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Nat Methods. 2017 Apr;14(4):381-387. doi: 10.1038/nmeth.4220. Epub 2017 Mar 6.

Power analysis of single-cell RNA-sequencing experiments.

Author information

1
European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK.
2
Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK.
3
Centre of Biological Engineering, University of Minho, Campus de Gualtar, Braga, Portugal.
4
Wellcome Trust - Medical Research Council Cambridge Stem Cell Institute, Cambridge, UK.
5
Department of Haematology, University of Cambridge, Cambridge, UK.
#
Contributed equally

Abstract

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.

PMID:
28263961
PMCID:
PMC5376499
DOI:
10.1038/nmeth.4220
[Indexed for MEDLINE]
Free PMC Article

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