Time-course Differential Expression analysis of omics data • TiDEomics

TiDEomics is designed to streamline Time-course Differential Expression analysis of omics data with multiple experimental groups / conditions, for example, different cell lines or different treatments sampled at several time points.

The package’s main goals are:

Compare multiple time courses systematically.
Identify features (e.g. genes, proteins) and pathways differentially expressed by time, condition, and both factors (time × condition interactions).
Provide utilities for quality control, data processing, sample-level and feature-level analysis, tailored for time-course multi-condition data.
Output high-quality tables and figures to facilitate interpretation and reporting.

The package supports datasets with missing values, and operates on SummarizedExperiment objects to ensure compatibility with the Bioconductor ecosystem.

Installation

Install the development version from GitHub with:

if (!require("remotes", quietly = TRUE)) install.packages("remotes")

remotes::install_github("hte123/TiDEomics")

Example

For detailed examples and explanations, please refer to the tutorial, applications and other package documentation.

Citation

Below is the citation output from using citation('TiDEomics') in R. Please run this yourself to check for any updates on how to cite TiDEomics.

print(citation("TiDEomics"), bibtex = TRUE)
#> To cite package 'TiDEomics' in publications use:
#> 
#>   He T (2026). _TiDEomics: Time-course Differential Expression analysis
#>   of omics data_. R package version 0.99.0,
#>   <https://github.com/hte123/TiDEomics>.
#> 
#> A BibTeX entry for LaTeX users is
#> 
#>   @Manual{,
#>     title = {TiDEomics: Time-course Differential Expression analysis of omics data},
#>     author = {Tianen He},
#>     year = {2026},
#>     note = {R package version 0.99.0},
#>     url = {https://github.com/hte123/TiDEomics},
#>   }

Please note that the TiDEomics was only made possible thanks to many other R and bioinformatics software authors, which are cited either in the vignettes and/or the paper(s) describing this package.

Code of Conduct

Please note that the TiDEomics project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

Development tools

Continuous code testing is possible thanks to GitHub actions through usethis, remotes, and rcmdcheck customized to use Bioconductor’s docker containers and BiocCheck.
Code coverage assessment is possible thanks to codecov and covr.
The documentation website is automatically updated thanks to pkgdown.
The code is styled automatically thanks to styler.
The documentation is formatted thanks to devtools and roxygen2.

This package was developed using biocthis.