scAnyOmics: Foundation-Model Distillation for Any-to-Any Completion of Multi-omics Virtual Cells

Authors: Zhaoqi Song , Ye Du , Yusuke Iwasawa , Yutaka Matsuo , Qian Niu

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Abstract: Recent advances in single-cell technologies have enabled the profiling of multiple molecular modalities, including transcriptomics, proteomics, and chromatin accessibility, providing a more comprehensive understanding of cellular states. However, integrating heterogeneous modalities and predicting missing molecular information remain challenging, particularly when paired measurements are limited. Here, we introduce scAnyOmics, a generative framework that distills knowledge from a frozen single-cell foundation model into a multimodal student model. scAnyOmics integrates scRNA-seq, protein-marker measurements, and scATAC-seq into a shared representation, enabling cross-modal prediction and reconstruction of missing modalities. We assembled 690,880 cells from six publicly available studies, covering both paired and partially paired multi-omics measurements, to systematically evaluate its predictive performance. We further evaluated scAnyOmics on held-out donor data, where it outperformed baseline methods across multiple evaluation metrics. Beyond cross-modal reconstruction, we explored its applications in cell-type annotation, perturbation-response prediction, and biological age estimation. Overall, scAnyOmics provides a unified framework for integrating incomplete single-cell multi-omics data and transferring pretrained biological knowledge, supporting the development of virtual cell models across diverse cellular contexts and experimental platforms.

Comments:

14 pages, 4 figures, 2 tables

Subjects:

Artificial Intelligence (cs.AI)

Cite as:

arXiv:2610.41399 [cs.AI]

(or

arXiv:2610.41399v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.41399

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arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Zhaoqi Song [ view email ]

[v1]

Fri, 30 Oct 2026 08:12:01 UTC (2,925 KB)

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