CellNiche 0.1.4

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

A successful example

conda create -n cellniche python=3.9
conda activate cellniche
pip install torch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2
pip install torch_cluster-1.6.3+pt20cu117-cp39-cp39-linux_x86_64.whl
pip install torch_scatter-2.1.2+pt20cu117-cp39-cp39-linux_x86_64.whl
pip install torch_sparse-0.6.18+pt20cu117-cp39-cp39-linux_x86_64.whl
pip install torch_spline_conv-1.2.2+pt20cu117-cp39-cp39-linux_x86_64.whl
pip install torch-geometric==2.6.1
pip install CellNiche

pip install pyyaml
...

Tutorials

Coming soon

Getting Started

bash(recommend)

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

python

import cellniche

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