https://github.com/apahl/cellpainting2

Analyzing Cell Painting results with Dask and Pandas - not yet ready for use by others
https://github.com/apahl/cellpainting2

cellprofiler dask jupyter-notebook pandas rdkit

Last synced: about 1 month ago
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Analyzing Cell Painting results with Dask and Pandas - not yet ready for use by others

• Host: GitHub
• URL: https://github.com/apahl/cellpainting2
• Owner: apahl
• License: mit
• Created: 2018-01-11T15:14:32.000Z (over 8 years ago)
• Default Branch: master
• Last Pushed: 2018-05-24T07:45:32.000Z (about 8 years ago)
• Last Synced: 2026-02-01T08:50:56.647Z (4 months ago)
• Topics: cellprofiler, dask, jupyter-notebook, pandas, rdkit
• Language: Python
• Homepage:
• Size: 172 KB
• Stars: 0
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Tools for Processing Results from CellPainting Assay

IMPORTANT: This package is very much WIP and probably not yet usable by others. Breaking changes are introduced on a daily basis and there is not yet an easy setup.

This is a set of tools used for processing results generated by [CellProfiler](https://cellprofiler.org) for the CellPainting asssay.

The tools are designed to be used in a Jupyter Notebook and have been written in Python3.

The starting point is a `Results.tsv` file that contains output from a CellProfiler pipeline aggregated as medians on site level (please visit [cellprofiler.org](https://cellprofiler.org/) for details on how to setup and run CellProfiler pipelines).

Further documentation, including Jupyter Notebooks with example workflows, will follow.

## CellPainting 2

* Switch from a categorical fingerprint to a continuous log2-fold scale

* Use 629 parameters selected based on reproducibility

    - do not remove correlated parameters