MetaboDynamics

MetaboDynamics: a framework of probabilistic models to analyze longitudinal metabolomics data

https://github.com/katjadanielzik/metabodynamics

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Keywords

dynamics functional-analysis longitudinal-analysis metabolomics-data metabolomics-pipeline

Last synced: 6 months ago · JSON representation ·

Repository

MetaboDynamics: a framework of probabilistic models to analyze longitudinal metabolomics data

Basic Info

Host: GitHub
Owner: KatjaDanielzik
License: gpl-3.0
Language: R
Default Branch: main
Homepage:
Size: 123 MB

Statistics

Stars: 5
Watchers: 2
Forks: 1
Open Issues: 0
Releases: 0

Topics

dynamics functional-analysis longitudinal-analysis metabolomics-data metabolomics-pipeline

Created over 1 year ago · Last pushed 6 months ago

Metadata Files

Readme License Citation

MetaboDynamics:

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 package available from Bioconductor.

URL: https://www.bioconductor.org/packages/release/bioc/html/MetaboDynamics.html

To install MetaboDynamics, start R (4.5) and enter:

``` r if (!require("BiocManager", quietly = TRUE)) install.packages("BiocManager")

BiocManager::install("MetaboDynamics") ``` You can also install the development version (current bug fixes and added features can be found in the NEWS file) of MetaboDynamics from GitHub with:

``` r if (!require("devtools", quietly = TRUE)) install.packages("devtools")

devtools::installgithub("KatjaDanielzik/MetaboDynamics",buildvignettes=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 or if package is installed:

r browseVignettes("MetaboDynamics")

Owner

Login: KatjaDanielzik
Kind: user

Repositories: 1
Profile: https://github.com/KatjaDanielzik

Citation (CITATION.cff)

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
- family-names: "Danielzik"
  given-names: "Katja"
  orcid: "https://orcid.org/0009-0007-5021-6212"
- family-names: "Kitanovski"
  given-names: "Simo"
  orcid: "https://orcid.org/0000-0003-2909-5376"
- family-names: "Matschke"
  given-names: "Johann"
  orcid: "https://orcid.org/0000-0003-4878-8741"
- family-names: "Hoffmann"
  given-names: "Daniel"
  orcid: "https://orcid.org/0000-0003-2973-7869"
title: "MetaboDynamics: Bayesian analysis of longitudinal metabolomics data"
version: 0.99.20
DOI: 10.18129/B9.bioc.MetaboDynamics
url: " https://bioconductor.org/packages/MetaboDynamics"

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Last Year

Issues event: 5
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Average comments per issue: 0.33
Average comments per pull request: 0.0
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Past Year

Issues: 3
Pull requests: 2
Average time to close issues: about 16 hours
Average time to close pull requests: less than a minute
Issue authors: 1
Pull request authors: 1
Average comments per issue: 0.33
Average comments per pull request: 0.0
Merged pull requests: 2
Bot issues: 0
Bot pull requests: 0

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Top Authors

Issue Authors

KatjaDanielzik (5)
snaketron (2)

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KatjaDanielzik (13)
snaketron (1)

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Packages

Total packages: 1
Total downloads:
- bioconductor 1,891 total

Total dependent packages: 0
Total dependent repositories: 0
Total versions: 2
Total maintainers: 1

bioconductor.org: MetaboDynamics

Bayesian analysis of longitudinal metabolomics data

Homepage: https://github.com/KatjaDanielzik/MetaboDynamics
Documentation: https://bioconductor.org/packages/release/bioc/vignettes/MetaboDynamics/inst/doc/MetaboDynamics.pdf
License: GPL (>= 3)
Latest release: 1.0.2
published 8 months ago

Versions: 2
Dependent Packages: 0
Dependent Repositories: 0
Downloads: 1,891 Total

Rankings

Dependent repos count: 0.0%

Dependent packages count: 30.3%

Average: 40.8%

Downloads: 92.1%

Maintainers (1)

katja.danielzik@uni-due.de