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"Ensemble-Based Method for Detecting Differential Expression in RNA Datasets Provides Greater Sensitivity and Less Biased p-Values Than Existing Methods"

This white paper from Q2 Solutions introduces a novel "stacking" ensemble method for detecting differential gene expression that combines the outputs of traditional methods through a logistic regression model to produce less biased p-values and demonstrates superior performance in various testing scenarios.

In examining two-group differential analysis, it is apparent that different statistical methods can yield very different results even given the same RNA-seq input data. In particular, parametric methods such as t and limma yield very similar results that are very different than results from methods based on count-based testing such as edgeR and DESeq (with edgeR and DESeq2 also yielding very similar results). Still another approach based on empirical Bayes modeling that seemingly provides yet another distinct set of information is EBSeq.

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