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

Tomislav Ilicic; Jong Kyoung Kim; Aleksandra A Kolodziejczyk; Frederik Otzen Bagger; Davis James McCarthy; John C Marioni; Sarah A Teichmann

doi:10.1186/s13059-016-0888-1

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

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

Authors

Tomislav Ilicic^{1

2}, Jong Kyoung Kim³, Aleksandra A Kolodziejczyk^{3

4}, Frederik Otzen Bagger^{3

5

6}, Davis James McCarthy^{3

7}, John C Marioni^{3

4

8}, Sarah A Teichmann^{9

10

11}

Affiliations

¹ European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. [email protected].
² Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SA, UK. [email protected].
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. [email protected].
¹⁰ Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SA, UK. [email protected].
¹¹ Cavendish Laboratory, Dept Physics, University of Cambridge, JJ Thomson Avenue, Cambridge, CB3 0HE, UK. [email protected].

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.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Animals
Base Sequence / genetics*
Bone Marrow Cells / classification
CD4-Positive T-Lymphocytes / classification
Dendritic Cells / classification
Embryonic Stem Cells / classification
Gene Expression Profiling
High-Throughput Nucleotide Sequencing
Mice
Oligonucleotide Array Sequence Analysis
RNA / genetics*
Single-Cell Analysis*

Substances

RNA

Abstract

Publication types

MeSH terms

Substances

Grants and funding