Robustly detecting differential expression in RNA sequencing data using observation weights

By Xiaobei Zhou, Helen Lindsay, Mark D. Robinson. Nucleic Acids Research, 2013

389 citations recorded by the source corpus.

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/.

Subfields: Molecular Biology, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/2c4272e5dd8a6f54caae33fec533ad5fc133c8c7

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Citations in the corpus

Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 (2014, neutral): “edgeR now includes an optional method to handle outliers by iteratively refitting the generalized linear model after down-weighting potential outlier counts [33].”
Why weight? Modelling sample and observational level variability improves power in RNA-seq analyses (2015, methodology): “For larger experiments, the usefulness of other approaches such as robust methods (34) that can determine unusual observations by looking at the residuals ‘within’ each gene would be expected to come to the fore.”
edgeR v4: powerful differential analysis of sequencing data with expanded functionality and improved support for small counts and larger datasets (2024, positive): “A variation of the weighted likelihood EB al-gorithm that is more robust to observation outliers was also implemented [ 31 ].”
RNA Sequencing Data: Hitchhiker's Guide to Expression Analysis (2018, methodology): “Second, edgeR-robust [139], for instance, adopts an M-estimation approach by iteratively downweighting outlying observations within the GLM fitting procedure, dampening the effect of outliers on both mean and variance estimates.”
A Mammalian enhancer trap resource for discovering and manipulating neuronal cell types (2016, methodology): “Gene counts data generated with HT-seq {Anders, 2015 #103} was used for differentially expressed gene analysis by edgeR (Zhou et al., 2014).”