Department of Statistics, Stanford University, 450 Serra Mall, Stanford, 94305, USA.
Department of Bioengineering, Stanford University, 443 Via Ortega, Stanford, 94305, USA.
Genome Biol. 2018 Oct 8;19(1):159. doi: 10.1186/s13059-018-1538-6.
Pooled CRISPR screens allow researchers to interrogate genetic causes of complex phenotypes at the genome-wide scale and promise higher specificity and sensitivity compared to competing technologies. Unfortunately, two problems exist, particularly for CRISPRi/a screens: variability in guide efficiency and large rare off-target effects. We present a method, CRISPhieRmix, that resolves these issues by using a hierarchical mixture model with a broad-tailed null distribution. We show that CRISPhieRmix allows for more accurate and powerful inferences in large-scale pooled CRISPRi/a screens. We discuss key issues in the analysis and design of screens, particularly the number of guides needed for faithful full discovery.
池 CRISPR 筛选允许研究人员在全基因组范围内研究复杂表型的遗传原因，与竞争技术相比，具有更高的特异性和灵敏度。不幸的是，存在两个问题，特别是对于 CRISPRi/a 筛选：向导效率的可变性和罕见的大脱靶效应。我们提出了一种方法，CRISPhieRmix，通过使用具有宽尾零分布的层次混合模型来解决这些问题。我们表明，CRISPhieRmix 允许在大规模池 CRISPRi/a 筛选中进行更准确和强大的推断。我们讨论了筛选分析和设计中的关键问题，特别是为实现完全发现所需的向导数量。
