Open AccessDesigning Drug‐Response Experiments and Quantifying their Results
Author(s) -
Hafner Marc,
Niepel Mario,
Subramanian Kartik,
Sorger Peter K.
Publication year - 2017
Publication title -
current protocols in chemical biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.503
H-Index - 14
ISSN - 2160-4762
DOI - 10.1002/cpch.19
Subject(s) - python (programming language) , computer science , drug response , throughput , pipeline (software) , sensitivity (control systems) , data mining , drug , programming language , operating system , medicine , engineering , pharmacology , electronic engineering , wireless
We developed a Python package to help in performing drug‐response experiments at medium and high throughput and evaluating sensitivity metrics from the resulting data. In this article, we describe the steps involved in (1) generating files necessary for treating cells with the HP D300 drug dispenser, by pin transfer or by manual pipetting; (2) merging the data generated by high‐throughput slide scanners, such as the Perkin Elmer Operetta, with treatment annotations; and (3) analyzing the results to obtain data normalized to untreated controls and sensitivity metrics such as IC 50 or GR 50 . These modules are available on GitHub and provide an automated pipeline for the design and analysis of high‐throughput drug response experiments, that helps to prevent errors that can arise from manually processing large data files. © 2017 by John Wiley & Sons, Inc.