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Cui et al. |

scGPT: Predicting Perturbations by Fine-Tuning a scGPT Pre-trained Model

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Step 1: Upload your data

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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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Step 2: Set Parameters
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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: Prediction for perturbation response by fine-tuning scGPT pre-trained model
Technology: Transformers
Limitations: 
   This app requires specific formatting in your data:
  1. In the adata.obs data frame, you need the 'condition' and 'cell_type' columns. Specify their corresponding column names from your dataset.
  2. For conditions, use 'ctrl' for control cells, 'A+ctrl' or 'ctrl+A' for single perturbations, and 'A+B' for combination perturbations.
  3. In the adata.var data frame, include a 'gene_name' column with gene symbols.
   Ensure this formatting before running the app.
   Some of the metrics are kept as default. Please see the tutorial.
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:
May-23-2024
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