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
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+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 .
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.
Jiabei Cheng, Jingbo Zhou, Jun Xia +4
Westlake University Hangzhou, Zhejiang, China · Shanghai Jiao Tong University Shanghai, China · Zhejiang University Hangzhou, Zhejiang, China +3
In this work, we introduce CellxPert, a scalable multimodal foundation model that unifies single-cell and spatial multi-omics within a common representation space. CellxPert jointly encodes transcriptomic (scRNA-seq), chromatin-accessibility (ATAC-seq), and surface-proteomic (CITE-seq) measurements, while directly incorporating MERFISH and imaging mass-cytometry data as 2D or 3D spatial-visual layers. CellxPert facilitates four key downstream tasks out of the box: (i) cell-type annotation across a broad ontology of 154 largely overlapping identities -- the largest label space addressed to date and a stringent test of fine-grained discrimination, (ii) efficient fine-tuning using Low Rank Adaptation (LoRA), (iii) genome-wide transcriptomic response prediction to in-silico perturbations (ISP), and (iv) seamless multi-omic integration across various assays and platforms. Unlike current single-cell foundation models, which approximate gene perturbations by deleting or reordering tokenized gene expression ranks, CellxPert employs a Metropolis-Hastings sampler whose proposal kernel uses the model's masked conditional distributions to transition to new transcriptomic states conditioned on the perturbed genes. This Markov-chain procedure mitigates out-of-distribution artifacts introduced by abrupt token manipulation and produces trajectories that are biologically interpretable. Evaluations on PBMC68K, Replogle Perturb-seq, Systema, and BMMC benchmarks show that CellxPert surpasses classical and state-of-the-art baselines in cell-type annotation, perturbation response prediction, and multi-omic integration.
Andac Demir, Erik W. Anderson, Jeremy L. Jenkins +1
The integration of single-cell proteomic data is often hindered by the fragmented nature of targeted antibody panels. To address this limitation, we introduce scpFormer, a transformer-based foundation model designed for single-cell proteomics. Pre-trained on over 390 million cells, scpFormer replaces standard index-based tokenization with a continuous, sequence-anchored approach. By combining Evolutionary Scale Modeling (ESM) with value-aware expression embeddings, it dynamically maps variable panels into a shared semantic space without artificial discretization. We demonstrate that scpFormer generates global cell representations that perform competitively in large-scale batch integration and unsupervised clustering. Moreover, its open-vocabulary architecture facilitates in silico panel expansion, assisting in the reconstruction of biological manifolds in sparse clinical datasets. Finally, this learned protein co-expression logic is transferable to bulk-omics tasks, supporting applications like cancer drug response prediction. scpFormer provides a versatile, panel-agnostic framework to facilitate scalable biomarker discovery and precision oncology.
Qifeng Zhou, Lei Yu, Yuzhi Guo +5
Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, 76010, TX, USA. · Quantitative Biomedical Research Center, Department of Health Data Science and Biostatistics, Peter O’Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, 75390, TX, USA.