MetNet: Inferring metabolic networks from untargeted high-resolution mass spectrometry data
15 April 2025
Abstract
A major bottleneck of mass spectrometry-based metabolomic analysis is still the rapid detection and annotation of unknown m/z features across biological matrices. Traditionally, the annotation was done manually imposing constraints in reproducibility and automatization. Furthermore, different analysis tools are typically used at different steps of analyses which requires parsing of data and changing of environments. I present here MetNet, a novel R package, that is compatible with the output of the xcms/CAMERA suite and that uses the data-rich output of mass spectrometry metabolomics to putatively link features on their relation to other features in the data set. MetNet uses both structural and quantitative information of metabolomics data for network inference that will guide metabolite annotation.
Package
MetNet 1.26.0
1 Introduction
Among the main challenges in mass spectrometric metabolomic analysis is the
high-throughput analysis of metabolic features, their fast detection and
annotation.
By contrast to the screening of known, previously characterized,
metabolic features in these data, the putative annotation of unknown
features is often cumbersome and requires a lot of manual work, hindering
the biological information retrieval of these data.
High-resolution mass spectrometric data is often very rich in information
content and metabolic conversions, and reactions can be derived from structural
properties of features (Breitling et al. 2006).
In addition to that, statistical associations between
features (based on their intensity values) can be a valuable resource to find
co-synthesized or co-regulated metabolites, which are synthesized in the same
biosynthetic pathways. Given that an analysis tool within the R framework
is still lacking that is
integrating the two features of mass spectrometric information commonly
acquired with mass spectrometers (m/z and intensity values), I developed
MetNet to close this gap.
The MetNet package comprises functionalities to infer network
topologies from high-resolution mass spectrometry data. MetNet
combines information from both structural data (differences in m/z values
of features) and statistical associations (intensity values of features per
sample) to propose putative metabolic networks that can be used for further
exploration.
The idea of using high-resolution mass spectrometry data for network construction was first proposed in Breitling et al. (2006) and followed soon afterwards by a Cytoscape plugin, MetaNetter (Jourdan et al. 2007), that is based on the inference of metabolic networks on molecular weight differences and correlation (Pearson correlation and partial correlation).
Inspired by the paper of Marbach et al. (2012) different algorithms for network
were implemented in MetNet to account for
biases that are inherent in these statistical methods, followed by the
calculation of a consensus adjacency matrix using the differently computed
individual adjacency matrices.
The two main functionalities of the package include the creation of adjacency matrices from structural properties, based on losses/addition of functional groups defined by the user, and statistical associations. Currently, the following statistical models are implemented to infer a statistical adjacency matrix: Least absolute shrinkage and selection operator (LASSO, L1-norm regression, (Tibshirani 1994)), Random Forest (Breiman 2001), Pearson and Spearman correlation (including partial and semipartial correlation, see Steuer (2006) for a discussion on correlation-based metabolic networks), correlation based on Gaussian Graphical Models (GGM, see Krumsiek et al. (2011);Benedetti et al. (2020) for the advantages of using GGM instead of Pearson and partial pearson correlation), context likelihood of relatedness (CLR, (Faith et al. 2007)), the algorithm for the reconstruction of accurate cellular networks (ARACNE, (Margolin et al. 2006)) and constraint-based structure learning (Bayes, (Scutari 2010)). Since all of these methods have advantages and disadvantages, the user has the possibility to select several of these methods, compute adjacency matrices from these models and create a consensus matrix from the different statistical frameworks.
After creating the statistical and structural adjacency matrices these two matrices can be combined to form a consensus matrix that has information from both structural and statistical properties of the data. This can be followed by network analyses (e.g. calculation of topological parameters), integration with other data sources (e.g. genomic information or transcriptomic data) and/or visualization.
Central to MetNet is the AdjacencyMatrix class, derived from the
SummarizedExperiment S4 class. The AdjacencyMatrix host the adjacency
matrices creates during the different steps within the assays slot. They
will furthermore store information on the type of the AdjacencyMatrix,
i.e. if it was derived from structural or statistical properties or if
it used the combined information from these layers (combine). It also
stores information if the information was thresholded, e.g. by
applying the rtCorrection or threshold function. Furthermore, the
AdjacencyMatrix object stores information on if the graphs are directed
or undirected (within the directed slot).
Questions and bugs
MetNet is currently under active development. If you
discover any bugs, typos or develop ideas of improving
MetNet feel free to raise an issue via
Github or
send a mail to the developer.
2 Prepare the environment and load the data
To install MetNet enter the following to the R console
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("MetNet")Before starting with the analysis, load the MetNet package. This
will also load the required packages glmnet, stabs, GENIE3, mpmi,
parmigene, Hmisc, ppcor and bnlearn that are needed
for functions in the statistical adjacency matrix inference.
library(MetNet)The data format that is compatible with the MetNet framework is
a xcms/CAMERA output-like \(m~\times~n\) matrix, where
columns denote the different samples \(n\) and where \(m\) features are present.
In such a matrix, information about the masses of the features and quantitative
information of the features (intensity or concentration values) are needed.
The information about the m/z values has to be stored in a vector of
length \(\vert m \vert\) in the column "mz".
MetNet does not impose any requirements for
data normalization, filtering, etc. However, the user has to make sure that
the data is properly preprocessed. These include division by internal standard,
log2 or vsn transformation, noise filtering, removal of features that do not
represent mass features/metabolites, removal of isotopes, etc.
We will load here the object x_test that contains m/z values
(in the column "mz"), together with the corresponding retention time
(in the column "rt") and intensity values. We will use here the object
x_test for guidance through the workflow of MetNet.
data("x_test", package = "MetNet")
x_test <- as.matrix(x_test)3 Creating the structural adjacency
The function structural will create an AdjacencyMatrix object of
type structural containing the adjacency
matrices based on structural properties (m/z values) of the features.
The function expects a matrix with a column "mz" that contains the
mass information of a feature (typically the m/z value). Furthermore,
structural takes a data.frame
object as argument transformation with the colnames
"mass" and additional columns (e.g. "group", "formula" or "rt").
structural looks for transformations (in the
sense of additions/losses of functional groups mediated by biochemical,
enzymatic reactions) in the data using the mass information.
Following the work of Breitling et al. (2006) and Jourdan et al. (2007), molecular weight difference wX is defined by \(w_X = \vert w_A - w_B \vert\)
where wA is the molecular weight of substrate A, and wB is the molecular weight of product B (typically, m/z values will be used as a proxy for the molecular weight since the molecular weight is not directly derivable from mass spectrometric data). As exemplified in Jourdan et al. (2007), specific enzymatic reactions refer to specific changes in the molecular weight, e.g. carboxylation reactions will result in a mass difference of 43.98983 (molecular weight of CO2) between metabolic features.
The search space for these transformation is adjustable by the
transformation argument in
structural allowing to look for specific
enzymatic transformations. Hereby,
structural will take into account the
ppm value, to adjust for inaccuracies in m/z values due to technical
reasons according to the formula
\[ppm = \frac{m_{exp} - m_{calc}}{m_{exp}} \cdot 10^{-6}\]
with mexp the experimentally determined m/z value and mcalc the
calculated accurate mass of a molecule. Within the function, a lower and upper
range is calculated depending on the supplied ppm value, differences
between the m/z feature values are calculated and matched against the
"mass"es of the transformation argument. If any
of the additions/losses defined in transformation is found in the
data, it will be reported as an (unweighted) connection in the assay
"binary" of the returned AdjacencyMatrix object.
Together with this assay, additional character adjacency matrices can be
written to the assay slot of the AdjacencyMatri object.
E.g. we can write the type of
connection/transformation (derived e.g. from the column "group" in the
transformation object) as a character matrix to the
assay "group" by setting var = "group".
Before creating the structural AdjacencyMatrix, one must define the
search space, i.e. the transformation that will be looked for in the mass spectrometric
data, by creating here the transformations object.
## define the search space for biochemical transformation
transformations <- rbind(
c("Hydroxylation (-H)", "O", 15.9949146221, "-"),
c("Malonyl group (-H2O)", "C3H2O3", 86.0003939305, "+"),
c("D-ribose (-H2O) (ribosylation)", "C5H8O4", 132.0422587452, "-"),
c("C6H10O6", "C6H10O6", 178.0477380536, "-"),
c("Rhamnose (-H20)", "C6H10O4", 146.057910, "-"),
c("Monosaccharide (-H2O)", "C6H10O5", 162.0528234315, "-"),
c("Disaccharide (-H2O) #1", "C12H20O10", 324.105649, "-"),
c("Disaccharide (-H2O) #2", "C12H20O11", 340.1005614851, "-"),
c("Trisaccharide (-H2O)", "C18H30O15", 486.1584702945, "-"),
c("Glucuronic acid (-H2O)", "C6H8O6", 176.0320879894, "?"),
c("coumaroyl (-H2O)", "C9H6O2", 146.0367794368, "?"),
c("feruloyl (-H2O)", "C9H6O2OCH2", 176.0473441231, "?"),
c("sinapoyl (-H2O)", "C9H6O2OCH2OCH2", 206.0579088094, "?"),
c("putrescine to spermidine (+C3H7N)", "C3H7N", 57.0578492299, "?"))
## convert to data frame
transformations <- data.frame(
group = transformations[, 1],
formula = transformations[, 2],
mass = as.numeric(transformations[, 3]),
rt = transformations[, 4])The function structural will then check for those
m/z differences that are stored in the column "mass" in the
object transformations. To create the AdjacencyMatrix object derived
from these structural information we enter
struct_adj <- structural(x = x_test, transformation = transformations,
var = c("group", "formula", "mass"), ppm = 10)in the R console.
As we set var = c("group", "formula", "mass"), the AdjacencyMatrix object
will contain the assays "group", "formula", and "mass" that store the
character adjacency matrices with the information defined in
the columns of transformations.
3.1 Advanced topic: Creating a directed structural graph
By default, the structural AdjacencyMatrix object and the contained
adjacency matrices are undirected (the
argument in structural is set to directed = FALSE by default; i.e. the
matrices are symmetric). MetNet,
however, also allows to include the information on the directionality of
the transformation (e.g. to distinguish between additions and losses).
This behaviour can be specified by setting directed = TRUE:
struct_adj_dir <- structural(x = x_test, transformation = transformations,
var = c("group", "formula", "mass"), ppm = 10, directed = TRUE)In the following we will visualize the results from the undirected and directed structural network.
We will set the mode of the igraph object
to "directed" in both cases to make the distinction between the returned
outputs of structural for setting directed = FALSE and directed = TRUE.
Alternatively, we could also set the mode for the first igraph object
(using the undirected output of structural) to "undirected" which results
in an igraph object where the directionality of the edges is not retained.
g_undirected <- igraph::graph_from_adjacency_matrix(
assay(struct_adj, "binary"), mode = "directed", weighted = NULL)
plot(g_undirected, edge.width = 1, edge.arrow.size = 0.5,
vertex.label.cex = 0.5, edge.color = "grey")
g_directed <- igraph::graph_from_adjacency_matrix(
assay(struct_adj_dir, "binary"), mode = "directed", weighted = NULL)
plot(g_directed, edge.width = 1, edge.arrow.size = 0.5,
vertex.label.cex = 0.5, edge.color = "grey")
Advanced topic: Refining the structural adjacency (optional)
The retention time will differ depending
on the chemical group added, e.g. an addition of a glycosyl group will
usually result in a lower retentiom time in reverse-phase chromatography.
This information can be used in refining the adjacency matrix derived from
the structural matrix. The rtCorrection function does this check, if the
predicted transformations correspond to the expected retention time shift,
in an automated fashion. It requires information about the expected retention
time shift in the data.frame passed to the transformation
argument (in the "rt" column). Within this column, information about
retention time shifts is encoded by "-", "+" and "?",
which means the feature with higher m/z value has lower, higher or unknown
retention time than the feature with the lower m/z value. The values for
m/z and retention time will be taken from the object passed to the
x argument. In case there is a discrepancy between the transformation
and the retention time shift, the adjacency matrix at the specific position
will be set to 0. rtCorrection will return the an AdjacencyMatrix object
with updated adjacency matrices ("binary" and the additional
character adjacecny matrices).
To account for retention time shifts we enter
struct_adj <- rtCorrection(am = struct_adj, x = x_test,
transformation = transformations, var = "group")in the R console. The character "group" defined with var will serve
here as the link between the assay "group" and the column in transformation
to calculate the retention time discrepancies between feature pairs.
For data analysis a data.frame can be generated from AdjacencyMatrix objects
by applying as.data.frame(). Further filtering displays only feature-pairs
which were matched to a transformation.
struct_df <- as.data.frame(struct_adj)
struct_df <- struct_df[struct_df$binary == 1, ]Some overview on the mass-difference distribution of the data can be observed
using the mz_summary function. The number of determined mass differences
can be displayed by using the mz_vis function.
mz_sum <- mz_summary(struct_adj, var = "group")
mz_vis(mz_sum, var = "group")
For larger data-sets, also a filter can be
applied to visualize mass-difference above a defined threshold.
A filter can be applied, by filter. Since the maximum count of any mass difference in struct_adj is 4, a filter of 5 results in 0 mass differences.
mz_summary(struct_adj, filter = 4)## group formula count
## 1 Hydroxylation (-H) O 15
## 2 Malonyl group (-H2O) C3H2O3 11
## 3 Monosaccharide (-H2O) C6H10O5 6
## 4 Rhamnose (-H20) C6H10O4 9
## 6 feruloyl (-H2O) C9H6O2OCH2 4
## 7 putrescine to spermidine (+C3H7N) C3H7N 4mz_summary(struct_adj, filter = 5)## group formula count
## 1 Hydroxylation (-H) O 15
## 2 Malonyl group (-H2O) C3H2O3 11
## 3 Monosaccharide (-H2O) C6H10O5 6
## 4 Rhamnose (-H20) C6H10O4 9The AdjacencyMatrix class allows storing further information on the features
as putative annotations, database identifier, SMILES, etc. using rowData().
A data.frame containing the same rownames as the test data needs to be
provided. The columns can store different information as annotations, identifier, etc.
We will load the x_annotation file, which contains an example annotation
and other identifier for feature x1856. All the other features contain NAs
in corresponding columns.
data("x_annotation", package = "MetNet")
x_annotation <- as.data.frame(x_annotation)
## add annotations to the structural AdjacencyMatrix object
rowData(struct_adj) <- x_annotation
## display annotation for the feature "1856"
rowData(struct_adj)["x1856", ]## DataFrame with 1 row and 11 columns
## database_mz database_identifier chemical_formula smiles
## <character> <character> <character> <character>
## x1856 308.2 N-caffeoylspermidine C16H25N3O3 C=1(C=C(C(=CC1)O)O)/..
## inchi inchikey metabolite_identification
## <character> <character> <character>
## x1856 InChI=1S/C16H25N3O3/.. AZSUJBAOTYNFDE-FNORW.. NA
## fragmentations modifications charge database
## <character> <character> <character> <character>
## x1856 NA NA 1 NA3.2 Adding spectral similarity to the structural adjacency
MetNet can also incorporate information from spectral similarity to the
structural AdjacencyMatrix.
addSpectralSimilarity uses a list of spectral similarity matrices
(e.g. that were created using functionality from the Spectra package)
and adds them to the structural AdjacencyMatrix.
Column- and rownames of the spectral similarity matrix should match to the
respective feature names in the respective MS1 data (i.e. colnames/rownames
in the structural AdjacencyMatrix)
The function will create weighted adjacency matrices using the spectral similarity methods defined by names of the list-entries (e.g. “ndotproduct”).
In the following example, we load a toy MS2 dataset, represented as Spectra
object. This object stores unique id’s, matching to the respective
MS1 data.
We will then create a spectral similarity adjacency matrices using the
normalized dotproduct and add them to the previously created “structural”
AdjacencyMatrix.
# required for ndotproduct calculus
library(MsCoreUtils)##
## Attaching package: 'MsCoreUtils'## The following object is masked from 'package:GenomicRanges':
##
## reduce## The following object is masked from 'package:IRanges':
##
## reduce## The following object is masked from 'package:MatrixGenerics':
##
## colCounts## The following object is masked from 'package:matrixStats':
##
## colCounts## The following object is masked from 'package:stats':
##
## smoothlibrary(Spectra)## Loading required package: BiocParallel##
## Attaching package: 'Spectra'## The following object is masked from 'package:MsCoreUtils':
##
## entropy## create spectral similarity matrix
f <- system.file("spectra_matrix/ms2_test.RDS", package = "MetNet")
sps_sub <- readRDS(f)
adj_spec <- Spectra::compareSpectra(sps_sub, FUN = ndotproduct)
colnames(adj_spec) <- sps_sub$id
rownames(adj_spec) <- sps_sub$id
spect_adj <- addSpectralSimilarity(am_structural = struct_adj,
ms2_similarity = list("ndotproduct" = adj_spec))Furthermore, the spectral similarity matrix can be thresholded using the
function threshold.
The user needs to define whether the data should be thresholded or not.
If multiple methods have been used to generate the spectral matrices,
different threshold parameters can be defined (for detailed description see
thresholding of statistical).
## the assayNames in spect_adj are used to define the filter criteria
assayNames(spect_adj)## [1] "binary" "group" "formula" "mass" "ndotproduct"## return edges with normalized dotproduct > 0.10
args_thr <- list(filter = "ndotproduct > 0.1")
## return edges with normalized dotproduct > 0.10, even if no mass-difference
## was detected between pairs of features
args_thr <- list(filter = "ndotproduct > 0.1 | binary == 1 & is.na(ndotproduct)")
## pass the filtering criteria to the args argument and set type to "threshold"
spect_adj_thr <- threshold(am = spect_adj, type = "threshold",
args = args_thr)4 Creating the statistical adjacency
4.1 Creating weighted adjacency matrices using statistical
The function statistical will create an AdjacencyMatrix object of type
statistical containing the adjacency
matrices based on statistical associations. The function will create
weighted adjacency matrices using the statistical models defined by the
model argument. Currently, the following models are available:
LASSO (using stabs,
(Hofner, Boccuto, and Göker 2015; Thomas et al. 2017)), Random Forest (using GENIE3,
CLR, ARACNE (the two latter using the package mpmi to calculate
Mutual Information using a nonparametric bias correction by
Bias Corrected Mutual Information, and the functions clr and
aracne.a from the parmigene package), Pearson and
Spearman correlation (based on the
psych package), partial and semipartial
Pearson and Spearman correlation (using the ppcor package), correlation based
on Gaussian graphical models (using the GeneNet package (Schäfer and Strimmer 2005)) and
score-based structure learning returning the strength of the probabilistic
relationships of the arcs of
a Bayesian network, as learned from bootstrapped data (using the
boot.strength with the Tabu greedy search as default
from the bnlearn package (Scutari 2010)).
For further information on the different models
take a look on the respective help pages of lasso,
randomForest, clr, aracne, correlation and/or
bayes. Arguments that are accepted by the respective underlying
functions can be passed directly to the statistical
function. In addition,
arguments that are defined in the functions lasso,
randomForest, clr, aracne, correlation and/or
bayes can be passed to the functions.
4.2 Creating an unweighted adjacency matrix using threshold
From the statistical AdjacencyMatrix object the function threshold
will create an AdjacencyMatrix object with the derived
unweighted adjacency matrix from the weighted adjacency matrices
unifying the information present from all statistical models. This
unweighted adjacency matrix is stored in the assay "consensus".
In the following example, we will create a list of unweighted adjacency matrices using Pearson and Spearman correlation using the intensity values as input data.
x_int <- x_test[, 3:ncol(x_test)] |>
as.matrix()
stat_adj <- statistical(x_int, model = c("pearson", "spearman"))## [1] "pearson finished."
## [1] "spearman finished."The reasoning behind this step is to circumvent disadvantages arising from each
model and creating a statistically reliable topology that reflects the actual
metabolic relations. threshold returns an unweighted adjacency
matrix with connections inferred from the respective models (in the
"consensus" assay).
There are four different types implemented how the unweighted adjacency
matrix can be created: threshold, top1, top2, mean.
For type = "threshold", threshold values have to be defined in the
args argument for the respective statistical model within the list entry
filter. Values above or below
these thresholds in each respective weighted adjacency matrix will be
reported as present (1) or absent (0) in the returned unweighted adjacency
matrix.
For the other three types (top1, top2, mean) the ranks per statistical model
will be calculated and from each respective link the top1, top2 or mean rank
across statistical models will be calculated (cf. (Hase et al. 2013)). The
top n unique ranks (defined by the entry n in args) will be returned
as links in the unweighted consensus adjacency matrix.
We will create here for all ways the thresholded AdjacencyMatrix objects of
type statistical containing the consensus adjacency matrix.
## type = "threshold"
## the assayNames in stat_adj are used to define the filter criteria
assayNames(stat_adj)## [1] "pearson_coef" "pearson_pvalue" "spearman_coef" "spearman_pvalue"## return edges with positive Pearson correlation coefficients > 0.95
args_thr <- list(filter = "pearson_coef > 0.95")
## return edges with positive Spearman correlation coefficients > 0.95
args_thr <- list(filter = "spearman_coef > 0.95")
## return edges with absolute Pearson correlation coefficients > 0.95 and
## associated p-values <= 0.05
args_thr <- list(filter = "abs(pearson_coef) > 0.95 & pearson_pvalue <= 0.05")
## return edges with absolute Pearson OR Spearman correlation coefficients > 0.95
args_thr <- list(filter = "abs(pearson_coef) > 0.95 | abs(spearman_coef) > 0.95")
## return edges with absolute Pearson AND Spearman correlation coefficients > 0.95
args_thr <- list(filter = "abs(pearson_coef) > 0.95 & abs(spearman_coef) > 0.95")
## pass the filtering criteria to the args argument and set type to "threshold"
stat_adj_thr <- threshold(am = stat_adj, type = "threshold",
args = args_thr)
## alternatively, use the types "top1", "top2", "mean"
## retrieve the feature pairs which have the 100 highest coefficients
args_top <- list(n = 100)
## type = "top1"
stat_adj_top1 <- threshold(am = stat_adj, type = "top1",
args = args_top)
## type = "top2"
stat_adj_top2 <- threshold(am = stat_adj, type = "top2",
args = args_top)
## type = "mean"
stat_adj_mean <- threshold(am = stat_adj, type = "mean",
args = args_top)5 Combining the structural and statistical matrix
After creating the structural and statistical AdjacencyMatrix objects,
the two objects are combined. The function combine
will combine these two objects and create an AdjacencyMatrix object of type
combine. The function accepts
the arguments am_structural and am_statistical for the respective
AdjacencyMatrix objects. Please note that for am_structural the
AdjacencyMatrix obtained via structural or rtCorrection can be used,
while for am_statistical the AdjacencyMatrix from threshold has to be
used.
The edges that are present both in the binary assay and the consensus assay
will be reported within the combine_binary assay in a combine
AdjacencyMatrix object. If there are additional assays in the
AdjacencyMatrix of type structural these matrices will be stored in the
AdjacencyMatrix of type combine and will contain the edges that are
supported by the combine_binary adjacency matrix (the other edges are
set to "").
We will use here the thresholded statistical AdjacencyMatrix from
type = "mean" to combine it with the structural AdjacencyMatrix,
struct_adj:
comb_adj <- combine(am_structural = struct_adj, am_statistical = stat_adj_mean)We can also combine the statistical AdjacencyMatrix
with the structural AdjacencyMatrix spect_adj_thr, based on the
thresholded spectral similarity values.
comb_spect_adj <- combine(am_structural = spect_adj_thr,
am_statistical = stat_adj_mean)6 Visualization and further analyses
To display the created consensus adjacency matrix, existing visualization
tools available in the R framework can be employed or any other visualization
tool after exporting the consensus matrix as a text file. In this example,
we will use the igraph (Csardi and Nepusz 2006) package to visualize the
adjacency matrix.
We use here the assay "combine_binary" from the AdjacencyMatrix of type
combine and pass it to the graph_from_adjacency_matrix function:
adj <- assay(comb_adj, "combine_binary")
g <- igraph::graph_from_adjacency_matrix(adj, mode = "undirected")
plot(g, edge.width = 2, vertex.label.cex = 0.5, edge.color = "grey")
Figure 1: Ab initio network inferred from structural and quantitative mass spectrometry data
Vertices are connected that are separated by given metabolic transformation and statistical association.
Furthermore, the network can be analysed by network analysis techniques
(topological parameters such as centrality, degree, clustering indices) that
are implemented in different packages in R
(e.g. igraph or sna) or other software tools outside of
the R environment.
Appendix
Session information
All software and respective versions to build this vignette are listed here:
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## [10] magrittr_2.0.3 compiler_4.5.0 rlang_1.1.6
## [13] sass_0.4.10 tools_4.5.0 igraph_2.1.4
## [16] yaml_2.3.10 S4Arrays_1.8.0 labeling_0.4.3
## [19] mnormt_2.1.1 DelayedArray_0.34.0 abind_1.4-8
## [22] withr_3.0.2 purrr_1.0.4 grid_4.5.0
## [25] colorspace_2.1-1 ggplot2_3.5.2 scales_1.3.0
## [28] MASS_7.3-65 tinytex_0.57 cli_3.6.4
## [31] rmarkdown_2.29 crayon_1.5.3 httr_1.4.7
## [34] cachem_1.1.0 bnlearn_5.0.2 GENIE3_1.30.0
## [37] parallel_4.5.0 GeneNet_1.2.17 BiocManager_1.30.25
## [40] XVector_0.48.0 vctrs_0.6.5 Matrix_1.7-3
## [43] jsonlite_2.0.0 bookdown_0.43 clue_0.3-66
## [46] parmigene_1.1.1 magick_2.8.6 longitudinal_1.1.13
## [49] jquerylib_0.1.4 tidyr_1.3.1 glue_1.8.0
## [52] stabs_0.6-4 codetools_0.2-20 gtable_0.3.6
## [55] UCSC.utils_1.4.0 munsell_0.5.1 tibble_3.2.1
## [58] pillar_1.10.2 htmltools_0.5.8.1 GenomeInfoDbData_1.2.14
## [61] R6_2.6.1 evaluate_1.0.3 lattice_0.22-7
## [64] corpcor_1.6.10 bslib_0.9.0 MetaboCoreUtils_1.16.0
## [67] Rcpp_1.0.14 fdrtool_1.2.18 SparseArray_1.8.0
## [70] nlme_3.1-168 xfun_0.52 fs_1.6.6
## [73] pkgconfig_2.0.3Transformations
The list of transformations is taken from Breitling et al. (2006). The numerical m/z values were calculated by using the structural formula and the Biological Magnetic Resonance Data Bank web tool.
transformations <- rbind(
c("Alanine", "C3H5NO", "71.0371137878"),
c("Arginine", "C6H12N4O", "156.1011110281"),
c("Asparagine", "C4H6N2O2", "114.0429274472"),
c("Guanosine 5-diphosphate (-H2O)", "C10H13N5O10P2", "425.0137646843"),
c("Guanosine 5-monophosphate (-H2O)", "C10H12N5O7P", "345.0474342759"),
c("Guanine (-H)", "C5H4N5O", "150.0415847765"),
c("Aspartic acid", "C4H5NO3", "115.0269430320"),
c("Guanosine (-H2O)", "C10H11N5O4", "265.0811038675"),
c("Cysteine", "C3H5NOS", "103.0091844778"),
c("Deoxythymidine 5'-diphosphate (-H2O)", "C10H14N2O10P2", "384.01236770"),
c("Cystine", "C6H10N2O3S2", "222.0132835777"),
c("Thymidine (-H2O)", "C10H12N2O4", "224.0797068840"),
c("Glutamic acid", "C5H7NO3", "129.0425930962"),
c("Thymine (-H)", "C5H5N2O2", "125.0351024151"),
c("Glutamine", "C5H8N2O2", "128.0585775114"),
c("Thymidine 5'-monophosphate (-H2O)", "C10H13N2O7P", "304.0460372924"),
c("Glycine", "C2H3NO", "57.0214637236"),
c("Uridine 5'-diphosphate (-H2O)", "C9H12N2O11P2", "385.9916322587"),
c("Histidine", "C6H7N3O", "137.0589118624"),
c("Uridine 5'-monophosphate (-H2O)", "C9H11N2O8P", "306.0253018503"),
c("Isoleucine", "C6H11NO", "113.0840639804"),
c("Uracil (-H)", "C4H3N2O2", "111.0194523509"),
c("Leucine", "C6H11NO", "113.0840639804"),
c("Uridine (-H2O)", "C9H10N2O5", "226.0589714419"),
c("Lysine", "C6H12N2O", "128.0949630177"),
c("Acetylation (-H)", "C2H3O2", "59.0133043405"),
c("Methionine", "C5H9NOS", "131.0404846062"),
c("Acetylation (-H2O)", "C2H2O", "42.0105646863"),
c("Phenylalanine", "C9H9NO", "147.0684139162"),
c("C2H2", "C2H2", "26.0156500642"),
c("Proline", "C5H7NO", "97.0527638520"),
c("Carboxylation", "CO2", "43.9898292442"),
c("Serine", "C3H5NO2", "87.0320284099"),
c("CHO2", "CHO2", "44.9976542763"),
c("Threonine", "C4H7NO2", "101.0476784741"),
c("Condensation/dehydration", "H2O", "18.0105646863"),
c("Tryptophan", "C11H10N2O", "186.0793129535"),
c("Diphosphate", "H3O6P2", "160.9404858489"),
c("Tyrosine", "C9H9NO2", "163.0633285383"),
c("Ethyl addition (-H2O)", "C2H4", "28.0313001284"),
c("Valine", "C5H9NO", "99.0684139162"),
c("Formic Acid (-H2O)", "CO", "27.9949146221"),
c("Acetotacetate (-H2O)", "C4H4O2", "84.0211293726"),
c("Glyoxylate (-H2O)", "C2O2", "55.9898292442"),
c("Acetone (-H)", "C3H5O", "57.0340397826"),
c("Hydrogenation/dehydrogenation", "H2", "2.0156500642"),
c("Adenylate (-H2O)", "C10H12N5O6P", "329.0525196538"),
c("Hydroxylation (-H)", "O", "15.9949146221"),
c("Biotinyl (-H)", "C10H15N2O3S", "243.0803380482"),
c("Inorganic phosphate", "P", "30.9737615100"),
c("Biotinyl (-H2O)", "C10H14N2O2S", "226.0775983940"),
c("Ketol group (-H2O)", "C2H2O", "42.0105646863"),
c("Carbamoyl P transfer (-H2PO4)", "CH2ON", "44.0136386915"),
c("Methanol (-H2O)", "CH2", "14.0156500642"),
c("Co-enzyme A (-H)", "C21H34N7O16P3S", "765.0995583014"),
c("Phosphate", "HPO3", "79.9663304084"),
c("Co-enzyme A (-H2O)", "C21H33N7O15P3S", "748.0968186472"),
c("Primary amine", "NH2", "16.0187240694"),
c("Glutathione (-H2O)", "C10H15N3O5S", "289.0732412976"),
c("Pyrophosphate", "PP", "61.9475230200"),
c("Isoprene addition (-H)", "C5H7", "67.0547752247"),
c("Secondary amine", "NH", "15.0108990373"),
c("Malonyl group (-H2O)", "C3H2O3", "86.0003939305"),
c("Sulfate (-H2O)", "SO3", "79.9568145563"),
c("Palmitoylation (-H2O)", "C16H30O", "238.2296655851"),
c("Tertiary amine", "N", "14.0030740052"),
c("Pyridoxal phosphate (-H2O)", "C8H8NO5P", "229.0140088825"),
c("C6H10O5", "C6H10O5", "162.0528234315"),
c("Urea addition (-H)", "CH3N2O", "59.0245377288"),
c("C6H10O6", "C6H10O6", "178.0477380536"),
c("Adenine (-H)", "C5H4N5", "134.0466701544"),
c("D-ribose (-H2O) (ribosylation)", "C5H8O4", "132.0422587452"),
c("Adenosine (-H2O)", "C10H11N5O3", "249.0861892454"),
c("Disaccharide (-H2O) #1", "C12H20O10", "324.105649"),
c("Disaccharide (-H2O) #2", "C12H20O11", "340.1005614851"),
c("Adenosine 5'-diphosphate (-H2O)", "C10H13N5O9P2", "409.0188500622"),
c("Glucose-N-phosphate (-H2O)", "C6H11O8P", "242.0191538399"),
c("Adenosine 5'-monophosphate (-H2O)", "C10H12N5O6P", "329.0525196538"),
c("Glucuronic acid (-H2O)", "C6H8O6", "176.0320879894"),
c("Cytidine 5'-diphosphate (-H2O)", "C9H13N3O10P2", "385.0076166739"),
c("Monosaccharide (-H2O)", "C6H10O5", "162.0528234315"),
c("Cytidine 5'-monophsophate (-H2O)", "C9H12N3O7P", "305.0412862655"),
c("Trisaccharide (-H2O)", "C18H30O15", "486.1584702945"),
c("Cytosine (-H)", "C4H4N3O", "110.0354367661"))
transformations <- data.frame(group = transformations[, 1],
formula = transformations[, 2],
mass = as.numeric(transformations[, 3]))References
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