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This is the development version of timeOmics; for the stable release version, see timeOmics.

Time-Course Multi-Omics data integration

Bioconductor version: Development (3.20)

timeOmics is a generic data-driven framework to integrate multi-Omics longitudinal data measured on the same biological samples and select key temporal features with strong associations within the same sample group. The main steps of timeOmics are: 1. Plaform and time-specific normalization and filtering steps; 2. Modelling each biological into one time expression profile; 3. Clustering features with the same expression profile over time; 4. Post-hoc validation step.

Author: Antoine Bodein [aut, cre], Olivier Chapleur [aut], Kim-Anh Le Cao [aut], Arnaud Droit [aut]

Maintainer: Antoine Bodein <antoine.bodein.1 at>

Citation (from within R, enter citation("timeOmics")):


To install this package, start R (version "4.4") and enter:

if (!require("BiocManager", quietly = TRUE))

# The following initializes usage of Bioc devel


For older versions of R, please refer to the appropriate Bioconductor release.


To view documentation for the version of this package installed in your system, start R and enter:

timeOmics HTML R Script
Reference Manual PDF


biocViews Classification, Clustering, DimensionReduction, FeatureExtraction, GenePrediction, ImmunoOncology, Metabolomics, Metagenomics, Microarray, MultipleComparison, Proteomics, Regression, Sequencing, Software, TimeCourse
Version 1.17.0
In Bioconductor since BioC 3.11 (R-4.0) (4 years)
License GPL-3
Depends mixOmics, R (>= 4.0)
Imports dplyr, tidyr, tibble, purrr, magrittr, ggplot2, stringr, ggrepel, lmtest, plyr, checkmate
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Follow Installation instructions to use this package in your R session.

Source Package timeOmics_1.17.0.tar.gz
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macOS Binary (x86_64) timeOmics_1.17.0.tgz
macOS Binary (arm64) timeOmics_1.17.0.tgz
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