arnaudkopp/transcellassay

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TransCellAssay

This Python packages is designed to analysis data from High Content Screening assay.
Data input are designed to be Single Cell data (1data/Well work well too) in csv/txt file.
This package has been designed with the aim to facilitate the development of data analysis pipeline, so programming skills are needed to make you own analysis pipeline.
It was developed and tested on python 3.4 platform and aim to replace CellHTS2 or similar software for our analysis.
It work on 96, 384 and 1536 (depend format) well plate with acceptable performance.

What TCA do :

• Quality Control for control, data consistency across replica (check if data are missing).
• Controls based and non-controls based normalization, logarithmic transformation, feature scaling.
• Systematic Error Detection Test (border effect on plate).
• Systematics error Correction with Bscore, BZscore, PMP, MEA, DiffusionModel, polynomial/lowess fitting.
• Scoring with SSMD, T-Statistics, T-Test, Rank product, single cell properties.
• Plotting features
• Easy use of Scikit-Learn machine learning package

Python Module Dependencies

See requirements.txt file, packages in most recent version is a must.

License

This work is under GPLv3 licence. [email protected]

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