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Nicheformer

⚠️ Status: partial | Version: v1.0

Overview

Niche-aware spatial transformer, jointly models spatial coordinates and gene expression

When to choose Nicheformer

User has spatial transcriptomics data (Visium, MERFISH, Slide-seq) and wants niche-aware or spatial-context embeddings

Specifications

Property Value
Model Nicheformer
Version v1.0
Tasks embed, integrate, spatial
Modalities RNA, Spatial
Species human, mouse
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("nicheformer")

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

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

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

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

Input Requirements

Requirement Detail
Gene ID scheme symbol
Preprocessing Standard preprocessing. For spatial tasks, include spatial coordinates in adata.obsm['spatial'].
Data format AnnData (.h5ad)
Batch key .obs column for batch integration (optional)

Output Keys

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

Key Location Description
X_nicheformer adata.obsm Cell embeddings (512-dim) 
import scanpy as sc

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

# Downstream analysis
sc.pp.neighbors(adata, use_rep="X_nicheformer")
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 Nicheformer Tutorial Notebook.