cosinor-lite

cosinor-lite is a lightweight toolkit for exploring circadian oscillations across multiomics and live-cell imaging experiments. It relies on widely used statistical models and offers two modes of use.

• Interactive Gradio dashboard to drag & drop your data files.
• Python package for fine-grained customisation beyond the settings offered in the Gradio UI.

🚀 Try the interactive analysis pipeline on Hugging Face Spaces → cosinor-lite.

Why cosinor-lite?

• Unified workflow for live cell data: Perform detrending and fit cosinor models with either a fixed 24 h, free-period or damped oscillation with consistent APIs across data modalities (i.e. bioluminescence, cytokines, qPCR etc.).
• Differential rhythmicity across any omics type: Compare two conditions for differential rhythmicity for any type of omics data using BIC model-selection strategies with widespread use in the circadian literature.
• Create publication-ready analysis with a simple UI: Launch a browser-based analysis console for visual quality control, plots, and parameter export.

Extracting circadian parameters from live-cell data

For live-cell data (see tutorial in notebooks/live_cell_tutorial), cosinor-lite offers the choice between three different cosinor models:

• a) Fixed 24-h period

• b) Free period

• c) Free period with damped amplitude

Once a model is chosen, it is fitted independently to each sample. The fitted parameters can be exported for downstream statistical analysis.

Differential rhytmicity analysis of omics datasets

cosinor-lite includes a toolbox for performing differential rhytmicity analysis of omics datasets (ee notebooks/omics_tutorial) for tutorial. The details of the method are nicely explained in the article:

Pelikan A, Herzel H, Kramer A, Ananthasubramaniam B. 2022. Venn diagram analysis overestimates the extent of circadian rhythm reprogramming. The FEBS Journal 289:6605–6621. doi:10.1111/febs.16095

Here is an adaptation of their figure explaining the methdology:

The tool is very similar to the dryR package in R, but implemented in Python for ease of use with other Python-based data analysis pipelines. Please see the dryR tool for more details, and if you prefer implementing in R (https://github.com/naef-lab/dryR/tree/master).

For condition 1 (i.e. alpha cells) and condition 2 (i.e. beta cells), we fit 5 different models:

• Model 1) Arrhythmic in alpha and beta cells

• Model 2) Rhythmic in beta cells only

• Model 3) Rhythmic in alpha cells only

• Model 4) Rhythmic in alpha and beta cells with the same rhythmic parameters (i.e. phase and amplitude)

• Model 5) Rhythmic in both but with differential rhythmicity in alpha vs beta cells

Installation

# clone the repository
git clone https://github.com/nick-e-p/cosinor-lite.git
cd cosinor-lite

# create and populate the local uv environment
uv sync

# activate the environment
source .venv/bin/activate

Quick Start

Launch the interactive app

uv run python app.py

Open the printed local URL to explore:

• Live cell: Upload CSVs with participant, replicate, and time-series data, then compare detrending strategies or cosinor fits across groups.
• Omics: Load expression matrices, build time vectors automatically or manually, compute differential rhythmicity, and download publication-ready plots.

Use the Python API

from cosinor_lite.livecell_dataset import LiveCellDataset
from cosinor_lite.livecell_cosinor_analysis import CosinorAnalysis

dataset = LiveCellDataset(
    ids=["mouse_a", "mouse_b"],
    group=["treated", "control"],
    replicate=[1, 1],
    time_series=my_expression_matrix,
    time=my_timepoints,
)

analysis = CosinorAnalysis(dataset=dataset)
fit = analysis.fit(group="treated", model="damped")
print(fit.parameters)

Sample Datasets

The data/ directory includes curated examples to help you get started:

• bioluminescence_example.csv: Bioluminescence time series for live-cell analysis.
• cytokine_example.csv: Cytokine time series for live-cell analysis.
• qpcr_example.csv: qPCR time series for live-cell analysis.
• GSE95156_Alpha_Beta.txt: RNA-seq data used in the omics differential rhythmicity workflow.

Each file is formatted to drag & drop into app.py as well as the library APIs.

Contributing

1. Fork the repository and create a feature branch.
2. Install dependencies with uv sync and activate the environment.
3. Implement your changes with tests.
4. Verify via pre-commit run --all-files and uv run pytest.
5. Open a pull request describing the motivation and results.

Bug reports and feature proposals are welcome through GitHub issues. Please include reproducible examples whenever possible.

License

Licensed under the Apache License 2.0. See LICENSE for details.