# MetaboDynamics:
[](https://lifecycle.r-lib.org/articles/stages.html#stable)
[](https://doi.org/10.18129/B9.bioc.MetaboDynamics )
[](https://cran.r-project.org/web/licenses/GPL)
MetaboDynamics provides a framework of Bayesian models for robust and
easy analysis of longitudinal metabolomics Data. It takes concentration
tables with at least three replicates and KEGG IDs of metabolites as input
and provides robust estimation of mean concentrations, functional enrichment
analysis employing the KEGG database and comparison of clusters of metabolite
dynamics patterns (“dynamics clusters”) under different biological
conditions.
## Installation
MetaboDynamics is an [R](https://cran.r-project.org/) package available
from the [Bioconductor](https://www.bioconductor.org) "devel branch".
URL: https://www.bioconductor.org/packages/devel/bioc/html/MetaboDynamics.html
To install MetaboDynamics, start R (>4.4) and enter:
``` r
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
# The following initializes usage of Bioc devel
BiocManager::install(version='devel')
BiocManager::install("MetaboDynamics")
```
You can also install the development version (current bug fixes and added features
can be found in the [NEWS](https://github.com/KatjaDanielzik/MetaboDynamics/blob/main/inst/NEWS.md)
file) of MetaboDynamics from
[GitHub](https://github.com/) with:
``` r
if (!require("devtools", quietly = TRUE))
install.packages("devtools")
devtools::install_github("KatjaDanielzik/MetaboDynamics",build_vignettes=TRUE)
```
## Overview
MetaboDynamics facilitates the analysis of longitudinal metabolomics
data e.g. from untargeted LC-MS. Common tools mostly only allow the comparison
between two time points instead of analyzing the full observed dynamics profile
of metabolite concentrations over multiple time points. Furthermore common tools
mostly only allow to compare two experimental conditions and are using
frequentist statistical methods. As metabolomics data is often noisy, robust
methods for the estimation of mean metabolite concentrations per time point
are needed.
MetaboDynamics allows longitudinal analysis over
multiple time points and experimental conditions employing three
probabilistic models:
1) A hierarchical Bayesian model for the robust estimation of means at
every time point despite varying spread between time points.Hierarchical
Bayesian models are known to balance between over- and underfitting, allowing
to gain as much information from the noisy data as possible while not being overly
confident about the estimates. Its outputs are A) differences between time
points for every metabolite (differential concentrations),
and B) metabolite specific dynamics profiles that can be used for
clustering.
2) Over-representation analysis of KEGG-functional modules such as Amino acid
metabolism or KEGG pathways in dynamics clusters with a quantitative model
that employs a hypergeometric distribution and reports probabilities of a
functional module or pathway being over-represented in a cluster. Can also
estimate under-representation of functional modules.
3) Estimation of the dynamics similarity between metabolite clusters of
different experimental conditions with a Bayesian model. This model infers
the mean pairwise Euclidean distance of composing metabolite dynamics between
two clusters (i.e. every metabolite dynamics from cluster A is compared with
every metabolite dynamics of cluster B). In combination with the comparison of
metabolites that compose two clusters this allows to spot differences and similarities between
experimental conditions. For examples clusters of metabolites with similar
metabolite composition but different dynamics between experimental conditions.
## Workflow
For a worked example on simulated data see [Vignette](https://www.bioconductor.org/packages/devel/bioc/vignettes/MetaboDynamics/inst/doc/MetaboDynamics.html) or if package is installed:
``` r
browseVignettes("MetaboDynamics")
```
