Warchal Scott J, Dawson John C, Carragher Neil O
Cancer Research UK Edinburgh Centre, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK.
Methods Mol Biol. 2018;1787:171-181. doi: 10.1007/978-1-4939-7847-2_13.
Principal component analysis enables dimensional reduction of multivariate datasets that are typical in high-content screening. A common analysis utilizing principal components is a distance measurement between a perturbagen-such as small-molecule treatment or shRNA knockdown-and a negative control. This method works well to identify active perturbagens, though it cannot discern between distinct phenotypic responses. Here, we describe an extension of the principal component analysis approach to multivariate high-content screening data to enable quantification of differences in direction in principal component space. The theta comparative cell scoring method can identify and quantify differential phenotypic responses between panels of cell lines to small-molecule treatment to support in vitro pharmacogenomics and drug mechanism-of-action studies.
主成分分析能够对高内涵筛选中常见的多变量数据集进行降维。利用主成分的一种常见分析方法是测量干扰因素（如小分子处理或短发夹RNA敲低）与阴性对照之间的距离。这种方法在识别活性干扰因素方面效果良好，不过它无法区分不同的表型反应。在此，我们描述了一种将主成分分析方法扩展应用于多变量高内涵筛选数据的方法，以实现对主成分空间中方向差异的量化。θ比较细胞评分方法能够识别并量化不同细胞系组对小分子处理的差异表型反应，以支持体外药物基因组学和药物作用机制研究。
