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Cui et al. |

scGPT: Fine-tuning on Pre-trained Model for Cell-type Annotation

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Genomics & Multi-Omics
Step 1: Upload your data

Upload Single Cell RNA-Seq Reference Data File

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  • Your file can be in the following formats:h5ad
    • The h5ad format in scRNA-seq refers to the Hierarchical Data Format 5 (HDF5) Annotated Data format. It is commonly used to store single-cell gene expression data.
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Upload Single Cell RNA-Seq Test Data File

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  • Your file can be in the following formats:h5ad
    • The h5ad format in scRNA-seq refers to the Hierarchical Data Format 5 (HDF5) Annotated Data format. Test dataset must contain X_UMAP key in the obsm section.
or
Don’t have a file?
Use our demo data to run
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Step 2: Set Parameters
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cell_ranger
human_all
1
1
400
-1
-1
1000
-1
-1
1000
-1
-1
5000

scGPT (v0.2.1) is a foundation model for single-cell biology, based on generative pre-trained transformers trained on a vast repository of over 33 million cells. The scGPT model demonstrates the ability to extract valuable biological insights and can be further optimized through transfer learning for various downstream applications, including cell-type annotation, multi-batch integration, genetic perturbation prediction, and gene network inference.

Example use case: Fine-tune a pre-trained model on a new dataset for the cell type annotation task

Technology: Transformers

Limitation:

  • Some of the parameters were kept default. Please see this page for more details.
  • The whole-human model is tested only.
Citation:
scGPT: Towards Building a Foundation Model for Single-Cell Multi-omics Using Generative AI Haotian Cui, Chloe Wang, Hassaan Maan, Kuan Pang, Fengning Luo, Bo Wang bioRxiv 2023.04.30.538439; doi: https://doi.org/10.1101/2023.04.30.538439
Released:
Aug-08-2023
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