A toolkit for predicting hormone producing and receiving strength in single cell datasets.
hormone2cell identifies hormone producing cell types (HPCs) and hormone receiving cell types (HRCs) from single-cell or single-nucleus expression data using a curated Hormone Receptor Reference Database. It applies cell-type-level expression filtering, multi-gene logic, and strength scoring to summarize predicted hormone production and reception across cell types.
Overview of the hormone2cell workflow, created with AI assistance.
We recommend installing hormone2cell in a clean conda environment.
# Create and activate conda environment
conda create -n hormone2cell_env python=3.10 -y
conda activate hormone2cell_env
# Install hormone2cell from PyPI
pip install hormone2cellIf you want to install the latest development version from GitHub:
# Create and activate conda environment
conda create -n hormone2cell_env python=3.10 -y
conda activate hormone2cell_env
# Clone repository
git clone https://github.com/Teichlab/hormone2cell.git
cd hormone2cell
# Install in editable mode
pip install -e .Alternatively, download the package via Code → Download ZIP from GitHub and install it locally:
cd hormone2cell
pip install -e .Function docstrings and package documentation are available on the ReadTheDocs page.
The detailed tutorial is available as a notebook and as a rendered ReadTheDocs page.
The Hormone-Receptor Reference Database, including gene definitions for each hormone and receptor, is available from the Hormone Cell Atlas download page.
For hormone-centric analyses and visualization, please visit the Hormone Cell Atlas website.
For the single-cell resource associated with this project, please visit the Hormone Cell Atlas single-cell resource.
Citation information will be added once available.
Parts of the method were informed by a previously published adaptive thresholding framework: Nature Communications, 2024.