Comprehensive benchmark of differential transcript usage analysis for bulk and single-cell RNA sequencing

Abstract RNA sequencing offers unique insights into transcriptome diversity, and a plethora of tools have been developed to analyze alternative splicing. One important task is to detect changes in the relative transcript abundance in differential transcript usage (DTU) analysis. The choice of the right analysis tool is nontrivial and depends on experimental factors such as the availability of single- or paired-end and bulk or single-cell data. To help users select the most promising tool, we performed a comprehensive benchmark of DTU detection tools. We cover a wide array of experimental settings, using simulated bulk and single-cell RNA-seq data as well as real transcriptomics datasets, including time-series data. Our results suggest that edgeR, DEXSeq, and LimmaDS are better choices for paired-end data, while DEXSeq and DSGseq can be used for single-end data. In single-cell simulation settings, we showed that satuRn performs better than DTUrtle. In addition, we showed that Spycone is optimal for time series DTU/isoform switch analysis based on the evidence provided using Gene Ontology (GO) terms enrichment analysis. Our study offers a comprehensive evaluation of DTU analysis in bulk and single-cell data and identifies the need for methods that differentiate DTU events.