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

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.

Maximizing statistical power to detect clinically associated cell states with scPOST

“…We found we could significantly increase power by adopting a balanced study design and obtaining 20 more samples. Given that we (and others 24,31 ) found that shallower sequencing or assaying fewer cells per sample does not generally compromise power, it is more effective for investigators to allocate these resources to obtaining more samples.…”

“…Current single-cell simulation strategies directly simulate individual genes and focus on estimating power to detect general differential gene expression 19–23 or associations with genetic variants 23–24 . These strategies are typically used to simulate small datasets ranging from 400-10,000 cells with few replicates, because sampling individual genes is slow and limits larger simulations.…”

Pleiotropic Roles of ABC Transporters in Breast Cancer

“…The heterogeneity of breast tumours also impacts the accuracy of measuring the ABC transporter expression levels in clinical settings. Clinically testing ABC transporters using single-cell transcriptome sequencing may allow the inference of cell type composition and a more accurate quantitation of gene transcripts in breast tumour tissues [ 92 , 93 ]. Additionally, drug resistance is usually derived from the combined activity of different ABC transporters and, presumably, many other mechanisms during cancer progression.…”

Counterfactual inference for single-cell gene expression analysis

“…14,15 Likewise, for genetic analysis (expression quantitative trait loci), the statistical power of eQTL discovery is primarily determined by the degree of genetic variation across individuals rather than the number of cells per individual. 16 Nonetheless, differential expression analysis of single-cell RNA-seq is a state-of-the-art and unbiased approach to characterize cell-type-specific transcriptomic changes.…”