Lazac is a tool for inferring single-cell resolution copy number phylogenies from copy number data. It is based on our zero-agnostic copy number transformation (ZCNT) model, which we recently introduced as a simplification of the copy number transformation (CNT) model. Our algorithm uses tree search and an efficient solver of the small parsimony problem to find trees.
If you find this tool useful in your research, please cite us at:
@article{schmidt2023zero,
title={A zero-agnostic model for copy number evolution in cancer},
author={Schmidt, Henri and Sashittal, Palash and Raphael, Benjamin J},
journal={PLoS computational biology},
volume={19},
number={11},
pages={e1011590},
year={2023},
publisher={Public Library of Science San Francisco, CA USA}
}
lazac can be installed automatically using Conda.
Simply execute,
$ conda install -c schmidt73 lazac
and Lazac will be available as lazac.
Lazac is implemented in C++ and is packaged with the dependencies
needed to execute the program. In particular, the only dependencies are
a recent version of CMAKE and a modern C++17 compliant compiler.
To build Lazac from source, first clone the repository and its submodules:
$ git clone --recurse-submodules https://github.com/raphael-group/lazac-copy-number.git
Then from the root of the project directory, execute the following sequence o commands:
$ mkdir build; cd build
$ cmake ..
$ make
The output binary will be located at build/src/lazac.
To run lazac, simply execute the binary.
Usage: lazac [--help] [--version] {distance,nni}
Optional arguments:
-h, --help shows help message and exits
-v, --version prints version information and exits
Subcommands:
distance Computes a distance matrix on copy number profiles
nni Infers a copy number tree using NNI operations
The tool has two modes: distance matrix construction using the ZCNT
distance and a heuristic tree search algorithm for the ZCNT large
parsimony problem. The distance mode takes copy number profiles as
input. The nni mode takes both copy number profiles and a Newick
tree as input. Note: Lazac infers an unrooted tree by default.
To obtain a rooted tree, add a diploid normal sample to the set
input profiles and then root the unrooted tree at this sample.
The input format for lazac consists of copy number profiles and
Newick trees on the profiles. The format for a copy number profile is
a CSV consisting of at least five columns named node, chrom,
start, end, and cn_X. In particular, node is the name of the
cell, the triple (chrom, start, end) describes the copy number
bin, and cn_a is the number of copies of the locus on the a
allele. Since you can have varying numbers of alleles, we allow the
number of columns to vary. The Newick file must specify a binary
tree and the leaves of the tree must identify the cells in the node
column of the copy number profiles.
An example input format CSV is given
examples/PTX008_cn_profiles.csv.
We will construct a topology on bulk tumor sequencing data from patient 8 studied in Gundem et al. 2015. First, we will compute the distance matrix using the following command:
$ lazac distance examples/PTX008_cn_profile.csv -o examples/PTX008
Then, we will perform neighbor joining on the distance matrix to obtain a starting tree for our large parsimony tree search algorithm and then arbitrarily resolve polytomies to make the tree binary
$ python scripts/nj.py examples/PTX008_dist_matrix.csv --output examples/PTX008_rcnt_nj_tree.newick
$ python scripts/resolve_polytomies.py examples/PTX008_rcnt_nj_tree.newick --output examples/PTX008_rcnt_nj_tree.binary.newick
Then, we will run lazac nni to improve upon the candidate
topology.
$ lazac nni examples/PTX008_cn_profile.csv examples/PTX008_rcnt_nj_tree.binary.newick -a 2 -o examples/PTX008_rcnt
Finally, we can root our tree at a desired node. For best results, add a fake, diploid normal sample to the input set of profiles and root the tree at this sample.
$ python scripts/root.py examples/PTX008_rcnt_tree.newick [ROOT] --output [OUTPUT.NEWICK]