSpectra call with parameters MAPFUN, ppm, tolerance and FUN (see the help on compareSpectra in the Spectra package for more details). In addition, we can pre-filter the target spectra for each individual query spectrum to speed-up the calculations. By setting requirePrecursor = TRUE we compare below each query spectrum only to target spectra with matching precursor m/z (accepting a deviation defined by parameters ppm and tolerance). By default, matchSpectra with CompareSpectraParam considers spectra with a similarity score higher than 0.7 as matching and these are thus reported.

mtches <- matchSpectra(pest_ms2, minimb,
                       param = CompareSpectraParam(requirePrecursor = TRUE,
                                                   ppm = 10))
mtches
## Object of class MatchedSpectra 
## Total number of matches: 16 
## Number of query objects: 13 (5 matched)
## Number of target objects: 100 (11 matched)

The results are reported as a MatchedSpectra object which represents the matching results for all query spectra. This type of object contains all query spectra, all target spectra, the matching information and the parameter object with the settings of the matching. The object can be subsetted to e.g. matching results for a specific query spectrum:

mtches[1]
## Object of class MatchedSpectra 
## Total number of matches: 0 
## Number of query objects: 1 (0 matched)
## Number of target objects: 100 (0 matched)

In this case, for the first query spectrum, no match was found among the target spectra. Below we subset the MatchedSpectra to results for the second query spectrum:

mtches[2]
## Object of class MatchedSpectra 
## Total number of matches: 4 
## Number of query objects: 1 (1 matched)
## Number of target objects: 100 (4 matched)

The second query spectrum could be matched to 4 target spectra. The matching between query and target spectra can be n:m, i.e. each query spectrum can match no or multiple target spectra and each target spectrum can be matched to none, one or multiple query spectra.

Data (spectra variables of either the query and/or the target spectra) can be extracted from the result object with the spectraData function or with $ (similar to a Spectra object). The spectraVariables function can be used to list all available spectra variables in the result object:

spectraVariables(mtches)
##  [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] "feature_id"                     "spectrum_id"                   
## [37] "target_msLevel"                 "target_rtime"                  
## [39] "target_acquisitionNum"          "target_scanIndex"              
## [41] "target_dataStorage"             "target_dataOrigin"             
## [43] "target_centroided"              "target_smoothed"               
## [45] "target_polarity"                "target_precScanNum"            
## [47] "target_precursorMz"             "target_precursorIntensity"     
## [49] "target_precursorCharge"         "target_collisionEnergy"        
## [51] "target_isolationWindowLowerMz"  "target_isolationWindowTargetMz"
## [53] "target_isolationWindowUpperMz"  "target_spectrum_id"            
## [55] "target_spectrum_name"           "target_date"                   
## [57] "target_authors"                 "target_license"                
## [59] "target_copyright"               "target_publication"            
## [61] "target_splash"                  "target_compound_id"            
## [63] "target_adduct"                  "target_ionization"             
## [65] "target_ionization_voltage"      "target_fragmentation_mode"     
## [67] "target_collision_energy_text"   "target_instrument"             
## [69] "target_instrument_type"         "target_formula"                
## [71] "target_exactmass"               "target_smiles"                 
## [73] "target_inchi"                   "target_inchikey"               
## [75] "target_cas"                     "target_pubchem"                
## [77] "target_synonym"                 "target_precursor_mz_text"      
## [79] "target_compound_name"           "score"

This lists the spectra variables from both the query and the target spectra, with the prefix "target_" being used for spectra variable names of the target spectra. Spectra variable "score" contains the similarity score.

We could thus use $target_compound_name to extract the compound name of the matching target spectra for the second query spectrum:

mtches[2]$target_compound_name
## [1] "Azaconazole" "Azaconazole" "Azaconazole" "Azaconazole"

The same information can also be extracted on the full MatchedSpectra. Below we use $spectrum_id to extract the query spectra identifiers we added above from the full result object.

mtches$spectrum_id
##  [1] "sp_1"  "sp_2"  "sp_2"  "sp_2"  "sp_2"  "sp_3"  "sp_4"  "sp_4"  "sp_5" 
## [10] "sp_6"  "sp_6"  "sp_6"  "sp_7"  "sp_8"  "sp_8"  "sp_8"  "sp_8"  "sp_8" 
## [19] "sp_9"  "sp_9"  "sp_10" "sp_11" "sp_12" "sp_13"

Because of the n:m mapping between query and target spectra, the number of values returned by $ (or spectraData) can be larger than the total number of query spectra. Also in the example above, some of the spectra IDs are present more than once in the result returned by $spectrum_id. The respective spectra could be matched to more than one target spectrum (based on our settings) and hence their IDs are reported multiple times. Both spectraData and $ for MatchedSpectra use a left join strategy to report/return values: a value (row) is reported for each query spectrum (even if it does not match any target spectrum) with eventually duplicated values (rows) if the query spectrum matches more than one target spectrum (each value for a query spectrum is repeated as many times as it matches target spectra). To illustrate this we use below the spectraData function to extract specific data from our result object, i.e. the spectrum and feature IDs for the query spectra we defined above, the MS2 spectra similarity score, and the target spectra’s ID and compound name.

mtches_df <- spectraData(mtches, columns = c("spectrum_id", "feature_id",
                                             "score", "target_spectrum_id",
                                             "target_compound_name"))
as.data.frame(mtches_df)
##    spectrum_id feature_id     score target_spectrum_id    target_compound_name
## 1         sp_1      FT001        NA               <NA>                    <NA>
## 2         sp_2      FT001 0.7869556           LU056604             Azaconazole
## 3         sp_2      FT001 0.8855473           LU056603             Azaconazole
## 4         sp_2      FT001 0.7234894           LU056602             Azaconazole
## 5         sp_2      FT001 0.7219942           LU056605             Azaconazole
## 6         sp_3      FT002        NA               <NA>                    <NA>
## 7         sp_4      FT003 0.7769746           KW108103 triphenylphosphineoxide
## 8         sp_4      FT003 0.7577286           KW108102 triphenylphosphineoxide
## 9         sp_5      FT003        NA               <NA>                    <NA>
## 10        sp_6      FT003 0.7433718           SM839501            Dimethachlor
## 11        sp_6      FT003 0.7019807           EA070705            Dimethachlor
## 12        sp_6      FT003 0.7081274           EA070711            Dimethachlor
## 13        sp_7      FT004        NA               <NA>                    <NA>
## 14        sp_8      FT004 0.7320465           SM839501            Dimethachlor
## 15        sp_8      FT004 0.8106258           EA070705            Dimethachlor
## 16        sp_8      FT004 0.7290458           EA070710            Dimethachlor
## 17        sp_8      FT004 0.8168876           EA070711            Dimethachlor
## 18        sp_8      FT004 0.7247800           EA070704            Dimethachlor
## 19        sp_9      FT004 0.7412586           KW108103 triphenylphosphineoxide
## 20        sp_9      FT004 0.7198787           KW108102 triphenylphosphineoxide
## 21       sp_10      FT005        NA               <NA>                    <NA>
## 22       sp_11      FT005        NA               <NA>                    <NA>
## 23       sp_12      FT006        NA               <NA>                    <NA>
## 24       sp_13      FT006        NA               <NA>                    <NA>

Using the plotSpectraMirror function we can visualize the matching results for one query spectrum. Note also that an interactive, shiny-based, validation of matching results is available with the validateMatchedSpectra function. Below we call this function to show all matches for the second spectrum.

plotSpectraMirror(mtches[2])

Not unexpectedly, the peak intensities of query and target spectra are on different scales. While this was no problem for the similarity calculation (the normalized dot-product which is used by default is independent of the absolute peak values) it is not ideal for visualization. Thus, we apply below a normalization function to both the query and target spectra and plot the spectra again afterwards (see the help for addProcessing in the Spectra package for more details on spectra data manipulations). This function will replace the absolute spectra intensities with intensities relative to the maximum intensity of the spectrum.

norm_int <- function(x) {
    x[, "intensity"] <- x[, "intensity"] / max(x[, "intensity"], na.rm = TRUE)
    x
}
mtches <- addProcessing(mtches, norm_int)
plotSpectraMirror(mtches[2])

The query spectrum seems to nicely match the identified target spectra. Below we extract the compound name of the target spectra for this second query spectrum.

mtches[2]$target_compound_name
## [1] "Azaconazole" "Azaconazole" "Azaconazole" "Azaconazole"

As alternative to the CompareSpectraParam we could also use the MatchForwardReverseParam with matchSpectra. This has the same settings and performs the same spectra similarity search than CompareSpectraParam, but reports in addition (similar to MS-DIAL) to the (forward) similarity score also the reverse spectra similarity score as well as the presence ratio for matching spectra. While the default forward score is calculated considering all peaks from the query and the target spectrum (the peak mapping is performed using an outer join strategy), the reverse score is calculated only on peaks that are present in the target spectrum and the matching peaks from the query spectrum (the peak mapping is performed using a right join strategy). The presence ratio is the ratio between the number of mapped peaks between the query and the target spectrum and the total number of peaks in the target spectrum. These values are available as spectra variables "reverse_score" and "presence_ratio" in the result object). Below we perform the same spectra matching as above, but using the MatchForwardReverseParam.

mp <- MatchForwardReverseParam(requirePrecursor = TRUE, ppm = 10)
mtches <- matchSpectra(pest_ms2, minimb, param = mp)
mtches
## Object of class MatchedSpectra 
## Total number of matches: 16 
## Number of query objects: 13 (5 matched)
## Number of target objects: 100 (11 matched)

Below we extract the query and target spectra IDs, the compound name and all scores.

as.data.frame(
    spectraData(mtches, c("spectrum_id", "target_spectrum_id",
                          "target_compound_name", "score", "reverse_score",
                          "presence_ratio")))
##    spectrum_id target_spectrum_id    target_compound_name     score
## 1         sp_1               <NA>                    <NA>        NA
## 2         sp_2           LU056604             Azaconazole 0.7869556
## 3         sp_2           LU056603             Azaconazole 0.8855473
## 4         sp_2           LU056602             Azaconazole 0.7234894
## 5         sp_2           LU056605             Azaconazole 0.7219942
## 6         sp_3               <NA>                    <NA>        NA
## 7         sp_4           KW108103 triphenylphosphineoxide 0.7769746
## 8         sp_4           KW108102 triphenylphosphineoxide 0.7577286
## 9         sp_5               <NA>                    <NA>        NA
## 10        sp_6           SM839501            Dimethachlor 0.7433718
## 11        sp_6           EA070705            Dimethachlor 0.7019807
## 12        sp_6           EA070711            Dimethachlor 0.7081274
## 13        sp_7               <NA>                    <NA>        NA
## 14        sp_8           SM839501            Dimethachlor 0.7320465
## 15        sp_8           EA070705            Dimethachlor 0.8106258
## 16        sp_8           EA070710            Dimethachlor 0.7290458
## 17        sp_8           EA070711            Dimethachlor 0.8168876
## 18        sp_8           EA070704            Dimethachlor 0.7247800
## 19        sp_9           KW108103 triphenylphosphineoxide 0.7412586
## 20        sp_9           KW108102 triphenylphosphineoxide 0.7198787
## 21       sp_10               <NA>                    <NA>        NA
## 22       sp_11               <NA>                    <NA>        NA
## 23       sp_12               <NA>                    <NA>        NA
## 24       sp_13               <NA>                    <NA>        NA
##    reverse_score presence_ratio
## 1             NA             NA
## 2      0.8764394      0.5833333
## 3      0.9239592      0.6250000
## 4      0.7573541      0.6250000
## 5      0.9519647      0.4285714
## 6             NA             NA
## 7      0.9025051      0.7500000
## 8      0.9164348      0.5000000
## 9             NA             NA
## 10     0.8915201      0.5000000
## 11     0.8687003      0.3333333
## 12     0.8687472      0.3703704
## 13            NA             NA
## 14     0.8444402      0.5000000
## 15     0.9267965      0.5000000
## 16     0.8765496      0.7500000
## 17     0.9236674      0.4814815
## 18     0.8714208      0.8571429
## 19     0.8743130      0.7500000
## 20     0.8937751      0.5000000
## 21            NA             NA
## 22            NA             NA
## 23            NA             NA
## 24            NA             NA

In these examples we matched query spectra only to target spectra if their precursor m/z is ~ equal and reported only matches with a similarity higher than 0.7. CompareSpectraParam, through its parameter THRESHFUN would however also allow other types of analyses. We could for example also report the best matching target spectrum for each query spectrum, independently of whether the similarity score is higher than a certain threshold. Below we perform such an analysis defining a THRESHFUN that selects always the best match.

select_top_match <- function(x) {
    which.max(x)
}
csp <- CompareSpectraParam(ppm = 10, requirePrecursor = FALSE,
                           THRESHFUN = select_top_match)
mtches <- matchSpectra(pest_ms2, minimb, param = csp)
res <- spectraData(mtches, columns = c("spectrum_id", "target_spectrum_id",
                                       "target_compound_name", "score"))
as.data.frame(res)
##    spectrum_id target_spectrum_id                   target_compound_name
## 1         sp_1           SM839603                             Flufenacet
## 2         sp_2           LU056603                            Azaconazole
## 3         sp_3           SM839501                           Dimethachlor
## 4         sp_4           KW108103                triphenylphosphineoxide
## 5         sp_5           LU100202        2,2'-(Tetradecylimino)diethanol
## 6         sp_6           SM839501                           Dimethachlor
## 7         sp_7           RP005503              Glycoursodeoxycholic acid
## 8         sp_8           EA070711                           Dimethachlor
## 9         sp_9           KW108103                triphenylphosphineoxide
## 10       sp_10           JP006901                  1-PHENYLETHYL ACETATE
## 11       sp_11           EA070711                           Dimethachlor
## 12       sp_12           EA070705                           Dimethachlor
## 13       sp_13           LU101704 2-Ethylhexyl 4-(dimethylamino)benzoate
##           score
## 1  0.000000e+00
## 2  8.855473e-01
## 3  6.313687e-01
## 4  7.769746e-01
## 5  1.772117e-05
## 6  7.433718e-01
## 7  1.906998e-03
## 8  8.168876e-01
## 9  7.412586e-01
## 10 4.085289e-04
## 11 4.323403e-01
## 12 3.469648e-03
## 13 7.612480e-06

Note that this whole example would work on any Spectra object with MS2 spectra. Such objects could also be extracted from an xcms-based LC-MS/MS data analysis with the chromPeaksSpectra or featureSpectra functions from the xcms package. Also,

Matches can be also further validated using an interactive Shiny app by calling validateMatchedSpectra on the MatchedSpectra object. Individual matches can be set to TRUE or FALSE in this app. By closing the app via the Save & Close button a filtered MatchedSpectra is returned, containing only matches manually validated.

Session information

## R version 4.2.1 (2022-06-23)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.4 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.16-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.16-bioc/R/lib/libRlapack.so
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_GB              LC_COLLATE=C              
##  [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       
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] msdata_0.37.0               Spectra_1.7.1              
##  [3] ProtGenerics_1.29.0         BiocParallel_1.31.10       
##  [5] QFeatures_1.7.1             MultiAssayExperiment_1.23.4
##  [7] SummarizedExperiment_1.27.1 Biobase_2.57.1             
##  [9] GenomicRanges_1.49.0        GenomeInfoDb_1.33.3        
## [11] IRanges_2.31.0              S4Vectors_0.35.1           
## [13] BiocGenerics_0.43.1         MatrixGenerics_1.9.1       
## [15] matrixStats_0.62.0          MetaboAnnotation_1.1.2     
## [17] BiocStyle_2.25.0           
## 
## loaded via a namespace (and not attached):
##  [1] xfun_0.31               bslib_0.4.0             lattice_0.20-45        
##  [4] htmltools_0.5.3         yaml_2.3.5              rlang_1.0.4            
##  [7] jquerylib_0.1.4         mzR_2.31.1              MetaboCoreUtils_1.5.1  
## [10] GenomeInfoDbData_1.2.8  stringr_1.4.0           zlibbioc_1.43.0        
## [13] codetools_0.2-18        evaluate_0.15           knitr_1.39             
## [16] fastmap_1.1.0           parallel_4.2.1          highr_0.9              
## [19] Rcpp_1.0.9              BiocManager_1.30.18     cachem_1.0.6           
## [22] DelayedArray_0.23.1     magick_2.7.3            jsonlite_1.8.0         
## [25] XVector_0.37.0          MsCoreUtils_1.9.1       fs_1.5.2               
## [28] digest_0.6.29           stringi_1.7.8           bookdown_0.27          
## [31] ncdf4_1.19              grid_4.2.1              clue_0.3-61            
## [34] cli_3.3.0               tools_4.2.1             bitops_1.0-7           
## [37] magrittr_2.0.3          sass_0.4.2              RCurl_1.98-1.7         
## [40] lazyeval_0.2.2          cluster_2.1.3           pkgconfig_2.0.3        
## [43] MASS_7.3-58             Matrix_1.4-1            rmarkdown_2.14         
## [46] AnnotationFilter_1.21.0 R6_2.5.1                igraph_1.3.4           
## [49] compiler_4.2.1

References

Rainer, Johannes, Andrea Vicini, Liesa Salzer, Jan Stanstrup, Josep M. Badia, Steffen Neumann, Michael A. Stravs, et al. 2022. “A Modular and Expandable Ecosystem for Metabolomics Data Annotation in R.” Metabolites 12 (2): 173. https://doi.org/10.3390/metabo12020173.