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Robustly detecting differential expression in RNA sequencing data using observation weights
A popular approach for comparing gene expression levels between (replicated) conditions of RNA sequencing data relies on counting reads that map to features of interest. Within such count-based methods, many flexible and advanced statistical approaches now exist and offer the ability to adjust for c...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4066750/ https://www.ncbi.nlm.nih.gov/pubmed/24753412 http://dx.doi.org/10.1093/nar/gku310 |
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author | Zhou, Xiaobei Lindsay, Helen Robinson, Mark D. |
author_facet | Zhou, Xiaobei Lindsay, Helen Robinson, Mark D. |
author_sort | Zhou, Xiaobei |
collection | PubMed |
description | A popular approach for comparing gene expression levels between (replicated) conditions of RNA sequencing data relies on counting reads that map to features of interest. Within such count-based methods, many flexible and advanced statistical approaches now exist and offer the ability to adjust for covariates (e.g. batch effects). Often, these methods include some sort of ‘sharing of information’ across features to improve inferences in small samples. It is important to achieve an appropriate tradeoff between statistical power and protection against outliers. Here, we study the robustness of existing approaches for count-based differential expression analysis and propose a new strategy based on observation weights that can be used within existing frameworks. The results suggest that outliers can have a global effect on differential analyses. We demonstrate the effectiveness of our new approach with real data and simulated data that reflects properties of real datasets (e.g. dispersion-mean trend) and develop an extensible framework for comprehensive testing of current and future methods. In addition, we explore the origin of such outliers, in some cases highlighting additional biological or technical factors within the experiment. Further details can be downloaded from the project website: http://imlspenticton.uzh.ch/robinson_lab/edgeR_robust/. |
format | Online Article Text |
id | pubmed-4066750 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-40667502014-06-24 Robustly detecting differential expression in RNA sequencing data using observation weights Zhou, Xiaobei Lindsay, Helen Robinson, Mark D. Nucleic Acids Res Methods Online A popular approach for comparing gene expression levels between (replicated) conditions of RNA sequencing data relies on counting reads that map to features of interest. Within such count-based methods, many flexible and advanced statistical approaches now exist and offer the ability to adjust for covariates (e.g. batch effects). Often, these methods include some sort of ‘sharing of information’ across features to improve inferences in small samples. It is important to achieve an appropriate tradeoff between statistical power and protection against outliers. Here, we study the robustness of existing approaches for count-based differential expression analysis and propose a new strategy based on observation weights that can be used within existing frameworks. The results suggest that outliers can have a global effect on differential analyses. We demonstrate the effectiveness of our new approach with real data and simulated data that reflects properties of real datasets (e.g. dispersion-mean trend) and develop an extensible framework for comprehensive testing of current and future methods. In addition, we explore the origin of such outliers, in some cases highlighting additional biological or technical factors within the experiment. Further details can be downloaded from the project website: http://imlspenticton.uzh.ch/robinson_lab/edgeR_robust/. Oxford University Press 2014-07-01 2014-04-20 /pmc/articles/PMC4066750/ /pubmed/24753412 http://dx.doi.org/10.1093/nar/gku310 Text en © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research. http://creativecommons.org/licenses/by/3.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited |
spellingShingle | Methods Online Zhou, Xiaobei Lindsay, Helen Robinson, Mark D. Robustly detecting differential expression in RNA sequencing data using observation weights |
title | Robustly detecting differential expression in RNA sequencing data using observation weights |
title_full | Robustly detecting differential expression in RNA sequencing data using observation weights |
title_fullStr | Robustly detecting differential expression in RNA sequencing data using observation weights |
title_full_unstemmed | Robustly detecting differential expression in RNA sequencing data using observation weights |
title_short | Robustly detecting differential expression in RNA sequencing data using observation weights |
title_sort | robustly detecting differential expression in rna sequencing data using observation weights |
topic | Methods Online |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4066750/ https://www.ncbi.nlm.nih.gov/pubmed/24753412 http://dx.doi.org/10.1093/nar/gku310 |
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