clusterde-py 0.1.0

Minimal implementation of the ClusterDE Algorithm for marker gene analysis vs. a synthetic null

clusterde_py

A Python implementation of the ClusterDE algorithm for differential expression analysis with synthetic null comparison.

Installation

pip install clusterde_py

Usage

from clusterde_py import find_markers
import scanpy as sc
from anndata import AnnData

# Load your data
adata = ...  # AnnData object with cluster labels in adata.obs

# Find markers between two clusters
result = find_markers(
    adata,
    cluster_key="cell_type",
    group1="cluster1", 
    group2="cluster2",
    fdr=0.05
)

# Result contains target p-values, null p-values, contrast scores, and q-values
print(result.head())

Features

• Synthetic null generation: Uses scDesigner to create null datasets without cluster structure
• Contrast scoring: Compares real vs null p-values to identify true differential expression
• FDR control: Uses Clipper algorithm for false discovery rate control
• Flexible pipeline: Supports different contrast methods and thresholding approaches

Development

To install in development mode:

git clone https://github.com/krisrs1128/clusterde-devel.git
cd clusterde-devel
pip install -e .

License

MIT