CellNiche 0.1.3

CellNiche represents cellular microenvironments in atlas-scale spatial omics data with contrastive learning

CellNiche

Overview

CellNiche is a scalable, cell-centric framework for identifying and characterizing cellular micro-environments from atlas-scale, heterogeneous spatial omics data.
Instead of processing entire tissue slices, CellNiche samples local subgraphs around each cell and learns context-aware embeddings via contrastive learning, while explicitly decoupling molecular identity (gene expression or cell-type labels) from spatial proximity modeling.

Key Features

Installation

From Source

git clone https://github.com/Super-LzzZ/CellNiche.git
cd cellniche

From PyPI

pip install CellNiche

Requirements

• Python ≥ 3.7
• PyTorch ≥ 1.12
• PyTorch Geometric (torch-geometric, torch-scatter, torch-sparse, torch-cluster, torch-spline-conv)
• Scanpy ≥ 1.9
• Anndata ≥ 0.9
• scikit-learn ≥ 1.3
• numpy ≥ 1.22
• scipy ≥ 1.10
• pandas ≥ 2.0
• networkx ≥ 3.1
• tqdm ≥ 4.67.1

You can install most dependencies with:

pip install torch torchvision torchaudio
pip install torch-geometric torch-scatter torch-sparse torch-cluster torch-spline-conv
pip install scanpy anndata scikit-learn numpy scipy pandas networkx tqdm

Tutorials

Coming soon

Getting Started

bash

python ./cellniche/main.py --config ./configs/xxx.yaml

python

import cellniche

# Parse arguments from a YAML config
opts = cellniche.parse_args([
    "--config", "configs/xxx.yaml"
])
# Run training/inference
cellniche.main(opts)