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

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 quantificati...

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Autores principales: Westoby, Jennifer, Herrera, Marcela Sjöberg, Ferguson-Smith, Anne C., Hemberg, Martin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6223048/
https://www.ncbi.nlm.nih.gov/pubmed/30404663
http://dx.doi.org/10.1186/s13059-018-1571-5
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author Westoby, Jennifer
Herrera, Marcela Sjöberg
Ferguson-Smith, Anne C.
Hemberg, Martin
author_facet Westoby, Jennifer
Herrera, Marcela Sjöberg
Ferguson-Smith, Anne C.
Hemberg, Martin
author_sort Westoby, Jennifer
collection PubMed
description 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13059-018-1571-5) contains supplementary material, which is available to authorized users.
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spelling pubmed-62230482018-11-19 Simulation-based benchmarking of isoform quantification in single-cell RNA-seq Westoby, Jennifer Herrera, Marcela Sjöberg Ferguson-Smith, Anne C. Hemberg, Martin Genome Biol Method 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13059-018-1571-5) contains supplementary material, which is available to authorized users. BioMed Central 2018-11-07 /pmc/articles/PMC6223048/ /pubmed/30404663 http://dx.doi.org/10.1186/s13059-018-1571-5 Text en © The Author(s). 2018 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.
spellingShingle Method
Westoby, Jennifer
Herrera, Marcela Sjöberg
Ferguson-Smith, Anne C.
Hemberg, Martin
Simulation-based benchmarking of isoform quantification in single-cell RNA-seq
title Simulation-based benchmarking of isoform quantification in single-cell RNA-seq
title_full Simulation-based benchmarking of isoform quantification in single-cell RNA-seq
title_fullStr Simulation-based benchmarking of isoform quantification in single-cell RNA-seq
title_full_unstemmed Simulation-based benchmarking of isoform quantification in single-cell RNA-seq
title_short Simulation-based benchmarking of isoform quantification in single-cell RNA-seq
title_sort simulation-based benchmarking of isoform quantification in single-cell rna-seq
topic Method
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6223048/
https://www.ncbi.nlm.nih.gov/pubmed/30404663
http://dx.doi.org/10.1186/s13059-018-1571-5
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