Dr. Neil Ashley

We are thrilled to share our new single-cell foundation model, Tabula (preprint: biorxiv.org/content/10.1101/…; package: github.com/aristoteleo/tabul…)—a privacy-preserving predictive foundation model for single-cell transcriptomics, leveraging federated learning and tabular modeling. Over the past year, we’ve seen a surge in foundation models for single-cell genomics, where genes are often arbitrarily ordered to mimic NLP paradigms. Furthermore, as we start to train on large datasets comprising thousands of individuals, the ethical and privacy concerns arise as well. To address these challenges, we introduce Tabula: a federated-learning-based, privacy-preserving foundation model that explicitly represents single-cell data using tabular modeling. Tabula demonstrates excellent performance across diverse tasks, including cell type annotation, multi-omics and multi-batch integration, gene imputation, denoising, and both gene perturbation and reverse perturbation predictions. Very excitingly, as one of the first examples of a truly predictive foundation model, Tabula accurately uncovers pairwise and even combinatorial regulatory logic across diverse biological systems, including hematopoiesis, pancreatic endogenesis, neurogenesis, and cardiogenesis, all of which have very well validated regulatory networks. For more details, please see @JiayuanDing 's excellent post here: teapawt.net/JiayuanDing/status/187… This is really an amazing collaboration with three brilliant young trainees, including @JiayuanDing Jianhui (@JilinJJ) and Shiyu (@shiyu_jiang23) and two other labs Jiliang (@tangjiliang), and Min Li.. Kudos to @JiayuanDing , the incredibly talented PhD student in my lab who led this project! Jianyuan is currently on the faculty job market and would be an outstanding addition to any institution. Please consider him and feel free to reach out to Jianyuan or me for more information. Additionally, Shiyun @shiyu_jiang23 , who contributed to innovative approaches for pairwise and combinatorial perturbation prediction, is applying to PhD programs. Please consider this rising star to join your PhD program as well! This work represents another important advance in my lab’s long time vision to establish a predictive “virtual embryo” model for human health. We are currently extending this approach to 3D Spatial Transcriptomics data as we previously reported in Spateo (teapawt.net/Xiaojie_Qiu/status/185…) . If you are a developmental biologist, technology developer, or a machine learning expert, please consider joining us on this exciting journey, please reach out regarding potential positions at all levels in my lab (devo-evo.com/research. Email: [email protected]). We also highly welcome graduate students from Stanford @StanfordEng @DevBioStanford @ChemSysBio @StanfordData @StanfordAILab for rotations in my new lab. I am excited about many collaboration opportunities within Stanford and the broader Bay Area as well.