This is to publish the code entered into GitHub by Derfel Terciano and Akshar Lohith in the Lokey Lab. It is also found at https://github.com/LokeyLab/QCGauntlet.

This code is used to visualize cytological profiling (CP) data early in the data analysis pipeline. It can be used on HistDiff outputs. It allows you to see if negative and/or positive control wells have a strong phenotype, and also how strong the screening wells' phenotypes were in comparison.

The version provided here was used in the paper published in Nature Communications, titled "Cell painting in activated cells illuminates phenotypic dark space and uncovers novel drug mechanisms of action." The supporting information for this paper is found at DOI:10.5281/zenodo.19392293. The preprint of the article is found at DOI:10.1101/2025.05.23.655853. 

This is what was entered onto GitHub by the creator:

This python program generates analytical figures that help determine the quality of experiments. This program can generate the following visuals:

• Scatter plots of CP Activity Scores (with threshold lines) NOTE: works only with two conditions)
• Elbow plots of CP Activity scores (with threshold lines)
• Clustermap files for Java TreeView
• Histograms for control condition correlations
• Barplots of CP Activity control wells that are over a threshold.

By default: thresholds are set to 0.5