splatPop: simulating population scale single-cell RNA sequencing data
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.
Citation
@article{b azodi2021,
author = {B Azodi, Christina and Zappia, Luke and Oshlack, Alicia and
J McCarthy, Davis},
title = {splatPop: Simulating Population Scale Single-Cell {RNA}
Sequencing Data},
journal = {Genome biology},
volume = {22},
number = {1},
pages = {341},
date = {2021-12-15},
url = {https://lazappi.id.au/publications/2021-azodi-splatPop},
doi = {10.1186/s13059-021-02546-1},
issn = {1465-6906},
langid = {en},
abstract = {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.}
}