# MetaboDynamics : developmental version
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 was build on R 4.4 [cran](https://cran.r-project.org/).
You can install the development version of MetaboDynamics from
[GitHub](https://github.com/) with:
``` r
if (!require("devtools", quietly = TRUE))
install.packages("devtools")
devtools::install_github("KatjaDanielzik/MetaboDynamics")
```
or from Bioconductor [Bioconductor](https://bioconductor.org/) with:
``` r
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
# The following initializes usage of Bioc devel
BiocManager::install(version='devel')
BiocManager::install("MetaboDynamics")
```
## Overview
MetaboDynamics facilitates the analysis of longitudinal metabolomics
data. Common tools mostly only allow the comparison between two time
points or experimental conditions and are using frequentist statistical
methods.
As metabolomics data is often noisy, robust methods for the estimation
of dynamics are needed. MetaboDynamics allows longitudinal analysis over
multiple time points and experimental conditions employing 3
probabilistic models:
1) A hierarchical Bayesian model for the robust estimation of means at
every time point despite varying spread between time points. Its
outputs are A) differences between time points for every metabolite,
and B) metabolite specific dynamics profiles that can be used for
clustering.
2) Over-representation analysis of KEGG-functional modules or KEGG pathways
in dynamics clusters with a quantitative model that employs a hypergeometric
distribution and reports probabilities of a functional module being
over-represented in a cluster. Can also estimate
under-representation of functional modules.
3) Estimation of the distances between dynamics clusters under
different experimental conditions with a Bayesian model that infers
the mean pairwise Euclidean distance between two clusters. In
combination with the comparison of metabolites that compose two
clusters this allows to spot differences and similarities between
experimental conditions.
## Workflow
For a worked example see Vignette ("Bioconductor-submission” branch: folder /vignettes,
file MetaboDynamics.html) or if package is installed:
``` r
browseVignettes("MetaboDynamics")
```
