CytoDataFrame extends Pandas functionality to help display single-cell profile data alongside related images.
CytoDataFrame is an advanced in-memory data analysis format designed for single-cell profiling, integrating not only the data profiles but also their corresponding microscopy images and segmentation masks. Traditional single-cell profiling often excludes the associated images from analysis, limiting the scope of research. CytoDataFrame bridges this gap, offering a purpose-built solution for comprehensive analysis that incorporates both the data and images, empowering more detailed and visual insights in single-cell research.
CytoDataFrame is best suited for work within Jupyter notebooks. With CytoDataFrame you can:
- View image objects alongside their feature data using a Pandas DataFrame-like interface.
- Highlight image objects using mask or outline files to understand their segmentation.
- Adjust image displays on-the-fly using interactive slider widgets.
- Automatically detect 3D image volumes and render interactive trame views in notebooks when 3D dependencies are installed (with graceful fallback otherwise).
For 3D notebook display behavior:
- 3D-aware rendering is enabled by default (
display_options={"auto_trame_for_3d": True}). - Disable automatic trame switching with
display_options={"auto_trame_for_3d": False}. - Force trame layout regardless of auto-detection with
display_options={"view": "trame"}.
📓 Want to see CytoDataFrame in action? Check out our example notebook for a quick tour of its key features.
✨ CytoDataFrame development began within coSMicQC - a single-cell profile quality control package. Please check out our work there as well!
Install CytoDataFrame from source using the following:
# install from pypi
pip install cytodataframe
# or install directly from source
pip install git+https://github.com/cytomining/CytoDataFrame.gitPlease see our contributing documentation for more details on contributions, development, and testing.