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dose-response

The script uses the formula for the 4PL-Dose-Response-Curve for fitting:

    Y = Bottom + (Top - Bottom) / (1 + 10 ** (HillSlope * (lg EC50 - lg X)))

CAVE: Default unit is µM and confidence level is 0.05 (to change: use the python class manually)

Input:

  • A csv file with doses and its corresponding responses

Output:

  • A logarithmic dose-response plot of the fitted curce with standard error bars
  • A csv file with the fitted parameters, their SD and the lower and upper bounds of the CI

Usage

  1. Install DRC
pip install dose-response
  1. Provide your data results in a CSV file without headers (Mark values to exclude with an * as suffix).
   
0.0100 0.9648 0.9071* 1.1355 1.0846
0.0200 1.0410 1.2995 1.0521 1.1535
0.0500 1.0967 1.1554 1.0780 1.2259
0.0100 1.2117 1.2174 1.3170 0.9917
0.0200 1.1644 1.6411* 1.3084 1.2987
0.0500 1.4881 1.7500 1.7305 1.3465
1.0000 2.0601 2.2339 2.2620 1.8493
2.0000 1.7824* 2.4520* 2.1712 2.1571
5.0000 2.7854 2.6934 2.8719 2.9488
10.0000 3.1894* 2.4637 2.3380 2.5128
  1. Execute DRC

     file: CSV file with data
     -d, --dose-col: Column index of doses: [Default: 0]
     -r, --response-cols: start and end index of response columns: [Default: every other column]
     -o, --out Output directory [Default: current working directory]
    
    
     drc DataFile -d DoseColumnIndex -r ResponseStartIndex ResponseEndIndex -o OutputDir
    
     drc demo/demo.csv -d 0 -r 1 4 -o demo
    
  2. Plot is stored in OutputDirectory/DataFileBase_plot.png

     demo/demo_plot.png
    

  1. Parameters are stored in OutputDirectory/DataFileBase_params.csv

     demo/demo_params.csv
    
Parameter Mean SD CI_Lower CI_Upper
Hill Slope 1.7123 0.3933 0.9101 2.5144
Top 2.6591 0.0494 2.5582 2.7599
Bottom 1.1178 0.0855 0.9433 1.2922
EC50 0.7874 0.1028 0.5778 0.9969
LogEC50 -0.1038 -0.2382 -0.0013

Test

  1. Create test data

     drc-test TestDataFile
    
  2. Execute DRC

     drc TestDataFile
    

Acknowledgments

Thanks to yannabraham (https://gist.github.com/yannabraham/5f210fed773785d8b638)

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