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license: mit |
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## 🗞️ Model description |
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**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. |
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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: |
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- `H5AD_PATH`: Path to your `.h5ad` single-cell data file (e.g., `H5AD_PATH = "path/to/your/data.h5ad"`). |
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- `GENE_VOCAB_PATH`: Path to your gene vocabulary file (e.g., `GENE_VOCAB_PATH = "path/to/your/gene_vocab.npy"`). |
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```python |
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from mmllm.module import InstructCell |
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import anndata |
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import numpy as np |
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from utils import unify_gene_features |
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# Load the pre-trained InstructCell model from HuggingFace |
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model = InstructCell.from_pretrained("zjunlp/InstructCell-instruct") |
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# Load the single-cell data (H5AD format) and gene vocabulary file (numpy format) |
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adata = anndata.read_h5ad(H5AD_PATH) |
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gene_vocab = np.load(GENE_VOCAB_PATH) |
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adata = unify_gene_features(adata, gene_vocab, force_gene_symbol_uppercase=False) |
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# Select a random single-cell sample and extract its gene counts and metadata |
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k = np.random.randint(0, len(adata)) |
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gene_counts = adata[k, :].X.toarray() |
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sc_metadata = adata[k, :].obs.iloc[0].to_dict() |
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# Define the model prompt with placeholders for metadata and gene expression profile |
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prompt = ( |
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"Can you help me annotate this single cell from a {species}? " |
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"It was sequenced using {sequencing_method} and is derived from {tissue}. " |
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"The gene expression profile is {input}. Thanks!" |
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) |
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# Use the model to generate predictions |
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for key, value in model.predict( |
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prompt, |
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gene_counts=gene_counts, |
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sc_metadata=sc_metadata, |
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do_sample=True, |
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top_p=0.95, |
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top_k=50, |
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max_new_tokens=256, |
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).items(): |
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# Print each key-value pair |
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print(f"{key}: {value}") |
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``` |
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For more detailed explanations and additional examples, please refer to the Jupyter notebook [demo.ipynb](https://github.com/zjunlp/InstructCell/blob/main/demo.ipynb). |
