This repository contains code to reproduce some of the plots and analysis in the paper "Leveraging DAGs to improve context-sensitive and abundance-aware tree estimation."
Running the scripts in this repo requires a python environment with gctree
and other dependencies installed. The dependencies are listed in
requirements.txt and are all installable with pip. However, the conda
environment file environment.yml is also provided.
git clone [email protected]:matsengrp/gctree2-validation-plots.git
cd gctree2-validation-plots
conda env create -f environment.yml
conda activate gctree2-validation-plots
To produce the plots, you must first edit gctree_benchmark_direct_run.sh to
point it toward the simulated data and inference outputs. This may be done by
editing the variables sim_prefix and inference_prefix at the top of the
script.
The values for sim_prefix and inference_prefix specify where to find the outputs
from running the simulation and inference code, which is provided with the
paper.
To produce the plots, run gctree_benchmark_direct_run.sh in the environment
set up as described above.
This script will send jobs to a cluster using sbatch. If you do not have
access to a cluster using sbatch, the use_cluster variable at the top of
the script will need to be set to 0.
The script will not finish until all sbatch jobs for your user are finished
running, so be sure all unrelated cluster jobs are finished before running this
script.
Figure 4 from the paper is the file hdag_comparison_faceted.pdf.
A variety of plots similar to Figure 5 will be placed in the directory
example_sim_scatters. For the paper, we selected one which showed the
following:
- improvement in RF distance in history sDAG trees compared to dnapars trees,
- many more history sDAG trees than dnapars trees, and
- qualitative correlation between RF distance improvement and the two likelihoods.