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Robustly detecting differential expression in RNA sequencing data using observation weights.
Journal article

Robustly detecting differential expression in RNA sequencing data using observation weights.

  • Zhou X Institute of Molecular Life Sciences, University of Zurich, CH-8057 Zurich, Switzerland SIB Swiss Institute of Bioinformatics, University of Zurich, CH-8057 Zurich, Switzerland.
  • Lindsay H Institute of Molecular Life Sciences, University of Zurich, CH-8057 Zurich, Switzerland SIB Swiss Institute of Bioinformatics, University of Zurich, CH-8057 Zurich, Switzerland.
  • Robinson MD Institute of Molecular Life Sciences, University of Zurich, CH-8057 Zurich, Switzerland SIB Swiss Institute of Bioinformatics, University of Zurich, CH-8057 Zurich, Switzerland mark.robinson@imls.uzh.ch.
  • 2014-04-23
Published in:
  • Nucleic acids research. - 2014
Computer Simulation
Gene Expression Profiling
Sequence Analysis, RNA
English 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/.
Language
  • English
Open access status
gold
Identifiers
Persistent URL
https://folia.unifr.ch/global/documents/228752
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