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

Zhaoqi Song; Ye Du; Yusuke Iwasawa; Yutaka Matsuo; Qian Niu

cs.AIOct 30, 2026

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

Organizations: Department of Technology Management for Innovation, Graduate School of Engineering, The University of Tokyo, Tokyo, 113-8656, Japan · State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Faculty of Medicine and Life Sciences, Xiamen University, Xiamen, 361102, China · Department of Computing, Hong Kong Polytechnic University, Hong Kong S.A.R., China

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.

Figures & tables

Figure 1: Overview of the scAnyOmics framework. scAnyOmics contains four main components: (A) single-cell multi-omics input, including scRNA-seq, surface proteins (ADT), and chromatin accessibility (ATAC); (B) knowledge distillation from three frozen foundation models ( UCE , scLinguist , and Atacformer ); (C) multimodal student training with masked inputs and self-distillation to learn unified cellular representations; and (D) downstream applications, including cell-type prediction, continuous perturbation-response prediction, and missing-modality inference.

Figure 2: Reconstruction performance across nine input–target combinations. Each panel corresponds to a withheld target modality, with rows indicating the available input modality or modalities. Blue circles denote the UCE-distilled scAnyOmics student, orange triangles the student without teacher distillation, and grey squares the stronger of ridge regression and 15-nearest-neighbour averaging. RNA and ADT outcomes are median Spearman correlations across 2,000 genes and 166 antibodies, respectively; ATAC outcomes are median AUROC values across 8,000 10-kb genomic bins. The vertical dashed line indicates chance-level AUROC (0.50). Numbers beside the blue markers are scAnyOmics scores. All tasks were evaluated on the same 28,239 trimodal cells from held-out samples.

Figure 3: Antibody-specific accuracy of RNA-to-ADT reconstruction. (a) Spearman correlation for each of 166 antibodies predicted from RNA, comparing ridge regression (horizontal axis) with scAnyOmics (vertical axis), using 10,000 trimodal test cells. Markers above the diagonal favour scAnyOmics (125 antibodies); markers below it favour ridge regression (41 antibodies). (b–e) Measured and predicted

\log(1+\mathrm{count})

values for CD19, CD3, CD14, and CX3CR1 in a random subset of 400 test cells. Panel annotations report the Spearman correlations of scAnyOmics and ridge regression. Measured values were jittered horizontally for visualization.

Figure 4: Effects of foundation-model teachers and self-distillation on cross-modal reconstruction. Rows indicate the teacher configuration and columns the observed input–target task. Entries show differences relative to a student trained without teacher distillation; positive changes (blue) indicate higher reconstruction accuracy and negative changes (orange) indicate lower accuracy. RNA and ADT panels report changes in median Spearman correlation, whereas the ATAC panel reports changes in median AUROC on a separate colour scale. Values in parentheses beneath each task label give the corresponding teacher-free score. All comparisons use the same 28,239 trimodal held-out sample cells as Figure 2 .

GEO series (PMID)	Biological material	Condition	Assay(s)	Blocks / GSMs	Donors	Anti- bodies
GSE158013 (33835024)	Leukapheresis PBMCs, dead-cell/neutrophil-depleted	Unstimulated	TEA-seq (5 libraries); 10x Multiome, nuclei or permeabilized cells (2 libraries)	7 / 7	1 (HMN200910)	45
GSE200417 (35739535)	Peripheral blood pan-T cells, healthy donors aged 18–35 years	12 h CD3/CD28/CD2 + IL-1 $\beta$ + IL-23 $\pm$ PGE2/TGF- $\beta$	DOGMA-seq, DIG (2 libraries) and LLL (1 library) permeabilization; CITE-seq (1 library)	7 / 4	Not resolved	154
GSE214546 (37845489)	PBMCs	Unstimulated	TEA-seq	19 / 19	16 donors + 1 control individual	54
GSE236422 (—)	Blood T cells from healthy donors	Bortezomib, lenalidomide, dexamethasone or DMSO $\times$ 4/24/72 h; untreated 0 h	TEA-seq	12 / 12	Not resolved	54
GSE156478 (34083792)	PBMCs	Control arm (16 h IL-2)	CITE-seq	1 / 1	Not resolved	113
GSE166188 (34083792)	PBMCs	Control arm (16 h IL-2)	DOGMA-seq, LLL permeabilization	1 / 1	Not resolved	107

Table 1: Source datasets.

GEO	Total cells	RNA+ADT+ATAC	RNA+ADT	RNA+ATAC	RNA	Split (train / test)
GSE158013	59,151	37,745	4,682	14,680	2,044	49,911 / 9,240
GSE200417	68,138	41,903	14,821	11,414	0	56,195 / 11,943
GSE214546	367,955	53,423	314,532	0	0	246,439 / 121,516
GSE236422	181,518	168,062	13,456	0	0	172,516 / 9,002
GSE156478	5,527	0	5,527	0	0	5,527 / 0
GSE166188	8,591	8,591	0	0	0	8,591 / 0

Table 2: Cells per dataset and modality combination.

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scTranslation: A Comprehensive Benchmark for Single-Cell Multi-Omics Modality Translation

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Westlake University Hangzhou, Zhejiang, China · Shanghai Jiao Tong University Shanghai, China · Zhejiang University Hangzhou, Zhejiang, China+3

Multimodal Model Evaluation Single-Cell Transcriptomics

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Simultaneous measurement of multiple omics modalities in single cells enables researchers to gain a more comprehensive understanding of cellular states and regulatory mechanisms. However, due to high experimental costs, significant noise, and incomplete modality coverage, a variety of computational methods for modality translation have emerged in recent years. Despite the development of translation models, there is still a lack of systematic benchmark evaluation in terms of datasets, evaluation metrics, and influencing factors. To address this, we present scTranslation, a comprehensive benchmark for single-cell multi-omics modality translation tasks. It includes diverse translation datasets, integrates state-of-the-art models, and provides a comprehensive evaluation metrics. In addition, we assess model performance under different scenarios, such as feature selection, feature quality, and few-shot settings. These factors significantly affect model performance but have rarely been systematically studied before. Leveraging this benchmark, we conduct a large-scale study of current methods, report many insightful findings that open up new possibilities for future development. The benchmark is open-sourced to facilitate future research. The code is anonymously released at https://github.com/Bunnybeibei/scTranslation.

Abstract

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