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README.Rmd
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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# MetaboDynamics : developmental version
<!-- badges: start -->
<!-- badges: end -->
MetaboDynamics provides a framework of Bayesian models for robust and easy
analysis of longitudinal metabolomics Data. It takes concentration tables 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
You can install the development version of MetaboDynamics from [GitHub](https://github.com/) with:
``` {r,eval=FALSE}
# install.packages("devtools")
devtools::install_github("KatjaDanielzik/MetaboDynamics")
```
or from Bioconductor with:
```{r,eval=FALSE}
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 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.
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 ("devel" branch: folder /vignettes, file MetaboDynamics.html)
or if package is installed:
```{r,eval=FALSE}
browseVignettes("MetaboDynamics")
```
![](man/figures/README-MetaboDynamics_pitch.png)