scDeepAPA: a deep learning framework for single-cell alternative polyadenylation identification.

Alternative polyadenylation (APA) is a widespread post-transcriptional regulatory mechanism that diversifies transcript isoforms and modulates mRNA stability, localization, and translation. Although single-cell RNA sequencing (scRNA-seq) provides an unprecedented opportunity to study cell-type-specific APA dynamics, existing computational tools are largely designed for bulk RNA-seq data or rely heavily on gene annotations, limiting their applicability to single-cell contexts. Here, we present scDeepAPA, a deep learning framework specifically optimized for scRNA-seq data to enable accurate polyadenylation site (PAS) detection, isoform quantification, and functional interpretation of APA events at single-cell resolution. Trained on high-confidence annotations from PolyASite v3.0, scDeepAPA integrates convolutional feature extraction with Mamba-based state-space modeling and bidirectional LSTM layers to capture both long-range and local sequence dependencies. Comprehensive benchmarking against five state-of-the-art PAS prediction models demonstrates that scDeepAPA consistently achieves superior performance across accuracy, F1 score, and area under the receiver operating characteristic metrics in both human and mouse datasets. Applying scDeepAPA to Alzheimer's disease mouse brain data revealed widespread, cell-type-specific APA remodeling across immune and glial populations, including shifts toward proximal PAS usage and 3' UTR shortening. In KRAS-mutant small cell lung cancer, scDeepAPA uncovered global proximal PAS activation and tumor-specific intronic polyadenylation events. Notably, several intronic APA events generated truncated transcripts encoding predicted neoantigenic peptides with strong major histocompatibility complex class I binding affinity, supported by structural modeling and tumor-specific expression patterns. By enabling accurate PAS identification and quantitative APA profiling, scDeepAPA facilitates in-depth downstream analyses of regulatory mechanisms and immunogenic consequences in single-cell transcriptomics, advancing the understanding of post-transcriptional regulation in neurodegeneration and cancer.