Statistical considerations for high throughput screening data

Abstract

High throughput screening (HTS) is a widely used effective approach in genome-wide association and large scale protein expression studies, drug discovery, and biomedical imaging research. How to accurately identify candidate ‘targets’ or biologically meaningful features with a high degree of confidence has led to extensive statistical research in an effort to minimize both false-positive and false-negative rates. A large body of literature on this topic with in-depth statistical contents is available. We examine currently available statistical methods on HTS and aim to summarize some selected methods into a concise, easy-to-follow introduction for experimental biologists.

Keywords

high throughput screen / false-positive rate / false-negative rate / target discovery / predictive modeling