Perplexity as a Metric for Isoform Diversity in the Human Transcriptome

Abstract

Characterizing the extensive isoform diversity revealed by long-read RNA-sequencing remains challenging. After removal of technical artifacts, existing pipelines apply arbitrary expression thresholds that filter out bona fide transcript structures, misrepresenting diversity and hindering reproducibility. Instead of discarding isoforms, we propose a fundamentally distinct approach to quantifying isoform diversity using perplexity-the effective number of isoforms for a gene, derived from Shannon entropy-wherein every isoform, including low-abundance ones, contributes proportionally to a genes diversity. Analyzing 124 ENCODE4 PacBio datasets spanning 55 human cell types, we show that perplexity provides interpretable and reproducible isoform diversity measurements across genes, regulatory levels, and tissues.