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, 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, researchers from the European Bioinformatics Institute and the Wellcome Trust Sanger Institute assessed the protocol sensitivity and accuracy of many published data sets, on the basis of spike-in standards and uniform data processing. For their workflow, they developed a flexible tool for counting the number of unique molecular identifiers (https://github.com/vals/umis/). The researchers compared 15 protocols computationally and 4 protocols experimentally for batch-matched cell populations, in addition to investigating the effects of spike-in molecular degradation. Thier analysis provides an integrated framework for comparing scRNA-seq protocols.

Performance metrics for scRNA-seq protocols


(a) Accuracy. Distributions of Pearson correlations (R) for all samples, stratified by protocol (without accounting for sequencing depth). BAT-seq, barcoded 3′-specific sequencing. (b) Sensitivity. Distributions of molecular-detection limits for all samples, stratified by protocol (without accounting for sequencing depth). n, number of samples. The implementation platforms and quantification strategies are indicated below the protocols. (c) UMI efficiency. Distributions of UMI counting efficiencies in samples, based on UMI-tag counting, stratified by protocol. Boxes, quartiles; whiskers, full range of values; white dots, median.

Svensson V, Natarajan KN, Ly LH, Miragaia RJ, Labalette C, Macaulay IC, Cvejic A, Teichmann SA. (2017) Power analysis of single-cell RNA-sequencing experiments. Nat Methods [Epub ahead of print]. [abstract]

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