Nicheformer

⚠️ Status: partial | Version: v1.0

Overview

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

!!! tip “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.