CytoTable

Diagram showing data flow relative to this project.

Summary

CytoTable enables single-cell morphology data analysis by cleaning and transforming CellProfiler (.csv or .sqlite), cytominer-database (.sqlite), and DeepProfiler (.npz), and other sources such as IN Carta data output data at scale. CytoTable creates parquet files for both independent analysis and for input into Pycytominer. The output files (such as Parquet and anndata file formats) have a documented data model, including referenceable schema where appropriate (for validation within Pycytominer or other image-based profiling projects).

The name for the project is inspired from:

• Cyto: "1. (biology) cell." (Wiktionary: Cyto-)
• Table:
  • "1. Furniture with a top surface to accommodate a variety of uses."
  • "3.1. A matrix or grid of data arranged in rows and columns."
    (Wiktionary: Table)

Installation

Install CytoTable from PyPI or from source:

# install from pypi
pip install cytotable

# install directly from source
pip install git+https://github.com/cytomining/CytoTable.git

Contributing, Development, and Testing

We test CytoTable using ubuntu-latest and macos-latest GitHub Actions runner images.

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

Relationship to other projects

CytoTable focuses on image-based profiling data harmonization and serialization. At scale, CytoTable transforms data into file formats which can be directly integrated with:

Please let us know how you use CytoTable (we'd love to add your project to this list)!

• Pycytominer for the bioinformatics pipeline for image-based profiling.
• coSMicQC for quality control.
• CytoDataFrame for interactive visualization of profiles with single cell images.

References

• pycytominer
• cytominer-database
• DeepProfiler
• CellProfiler
• cytominer-eval