medicc2 0.5b3

Minimum Event Distance for Intra-tumour Copy-number Comparisons

MEDICC2 - Minimum Event Distance for Intra-tumour Copy number Comparisons

Version 0.5 beta (May 2021)

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

Setup

Due to the dependency on OpenFST, MEDICC2 cannot be installed on Windows machines.

Download

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 OpenFST submodule

Installing dependencies

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

Installation

Run python setup.py build_ext --inplace to compile the fstlib C extension.

Usage

General usage is python medicc2.py path/to/input/file path/to/output/folder. Run python medicc2.py --help for information on optional arguments.

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

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.

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 python medicc2.py 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 python medicc2.py 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.