splatPop: simulating population scale single-cell RNA sequencing data

Genome Biology

Luke Zappia¹  Christina B. Azodi²  Davis J. McCarthy²  Alicia Oshlack³ 

[1] Department of Mathematics, Technical University of Munich, Boltzmannstraße 3, 85748, Garching bei München, Germany;Institute of Computational Biology, Helmholtz Zentrum München, Ingolstädter Landstraße 1, 85764, Neuherberg, Germany;St. Vincent’s Institute of Medical Research, 9 Princes Street, 3065, Fitzroy, VIC, Australia;University of Melbourne, Royal Parade, 3010, Parkville, VIC, Australia;University of Melbourne, Royal Parade, 3010, Parkville, VIC, Australia;Peter MacCallum Cancer Centre, Grattan Street, 3000, Melbourne, VIC, Australia;

关键词: Single-cell RNA-sequencing;    Simulation;    Software;   

DOI  :  10.1186/s13059-021-02546-1

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY