scPhoenix: A Contrastive Learning-based Framework with Aux-Core Feature Disentanglement Enhances Sparse Cellular Multi-omics Translation

Yan, C.; He, Z.; Ye, L.; Zheng, S.; Lin, N.; Li, G.; Li, W.

doi:10.1101/2025.09.27.678942

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bioRxiv · 10.1101/2025.09.27.678942

scPhoenix: A Contrastive Learning-based Framework with Aux-Core Feature Disentanglement Enhances Sparse Cellular Multi-omics Translation

Yan, C.·He, Z.·Ye, L.·Zheng, S.·Lin, N.·Li, G.·Li, W.

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Abstract

Recent advances in single-cell multi-omics co-assays and spatiotemporal sequencing technologies have provided unprecedented opportunities for systematically characterizing cellular heterogeneity. However, severe sparsity and pronounced spatial heterogeneity--hallmark features of complex diseases and tumor microenvironment--remain major obstacles in deciphering cellular multi-omics data. Here, we present scPhoenix, a contrastive learning-based framework for single-cell cross-modality translation. scPhoenix adopts a two-stage Aux-Core strategy to disentangle modality-specific feature extraction from cross-modality feature interaction. Across diverse datasets, it preserves cellular heterogeneity during translation and demonstrates significant advantages for data with high sparsity. In addition, the framework integrates a contrastive learning framework with five effective data augmentation methods tailored to single-cell data. Moreover, scPhoenixs design supports extensions to unpaired data training and spatial multi-omics translation, enabling robust performance in scenarios with high spatial heterogeneity. scPhoenix is freely available at https://github.com/liwz-lab/scPhoenix.

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Yan, C., He, Z., Ye, L., Zheng, S., Lin, N., Li, G., Li, W.. 2025-09-29. scPhoenix: A Contrastive Learning-based Framework with Aux-Core Feature Disentanglement Enhances Sparse Cellular Multi-omics Translation. https://doi.org/10.1101/2025.09.27.678942

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