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Genome Biol. 2016 Feb 17;17:29. doi: 10.1186/s13059-016-0888-1.

Classification of low quality cells from single-cell RNA-seq data.

Author information

1
European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. ti243@cam.ac.uk.
2
Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SA, UK. ti243@cam.ac.uk.
3
European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK.
4
Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SA, UK.
5
Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Cambridge, CB2 0PT, UK.
6
National Health Service (NHS) Blood and Transplant, Cambridge Biomedical Campus, Cambridge, CB2 0PT, UK.
7
St Vincent's Institute of Medical Research, Fitzroy, Victoria, 3065, Australia.
8
University of Cambridge, Cancer Research UK Cambridge Institute, Robinson Way, Cambridge, CB2 0RE, UK.
9
European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. st9@sanger.ac.uk.
10
Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SA, UK. st9@sanger.ac.uk.
11
Cavendish Laboratory, Dept Physics, University of Cambridge, JJ Thomson Avenue, Cambridge, CB3 0HE, UK. st9@sanger.ac.uk.

Abstract

Single-cell RNA sequencing (scRNA-seq) has broad applications across biomedical research. One of the key challenges is to ensure that only single, live cells are included in downstream analysis, as the inclusion of compromised cells inevitably affects data interpretation. Here, we present a generic approach for processing scRNA-seq data and detecting low quality cells, using a curated set of over 20 biological and technical features. Our approach improves classification accuracy by over 30 % compared to traditional methods when tested on over 5,000 cells, including CD4+ T cells, bone marrow dendritic cells, and mouse embryonic stem cells.

PMID:
26887813
PMCID:
PMC4758103
DOI:
10.1186/s13059-016-0888-1
[Indexed for MEDLINE]
Free PMC Article

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