Power analysis of single-cell RNA-sequencing experiments

Single-cell RNA sequencing (scRNA-seq) has become an established and powerful method to investigate transcriptomic cell-to-cell variation, revealing new cell types, and providing insights into developmental processes and transcriptional stochasticity. A key question is how the variety of available protocols compare in terms of their ability to detect and accurately quantify gene expression. Here, we assess protocol sensitivity and accuracy on many published data sets based on spike-in standards and uniform data processing, which includes the development of a flexible Unique Molecular Identifier counting tool (https://github.com/vals/umis). We compare 15 protocols computationally and 4 protocols experimentally on batch-matched cell populations, in addition to investigating the impact of spike-in molecule degradation. Our analysis provides an integrated framework for comparing scRNA-seq protocols.