TiDEomics: Time-course Differential Expression analysis of omics data

TiDEomics provides an integrated framework for Time-course Differential Expression analysis of omics data with multiple experimental groups / conditions, including integration across datasets or omics layers.

TiDEomics enables:

• Analysis of feature x sample format data from different omics technologies.
• Comparison of ≥2 time courses systematically.
• Filtering of features by residual variance, accounting for group and temporal structure.
• Identification of features (e.g. genes, proteins) differentially expressed by time, group, and group-by-time effects.
• Integration of quality control, data processing, sample-level and feature-level analysis, tailored for time-course multi-group data.
• Functional enrichment with integrated visualisation.
• Multi-dataset/multi-omics comparison and consensus module identification using shared features.

Existing tools for time-course analysis often focus on specific analysis tasks or experimental designs, while TiDEomics provides a comprehensive workflow with multi-group and multi-omics support.

Example applications include:

• Transcriptomics analysis of a cell line’s response to different stimuli over time.
• Transcriptomics + proteomics analysis of multiple cell lines sampled at several time points.

TiDEomics supports datasets with missing values (e.g. mass spectrometry-based proteomics), and operates on SummarizedExperiment objects to ensure compatibility with the Bioconductor ecosystem.

TiDEomics supports both independent samples (e.g. cell culture) and repeated measurements (e.g. patient longitudinal studies). Subject ID can be supplied in create_input() and used in subject-aware functions, e.g. DE_timecourse(), decomp_variance().

Installation

Get the latest stable R release from CRAN. Then install TiDEomics from Bioconductor using the following code:

if (!requireNamespace("BiocManager", quietly = TRUE)) {
    install.packages("BiocManager")
}

BiocManager::install("TiDEomics")

The development version can be installed 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 tutorials, 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 0.99.6,
#>   <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 0.99.6},
#>     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 in the vignettes.

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

• Code testing is possible thanks to rcmdcheck and BiocCheck.
• Code coverage assessment is possible thanks to codecov and covr.
• The documentation website is updated thanks to pkgdown.
• The documentation is formatted thanks to devtools and roxygen2.

This package was developed using biocthis.