cytodataframe 0.3.0

An in-memory data analysis format for single-cell profiles alongside their corresponding images and segmentation masks.

CytoDataFrame

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"}.

For row display in notebook/widget tables:

• CytoDataFrame respects pandas display settings (display.max_rows, display.min_rows).
• When the table is larger than display.max_rows, the widget table inserts a midpoint ellipsis row (…) to indicate omitted rows.
• You can control truncation behavior by changing pandas display options before rendering.

📓 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!

Installation

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.git

Contributing, Development, and Testing

Please see our contributing documentation for more details on contributions, development, and testing.

References

• coSMicQC
• pycytominer
• CellProfiler
• CytoTable