CopyKAT-Python

CopyKAT-Python is a Python implementation of the CopyKAT workflow for inferring large-scale copy number alterations from single-cell RNA-seq data. It is designed for Python-based single-cell analysis pipelines and aims to improve usability, scalability, and integration with modern AnnData/Scanpy workflows.

The original CopyKAT-R package is widely used for distinguishing aneuploid tumor cells from diploid normal cells using scRNA-seq data. However, users have reported practical limitations when applying CopyKAT-R to large modern datasets.

Open GitHub issues in the original CopyKAT repository highlight several recurring needs:

• Long runtime, including reports of >1 hour for ~8,000 cells.
• Difficulty running very large datasets, including hundreds of thousands to millions of cells.

CopyKAT-Python was developed to address these practical issues while preserving the main biological idea of CopyKAT: large-scale chromosomal expression patterns can be used to infer copy number profiles and separate malignant from non-malignant cells.

Compared with CopyKAT-R, CopyKAT-Python focuses on:

• Native Python workflow support.
• Easier integration with AnnData, Scanpy, and Python pipelines.
• Improved handling of large datasets.
• More transparent intermediate outputs.
• Clearer confidence reporting for uncertain cells.
• More flexible downstream use of CNV matrices and cell-level annotations.
• Better reproducibility through Python package management and scripted workflows.

CopyKAT-Python is not intended to be a line-by-line clone of CopyKAT-R. It is a Python reimplementation designed to reproduce the core CopyKAT strategy while improving scalability and usability.

CopyKAT-Python reports tumor/normal predictions together with confidence-related outputs. High-confidence results usually show clear chromosome-arm or whole-chromosome CNV patterns, consistent CNV profiles within clusters, and strong separation between inferred diploid and aneuploid cells.

Lower-confidence results may occur in samples with weak CNV signal, low sequencing depth, few normal reference cells, strong batch effects, or tumors with near-diploid genomes.

CopyKAT-Python results may not be identical to CopyKAT-R because of differences in:

• Gene annotation versions.
• Filtering and preprocessing.
• Numerical implementation.
• Smoothing and segmentation details.
• Clustering behavior and random seeds.
• Handling of uncertain or not.defined cells.

These differences are expected for an independent Python implementation.

Input expression matrix → gene genomic ordering → smoothing → CNV inference → clustering → tumor/normal prediction.

Show inferred CNV profiles across chromosomes with cells grouped by predicted tumor/normal status.

Show side-by-side CNV heatmaps or classification agreement on the same dataset.

git clone https://github.com/NavinLab/copykat-python.git
cd copykat-python
pip install -e .