设计药物反应实验并对其结果进行量化。
Designing Drug-Response Experiments and Quantifying their Results.
作者信息
Hafner Marc, Niepel Mario, Subramanian Kartik, Sorger Peter K
机构信息
HMS LINCS Center, Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, Massachusetts.
出版信息
Curr Protoc Chem Biol. 2017 Jun 19;9(2):96-116. doi: 10.1002/cpch.19.
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 IC or GR . 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.
摘要
我们开发了一个Python软件包，以帮助进行中高通量的药物反应实验，并从所得数据中评估敏感性指标。在本文中，我们描述了以下步骤：（1）通过针式转移或手动移液生成使用惠普D300药物分配器处理细胞所需的文件；（2）将高通量载玻片扫描仪（如珀金埃尔默Operetta）生成的数据与处理注释合并；（3）分析结果以获得归一化至未处理对照的数据以及诸如IC或GR等敏感性指标。这些模块可在GitHub上获取，并提供了一个用于高通量药物反应实验设计和分析的自动化流程，有助于防止在手动处理大型数据文件时可能出现的错误。© 2017约翰威立父子公司版权所有。
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