Recent advancements for simultaneously profiling multi-omic modalities within individual cells have enabled the interrogation of cellular heterogeneity and molecular hierarchy. However, technical limitations lead to highly noisy multi-modal data and substantial costs. Although computational methods have been proposed to translate single-cell data across modalities, b broad applications of the methods still remain impeded b by formidable challenges. Here, we proposed scButterfly, a versatile single-cell cross-modality translation method based on dual-aligned variational autoencoders and innovative data augmentation schemes. With comprehensive experiments on multiple datasets, we provide compelling evidence of scButterfly's superiority over baseline methods in preserving cellular heterogeneity while translating d datasets o of various contexts and i in revealing cell type-specific biological insights.Besides, we d demonstrate the extensive applications of scButterfly for integrative multi-omics analysis of single-modality data, data enhancement of poor-quality single-cell multi-omics, and automatic cell type annotation of scATAC-seq data. Additionally, scButterfly can be generalized to unpaired data training and perturbation-response analysis via our data augmentation and optimal transport strategies. Moreover, scButterfly exhibits the capability i in consecutive translation from epigenome to transcriptome to proteome and has the potential to decipher novel biomarkers.
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