dose-response 0.0.2

Create Dose-Response-Curves from csv files

DRC

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

    git clone https://github.com/tim25651/DRC
    cd DRC/
    pip install .
   

2. 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

2. 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
   

3. Plot is stored in OutputDirectory/DataFileBase_plot.png

    demo/demo_plot.png
   

4. 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)