Optimized design of single-cell RNA sequencing experiments for cell-type-specific eQTL analysis

Igor Mandric, Tommer Schwarz, Arunabha Majumdar, Kangcheng Hou, Leah Briscoe, Richard Perez, Meena Subramaniam, Christoph Hafemeister, Rahul Satija, Chun Jimmie Ye, Bogdan Pasaniuc () and Eran Halperin
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Igor Mandric: University of California Los Angeles
Tommer Schwarz: Bioinformatics Interdepartmental Program, University of California Los Angeles
Arunabha Majumdar: David Geffen School of Medicine, University of California Los Angeles
Kangcheng Hou: Bioinformatics Interdepartmental Program, University of California Los Angeles
Leah Briscoe: Bioinformatics Interdepartmental Program, University of California Los Angeles
Richard Perez: University of California San Francisco
Meena Subramaniam: University of California San Francisco
Christoph Hafemeister: New York Genome Center
Rahul Satija: New York Genome Center
Chun Jimmie Ye: University of California San Francisco
Bogdan Pasaniuc: Bioinformatics Interdepartmental Program, University of California Los Angeles
Eran Halperin: University of California Los Angeles

Nature Communications, 2020, vol. 11, issue 1, 1-9

Abstract: Abstract Single-cell RNA-sequencing (scRNA-Seq) is a compelling approach to directly and simultaneously measure cellular composition and state, which can otherwise only be estimated by applying deconvolution methods to bulk RNA-Seq estimates. However, it has not yet become a widely used tool in population-scale analyses, due to its prohibitively high cost. Here we show that given the same budget, the statistical power of cell-type-specific expression quantitative trait loci (eQTL) mapping can be increased through low-coverage per-cell sequencing of more samples rather than high-coverage sequencing of fewer samples. We use simulations starting from one of the largest available real single-cell RNA-Seq data from 120 individuals to also show that multiple experimental designs with different numbers of samples, cells per sample and reads per cell could have similar statistical power, and choosing an appropriate design can yield large cost savings especially when multiplexed workflows are considered. Finally, we provide a practical approach on selecting cost-effective designs for maximizing cell-type-specific eQTL power which is available in the form of a web tool.

Date: 2020
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DOI: 10.1038/s41467-020-19365-w

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