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

作者：Chaoyu Yan, Zijian He, Lihang Ye, Shikang Zheng, Nan Lin, Guanbin Li, Weizhong Li · 发表于：bioRxiv (Cold Spring Harbor Laboratory) · 年份：2025 · DOI：10.1101/2025.09.27.678942 · 研究领域：Gene expression and cancer classification、Bioinformatics and Genomic Networks、Molecular Biology Techniques and Applications

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, scPhoenix’s 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 .