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Genome Biol. 2018 Nov 7;19(1):191. doi: 10.1186/s13059-018-1571-5.

Simulation-based benchmarking of isoform quantification in single-cell RNA-seq.

Author information

1
Department of Genetics, University of Cambridge, Downing Street, Cambridge, CB2 3EH, UK.
2
Departamento de Biología Celular y Molecular, Facultad de Ciencias Biológicas, Pontificia Universidad Católica de Chile, Av. Libertador Bernardo O'Higgins 340, 8331150, Santiago, Chile.
3
Department of Genetics, University of Cambridge, Downing Street, Cambridge, CB2 3EH, UK. afsmith@mole.bio.cam.ac.uk.
4
Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SA, UK. mh26@sanger.ac.uk.

Abstract

Single-cell RNA-seq has the potential to facilitate isoform quantification as the confounding factor of a mixed population of cells is eliminated. However, best practice for using existing quantification methods has not been established. We carry out a benchmark for five popular isoform quantification tools. Performance is generally good for simulated data based on SMARTer and SMART-seq2 data. The reduction in performance compared with bulk RNA-seq is small. An important biological insight comes from our analysis of real data which shows that genes that express two isoforms in bulk RNA-seq predominantly express one or neither isoform in individual cells.

KEYWORDS:

Benchmark; Bulk RNA-seq; Isoform quantification; Single cell; scRNA-seq

PMID:
30404663
PMCID:
PMC6223048
DOI:
10.1186/s13059-018-1571-5
[Indexed for MEDLINE]
Free PMC Article

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