AI Single cell

scGPT is a generative pretrained transformer foundation model for single-cell biology, pretrained on over 33 million human cells from 51 organs across 441 studies. Uses a GPT architecture adapted for gene expression data with a specialized attention mask. Outperforms traditional methods on cell type annotation, multi-batch integration, multi-omic integration, perturbation response prediction, and gene network inference. Represents a foundational AI model for cellular biology analogous to GPT for natural language.

Generative pretrained models have achieved remarkable success in various domains such as language and computer vision. Using burgeoning single-cell sequencing data, we have constructed a foundation model for single-cell biology, scGPT, based on a generative pretrained transformer across a repository of over 33 million cells. Our findings illustrate that scGPT effectively distills critical biological insights concerning genes and cells. Through further adaptation of transfer learning, scGPT can be optimized to achieve superior performance across diverse downstream applications including cell type annotation, multi-batch integration, multi-omic integration, perturbation response prediction and gene network inference.