Abstract Background Single-cell RNA sequencing (scRNA-seq) offers exciting possibilities to address biological and medical questions, but a systematic comparison of recently developed protocols is still lacking. Results We generated data from 447 mouse embryonic stem cells using Drop-seq, SCRB-seq, Smart-seq (on Fluidigm C1) and Smart-seq2 and analyzed existing data from 35 mouse embryonic stem cells prepared with CEL-seq. We find that Smart-seq2 is the most sensitive method as it detects the most genes per cell and across cells. However, it shows more amplification noise than CEL-seq, Drop-seq and SCRB-seq as it cannot use unique molecular identifiers (UMIs). We use simulations to model how the observed combinations of sensitivity and amplification noise affects detection of differentially expressed genes and find that SCRB-seq reaches 80% power with the fewest number of cells. When considering cost-efficiency at different sequencing depths at 80% power, we find that Drop-seq is preferable when quantifying transcriptomes of a large numbers of cells with low sequencing depth, SCRB-seq is preferable when quantifying transcriptomes of fewer cells and Smart-seq2 is preferable when annotating and/or quantifying transcriptomes of fewer cells as long one can use in-house produced transposase. Conclusions Our analyses allows an informed choice among five prominent scRNA-seq protocols and provides a solid framework for benchmarking future improvements in scRNA-seq methodologies.

Comparative Analysis of Single-Cell RNA Sequencing Methods