ScIsoX: a multidimensional framework for measuring isoform-level transcriptomic complexity in single cells

Single-cell isoform analysis enables high-resolution characterization of transcript expression, yet analytical frameworks to systematically measure transcriptomic complexity are lacking. Here, we introduce , a computational framework that integrates a novel hierarchical data structure, a suite of complexity metrics, and dedicated visualization tools for isoform-level analysis. supports systematic exploration of global and cell-type-specific isoform expression patterns arising from alternative splicing, revealing multidimensional complexity signatures across diverse datasets—insights often missed by conventional gene-level approaches. We demonstrate the utility of across multiple real-world single-cell isoform sequencing datasets, showcasing its potential as a general framework for transcriptomic complexity analysis.