Single-cell RNA sequencing (scRNA-seq) has become an established and powerful method to investigate transcriptomic cell-to-cell variation, thereby 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 assessed the protocol sensitivity and accuracy of many published data sets, on the basis of spike-in standards and uniform data processing. For our workflow, we developed a flexible tool for counting the number of unique molecular identifiers (https://github.com/vals/umis/). We compared 15 protocols computationally and 4 protocols experimentally for batch-matched cell populations, in addition to investigating the effects of spike-in molecular degradation. Our analysis provides an integrated framework for comparing scRNA-seq protocols.The study was supported by Cancer Research UK grant number C45041/A14953 to A Cvejic and C Labalette, European Research Council project 677501-ZF_Blood to A Cvejic and a core support grant from the Wellcome Trust and MRC to the Wellcome Trust–Medical Research Council Cambridge Stem Cell Institute. The ERC grant ThSWITCH to SA Teichmann (grant no. 260507) and a Lister Institute Research Prize to SA Teichmann. KN Natarajan was supported by the Wellcome Trust Strategic Award “Single cell ge nomics of mouse gastrulation”
