PASTRI

---

Version 0.1

PASTRI is an algorithm that infers tumor phylogenies from one or more bulk DNA sequencing samples.

If you use this software in your research, please cite

Satas, G., & Raphael, B. J. (2017). Tumor phylogeny inference using tree-constrained importance sampling. Bioinformatics, 33(14), i152-i160.

Table of Contents

1. License
2. Dependencies
3. Running PASTRI
   3.1 Usage
   3.2 File Format
   3.3 Input Files
   3.4 Output Files
4. Basic Examples
5. Development
6. Support

1 License

---

Copyright 2017 Brown University, Providence, RI.

                         All Rights Reserved

Permission to use, copy, modify, and distribute this software and its  
documentation for any purpose other than its incorporation into a  
commercial product is hereby granted without fee, provided that the  
above copyright notice appear in all copies and that both that  
copyright notice and this permission notice appear in supporting  
documentation, and that the name of Brown University not be used in  
advertising or publicity pertaining to distribution of the software  
without specific, written prior permission.  

BROWN UNIVERSITY DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS SOFTWARE,  
INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR ANY  
PARTICULAR PURPOSE.  IN NO EVENT SHALL BROWN UNIVERSITY BE LIABLE FOR  
ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES  
WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN    
ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF  
OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.  

2 Dependencies

---

We recommend the Anaconda distribution of Python 2 which contains all required dependencies. PASTRI was tested with Anaconda 4.4.0.

Dependencies:

1. Python 2.x
2. numpy
3. scipy

3 Running PASTRI

---

3.1 Usage

RunPASTRI.py calculates the posterior likelihood over trees.

python src/RunPASTRI.py [-h] [-n NUM_ITERS] [-o OUTPUT_PREFIX]
                    path/to/data_file path/to/proposal_file

positional arguments:
  data_file
  proposal_file

optional arguments:
  -h, --help            show help message and exit
  -n NUM_ITERS, --num_iters NUM_ITERS
  -o OUTPUT_PREFIX, --output_prefix OUTPUT_PREFIX

For a particular tree, get_F_and_C.py calculates best observed frequency matrix F, and cluster assignments C.

python get_F_and_C.py [-h] [-i TREE_POS] [-o OUTPUT_PREFIX]
                      data_file result_file sample_file

positional arguments:
    data_file
    result_file ( [PREFIX].trees from RunPASTRI.py )
    sample_file ( [PREFIX].fsamples from RunPASTRI.py )

optional arguments:
    -h, --help      show help message and exit
    -i, --tree_pos  (default = 1) the position of the tree of interest in the results file.
                    by default calculates for the highest likelihood tree. 

3.2 File Format

All files are organized as a series of lists or matrices, separated by a blank line The format features a header giving the name or a description of the component, followed by the shape of the matrix, and then the matrix in tab separated format.

> [Name]
(# of columns, # of rows)
x_1,1    x_1,2  ...   x_1,c
|        |            | 
x_r,1    x_r,2  ...   x_r,c

3.3 Input Files

1. Allele Counts File

See example/example.input for an example. Matrix A is the variant read count matrix. Each row is an SNV, and each column is a sample. Matrix D is the total (variant + reference) read count matrix. Each row is an SNV and each column is a sample.

2. Proposal Distribution File

See example/example.proposal for an example. Matrices Alpha and Beta correspond to parameters for a beta distribution, where each row corresponds to a cluster of SNVs and each column corresponds to a sample.

3.4 Output Files

1. Tree Posterior

See example/example.trees for example, after running basic example in section 3. Each matrix correponds to an unlabeled tree topology. The name is formatted as:

 > rank:id:Log-likelihood

The provided matrix is in perfect phylogeny format.

2. Samples

See example/Example.fsamples. Each matrix corresponds to a sampled frequency matrix. The header indicates the data likelihood of the sample.

Get F and C output.

3. Labeled Trees See example/Example.1.labeled_trees. An edge_list corresponding to a labeling of the highest likelihood (unlabeled tree). These correspond to permutations pi in the manuscript. Indexes correspond to rows of F, and cluster indexes of C.
   If more than one labeling is listed, all listed labelings have equal likelihood.

4. Frequency matrix

See example/Example.1.F. The maximum likelihood frequency matrix for the highest likelihood tree.

5. Cluster assignments

See example/Example.1.C. The maximum likelihood cluster assignments for the given F, and the highest likelihood tree. Each row lists first the cluster index (corresponding the the same row in F, and the same node in the labeled tree), then the list of mutations assigned to that cluster (indexed according to their row in the input file (0-based)).

4 Basic Example

---

An example input file is provided in example/ directory. This example uses tree with 5 samples, 20 mutations, and 8 clusters. Clone PASTRI repository to your local machine. In the repository run

python src/RunPASTRI.py example/Example.input example/Example.proposal -o example/Example

This will run PASTRI on a basic example with 20 mutations and 5 samples, with 8 clusters of mutations. PASTRI will execute 1000 iterations and then report the posterior distributions over trees in a example/Example.trees.

Following, to obtain the best frequency matrix and cluster assignments run:

python src/get_F_and_C.py example/Example.input example/Example.trees example/Example.fsamples -o example/Example

5 Development

---

Follow PASTRI development on our Trello board to see in progress and upcoming features.

6 Support

---

For support, please open an issue on the GithHub page or email gryte_satas (at) brown (dot) edu.