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Optimized design of single-cell RNA sequencing experiments for cell-type-specific eQTL analysis.
Igor Mandric1, Tommer Schwarz2, Arunabha Majumdar3
1Department of Computer Science, University of California Los Angeles, 404 Westwood Plaza, Los Angeles, CA, 90095, USA.
Nature Communications
|October 31, 2020
Summary
Low-coverage single-cell RNA sequencing (scRNA-Seq) of more samples boosts statistical power for cell-type-specific expression quantitative trait loci (eQTL) mapping more than high-coverage sequencing of fewer samples within the same budget.
Area of Science:
- Genomics
- Computational Biology
- Population Genetics
Background:
- Single-cell RNA sequencing (scRNA-Seq) offers direct measurement of cellular states but is limited by high costs for population-scale studies.
- Bulk RNA sequencing (RNA-Seq) with deconvolution methods provides estimates but lacks the resolution of scRNA-Seq.
- Cell-type-specific expression quantitative trait loci (eQTL) mapping is crucial for understanding gene regulation but requires substantial data.
Purpose of the Study:
- To determine cost-effective experimental designs for maximizing statistical power in cell-type-specific eQTL mapping using scRNA-Seq.
- To compare the power of low-coverage, high-sample-number designs versus high-coverage, low-sample-number designs under budget constraints.
- To provide a practical tool for selecting optimal scRNA-Seq experimental parameters for eQTL studies.
Main Methods:
- Simulations based on large-scale real scRNA-Seq data from 120 individuals.
- Evaluation of various experimental designs varying in sample number, cells per sample, and reads per cell.
- Statistical power analysis for cell-type-specific eQTL detection.
Main Results:
- Low-coverage per-cell sequencing of a larger number of samples yields higher statistical power for eQTL mapping than high-coverage sequencing of fewer samples, given a fixed budget.
- Multiple experimental designs can achieve similar statistical power, indicating flexibility in optimizing cost-effectiveness.
- Cost savings are substantial, particularly when incorporating multiplexed library preparation workflows.
Conclusions:
- Prioritizing a higher number of samples with lower sequencing depth per cell is a more powerful strategy for population-scale eQTL studies using scRNA-Seq.
- Careful selection of experimental design parameters can significantly reduce costs while maintaining or enhancing statistical power.
- A web tool is available to guide researchers in selecting cost-effective designs for maximizing eQTL discovery.
