https://github.com/abodein/timeOmics

Time-Course Multi-Omics data integration
https://github.com/abodein/timeOmics

cluster integration multi-omics time-series

Last synced: over 1 year ago
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Time-Course Multi-Omics data integration

• Host: GitHub
• URL: https://github.com/abodein/timeOmics
• Owner: abodein
• License: gpl-3.0
• Created: 2019-09-23T14:44:59.000Z (almost 7 years ago)
• Default Branch: master
• Last Pushed: 2024-04-09T19:32:33.000Z (about 2 years ago)
• Last Synced: 2024-10-30T01:58:11.422Z (over 1 year ago)
• Topics: cluster, integration, multi-omics, time-series
• Language: R
• Homepage:
• Size: 3.65 MB
• Stars: 24
• Watchers: 3
• Forks: 6
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Changelog: NEWS.md
  • License: LICENSE.md

Awesome Lists containing this project

• awesome-rm-omics - **timeOmics** - Bodein - [A Generic Multivariate Framework for the Integration of Microbiome Longitudinal Studies With Other Data Types](https://www.frontiersin.org/articles/10.3389/fgene.2019.00963) [vignette](http://www.bioconductor.org/packages/release/bioc/vignettes/timeOmics/inst/doc/vignette.html) (Software packages and methods / Longitudinal Multi-omics Integration)

README

          

[![Build Status](https://travis-ci.com/abodein/timeOmics_BioC.svg?branch=master)](https://travis-ci.com/abodein/timeOmics)

[![codecov](https://codecov.io/gh/abodein/timeOmics_BioC/branch/master/graph/badge.svg)](https://codecov.io/gh/abodein/timeOmics)

[![License: GPL v3](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0)

# timeOmics

***timeOmics*** is a generic data-driven framework to integrate multi-Omics longitudinal data (**A.**) measured on the same biological samples and select key temporal features with strong associations within the same sample group.

![](./man/figures/method_overview.png)

The main steps of ***timeOmics*** are:

* a pre-processing step (**B.**) Normalize and filter low-expressed features, except those not varying over time,

* a modelling step (**C.**)  Capture inter-individual variability in biological/technical replicates and accommodate heterogeneous experimental designs,

* a clustering step (**D.**) Group features with the same expression profile over time. Feature selection step can also be used to identify a signature per cluster,

* a post-hoc validation step (**E.**) Ensure clustering quality.

***timeOmics*** can be applied on both single-Omic or multi-Omics experimental design.

* If you came to this page thanks to our article and you wish to access its example scripts please follow this

 link .*

## Installation

### Latest `GitHub` Version

Install the devtools package in R, then load it and install the latest stable version of `timeOmics` from `GitHub`

```r

## install devtools if not installed

if (!requireNamespace("devtools", quietly = TRUE))

    install.packages("devtools")

## install timeOmics

devtools::install_github("abodein/timeOmics")

```

## Citing

*Bodein A, Chapleur O, Droit A and Lê Cao K-A (2019) A Generic Multivariate Framework for the Integration of Microbiome Longitudinal Studies With Other Data Types. Front. Genet. 10:963.  doi:10.3389/fgene.2019.00963*

## Maintainer

Antoine Bodein ()

## Bugs/Feature requests

If you have any bugs or feature requests, [let us know](https://github.com/abodein/timeOmics_BioC/issues). Thanks!