CRISPhieRmix：用于 CRISPR 池筛选的层次混合模型。
CRISPhieRmix: a hierarchical mixture model for CRISPR pooled screens.
机构信息
Department of Statistics, Stanford University, 450 Serra Mall, Stanford, 94305, USA.
Department of Bioengineering, Stanford University, 443 Via Ortega, Stanford, 94305, USA.
出版信息
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。
Zotero 插件