Inference of Clonal Copy Number Alterations in Single Cells
XClone is an algorithm to infer allele- and haplotype-specific copy numbers in individual cells from low-coverage and sparse single-cell RNA sequencing data (e.g., those generated by 10x Genomics, Smart-seq, etc.).
The demo of XClone and results on the all processed cancer datasets are available at xclone-data.
Please frequently read the tutorials and release history and keep software up to date since XClone is being updated and improved frequently at this stage.
XClone requires Python 3.7 or Python >=3.9 (Recommend 3.9 for stable performance in latest version). Details of the environment requirements, see XClone FAQs.
We recommend to use Anaconda environment for version control and to avoid potential conflicts:
conda create -n xclone python=3.9 conda activate xclone
XClone package can be conveniently (1~2mins) installed via PyPI:
pip install xclone
or directly from GitHub repository (for development version):
pip install git+https://github.com/single-cell-genetics/XClone
xcltk is a toolkit for XClone preprocessing. xcltk is avaliable through pypi. To install, type the following command line, and add -U for upgrading:
pip install -U xcltk
Alternatively, you can install from this GitHub repository for latest (often development) version by following command line:
pip install -U git+https://github.com/hxj5/xcltk
For a complete guide, please see XClone Documentation.
Tutorials on demo dataset (Glioma sample, BCH869)
Tutorials on demo dataset (Triple-negative breast cancer sample, TNBC1)
Download the Jupyter Notebooks by clicking the following links:
Notebook on demo dataset (Glioma sample, BCH869)
Notebook on demo dataset (Triple-negative breast cancer sample, TNBC1)
Notebook on demo dataset (Anaplastic thyroid cancer sample, ATC2)
Notebook on demo dataset (Astrocytoma sample, GBM_10XsnRNA)
For details of the method, please checkout our paper Robust analysis of allele-specific copy number alterations from scRNA-seq data with XClone.
Licensed under the Apache License, Version 2.0 (see the LICENSE);
Copyright 2024 Rongting Huang, Yuanhua Huang, StatBiomed Lab