St. Vincent's Institute of Medical Research, 9 Princes Street, Fitzroy, 3065, VIC, Australia.
University of Melbourne, Royal Parade, Parkville, 3010, VIC, Australia.
Genome Biol. 2021 Dec 15;22(1):341. doi: 10.1186/s13059-021-02546-1.
Population-scale single-cell RNA sequencing (scRNA-seq) is now viable, enabling finer resolution functional genomics studies and leading to a rush to adapt bulk methods and develop new single-cell-specific methods to perform these studies. Simulations are useful for developing, testing, and benchmarking methods but current scRNA-seq simulation frameworks do not simulate population-scale data with genetic effects. Here, we present splatPop, a model for flexible, reproducible, and well-documented simulation of population-scale scRNA-seq data with known expression quantitative trait loci. splatPop can also simulate complex batch, cell group, and conditional effects between individuals from different cohorts as well as genetically-driven co-expression.
现在，基于人群的单细胞 RNA 测序（scRNA-seq）已经可行，这使得更精细的功能基因组学研究成为可能，并促使人们急于采用批量方法并开发新的单细胞特异性方法来进行这些研究。模拟在开发、测试和基准测试方法方面很有用，但目前的 scRNA-seq 模拟框架不能模拟具有遗传效应的基于人群的数据。在这里，我们提出了 splatPop，这是一种用于灵活、可重复和记录良好的基于人群的 scRNA-seq 数据模拟的模型，这些数据具有已知的表达数量性状基因座。splatPop 还可以模拟来自不同队列的个体之间的复杂批量、细胞群和条件效应，以及遗传驱动的共表达。
