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Comparison of methods to detect differentially expressed genes between single-cell populations

We compared five statistical methods to detect differentially expressed genes between two distinct single-cell populations. Currently, it remains unclear whether differential expression methods developed originally for conventional bulk RNA-seq data can also be applied to single-cell RNA-seq data an...

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Detalles Bibliográficos
Autores principales: Jaakkola, Maria K, Seyednasrollah, Fatemeh, Mehmood, Arfa, Elo, Laura L
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5862313/
https://www.ncbi.nlm.nih.gov/pubmed/27373736
http://dx.doi.org/10.1093/bib/bbw057
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author Jaakkola, Maria K
Seyednasrollah, Fatemeh
Mehmood, Arfa
Elo, Laura L
author_facet Jaakkola, Maria K
Seyednasrollah, Fatemeh
Mehmood, Arfa
Elo, Laura L
author_sort Jaakkola, Maria K
collection PubMed
description We compared five statistical methods to detect differentially expressed genes between two distinct single-cell populations. Currently, it remains unclear whether differential expression methods developed originally for conventional bulk RNA-seq data can also be applied to single-cell RNA-seq data analysis. Our results in three diverse comparison settings showed marked differences between the different methods in terms of the number of detections as well as their sensitivity and specificity. They, however, did not reveal systematic benefits of the currently available single-cell-specific methods. Instead, our previously introduced reproducibility-optimization method showed good performance in all comparison settings without any single-cell-specific modifications.
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spelling pubmed-58623132018-08-31 Comparison of methods to detect differentially expressed genes between single-cell populations Jaakkola, Maria K Seyednasrollah, Fatemeh Mehmood, Arfa Elo, Laura L Brief Bioinform Papers We compared five statistical methods to detect differentially expressed genes between two distinct single-cell populations. Currently, it remains unclear whether differential expression methods developed originally for conventional bulk RNA-seq data can also be applied to single-cell RNA-seq data analysis. Our results in three diverse comparison settings showed marked differences between the different methods in terms of the number of detections as well as their sensitivity and specificity. They, however, did not reveal systematic benefits of the currently available single-cell-specific methods. Instead, our previously introduced reproducibility-optimization method showed good performance in all comparison settings without any single-cell-specific modifications. Oxford University Press 2017-09 2016-07-02 /pmc/articles/PMC5862313/ /pubmed/27373736 http://dx.doi.org/10.1093/bib/bbw057 Text en © The Author 2016. Published by Oxford University Press. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Papers
Jaakkola, Maria K
Seyednasrollah, Fatemeh
Mehmood, Arfa
Elo, Laura L
Comparison of methods to detect differentially expressed genes between single-cell populations
title Comparison of methods to detect differentially expressed genes between single-cell populations
title_full Comparison of methods to detect differentially expressed genes between single-cell populations
title_fullStr Comparison of methods to detect differentially expressed genes between single-cell populations
title_full_unstemmed Comparison of methods to detect differentially expressed genes between single-cell populations
title_short Comparison of methods to detect differentially expressed genes between single-cell populations
title_sort comparison of methods to detect differentially expressed genes between single-cell populations
topic Papers
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5862313/
https://www.ncbi.nlm.nih.gov/pubmed/27373736
http://dx.doi.org/10.1093/bib/bbw057
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