Single-cell copy number calling and event history reconstruction.

A statistical model and MCMC algorithm tailored to single-cell copy number profiling from shallow whole-genome DNA sequencing data. SCICoNE reconstructs the history of copy number events in the tumour and uses these evolutionary relationships to identify the copy number profiles of the individual cells.

Quick start

SCICoNE takes a read counts matrix of cells by genomic bins and outputs the copy number profile of each cell and the underlying event history.

• Command line interface tutorial
• Python package tutorial
• Example run on 10X Genomics data using the Python package

Requirements

• C++ compiler that supports C++14 standards (e.g. gcc>=5.2.0, clang>=5.0.0))
• CMake >= 3.9
• Boost >= 1.6.x
• OpenMP >= 4.5
• NLopt >= 2.6.2

Installation

Once the requirements are in place, downloading and installing SCICoNE takes about 5 minutes.

git clone https://github.com/cbg-ethz/SCICoNE.git # Clone the repository
cd SCICoNE
mkdir build && cd build                       # Create and enter the build directory
cmake ../scicone/                             # Compile the program with cmake
make                                          # Build the executables
cd ..                                         # Return to root directory

Python package

We also provide a Python 3 package to facilitate plotting and easily integrate SCICoNE with other data analysis tools. This interface runs the C++ binaries and reads the outputs into numpy arrays. Even if you don't want to use the complete package, we recommend you install it to facilitate usage of the C++ command line interface.

pip install pyscicone/