Stack: In-Context Learning of Single-Cell Biology

Abstract Single-cell transcriptomics offers the promise of measuring the diversity of cellular phenotypes across species, diseases, and other biological conditions. Recently, foundation models have emerged to identify this variation, yet most methods represent each cell independently, despite technical limitations that reduce measurement precision at the single-cell level. Here, we present S tack , a foundation model trained on 149 million uniformly preprocessed human single cells that leverages tabular attention to generate representations for each cell informed by the cells in its context. S tack offers substantial improvements for downstream tasks in the zero-shot setting compared to baselines, whether they…