Annotation of MS-based Metabolomics Data
3 October 2023
Package
MetaboAnnotation 1.4.3
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),
Nir Shachaf [ctb]
Compiled: Tue Oct 3 17:19:19 2023
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(formerlymatchMz): 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 (as created by the CompoundDb package) 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 8.137535target_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 evenFor 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 8.137535head(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 evenFunctions 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 100whichTarget(matched_features)## [1] 3149The 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 10.11476Individual 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 8.137535The 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) + 2We 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 -4.087194.3 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: 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## The following object is masked from 'package:AnnotationHub':
##
## cachese <- SummarizedExperiment(
assays = matrix(rnorm(nrow(ms1_features) * 4), ncol = 4,
dimnames = list(NULL, c("a", "b", "c", "d"))),
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 0.435885Subsetting the result object, to e.g. just matched elements will also subset the
SummarizedExperiment.
matched_sub <- matched_features[whichQuery(matched_features)]
MetaboAnnotation::query(matched_sub)## class: SummarizedExperiment
## dim: 1969 4
## metadata(0):
## assays(1): ''
## rownames: NULL
## rowData names(3): feature_id mz rtime
## colnames(4): a b c d
## 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':
##
## sweepqf <- QFeatures(list(features = se))
qf## An instance of class QFeatures containing 1 assays:
## [1] features: SummarizedExperiment with 2842 rows and 4 columnsmatchValues 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 0.4358854.4 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 [Tue Oct 3 17:19:48 2023]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 with reference fragment spectra instead (see
also the SpectraTutorials
workshop). As an alternative, it would also be possible to use a CompDb object
representing a compound annotation database (defined in the
CompoundDb package) with parameter target. See the
matchSpectra help page or section Query against multiple reference
databases below for more details and options to retrieve such annotation
resources from Bioconductor’s AnnotationHub.
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.
csp <- CompareSpectraParam(requirePrecursor = TRUE, ppm = 10)
mtches <- matchSpectra(pest_ms2, minimb, param = csp)
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 simple
scaling 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 each spectrum. Note that functions for addProcessing need
to have (like in the example below) the ... parameter.
scale_int <- function(x, ...) {
x[, "intensity"] <- x[, "intensity"] / max(x[, "intensity"], na.rm = TRUE)
x
}
mtches <- addProcessing(mtches, scale_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 NAIn 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)
}
csp2 <- CompareSpectraParam(ppm = 10, requirePrecursor = FALSE,
THRESHFUN = select_top_match)
mtches <- matchSpectra(pest_ms2, minimb, param = csp2)
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-06Note 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. Note also that retention times could in addition be
considered in the matching by selecting a non-infinite value for the
toleranceRt of any of the parameter classes. By default this uses the
retention times provided by the query and target spectra (i.e. spectra variable
"rtime") but it is also possible to specify any other spectra variable for the
additional retention time matching (e.g. retention indices instead of times)
using the rtColname parameter of the matchSpectra function (see
?matchSpectra help page for more information).
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.
4.4.1 Query against multiple reference databases
Getting access to reference spectra can sometimes be a little cumbersome since
it might involve lookup and download of specific resources or eventual
conversion of these into a format suitable for import. MetaboAnnotation
provides compound annotation sources to simplify this process. These
annotation source objects represent references (links) to annotation resources
and can be used in the matchSpectra call to define the targed/reference
spectra. The annotation source object takes then care, upon request, of
retrieving the annotation data or connecting to the annotation resources.
Also, compound annotation sources can be combined to allow matching query spectra against multiple reference libraries in a single call.
At present MetaboAnnotation supports the following types of compound
annotation sources (i.e. objects extending CompAnnotationSource):
Annotation resources that provide their data as a
CompDbdatabase (defined by the CompoundDb) package. These are supported by theCompDbSourceclass.Annotation resources for which a dedicated
MsBackendbackend is available hence supporting to access the data via aSpectraobject. These are supported by theSpectraDbSourceclass.
Various helper functions, specific for the annotation resource, are available to create such annotation source objects:
CompDbSource: creates a compound annotation source object from the providedCompDbSQLite data base file. This function can be used to integrate an existing (locally available)CompDbannotation database into an annotation workflow.MassBankSource: creates a annotation source object for a specific MassBank release. The desired release can be specified with thereleaseparameter (e.g.release = "2021.03"orrelease = "2022.06"). The function will then download the respective annotation database from Bioconductor’s AnnotationHub.
In the example below we create a annotation source for MassBank release
2022.06. This call will lookup the requested version in Biocondutor’s (online)
AnnotationHub and download the data. Subsequent requests for the same
annotation resource will load the locally cached version instead. Upcoming
MassBank database releases will be added to AnnotationHub after their official
release and all previous releases will be available as well.
mbank <- MassBankSource("2022.06")
mbank## Object of class CompDbSource
## Metadata information:
## - source: MassBank
## - url: https://massbank.eu/MassBank/
## - source_version: 2022.06
## - source_date: 2022-06-21
## - organism: NA
## - db_creation_date: Tue Aug 30 06:51:39 2022
## - supporting_package: CompoundDb
## - supporting_object: CompDbWe can now use that annotation source object in the matchSpectra call to
compare the experimental spectra from the previous examples against that release
of MassBank.
res <- matchSpectra(
pest_ms2, mbank,
param = CompareSpectraParam(requirePrecursor = TRUE, ppm = 10))## 'MsBackendCompDb' does not support parallel processing. Switching to serial processing.
## 'MsBackendCompDb' does not support parallel processing. Switching to serial processing.
## 'MsBackendCompDb' does not support parallel processing. Switching to serial processing.
## 'MsBackendCompDb' does not support parallel processing. Switching to serial processing.
## 'MsBackendCompDb' does not support parallel processing. Switching to serial processing.
## 'MsBackendCompDb' does not support parallel processing. Switching to serial processing.
## 'MsBackendCompDb' does not support parallel processing. Switching to serial processing.
## 'MsBackendCompDb' does not support parallel processing. Switching to serial processing.
## 'MsBackendCompDb' does not support parallel processing. Switching to serial processing.
## 'MsBackendCompDb' does not support parallel processing. Switching to serial processing.
## 'MsBackendCompDb' does not support parallel processing. Switching to serial processing.res## Object of class MatchedSpectra
## Total number of matches: 14
## Number of query objects: 13 (6 matched)
## Number of target objects: 10 (10 matched)The result object contains only the matching fragment spectra from the reference database.
target(res)## MSn data (Spectra) with 10 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
## 6 2 NA NA
## 7 2 NA NA
## 8 2 NA NA
## 9 2 NA NA
## 10 2 NA NA
## ... 46 more variables/columns.
## Processing:
## Switch backend from MsBackendCompDb to MsBackendDataFrame [Tue Oct 3 17:19:59 2023]And the names of the compounds with matching fragment spectra.
matchedData(res)$target_name## [1] NA "Azaconazole"
## [3] "Azaconazole" "Azaconazole"
## [5] "Azaconazole" NA
## [7] "triphenylphosphineoxide" "triphenylphosphineoxide"
## [9] "Triphenylphosphine oxide" "N,N-Dimethyldodecylamine"
## [11] "Dimethachlor" NA
## [13] "Dimethachlor" "Triphenylphosphine oxide"
## [15] "triphenylphosphineoxide" "triphenylphosphineoxide"
## [17] "Triphenylphosphine oxide" NA
## [19] NA NA
## [21] NA4.4.2 Performance and parallel processing
Pre-filtering the target spectra based on similar precursor m/z (using
requirePrecursor = TRUE generally speeds up the call because a spectra
comparison needs only to be performed on subsets of target spectra. Performance
of the matchSpectra function depends however also on the backend used for the
query and target Spectra. For some backends the peaks data (i.e. m/z and
intensity values) might not be already loaded into memory and hence spectra
comparisons might be slower because that data needs to be first loaded. As an
example, for Spectra objects, such as our pest_ms2 variable, that use the
MsBackendMzRbackend, the peaks data needs to be loaded from the raw data files
before the spectra similarity scores can be calculated. Changing the backend to
an in-memory data representation before matchSpectra can thus improve the
performance (at the cost of a higher memory demand).
Below we change the backends of the pest_ms2 and minimb objects to
MsBackendMemory which keeps all data (spectra and peaks data) in memory and we
compare the performance against the originally used MsBackendMzR (for
pest_ms2) and MsBackendDataFrame (for minimb).
pest_ms2_mem <- setBackend(pest_ms2, MsBackendMemory())
minimb_mem <- setBackend(minimb, MsBackendMemory())
library(microbenchmark)
microbenchmark(compareSpectra(pest_ms2, minimb, param = csp),
compareSpectra(pest_ms2_mem, minimb_mem, param = csp),
times = 5)## Unit: milliseconds
## expr min lq
## compareSpectra(pest_ms2, minimb, param = csp) 71.46379 78.11553
## compareSpectra(pest_ms2_mem, minimb_mem, param = csp) 41.92969 43.38181
## mean median uq max neval cld
## 253.05484 79.45892 79.49171 956.74424 5 a
## 46.70164 44.48420 44.80379 58.90873 5 aThere is a considerable performance gain by using the MsBackendMemory over the
two other backends, that comes however at the cost of a higher memory
demand. Thus, for large data sets (or reference libraries) this might not be an
option. See also issue
#93 in the
MetaboAnnotation github repository for more benchmarks and information on
performance of matchSpectra.
If for target a Spectra using a SQL database-based backend is used (such as
a MsBackendMassbankSql, MsBackendCompDb or MsBackendSql) and spectra
matching is performed with requirePrecursorMz = TRUE, simply caching the
precursor m/z values of all target spectra in memory improves the performance of
matchSpectra considerably. This can be easily done with e.g.
target_sps$precursorMz <- precursorMz(target_sps) where target_sps is the
Spectra object that uses one of the above mentioned backends. With this call
all precursor m/z values will be cached within target_sps and any
precursorMz(target_sps) call (which is used by matchSpectra to select the
candidate spectra against which to compare a query spectrum) will not require
a separate SQL call.
Parallel processing can also improve performance, but might not be possible for all backends. In particular, backends based on SQL databases don’t allow parallel processing because the database connection can not be shared across different processes.
Session information
## R version 4.3.1 (2023-06-16)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.3 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.17-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
##
## 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
##
## time zone: America/New_York
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] microbenchmark_1.4.10 msdata_0.40.0
## [3] QFeatures_1.10.0 MultiAssayExperiment_1.26.0
## [5] SummarizedExperiment_1.30.2 Biobase_2.60.0
## [7] GenomicRanges_1.52.0 GenomeInfoDb_1.36.4
## [9] IRanges_2.34.1 MatrixGenerics_1.12.3
## [11] matrixStats_1.0.0 Spectra_1.10.3
## [13] ProtGenerics_1.32.0 BiocParallel_1.34.2
## [15] S4Vectors_0.38.2 MetaboAnnotation_1.4.3
## [17] AnnotationHub_3.8.0 BiocFileCache_2.8.0
## [19] dbplyr_2.3.4 BiocGenerics_0.46.0
## [21] BiocStyle_2.28.1
##
## loaded via a namespace (and not attached):
## [1] DBI_1.1.3 bitops_1.0-7
## [3] gridExtra_2.3 sandwich_3.0-2
## [5] rlang_1.1.1 magrittr_2.0.3
## [7] multcomp_1.4-25 clue_0.3-65
## [9] compiler_4.3.1 RSQLite_2.3.1
## [11] png_0.1-8 vctrs_0.6.3
## [13] pkgconfig_2.0.3 MetaboCoreUtils_1.8.0
## [15] crayon_1.5.2 fastmap_1.1.1
## [17] magick_2.8.0 XVector_0.40.0
## [19] ellipsis_0.3.2 utf8_1.2.3
## [21] promises_1.2.1 rmarkdown_2.25
## [23] purrr_1.0.2 bit_4.0.5
## [25] xfun_0.40 zlibbioc_1.46.0
## [27] cachem_1.0.8 ChemmineR_3.52.0
## [29] jsonlite_1.8.7 blob_1.2.4
## [31] later_1.3.1 DelayedArray_0.26.7
## [33] interactiveDisplayBase_1.38.0 parallel_4.3.1
## [35] cluster_2.1.4 R6_2.5.1
## [37] bslib_0.5.1 jquerylib_0.1.4
## [39] Rcpp_1.0.11 bookdown_0.35
## [41] knitr_1.44 zoo_1.8-12
## [43] base64enc_0.1-3 splines_4.3.1
## [45] igraph_1.5.1 Matrix_1.6-1.1
## [47] httpuv_1.6.11 tidyselect_1.2.0
## [49] abind_1.4-5 yaml_2.3.7
## [51] codetools_0.2-19 curl_5.1.0
## [53] lattice_0.21-9 tibble_3.2.1
## [55] withr_2.5.1 shiny_1.7.5
## [57] KEGGREST_1.40.1 evaluate_0.22
## [59] survival_3.5-7 xml2_1.3.5
## [61] Biostrings_2.68.1 pillar_1.9.0
## [63] BiocManager_1.30.22 filelock_1.0.2
## [65] DT_0.29 ncdf4_1.21
## [67] generics_0.1.3 RCurl_1.98-1.12
## [69] BiocVersion_3.17.1 ggplot2_3.4.3
## [71] munsell_0.5.0 scales_1.2.1
## [73] xtable_1.8-4 glue_1.6.2
## [75] lazyeval_0.2.2 tools_4.3.1
## [77] mzR_2.34.1 mvtnorm_1.2-3
## [79] fs_1.6.3 grid_4.3.1
## [81] MsCoreUtils_1.12.0 AnnotationDbi_1.62.2
## [83] colorspace_2.1-0 GenomeInfoDbData_1.2.10
## [85] cli_3.6.1 rappdirs_0.3.3
## [87] rsvg_2.5.0 fansi_1.0.4
## [89] S4Arrays_1.0.6 dplyr_1.1.3
## [91] AnnotationFilter_1.24.0 gtable_0.3.4
## [93] sass_0.4.7 digest_0.6.33
## [95] TH.data_1.1-2 rjson_0.2.21
## [97] htmlwidgets_1.6.2 memoise_2.0.1
## [99] htmltools_0.5.6 lifecycle_1.0.3
## [101] httr_1.4.7 CompoundDb_1.4.0
## [103] mime_0.12 bit64_4.0.5
## [105] MASS_7.3-60References
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.