zjunlp / InstructCell-instruct

🗞️ 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.

Instruct Version : Supports generating only the answer portion without additional explanatory text, providing concise and task-specific outputs.

🚀 How to use

We provide a simple example for quick reference. This demonstrates a basic cell type annotation workflow.

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" ).

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 .