lionheart 1.0.1

LIONHEART is a method for detecting cancer from whole genome sequenced plasma cell-free DNA. Check README for additional installation steps.

LIONHEART Cancer Detector

LIONHEART is a method for detecting cancer from whole genome sequenced plasma cell-free DNA.

This software lets you run feature extraction and predict the cancer status of your samples. Further, you can train a model on your own data.

Developed for hg38. See the remap directory for the applied remapping pipeline.

Preprint: https://www.medrxiv.org/content/10.1101/2024.11.26.24317971v1

The code was developed and implemented by @ludvigolsen.

Installation

Install the main package:

# Create and activate conda environment
$ conda env create -f https://raw.githubusercontent.com/BesenbacherLab/lionheart/refs/heads/main/environment.yml
$ conda activate lionheart

# Install package from PyPI
$ pip install lionheart

# OR install from GitHub
$ pip install git+https://github.com/BesenbacherLab/lionheart.git

Custom mosdepth

We use a modified version of mosdepth available at https://github.com/LudvigOlsen/mosdepth/

To install this, it requires an installation of nim so we can use nimble install. Note that we use nim 1.6.14.

# Download nim installer and run
$ curl https://nim-lang.org/choosenim/init.sh -sSf | sh

# Add to PATH
# Change the path to fit with your system
# Tip: Consider adding it to the terminal configuration file (e.g. ~/.bashrc)
$ export PATH=/home/<username>/.nimble/bin:$PATH

# Install and use nim 1.6.4 
# NOTE: This step should be done even when nim is already installed
$ choosenim 1.6.14

Now that nim is installed, we can install the custom mosdepth with:

# Install modified mosdepth
$ nimble install -y https://github.com/LudvigOlsen/mosdepth

Get Resources

Download and unzip the required resources.

$ wget https://zenodo.org/records/14215762/files/inference_resources_v002.tar.gz
$ tar -xvzf inference_resources_v002.tar.gz 

Examples

Run via command-line interface

This example shows how to run lionheart from the command-line.

Note: If you don't have a BAM file at hand, you can download an example BAM file from: https://zenodo.org/records/13909979 It is a downsampled version of a public BAM file from Snyder et al. (2016; 10.1016/j.cell.2015.11.050) that has been remapped to hg38. On our system, the feature extraction for this sample takes ~1h15m using 12 cores (n_jobs).

# Start by skimming the help page
$ lionheart -h

# Extract feature from a given BAM file
# `mosdepth_path` is the path to the customized `mosdepth` installation
# E.g. "/home/<username>/mosdepth/mosdepth"
# `ld_library_path` is the path to the `lib` folder in the conda environment
# E.g. "/home/<username>/anaconda3/envs/lionheart/lib/"
$ lionheart extract_features --bam_file {bam_file} --resources_dir {resources_dir} --out_dir {out_dir} --mosdepth_path {mosdepth_path} --ld_library_path {ld_library_path} --n_jobs {cores}

# `sample_dir` is the `out_dir` of `extract_features`
$ lionheart predict_sample --sample_dir {sample_dir} --resources_dir {resources_dir} --out_dir {out_dir} --thresholds max_j spec_0.95 spec_0.99 sens_0.95 sens_0.99 0.5 --identifier {sample_id}

After running these commands for a set of samples, you can use lionheart collect to collect features and predictions across the samples. You can then use lionheart train_model to train a model on your own data (and optionally the included features).

Via gwf workflow

We provide a simple workflow for submitting jobs to slurm via the gwf package. Make a copy of the workflow directory, open workflow.py, change the paths and list the samples to run lionheart on.

The first time running a workflow it's required to first set the gwf backend to slurm or one of the other :

# Start by downloading the repository
$ wget -O lionheart-main.zip https://github.com/BesenbacherLab/lionheart/archive/refs/heads/main.zip
$ unzip lionheart-main.zip

# Copy workflow directory to a location
$ cp -r lionheart-main/workflow <location>/workflow

# Navigate to your copy of the the workflow directory
$ cd <location>/workflow

# Activate conda environment
$ conda activate lionheart

# Set `gwf` backend to slurm (or another preferred backend)
$ gwf config set backend slurm

Open the workflow.py file and change the various paths. When you're ready to submit the jobs, run:

$ gwf run

gwf allows seeing a status of the submitted jobs:

$ gwf status
$ gwf status -f summary