Hypatia: Comparative Isoform Profiling Across Cell Populations from Long-Read Single-Cell Transcriptomes

Abstract

Long-read single-cell RNA-sequencing enables isoform-level quantification in individual cells, yet methods for delineating cell population-specific isoforms remain limited. Here, we develop Hypatia to systematically compare isoform complexities across three complementary dimensions, including isoform usage, diversity, and expression. Our analyses demonstrated that Cramer's V improved the precision of detecting isoform usage shifts, at the cost of detecting fewer events in sparser data. Results further revealed Tsallis entropy as a refined, tunable measure of isoform diversity, improving comparability across genes and cells. Critically, integrating these analyses in real datasets provided complementary views of isoform variation across cell populations, highlighting Hypatia's important applications.