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Curr Protoc Chem Biol. 2017 Jun 19;9(2):96-116. doi: 10.1002/cpch.19.

Designing Drug-Response Experiments and Quantifying their Results.

Author information

1
HMS LINCS Center, Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, Massachusetts.

Abstract

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 IC50 or GR50 . 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.

KEYWORDS:

computational pipeline; data processing; drug response; experimental design

PMID:
28628201
PMCID:
PMC5729909
DOI:
10.1002/cpch.19
[Indexed for MEDLINE]
Free PMC Article

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