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Cell Type-Specific Isoform Function Prediction by Multiplex Heterogeneous Network
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DOI：10.1109/tcbbio.2026.3728504.png)
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Abstract
En 中文
Alternatively spliced isoforms from the same gene can perform distinct functions; however, their cell-type-specific roles remain largely uncharacterized, limiting our ability to understand cellular diversity and development beyond traditional gene-level analyses. We present cIsoFun, a multi-modal fusion framework for cell-type-specific isoform function prediction from single-cell transcriptomics data. cIsoFun leverages pre-trained ESM-2 and BERT models to extract initial sequence features, constructs a multiplex heterogeneous network over genes, isoforms, GO terms, and cell types to represent their complex relationships, and applies relation-aware attention to integrate multi-modal information and refine node embeddings. It then optimizes a multi-component loss on the updated embeddings to predict isoform functions, enabling biological interpretability via sequence-importance and cell-type-specific analyses. Experiments demonstrate that cIsoFun outperforms existing methods, particularly for sparse GO terms, and reveal distinct functional programs across contexts: kidney tumor cells are enriched for metabolism and growth regulation, skin tumor cells emphasize immune surveillance and migration, and cell lines prioritize DNA repair and telomere maintenance. Sequence-importance analysis highlights critical amino-acid regions and shows that domains annotated with the same function can exhibit distinct importance profiles across spliced isoforms. Together, these results provide new insights into cell-type-specific isoform functionality and establish cIsoFun as a practical tool for single-cell isoform analysis.
Keywords:
Single-cell transcriptomics
isoform function prediction
heterogeneous graph neural networks
gene ontology
data fusion
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