medicc2 0.5b4

Whole-genome doubling-aware copy number phylogenies for cancer evolution

MEDICC2 - Whole-genome doubling-aware copy number phylogenies for cancer evolution

For more information see the accompanying paper Whole-genome doubling-aware copy number phylogenies for cancer evolution with MEDICC2.

Installation

Install MEDICC2 via conda (recommended), pip or from source. MEDICC2 was developed and tested on unix-built systems (Linux and MacOS). For Windows users we recommended WSL2.

Note that the notebooks and examples are not included when installing from conda or pip. When installing from pip or source, you need to make sure to have a working version of gcc and gxx installed.

Installation via conda (recommended)

MEDICC2 can be installed via conda install -c bioconda -c conda-forge medicc2.

Installation via pip

As MEDICC2 relies on OpenFST version 1.8.1 which is not packaged on PyPi you have to first install it using conda with conda install -c conda-forge openfst. Next you can install MEDICC2 via pip install medicc2.

Installation from source

Clone the MEDICC2 repository and its submodules using git clone --recursive https://bitbucket.org/schwarzlab/medicc2.git. It is important to use the --recursive flag to also download the modified OpenFST submodule.

All dependencies including OpenFST (v1.8.1) should be directly installable via conda. A yaml file with a suggested MEDICC2 conda environment is provided in 'doc/medicc2.yml'. You can create a new conda environment with all requirements using conda env create -f doc/medicc2.yml -n medicc_env.

Then, inside the medicc2 folder, run pip install . to install MEDICC2 to your environment.

Usage

After installing MEDICC2, you can use MEDICC2 functions in python scripts (through import medicc) and from the command line. General usage from the command line is medicc2 path/to/input/file path/to/output/folder. Run medicc2 --help for information on optional arguments.

Logging settings can be changed using the medicc/logging_conf.yaml file with the standard python logging syntax.

Command line Flags

• input_file: path to the input file
• output_dir: path to the output folder
• --input-type, -i: Choose the type of input: f for FASTA, t for TSV. Default: 'TSV'
• --input-allele-columns, -a: Name of the CN columns (comma separated) if using TSV input format. This also adjusts the number of alleles considered (min. 1, max. 2). Default: 'cn_a, cn_b'
• --input-chr-separator: Character used to separate chromosomes in the input data (condensed FASTA only). Default: 'X'
• --tree: Do not reconstruct tree, use provided tree instead (in newick format) and only perform ancestral reconstruction. Default: None
• --topology-only, -s: Output only tree topology, without reconstructing ancestors. Default: False
• --normal-name, -n: ID of the sample to be treated as the normal sample. Trees are rooted at this sample for ancestral reconstruction. If the sample ID is not found, an artificial normal sample of the same name is created with CN states = 1 for each allele. Default: 'diploid'
• --exclude-samples, -x: Comma separated list of sample IDs to exclude. Default: None
• --filter-segment-length: Removes segments that are smaller than specified length. Default: None
• --bootstrap-method: Bootstrap method. Has to be either 'chr-wise' or 'segment-wise'. Default: 'chr-wise'
• --bootstrap-nr: Number of bootstrap runs to perform. Default: None
• --prefix, '-p': Output prefix to be used. None uses input filename. Default: None
• --no-wgd: Disable whole-genome doubling events. Default: False
• --no-plot: Disable plotting. Default: False
• --legacy-version: Use legacy version in which alleles are treated separately. Default: False
• --total-copy-numbers: Run for total copy number data instead of allele-specific data. Default: False
• -j, --n-cores: Number of cores to run on. Default: None
• -v, --verbose: Enable verbose output. Default: False
• --maxcn: Expert option: maximum CN at which the input is capped. Does not change FST. Default: 8
• --prune-weight: Expert option: Prune weight in ancestor reconstruction. Values >0 might result in more accurate ancestors but will require more time and memory. Default: 0
• --fst: Expert option: path to an alternative FST. Default: None
• --fst-chr-separator: Expert option: character used to separate chromosomes in the FST. Default: 'X'

Input files

Input files can be either in fasta or tsv format:

• fasta: A description file should be provided to MEDICC. This file should include one line per file with the name of the chromosome and the corresponding file names. If fasta files are provided you have to use the flag --input-type fasta.
• tsv: Files should have the following columns: sample_id, chrom, start, end as well as columns for the copy numbers. MEDICC expects the copy number columns to be called cn_a and cn_b. Using the flag --input-allele-columns you can set your own copy number columns. If you want to use total copy numbers, make sure to use the flag --total-copy-numbers.

MEDICC2 follows the BED convention for segment coordinates, i.e. segment start is at 0 and the segment end is non-inclusive.

The folder examples/simple_example contains a simple example input both in fasta and tsv format. The folder examples/OV03-04 contains a larger example consisting of multiple fasta files. If you want to run MEDICC on this data run medicc2 examples/OV03-04/OV03-04_descr.txt path/to/output/folder --input-type fasta.

Usage examples

For first time users we recommend to have a look at examples/simple_example to get an idea of how input data should look like. Then run medicc2 examples/simple_example/simple_example.tsv path/to/output/folder as an example of a standard MEDICC run. Finally, the notebook notebooks/example_workflows.py shows how the individual functions in the workflow are used.

The notebook notebooks/bootstrap_demo.py demonstrates how to use the bootstrapping routine and notebooks/plot_demo.py shows how to use the main plotting functions.

Contact

Email questions, feature requests and bug reports to Tom Kaufmann, [e-mail].

License

MEDICC2 is available under GPLv3. It contains modified code of the pywrapfst Python module from OpenFST as permitted by the Apache 2 license.

Please cite

Kaufmann TL, Petkovic M, Watkins TBK, Colliver EC, Laskina S, Thapa N, Minussi DC, Navin N, Swanton C, Van Loo P, Haase K, Tarabichi M, Schwarz RF. MEDICC2: whole-genome doubling aware copy-number phylogenies for cancer evolution
bioRxiv 2021 Sep 6; doi: 10.1101/2021.02.28.433227

Schwarz RF, Trinh A, Sipos B, Brenton JD, Goldman N, Markowetz F.
Phylogenetic quantification of intra-tumour heterogeneity.
PLoS Comput Biol. 2014 Apr 17;10(4):e1003535. doi: 10.1371/journal.pcbi.1003535.