  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/
