A Real‐Data‐Driven Framework for Evaluating Differential Transcript Usage Methods Across Long‐Read Bulk, Single‐Cell, and Spatial Transcriptomics

Differential transcript usage (DTU) analysis reveals transcript-level regulation in alternative splicing. With the rapid adoption of long-read sequencing in bulk, single-cell, and spatial transcriptomics, reliable evaluation of DTU methods under real biological conditions becomes essential. Current evaluation frameworks mainly rely on simulated data, which can introduce bias and may not reflect true regulatory mechanisms. A real-data-driven framework is constructed to evaluate DTU methods across long-read bulk, single-cell, and spatial transcriptomics. The framework includes two key components. First, a biologically grounded reference transcript set is defined using RNA-binding protein (RBP) knockout or knockdown RNA-seq data together with experimentally validated RBP-transcript interactions. Second, a DTU-specific evaluation metric, the transcript set enrichment score, is introduced to quantify how effectively a method prioritizes reference transcripts in ranked results. The framework is systematically validated for reliability, unbiasedness, stability, effectiveness, and robustness using multiple real RNA-seq datasets. Supported by this validation, ten representative DTU methods are evaluated across long-read and short-read data, revealing performance differences across data types. Beyond evaluating DTU methods, the framework is further extended to predict transcript-level RBP activity, recovering perturbed RBPs more consistently than gene-level differential expression strategies. Together, this study establishes a biologically interpretable and data-driven standard for DTU method evaluation.