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

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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.

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The online version of this article (doi:10.1186/s13059-016-0888-1) contains supplementary material, which is available to authorized users.

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Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences

Jeremy Goecks, Anton Nekrutenko, James E. Taylor (2010)

Increased reliance on computational approaches in the life sciences has revealed grave concerns about how accessible and reproducible computation-reliant results truly are. Galaxy http://usegalaxy.org, an open web-based platform for genomic research, addresses these problems. Galaxy automatically tracks and manages data provenance and provides support for capturing the context and intent of computational methods. Galaxy Pages are interactive, web-based documents that provide users with a medium to communicate a complete computational analysis.

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Comparison of the predicted and observed secondary structure of T4 phage lysozyme.

B W Matthews (1975)

Predictions of the secondary structure of T4 phage lysozyme, made by a number of investigators on the basis of the amino acid sequence, are compared with the structure of the protein determined experimentally by X-ray crystallography. Within the amino terminal half of the molecule the locations of helices predicted by a number of methods agree moderately well with the observed structure, however within the carboxyl half of the molecule the overall agreement is poor. For eleven different helix predictions, the coefficients giving the correlation between prediction and observation range from 0.14 to 0.42. The accuracy of the predictions for both beta-sheet regions and for turns are generally lower than for the helices, and in a number of instances the agreement between prediction and observation is no better than would be expected for a random selection of residues. The structural predictions for T4 phage lysozyme are much less successful than was the case for adenylate kinase (Schulz et al. (1974) Nature 250, 140-142). No one method of prediction is clearly superior to all others, and although empirical predictions based on larger numbers of known protein structure tend to be more accurate than those based on a limited sample, the improvement in accuracy is not dramatic, suggesting that the accuracy of current empirical predictive methods will not be substantially increased simply by the inclusion of more data from additional protein structure determinations.

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Quantitative single-cell RNA-seq with unique molecular identifiers.

Saiful Islam, Amit Zeisel, Simon Joost … (2014)

Single-cell RNA sequencing (RNA-seq) is a powerful tool to reveal cellular heterogeneity, discover new cell types and characterize tumor microevolution. However, losses in cDNA synthesis and bias in cDNA amplification lead to severe quantitative errors. We show that molecular labels--random sequences that label individual molecules--can nearly eliminate amplification noise, and that microfluidic sample preparation and optimized reagents produce a fivefold improvement in mRNA capture efficiency.

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Author and article information

Contributors

Tomislav Ilicic: ti243@cam.ac.uk

Sarah A. Teichmann: st9@sanger.ac.uk

Journal

Journal ID (nlm-ta): Genome Biol

Journal ID (iso-abbrev): Genome Biol

Title: Genome Biology

Publisher: BioMed Central (London )

ISSN (Print): 1474-7596

ISSN (Electronic): 1474-760X

Publication date (Electronic): 17 February 2016

Publication date PMC-release: 17 February 2016

Publication date Collection: 2016

Volume: 17

Electronic Location Identifier: 29

Affiliations

[ ]European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD UK

[ ]Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SA UK

[ ]Cavendish Laboratory, Dept Physics, University of Cambridge, JJ Thomson Avenue, Cambridge, CB3 0HE UK

[ ]University of Cambridge, Cancer Research UK Cambridge Institute, Robinson Way, Cambridge, CB2 0RE UK

[ ]Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Cambridge, CB2 0PT UK

[ ]National Health Service (NHS) Blood and Transplant, Cambridge Biomedical Campus, Cambridge, CB2 0PT UK

[ ]St Vincent’s Institute of Medical Research, Fitzroy, Victoria 3065 Australia

Article

Publisher ID: 888

DOI: 10.1186/s13059-016-0888-1

PMC ID: 4758103

PubMed ID: 26887813

SO-VID: dd891532-9bbc-4e7f-8e53-8ab32ebf5bea

License:

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

History

Date received : 21 August 2015

Date accepted : 27 January 2016

Funding

Funded by: FundRef http://dx.doi.org/http://dx.doi.org/10.13039/501100000268, Biotechnology and Biological Sciences Research Council;

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ScienceOpen disciplines: Genetics

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ScienceOpen disciplines: Genetics

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

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RNA drug delivery

Most cited references 19

Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences

Comparison of the predicted and observed secondary structure of T4 phage lysozyme.

Quantitative single-cell RNA-seq with unique molecular identifiers.

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Cited by 289

Most referenced authors 1,158