scDesignPop

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scDesignPop is a simulator for population-scale single-cell RNA-sequencing (scRNA-seq) data. By incorporating eQTL effects from genotype data with other covariates, scDesignPop has several key applications:

• 1. performing eQTL power analysis across cell types,
• 2. benchmarking single-cell eQTL mapping methods with user-specified eQTLs,
• 3. protecting genomic privacy by mitigating eQTL-based re-identification of individuals via linking attacks

Detailed tutorials that illustrate various functionalities of scDesignPop are available at this website. The following illustration figure summarizes the workflow of scDesignPop:

Table of contents

1. Installation
2. Tutorials
3. Contact
4. Related Manuscripts

Installation

To install the key dependence of scDesignPop, we recommend:

if(!require("SingleCellExperiment")){
  BiocManager::install("SingleCellExperiment")
}

You can install the development version of scDesignPop from GitHub with:

if(!require("remotes")){
    install.packages("remotes")
}
remotes::install_github("chrisycd/scDesignPop")

Tutorials

For tutorials, please check the website. The tutorials will demonstrate the applications of scDesignPop as follows:

• Model population-scale scRNA-seq data

• Modeling batch effect in population-scale scRNA-seq data

• Modeling trans-eQTL effect using public data

• Model linear dynamic eQTL effects in continuous cell states

• Model Nonlinear dynamic eQTL effects in continuous cell states

• Model cell type proportions for new individuals

• Modify eQTL effect for eGenes / non-eGenes

• Power analysis for selected genes

• Power analysis based on user specified eQTL effect sizes

Contact

Any questions or advice on scDesignPop are welcomed! Please report it on issues, or contact Chris Dong (cycd@g.ucla.edu) or Yihui Cen (yihuicen@g.ucla.edu).

Related Manuscripts

• The original scDesignPop paper (TBD. Coming soon…)

• The simulator for general single-cell counts: scDesign3

  • scDesign3: Song, D., Wang, Q., Yan, G. et al. scDesign3 generates realistic in silico data for multimodal single-cell and spatial omics. Nat Biotechnol 42, 247–252 (2024).

• The predecessors of scDesign3

  • scDesign: Li, W. V., & Li, J. J. (2019). A statistical simulator scDesign for rational scRNA-seq experimental design. Bioinformatics, 35(14), i41-i50.
  • scDesign2: Sun, T., Song, D., Li, W. V., & Li, J. J. (2021). scDesign2: a transparent simulator that generates high-fidelity single-cell gene expression count data with gene correlations captured. Genome biology, 22(1), 1-37.

• The simulator for single-cell multi-omics reads developed by our lab member Guanao Yan

  • scReadSim: Yan, G., Song, D. & Li, J.J. scReadSim: a single-cell RNA-seq and ATAC-seq read simulator. Nat Commun 14, 7482 (2023)