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ChatCell

⚠️ Status: partial | Version: v1.0

Overview

Conversational chat interface for single-cell analysis, zero-shot annotation via dialogue

When to choose ChatCell

User wants interactive chat-based cell analysis, conversational annotation, or dialogue-driven exploration

Specifications

Property Value
Model ChatCell
Version v1.0
Tasks embed, annotate
Modalities RNA
Species human
Gene IDs symbol
Embedding Dim 512
GPU Required Yes
Min VRAM 16 GB
Recommended VRAM 32 GB
CPU Fallback No
Adapter Status ⚠️ partial

Quick Start

import omicverse as ov

# 1. Check model spec
info = ov.fm.describe_model("chatcell")

# 2. Profile your data
profile = ov.fm.profile_data("your_data.h5ad")

# 3. Validate compatibility
check = ov.fm.preprocess_validate("your_data.h5ad", "chatcell", "embed")

# 4. Run inference
result = ov.fm.run(
    task="embed",
    model_name="chatcell",
    adata_path="your_data.h5ad",
    output_path="output_chatcell.h5ad",
    device="auto",
)

# 5. Interpret results
metrics = ov.fm.interpret_results("output_chatcell.h5ad", task="embed")

Input Requirements

Requirement Detail
Gene ID scheme symbol
Preprocessing Standard preprocessing. Conversational prompts can guide annotation.
Data format AnnData (.h5ad)
Label key .obs column for cell type labels (optional)

Output Keys

After running ov.fm.run(), results are stored in the AnnData object:

Key Location Description
X_chatcell adata.obsm Cell embeddings (512-dim)
chatcell_pred adata.obs Predicted cell type labels 
import scanpy as sc

adata = sc.read_h5ad("output_chatcell.h5ad")
embeddings = adata.obsm["X_chatcell"]  # shape: (n_cells, 512)

# Downstream analysis
sc.pp.neighbors(adata, use_rep="X_chatcell")
sc.tl.umap(adata)
sc.tl.leiden(adata, resolution=0.5)
sc.pl.umap(adata, color=["leiden"])

Resources

Hands-On Tutorial

For a step-by-step walkthrough with code, see the ChatCell Tutorial Notebook.