Tabula: A Tabular Self-Supervised Foundation Model for Single-Cell Transcriptomics

Foundation models (FMs) have shown great promise in single-cell genomics, yet current approaches, such as scGPT, Geneformer, and scFoundation, rely on centralized training and language modeling objectives that overlook the tabular nature of single-cell data and raise significant privacy concerns. We present T ABULA , a foundation model designed for single-cell transcriptomics, which integrates a novel tabular modeling objective and federated learning framework to enable privacy-preserving pretraining across decentralized datasets. T ABULA directly models the cell-by-gene expression matrix through column-wise gene reconstruction and row-wise cell contrastive learning, capturing both gene-level relationships and cell-level heterogeneity without imposing artificial gene sequence order. Extensive experiments demonstrate the effectiveness of T ABULA : despite using only half the pretraining data, T ABULA achieves state-of-the-art performance across key tasks, including gene imputation, perturbation prediction, cell type annotation, and multi-omics integration. It is important to note that as public single-cell datasets continue to grow, T ABULA provides a scalable and privacy-aware foundation that not only validates the feasibility of federated tabular modeling, but also establishes a generalizable framework for training future models under similar privacy-preserving settings. All resources are openly available at https://github.com/aristoteleo/tabula to support broad community adoption and future methodological advances.