Stack is 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. Stack can perform in-context learning from unlabeled cells representing arbitrary conditions such as a chemical perturbation or a different donor, predicting the effect of those conditions on a target cell population without requiring data-specific fine-tuning. We apply Stack to generate Perturb Sapiens, the first human whole-organism atlas of perturbed cells, spanning 28 tissues, 40 cell classes, and 201 perturbations (∼20,000 perturbation effect profiles). Stack presents a new modeling framework where cells themselves act as guiding examples at inference time, unlocking general-purpose in-context learning capabilities for single-cell biology.

Github

Stack on Github

HuggingFace

Perturb Sapiens