Todd Bartkowiak

🎉Exciting Update of scGPT 🎉: After receiving significant attention from the community since our April release, we're thrilled to announce the first major update for scGPT - a foundation model for single-cell multi-omic data. This update integrates community feedback and leverages the latest data release from @cellxgene. It boasts larger pretraining data, and more robust models, and expands the range of application tasks. Pre-print (V2) available: biorxiv.org/content/10.1101/… Access our open-source code and models here: github.com/bowang-lab/scGPT Detailed tutorials: scgpt.readthedocs.io/en/late… Highlights of this update include: 🔬 Introducing the first GPT-style foundation model for single-cell multi-omic data, pretrained on over 33M human cell atlas data. 💡 Our generalist approach enables one model to accomplish multiple tasks in single-cell analysis, including multi-omic integrative analysis and perturbation prediction. 🧬 Discovering gene-gene interactions specific to various conditions using learned attention weights and gene embeddings. 🚀 Uncovered a scaling law showcasing continuous model performance enhancement as data volume increases. 🐾 scGPT model zoo (see github) now offers multiple pre-trained foundation models for various solid organs and a comprehensive pan-cancer model. Begin exploring your data with the most fitting foundation model. We welcome your thoughts on specialist vs generalist approaches in single-cell studies. Many thanks for the following technical analyses of scGPT: 1. Excellent twittorial by @simocristea: nitter.cf/simocristea/stat… 2. Comprehensive review on LLM in cell biology by @s_batzoglou: towardsdatascience.com/large… 3. Insightful blog by @SalvatoreRaieli: levelup.gitconnected.com/scg… Kudos to Haotian (@HAOTIANCUI1) & Chloe (@chloexwang1) for their exemplary work on this project. @VectorInst @pmcc_ai @UHNAIHUB @bradwouters @drbarryrubin @UofT_TCAIREM @UofT_LMP @UofTCompSci @uoftmedicine