English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

A popular approach for comparing gene expression levels between (replicated)conditions of RNA sequencing data relies on counting reads that map to featuresof interest. Within such count-based methods, many flexible and advancedstatistical approaches now exist and offer the ability to adjust for covariates(e.g., batch effects). Often, these methods include some sort of (sharing ofinformation) across features to improve inferences in small samples. It isimportant to achieve an appropriate tradeoff between statistical power andprotection against outliers. Here, we study the robustness of existingapproaches for count-based differential expression analysis and propose a newstrategy based on observation weights that can be used within existingframeworks. The results suggest that outliers can have a global effect ondifferential analyses. We demonstrate the effectiveness of our new approachwith real data and simulated data that reflects properties of real datasets(e.g., dispersion-mean trend) and develop an extensible framework forcomprehensive testing of current and future methods. In addition, we explorethe origin of such outliers, in some cases highlighting additional biologicalor technical factors within the experiment. Further details can be downloadedfrom the project website:http://imlspenticton.uzh.ch/robinson_lab/edgeR_robust/

Robustly detecting differential expression in RNA sequencing data using observation weights