Tree-based differential testing using inferential uncertainty for RNA-Seq

Abstract

Identifying differentially expressed transcripts poses a crucial yet challenging problem in transcriptomics. Substantial uncertainty is associated with the abundance estimates of certain transcripts which, if ignored, can lead to the exaggeration of false positives and, if included, may lead to reduced power. For a given set of RNA-Seq samples,TreeTerminusarranges transcripts in a hierarchical tree structure that encodes different layers of resolution for interpretation of the abundance of transcriptional groups, with uncertainty generally decreasing as one ascends the tree from the leaves. We introducetrenDi, which utilizes the tree structure fromTreeTerminusfor differential testing. The candidate nodes are determined in a data-driven manner to maximize the signal that can be extracted from the data while controlling for the uncertainty associated with estimating the transcript abundances. The identified candidate nodes can include transcripts and inner nodes, with no two nodes having an ancestor/descendant relationship. We evaluated our method on both simulated and experimental datasets, comparing its performance with other tree-based differential methods as well as with uncertainty-aware differential transcript/gene expression methods. Our method detects inner nodes that show a strong signal for differential expression, which would have been overlooked when analyzing the transcripts alone.