Improved statistical methods for hit selection in high-throughput screening
Improved statistical methods for hit selection in high-throughput screening
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DOI：
10.1177/1087057103258285
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发表时间：
2003-12-01
影响因子：
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通讯作者：
Liaw, A
中科院分区：
文献类型：
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作者：
Brideau, C;Gunter, B;Liaw, A
高通量筛选（HTS）在现代药物发现中起着核心作用，允许针对各种推定的药物靶点快速筛选大的化合物集合。HTS是一个工业规模的过程，依靠复杂的自动化，控制和最先进的检测技术来组织，测试和测量纳升至微升体积的数十万至数百万种化合物。尽管有这种高技术，HTS的命中选择仍然通常使用简单的数据分析和基本的统计方法来完成。作者在这篇文章中讨论了这些方法的一些缺点，并提出了基于现代统计数据分析方法的替代方案。最重要的是，它们描述并展示了来自生物学家友好型StatServer（R）HTS应用程序（SHS）的许多真实的例子，该应用程序是一种定制开发的软件工具，建立在市售的S-PLUS（R）和StatServer（R）统计分析和服务器软件上。该系统使用强大而复杂的统计方法远程处理HTS数据，但通过以各种易于解释的图形和表格输出结果，将用户与技术细节隔离开来。
High-throughput screening (HTS) plays a central role in modem drug discovery, allowing the rapid screening of large compound collections against a variety of putative drug targets. HTS is an industrial-scale process, relying on sophisticated automation, control, and state-of-the art detection technologies to organize, test, and measure hundreds of thousands to millions of compounds in nano- to microliter volumes. Despite this high technology, hit selection for HTS is still typically done using simple data analysis and basic statistical methods. The authors discuss in this article some shortcomings of these methods and present alternatives based on modem methods of statistical data analysis. Most important, they describe and show numerous real examples from the biologist-friendly StatServer((R)) HTS application (SHS), a custom-developed software tool built on the commercially available S-PLUS(R) and StatServer((R)) statistical analysis and server software. This system remotely processes HTS data using powerful and sophisticated statistical methodology but insulates users from the technical details by outputting results in a variety of readily interpretable graphs and tables.