OmniCellTOSG: The First Cell Text-Omic Signaling Graphs Dataset for Joint LLM and GNN Modeling
Journal:
arXiv
Published Date:
Apr 2, 2025
Abstract
Complex cell signaling systems -- governed by varying protein abundances and
interactions -- generate diverse cell types across organs. These systems evolve
under influences such as age, sex, diet, environmental exposures, and diseases,
making them challenging to decode given the involvement of tens of thousands of
genes and proteins. Recently, hundreds of millions of single-cell omics data
have provided a robust foundation for understanding these signaling networks
within various cell subpopulations and conditions. Inspired by the success of
large foundation models (for example, large language models and large vision
models) pre-trained on massive datasets, we introduce OmniCellTOSG, the first
dataset of cell text-omic signaling graphs (TOSGs). Each TOSG represents the
signaling network of an individual or meta-cell and is labeled with information
such as organ, disease, sex, age, and cell subtype. OmniCellTOSG offers two key
contributions. First, it introduces a novel graph model that integrates
human-readable annotations -- such as biological functions, cellular locations,
signaling pathways, related diseases, and drugs -- with quantitative gene and
protein abundance data, enabling graph reasoning to decode cell signaling. This
approach calls for new joint models combining large language models and graph
neural networks. Second, the dataset is built from single-cell RNA sequencing
data of approximately 120 million cells from diverse tissues and conditions
(healthy and diseased) and is fully compatible with PyTorch. This facilitates
the development of innovative cell signaling models that could transform
research in life sciences, healthcare, and precision medicine. The OmniCellTOSG
dataset is continuously expanding and will be updated regularly. The dataset
and code are available at https://github.com/FuhaiLiAiLab/OmniCellTOSG.
