Year: 2021
Type: article
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.
Source: Genome biology
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $4,890
Awards: Harnessing the power of single cell sequencing to advance biomedical research, Cloud-ready analysis methods and software to study effects of DNA variants on single-cell gene expression, Combining human genetics with single-cell genomics to unravel the differentiation dynamics of human induced pluripotent stem cells, Advancing the analysis of single cell transcriptomics to facilitate biomedical research, Developing interpretable machine learning models for clinical imaging and single-cell genomics, G7716934159
OpenAlex ID: W4226092339