This Python/C++ code is the accompanying software for the paper PhyloWGS: Reconstructing subclonal composition and evolution from whole-genome sequencing of tumors, with authors Amit G. Deshwar, Shankar Vembu, Christina K. Yung, Gun Ho Jang, Lincoln Stein, and Quaid Morris.

The input to evolve.py is two tab-delimited text files -- one for SSM data and one for CNV data. Please see the files ssm_data.txt and cnv_data.txt included with PhyloWGS for examples.

To see how to generate ssm_data.txt and cnv_data.txt from a VCF file and Battenberg CNV file, please see the included parser.

ssm_data.txt:

• id: identifier for each SSM. Identifiers must start at s0 and increment, so the first data row will have s0, the second row s1, and so forth.
• gene: any string identifying the variant -- this need not be a gene name. <chr>_<pos> (e.g., 2_234577) works well.
• a: number of reference-allele reads at the variant locus.
• d: total number of reads at the variant locus.
• mu_r: fraction of expected reference allele sampling from the reference population. E.g., if the tumor has an A->T somatic mutation at the locus, the genotype of the reference population should be AA. Thus, mu_r should be 1 - (sequencing error rate). Given the 0.001 error rate in Illumina sequencing, setting this column to 0.999 works well.
• mu_v: fraction of expected reference allele sampling from variant population. Suppose an A->T somatic mutation occurred at the locus. mu_v always uses normal ploidy (i.e., the copy number in non-CNV regions). As humans are diploid, copy number will thus always be 2. So, the variant population genotype should be AT, meaning we will observe the reference allele with frequency 0.5 - (sequencing error rate). Given the 0.001 error rate in Illumina sequencing, setting this column to 0.499 works well.

cnv_data.txt: Note that if you are running without any CNVs, this file should be empty. You can create the empty file via the command touch cnv_data.txt.

• cnv: identifier for each CNV. Identifiers must start at c0 and increment, so the first data row will have c0, the second row c1, and so forth.
• a: number of reference reads covering the CNV.
• d: total number of reads covering the CNV. This will be affected by factors such as total copy number at the locus, sequencing depth, and the size of the chromosomal region spanned by the CNV.
• ssms: SSMs that overlap with this CNV. Each entry is a comma-separated triplet consisting of SSM ID, maternal copy number, and paternal copy number. These triplets are separated by semicolons.

1. Install dependencies.

• Install Python 2 versions of NumPy (www.numpy.org) and SciPy (www.scipy.org).
• Install Python 2 version of ETE2 (e.g.: pip2 install --user ete2).
• Install GSL (http://www.gnu.org/software/gsl/).

2. Compile the C++ file.

    g++ -o mh.o -O3 mh.cpp  util.cpp `gsl-config --cflags --libs`
   

3. Run PhyloWGS. Minimum invocation on sample data set:

    python2 evolve.py ssm_data.txt cnv_data.txt
   

All options:

    usage: evolve.py [-h] [-b WRITE_BACKUPS_EVERY] [-k TOP_K_TREES]
                     [-f CLONAL_FREQS] [-s MCMC_SAMPLES] [-i MH_ITERATIONS]
                     [-r RANDOM_SEED]
                     ssm_file cnv_file

    Run PhyloWGS to infer subclonal composition from SSMs and CNVs

    positional arguments:
      ssm_file              File listing SSMs (simple somatic mutations, i.e.,
                            single nucleotide variants. For proper format, see
                            README.md.
      cnv_file              File listing CNVs (copy number variations). For proper
                            format, see README.md.

    optional arguments:
      -h, --help            show this help message and exit
      -b WRITE_BACKUPS_EVERY, --write-backups-every WRITE_BACKUPS_EVERY
                            Number of iterations to go between writing backups of
                            program state (default: 100)
      -k TOP_K_TREES, --top-k-trees TOP_K_TREES
                            Output file to save top-k trees in text format
                            (default: top_k_trees)
      -f CLONAL_FREQS, --clonal-freqs CLONAL_FREQS
                            Output file to save clonal frequencies (default:
                            clonalFrequencies)
      -s MCMC_SAMPLES, --mcmc-samples MCMC_SAMPLES
                            Number of MCMC samples (default: 2500)
      -i MH_ITERATIONS, --mh-iterations MH_ITERATIONS
                            Number of Metropolis-Hastings iterations (default:
                            5000)
      -r RANDOM_SEED, --random-seed RANDOM_SEED
                            Random seed for initializing MCMC sampler (default:
                            None)

4. Generate JSON results.

    mkdir test_results
    cd test_results
    # To work with viewer in Step 5, the naming conventions used here must be
    # followed.
    # "example_data" is simply the name by which you want your results to be identified.
    python2 /path/to/phylowgs/write_results.py example_data ../trees.zip example_data.summ.json.gz example_data.muts.json.gz example_data.mutass.zip
    cd ..
   

All options:

    usage: write_results.py [-h] [--include-ssm-names] [--min-ssms MIN_SSMS]
                            dataset_name tree_file tree_summary_output
                            mutlist_output mutass_output

    Write JSON files describing trees

    positional arguments:
      dataset_name         Name identifying dataset
      tree_file            File containing sampled trees
      tree_summary_output  Output file for JSON-formatted tree summaries
      mutlist_output       Output file for JSON-formatted list of mutations
      mutass_output        Output file for JSON-formatted list of SSMs and CNVs
                           assigned to each subclone

    optional arguments:
      -h, --help           show this help message and exit
      --include-ssm-names  Include SSM names in output (which may be sensitive
                           data) (default: False)
      --min-ssms MIN_SSMS  Minimum number or percent of SSMs to retain a subclone
                           (default: 0.01)

5. View results.

    mv test_results /path/to/phylowgs/witness/data
    cd /path/to/phylowgs/witness
    gunzip data/*/*.gz
    python2 index_data.py
    python2 -m SimpleHTTPServer
    # Open http://127.0.0.1:8000 in your web browser. Note that, by
    # default, the server listens for connections from any host.
   

If PhyloWGS is interrupted for whatever reason, you can resume your existing run by simply running evolve.py from the same directory as the previous run, without any command-line params:

# Start initial run.
python2 evolve.py ssm_data.txt cnv_data.txt

# Hit CTRL+C to send SIGINT, halting run partway through.

# Resume run:
python2 evolve.py

Copyright (C) 2015 Quaid Morris

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/.