Sparse Signaling Pathway Sampling

Code related to the manuscript Inferring signaling pathways with probabilistic programming (Merrell & Gitter, 2020) Bioinformatics, 36:Supplement_2, i822–i830.

This repository contains the following:

• SSPS: A method that infers relationships between variables using time series data.
  • Modeling assumption: the time series data is generated by a Dynamic Bayesian Network (DBN).
  • Inference strategy: MCMC sampling over possible DBN structures.
  • Implementation: written in Julia, using the Gen probabilistic programming language
• Analysis code:
  • simulation studies;
  • convergence analyses;
  • evaluation on experimental data;
  • a Snakefile for managing all of the analyses.

Installation and basic setup

(If you plan to reproduce all of the analyses, then make sure you're on a host with access to plenty of CPUs. Ideally, you would have access to a cluster of some sort.)

1. Clone this repository

git clone git@github.com:gitter-lab/ssps.git

2. Install Julia 1.6 (and all Julia dependencies)
   • Download the correct Julia binary here: https://julialang.org/downloads/.
     E.g., for Linux x86_64:

   $ wget https://julialang-s3.julialang.org/bin/linux/x64/1.6/julia-1.6.7-linux-x86_64.tar.gz 
   $ tar -xvzf julia-1.6.7-linux-x86_64.tar.gz
   

   • Find additional installation instructions here: https://julialang.org/downloads/platform/.
   • Use Pkg -- Julia's package manager -- to install the project's julia dependencies:

   $ cd ssps/SSPS
   $ julia --project=. 
                  _
      _       _ _(_)_     |  Documentation: https://docs.julialang.org
     (_)     | (_) (_)    |
      _ _   _| |_  __ _   |  Type "?" for help, "]?" for Pkg help.
     | | | | | | |/ _` |  |
     | | |_| | | | (_| |  |  Version 1.6.7 (2022-07-19)
    _/ |\__'_|_|_|\__'_|  |  Official https://julialang.org/ release
   |__/                   |
   
   julia> using Pkg
   julia> Pkg.instantiate()
   julia> exit()
   

Reproducing the analyses

In order to reproduce the analyses, you will need some extra bits of software.

• We use Snakemake -- a python package -- to manage the analysis workflow.
• We use some other python packages to postprocess the results, produce plots, etc.
• Some of the baseline methods are implemented in R or MATLAB.

Hence, the analyses entail some extra setup:

1. Install python dependencies (using conda)

   • For the purposes of these instructions, we assume you have Anaconda3 or Miniconda3 installed, and have access to the conda environment manager.
     (We recommend using Miniconda; find full installation instructions here.)
   • We recommend setting up a dedicated virtual environment for this project. The following will create a new environment named ssps and install the required python packages:

   $ conda create -n ssps -c conda-forge pandas matplotlib numpy bioconda::snakemake-minimal
   $ conda activate ssps
   (ssps) $
   

   • If you plan to reproduce the analyses on a cluster, then install cookiecutter and the complete version of snakemake

   (ssps) $ conda install -c conda-forge cookiecutter bioconda::snakemake
   

   and find the appropriate Snakemake profile from this list: https://github.com/Snakemake-Profiles/doc install the Snakemake profile using cookiecutter:

   (ssps) $ cookiecutter https://github.com/Snakemake-Profiles/htcondor.git
   

   replacing the example with the desired profile.

2. Install R packages

   • Ckmeans.1d.dp
   • FunChisq
   • mclust

3. Check whether MATLAB is installed.

   • If you don't have MATLAB, then you won't be able to run the exact DBN inference method of Hill et al., 2012.
   • You'll need to comment out the hill method wherever it appears in analysis_config.yaml.

After completing this additional setup, we are ready to run the analyses.

1. Make any necessary modifications to the configuration file: analysis_config.yaml. This file controls the space of hyperparameters and datasets explored in the analyses.
2. Run the analyses using snakemake:
   • If you're running the analyses on your local host, simply move to the directory containing Snakefile and call snakemake.

   (ssps) $ cd ssps
   (ssps) $ snakemake
   

   • Since Julia is a dynamically compiled language, some time will be devoted to compilation when you run SSPS for the first time. You may see some warnings in stdout -- this is normal.
   • If you're running the analyses on a cluster, call snakemake with the same Snakemake profile you found here:

   (ssps) $ cd ssps
   (ssps) $ snakemake --profile YOUR_PROFILE_NAME
   

   (You will probably need to edit the job submission parameters in the profile's config.yaml file.)
3. Relax. It will take tens of thousands of cpu-hours to run all of the analyses.

Running SSPS on your data

Follow these steps to run SSPS on your dataset. You will need

• a CSV file (tab separated) containing your time series data
• a CSV file (comma separated) containing your prior edge confidences.
• Optional: a JSON file containing a list of variable names (i.e., node names).

1. Install the python dependencies if you haven't already. Find detailed instructions above.
2. cd to the run_ssps directory
3. Configure the parameters in ssps_config.yaml as appropriate
4. Run Snakemake: $ snakemake --cores 1. Increase 1 to increase the maximum number of CPU cores to be used.

A note about parallelism

SSPS allows two levels of parallelism: (1) at the Markov chain level and (2) at the iteration level.

• Chain-level parallelism is provided via Snakemake. For example, Snakemake can run 4 chains simultaneously if you specify --cores 4 at the command line: $ snakemake --cores 4. In essence, this just creates 4 instances of SSPS that run simultaneously.
• Iteration-level parallelism is provided by Julia's multi-threading features. The number of threads available to a SSPS instance is specified by an environment variable: JULIA_NUM_THREADS.
• The total number of CPUs used by your SSPS jobs is the product of Snakemake's --cores parameter and Julia's JULIA_NUM_THREADS environment variable. Concretely: if we run snakemake --cores 2 and have JULIA_NUM_THREADS=4, then up to 8 CPUs may be used at one time by the SSPS jobs.

Licenses

SSPS is available under the MIT License, Copyright © 2020 David Merrell.

The MATLAB code dynamic_network_inference.m has been modified from the original version, Copyright © 2012 Steven Hill and Sach Mukherjee.

The dream-challenge data is described in Hill et al., 2016 and is originally from Synapse.