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.
MetNet 1.24.0
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).
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.
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)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.
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")