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 UCLA researchers 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. The researchers 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, they 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.
Experimental designs for CD4 T cells ct-eQTL with effective sample size Neff = 40
a Comparison of different experimental designs. Experimental design N = 88, M = 2250, r = 4500 yields 2-fold reduction in cost than the standard design. b For a fixed sample size and number of cells per individual, increasing coverage implies increasing the effective sample size (i.e., power) only up to a point. There is little gain in power at coverages greater than 12,500 reads per cell. Red solid line corresponds to budget and blue dashed line corresponds to effective sample size.
Availability – The online calculator for the design of ct-eQTL studies is available at https://mandricigor.github.io/ct-eqtl-design/.