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I'm creating 3D tours of open single cell RNA-seq data in UMAP space. Here, using data from 50k brain cells by the allen institute . ( The UMAP position and class of every cell is read into Blender, realized as a stylized, class-specific model I sculpted

48,138 次观看 • 3 年前 •via X (Twitter)

10 条评论

Brad Krajina 的头像
Brad Krajina3 年前

Relevant to the dataset used to produce this video are the following papers from researchers at the @AllenInstitute : I did not contribute to the original seq data, and I am grateful to the researchers for making it open

Brad Krajina 的头像
Brad Krajina3 年前

After projecting the RNA-seq data from the Allen Brain Map into 3D UMAP space using Seurat, reading the UMAP data into Blender was achieved using a csv importer add-on from @smonbrogg

Brad Krajina 的头像
Brad Krajina3 年前

Rendering all of the 3D models without overfilling GPU memory was possible using LOD and camera culling nodes from @erindale_xyz in the Erindale Toolkit- Advanced Geometry Nodes Groups.

Brad Krajina 的头像
Brad Krajina3 年前

Thanks to @CgFigures for a great tutorial on importing csv data into geometry nodes in Blender

Chanchal Kumar 的头像
Chanchal Kumar3 年前

@AllenInstitute Amazing work!!

Brad Krajina 的头像
Brad Krajina3 年前

@AllenInstitute Thanks so much!

Nader 的头像
Nader3 年前

@AllenInstitute Wow, what an incredible endeavor! Thanks for sharing!

Amy Robinson Sterling 的头像
Amy Robinson Sterling3 年前

@AllenInstitute Also I really like the shaders and how you made the nucleus glow

Christopher Akiki 的头像
Christopher Akiki3 年前

@AllenInstitute This is next level 🤯

Zafer Kosar 的头像
Zafer Kosar3 年前

@AllenInstitute Astonishing work

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387,977 次观看 • 7 个月前

🔬 Exciting News! Our manuscript, "scGPT: toward building a foundation model for single-cell multi-omics using generative AI" is now finally published in Nature Methods (Nature Methods) 🎉 !!! (Re-)Introducing scGPT: A transformative foundation model engineered for single-cell omics analysis. Developed through the analysis of over 33 million human cells, scGPT sets a new benchmark for application versatility, offering both fine-tuning and zero-shot capabilities. Since its preprint in May 2023, scGPT has significantly impacted the field, evidenced by 13K+ installations, 600+ GitHub stars 🌟, and 40+ citations before its official publication! scGPT has been validated by numerous benchmark studies as a leading foundation model in single-cell analysis. Its pre-trained embeddings extend its utility beyond single-cell studies, enhancing a variety of downstream tasks including protein enrichment and genetic perturbation predictions. Some key updates lately: ---Expanded zero-shot applications for efficient reference mapping and integration, now with CellXGene census integration. ---Advanced perturbation analysis capabilities, including genome-scale perturb-seq data analysis and bulk sequencing data generalization. ---Upgraded scGPT package, offering versatile model loading compatible with PyTorch and flash-attn, for both GPU and CPU. ---Cloud-based scGPT applications for reference mapping, cell annotation, and gene regulatory network inference are available on ---Integration with Hugging Face for easier model training. Limitations: scGPT is an early foray into foundation models for single-cell omics, facing challenges like limited zero-shot learning in some tasks, pretraining constraints, data quality issues, and evaluation limitations. See our Supplementary Notes for details. 🚀 Future Work? Short-Term Goals: 1. Releasing a Mouse Model for broader analysis. 2. Developing a comprehensive evaluation suite for foundation models in single-cell analysis. 3. Creating a foundation model for single-cell spatial omics. 4. Enhancing zero-shot capacity by integrating scGPT with RAG (e.g., knowledge graphs). Long-Term Goals: 1. Expanding scGPT for comprehensive single-cell multi-omics analysis. 2. Developing an in-silico perturbation model for predicting genetic perturbation effects. 3. Merging scGPT with multi-modal genomic sequence models for a deeper understanding of cell biology. 📚 Access the paper on Nature Methods: 🔬Preprint in Bioarixv: 💻 All our codes/data/weights are open source: Wholehearted congratulations to all the authors, especially the two co-first authors, Haotian (Haotian Cui ) and Chloe (ChloeXWang), who are really the emerging superstars in AI and biology! Vector Institute Peter Munk Cardiac Centre AI U of T Department of Computer Science Department of Laboratory Medicine & Pathobiology University Health Network University of Toronto #scGPT #GenerativeAI #AI4Science #Combio #opensource

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22,440 次观看 • 4 个月前