Git Product home page Git Product logo

optimize-mod-resolution's Introduction

Determining an optimal modularity resolution parameter for MolTI-DREAM

Discover the optimal modularity resolution parameter for your multilayer network when using MolTI-DREAM.

Requirements

This script depends on depends on:

  • Python (>=2.6, tested on 3.9.9)
  • Pandas (tested on 1.2.0)
  • Numpy (tested on 1.19.5)

Input

Add one or more networks with a comma separated file, .csv, in the Networks folder, as seen in the Sample Networks Folder

Run

- Run script: bash optimal_mod.sh 
- Optional arguments: 
    -u upper_limit    set maximum modularity value tested                 [default is 0]  
    -l lower_limit    set minimum modularity value tested                 [default is 50]
    -s steps          set steps to cover the defined range                [default is 5]
    -m min_nodes      set minimal num. of nodes to define a community     [default is 6]
    -r randomizations set number of Louvain randomizations [default 5]

Output

The result is presented at the terminal and at Output directory, determining an optimal modularity resolution parameter as well as the corresponding number of communities and average community size, after filtering for disease modules (communities > 6 nodes). Additional results can be found at the output resulting folder.

Sample Output

molti-output-analysis.txt

mod_param num_communities avg_community_size
5 13 16.0
10 13 14.0
15 12 15.0
20 14 12.0
25 13 12.0
30 13 12.0
35 12 12.0
40 11 11.0
45 11 11.0
50 11 10.0

optimal_mod_parameter.txt

Results:
    Optimal modularity parameter: 25
    - Number of communities: 13
    - Average community size: 12.0

References

Didier G, Valdeolivas A, Baudot A. Identifying communities from multiplex biological networks by randomized optimization of modularity. F1000Res. 2018 Jul 10;7:1042. doi: 10.12688/f1000research.15486.2. PMID: 30210790; PMCID: PMC6107982.

optimize-mod-resolution's People

Contributors

marbatlle avatar

Watchers

 avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.