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High-throughput calculation of EC50 values.

Project description

ECCpy, a program for EC50 calculation in python.

The EC50, or the “half maximal effective concentration”, is a key measure of the effectiveness of a compound to affect a biological system. It is commonly used in pharmacology, biology and biochemistry. The EC50 is calculated by fitting the dose-response data to a sigmoidal curve, typically using the Hill equation. Variants include the half maximal “lethal dose” (LD50), and “inhibitor concentration” (IC50).


Robust data analysis

  • fully automated
    • fitting of sigmoidal curves to dose-response data
    • calculation of EC50, LD50 or IC50 values.
    • high-throughput analysis
    • comparison of EC50 values from different experiments
    • calculation of EC25 or EC75 values
  • accepts REAL biological data
    • pre-filtering excludes nonsense data
    • judgefit module identifies low-quality EC50 values

Designed for humans

  • easy-to-use excel files:
    • excel settings file
    • excel input file with dose and response data
    • excel output file with EC50 values
  • simple graphical output: - sigmoidal curves with EC50 shown on graph - daily summary barcharts and curves


  • simple python syntax
  • open-source software
  • built on powerful numpy, scipy, and pandas packages

Development status

ECCpy has been used extensively for the analysis of LD50 assays by bachelor, master and PhD students within the lab of Dieter Langosch at the Technical University of Munich in Germany. However this software is released “as is”, and is likely to contain bugs related to particular data types, python versions or operating systems.


pip install eccpy

ECCpy is written for python 3.x. We recommend the Anaconda python distribution, which contains all the required python packages (numpy, scipy, pandas and matplotlib).


Using ECCpy requires only the following: 1) Prepare your data, 2) update an excel settings file, 3) tell ECCpy to “run”.

See the wiki for more details (

1) Prepare your data.
  • use the excel or microplate templates in the eccpy/templates folder
  • for the generic excel format, simply open the template and paste in your dose and response data.
2) Update an excel settings file
  • copy the ECCpy_settings_template.xlsx from eccpy/templates
  • open the excel file, input the name and location of your datafiles, and the desired location for your output files
  • write “TRUE” next to the files you want to examine
3) tell ECCpy to “run”.
  • run the ipython/jupyter notebook, which opens a python interpreter in your web browser
  • paste in the following three lines. Replace the location of your settings file.
  • hit Ctrl-Enter to run
  • based on your output, adjust the quality thresholds in the settings file to suit your data


import eccpy
settings = r"D:\data\ECCpy_settings.xlsx"


  • run eccpy on the provided example files (eccpy/examples) before processing your own data.

ECCpy output

run_curvefit program
  • individual dose-response curves
  • automatic judging of data quality
  • daily summary curves, barchart and more!

run_gatherer program

  • combines data from multiple experiments
  • excludes EC50 values that are not of sufficient quality, according to user-defined thresholds
  • bar charts with mean and SEM over all selected experiments
  • scatter plots showing individual datapoints for each day/experiment, and more!

compare_rawdata program

  • collects raw data and dose-response curves from multiple experiments
  • compares datapoints and fitted curves between the selected samples


If you encounter a bug or ECCpy doesn’t work for any reason, please send an email to Mark Teese (contact details below) or initiate an issue in Github.

Non-programmers can contribute by:
  • testing ECCpy with your particular datasets
  • suggesting features
  • improving the readme and documentation

Pull requests are also very welcome.


ECCpy is free software distributed under the GNU General Public License version 3.


If you use ECCpy in your research, please use the following citation.

Schanzenbach C, Schmidt FC, Breckner P, Teese MG, & Langosch D (2017) Identifying ionic interactions within a membrane using BLaTM, a genetic tool to measure homo-and heterotypic transmembrane helix-helix interactions. Scientific Reports 7(7):43476.


Currently the code is maintained by Mark Teese at the Technical University of Munich.

Contact details are at the Langosch lab website of the Technical University of Munich, and/or in the image below.

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