# OmniCellTOSG: The First Cell Text-Omic Signaling Graphs Dataset for Graph Language Foundation Modeling

## TL;DR

This paper introduces a new dataset and model that combine text, gene data, and signaling networks to improve biomedical research and precision medicine.

## Contribution

The novel Text-Omic Signaling Graph (TOSG) unifies textual knowledge, omic data, and signaling networks for foundation modeling.

## Key findings

- OmniCellTOSG includes half a million TOSGs from 80 million single-cell RNA-seq profiles.
- CellTOSG-FM outperforms existing omic models in downstream tasks and provides interpretable insights.

## Abstract

With the rapid growth of large-scale single-cell omic datasets, omic foundation models (FMs) have emerged as powerful tools for advancing research in life sciences and precision medicine. However, most existing omic FMs rely primarily on numerical transcriptomic data by sorting genes as sequences, while lacking explicit integration of biomedical prior knowledge and signaling interactions that are critical for scientific discovery. Here, we introduce the Text-Omic Signaling Graph (TOSG), a novel data structure that unifies human-interpretable biomedical textual knowledge, quantitative omic data, and signaling network information. Using this framework, we construct OmniCellTOSG, a large-scale resource comprising approximately half million meta-cell TOSGs derived from around 80 million single-cell and single-nucleus RNA-seq profiles across organs and diseases. We further develop CellTOSG-FM, a multimodal graph language FM, to jointly analyze textual, omic and signaling network context. Across diverse downstream tasks, CellTOSG-FM outperforms existing omic FMs, and provides interpretable insights into disease-associated targets and signaling pathways.