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Multi-Omics Binary Integration via Lasso Ensembles (MOBILE)

https://github.com/cerdem12/mobile

Science Score: 67.0%

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Repository

Multi-Omics Binary Integration via Lasso Ensembles (MOBILE)

Basic Info
  • Host: GitHub
  • Owner: cerdem12
  • License: gpl-2.0
  • Language: Fortran
  • Default Branch: main
  • Size: 36.8 MB
Statistics
  • Stars: 3
  • Watchers: 1
  • Forks: 3
  • Open Issues: 0
  • Releases: 1
Created over 3 years ago · Last pushed about 2 years ago
Metadata Files
Readme License Citation

README.md

DOI

MOBILE

Multi-Omics Binary Integration via Lasso Ensembles (MOBILE)

The MOBILE pipeline finds context-specific association networks. It integrates multi-omics datasets in a data-driven, biologically-structured manner. The gene-level association networks are used to nominate differentially enriched pathways. More info and application examples can be found here in the MOBILE paper.

Dependencies

  • MATLAB
  • glmnet package
  • RStudio and R (for image quantification functions used in the paper)

Instructions

  1. Clone this repository from the command-line using

    git clone --recursive https://github.com/cerdem12/MOBILE.git

  2. Make sure all the folders are added to the MATLAB path.

  3. Run the scripts in order:

    MOBILE_runATACseqRNAseq.m + MOBILE_runRNAseqRPPA.m => MOBILE_summarize.m => MOBILE_select.m

Model testing and performance

The MOBILE simulation of RPPA-RNAseq inference takes around 2-3 second per run (10000 instances are run in total: ~8 hours) including the save function in a normal desktop/laptop.

The RNAseq-ATACseq inference simulations were run on Clemson University Palmetto HPC and took around 8 hours per 1000 iteration of the 10000 instances (sources used per batch job: number of nodes=1, number of CPUs=40, memory=360gb). Ten batch jobs were run in parallel and the results were concatanated offline afterwards.

Tested environments include:

  • Ubuntu 18.04, Intel Core i7 3930 CPU @ 3.20 GHz, 32 GB DDR3, Nvidia GTX 690 GPU
  • Windows 10 Education, Intel Core i5-3470 CPU @ 3.20 GHz, 8.00 GB RAM, Nvidia GTX 650 GPU, 64-bit operating system
  • Windows 10 Pro, Intel Core i7-8550U CPU @ 2.00 GHz, 16.00 GB RAM, Intel UHD 620 GPU, 64-bit operating system

Citation

Erdem C, Gross SM, Heiser LM, Birtwistle MR (2023). MOBILE pipeline enables identification of context-specific networks and regulatory mechanisms. Nature Communications. 14, 3991. doi: https://doi.org/10.1038/s41467-023-39729-2

Owner

  • Name: Cemal Erdem
  • Login: cerdem12
  • Kind: user

Cemal Erdem

Citation (CITATION.cff) 

cff-version: 1.1.0
message: "If you use this software, please cite it as below."
authors:
  - family-names: "Erdem"
    given-names: "Cemal"
    orcid: "https://orcid.org/0000-0003-3663-3646"
title: "MOBILE"
version: 1.0.0
date-released: 2023-03-23
url: "https://github.com/cerdem12/MOBILE"

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