recipe bioconductor-timeomics

Time-Course Multi-Omics data integration






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.

package bioconductor-timeomics

(downloads) docker_bioconductor-timeomics



depends bioconductor-mixomics:


depends r-base:


depends r-dplyr:

depends r-ggplot2:

depends r-ggrepel:

depends r-lmtest:

depends r-magrittr:

depends r-plyr:

depends r-purrr:

depends r-stringr:

depends r-tibble:

depends r-tidyr:



You need a conda-compatible package manager (currently either micromamba, mamba, or conda) and the Bioconda channel already activated (see set-up-channels).

While any of above package managers is fine, it is currently recommended to use either micromamba or mamba (see here for installation instructions). We will show all commands using mamba below, but the arguments are the same for the two others.

Given that you already have a conda environment in which you want to have this package, install with:

   mamba install bioconductor-timeomics

and update with::

   mamba update bioconductor-timeomics

To create a new environment, run:

mamba create --name myenvname bioconductor-timeomics

with myenvname being a reasonable name for the environment (see e.g. the mamba docs for details and further options).

Alternatively, use the docker container:

   docker pull<tag>

(see `bioconductor-timeomics/tags`_ for valid values for ``<tag>``)

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