| --- |
| license: mit |
| --- |
| |
| ## 🗞️ Model description |
| **InstructCell** is a multi-modal AI copilot that integrates natural language with single-cell RNA sequencing data, enabling researchers to perform tasks like cell type annotation, pseudo-cell generation, and drug sensitivity prediction through intuitive text commands. |
| By leveraging a specialized multi-modal architecture and our multi-modal single-cell instruction dataset, InstructCell reduces technical barriers and enhances accessibility for single-cell analysis. |
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| **Instruct Version**: Supports generating only the answer portion without additional explanatory text, providing concise and task-specific outputs. |
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|
| ### 🚀 How to use |
|
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| We provide a simple example for quick reference. This demonstrates a basic **cell type annotation** workflow. |
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| Make sure to specify the paths for `H5AD_PATH` and `GENE_VOCAB_PATH` appropriately: |
| - `H5AD_PATH`: Path to your `.h5ad` single-cell data file (e.g., `H5AD_PATH = "path/to/your/data.h5ad"`). |
| - `GENE_VOCAB_PATH`: Path to your gene vocabulary file (e.g., `GENE_VOCAB_PATH = "path/to/your/gene_vocab.npy"`). |
|
|
| ```python |
| from mmllm.module import InstructCell |
| import anndata |
| import numpy as np |
| from utils import unify_gene_features |
| |
| # Load the pre-trained InstructCell model from HuggingFace |
| model = InstructCell.from_pretrained("zjunlp/InstructCell-instruct") |
| |
| # Load the single-cell data (H5AD format) and gene vocabulary file (numpy format) |
| adata = anndata.read_h5ad(H5AD_PATH) |
| gene_vocab = np.load(GENE_VOCAB_PATH) |
| adata = unify_gene_features(adata, gene_vocab, force_gene_symbol_uppercase=False) |
| |
| # Select a random single-cell sample and extract its gene counts and metadata |
| k = np.random.randint(0, len(adata)) |
| gene_counts = adata[k, :].X.toarray() |
| sc_metadata = adata[k, :].obs.iloc[0].to_dict() |
| |
| # Define the model prompt with placeholders for metadata and gene expression profile |
| prompt = ( |
| "Can you help me annotate this single cell from a {species}? " |
| "It was sequenced using {sequencing_method} and is derived from {tissue}. " |
| "The gene expression profile is {input}. Thanks!" |
| ) |
| |
| # Use the model to generate predictions |
| for key, value in model.predict( |
| prompt, |
| gene_counts=gene_counts, |
| sc_metadata=sc_metadata, |
| do_sample=True, |
| top_p=0.95, |
| top_k=50, |
| max_new_tokens=256, |
| ).items(): |
| # Print each key-value pair |
| print(f"{key}: {value}") |
| ``` |
|
|
| For more detailed explanations and additional examples, please refer to the Jupyter notebook [demo.ipynb](https://github.com/zjunlp/InstructCell/blob/main/demo.ipynb). |
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|
|
| ### 🔖 Citation |
|
|
| If you use the code or data, please cite the following paper: |
|
|
| ```bibtex |
| @article{fang2025instructcell, |
| title={A Multi-Modal AI Copilot for Single-Cell Analysis with Instruction Following}, |
| author={Fang, Yin and Deng, Xinle and Liu, Kangwei and Zhang, Ningyu and Qian, Jingyang and Yang, Penghui and Fan, Xiaohui and Chen, Huajun}, |
| journal={arXiv preprint arXiv:2501.08187}, |
| year={2025} |
| } |
| ``` |
