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

ABSTRACT: 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 outlier

SUBMITTER: Zhou X 

PROVIDER: S-EPMC4066750 | biostudies-literature | 2014 Jun

REPOSITORIES: biostudies-literature