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Dose response curves and DIP rates for cell proliferation data in Python

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Thunor

Thunor (pronounced THOO-nor) is a free software platform for managing, visualizing, and analyzing high throughput screen (HTS) data, which measure the dose-dependent response of cells to one or more drug(s).

This repository, Thunor Core, is a Python package which can be used for standalone analysis or integration into computational pipelines. There is also a web interface, Thunor Web, built around this package with added database, multi-user capabilities, drag-and-drop upload of cell count data, automatic calculation of dose response curves, and an interactive multi-panelled plot system (demo).

Implementation

Thunor is written in pure Python and is compatible with Python 3 only. It makes extensive use of pandas and plotly.

Installation

Thunor Core is tested against the three most recent stable releases of Python (currently 3.10-3.12), so one of these versions is recommended. Install Thunor Core using pip:

pip install thunor

Examples and documentation

The Thunor Core documentation is available online, or you can build it locally for offline use. To do so, clone this git repository and change into the thunor directory.

To build documentation locally, you'll need a few software dependencies:

pip install -e '.[docs]'

You'll also need to install pandoc.

Then, you can build the documentation like so:

cd doc
make html

After the build completes, open _build/html/index.html in your web browser.

Tutorial

Like the docs, you can also view the tutorial online. To work through the tutorial locally, build the documentation as per the previous section. You can then open the file with Jupyter Notebook:

jupyter notebook --NotebookApp.iopub_data_rate_limit=1.0e10 doc/tutorial.ipynb

Citation

Lubbock A.L.R., Harris L.A., Quaranta V., Tyson D.R., Lopez C.F. Thunor: visualization and analysis of high-throughput dose–response datasets Nucleic Acids Research (2021), gkab424.

Further help and resources

See the Thunor website for further links, documentation and related projects.

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Dose response curves and DIP rates for cell proliferation data in Python

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