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Φ-score: A cell-to-cell phenotypic scoring method for sensitive and selective hit discovery in cell-based assays

机译:Φ评分:一种基于细胞的表型评分方法用于基于细胞的检测中的敏感和选择性命中发现

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摘要

Phenotypic screening monitors phenotypic changes induced by perturbations, including those generated by drugs or RNA interference. Currently-used methods for scoring screen hits have proven to be problematic, particularly when applied to physiologically relevant conditions such as low cell numbers or inefficient transfection. Here, we describe the Φ-score, which is a novel scoring method for the identification of phenotypic modifiers or hits in cell-based screens. Φ-score performance was assessed with simulations, a validation experiment and its application to gene identification in a large-scale RNAi screen. Using robust statistics and a variance model, we demonstrated that the Φ-score showed better sensitivity, selectivity and reproducibility compared to classical approaches. The improved performance of the Φ-score paves the way for cell-based screening of primary cells, which are often difficult to obtain from patients in sufficient numbers. We also describe a dedicated merging procedure to pool scores from small interfering RNAs targeting the same gene so as to provide improved visualization and hit selection.
机译:表型筛选监测由干扰引起的表型变化,包括药物或RNA干扰产生的变化。已经证明,当前使用的用于对屏幕命中进行评分的方法是有问题的,特别是当应用于生理上相关的条件,例如低细胞数或低效率的转染时。在这里,我们描述了Φ评分,这是一种用于在基于细胞的屏幕中识别表型修饰符或匹配的新颖评分方法。通过模拟,验证实验及其在大规模RNAi筛选中的基因鉴定中评估了Φ评分性能。使用稳健的统计数据和方差模型,我们证明了与传统方法相比,Φ得分表现出更好的灵敏度,选择性和可重复性。 Φ评分的改进性能为原代细胞的基于细胞的筛选铺平了道路,而原代细胞通常难以从足够数量的患者那里获得。我们还描述了一种专用合并程序,以合并来自靶向同一基因的小干扰RNA的分数,从而提供改进的可视化效果和命中选择。

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