GithubHelp home page GithubHelp logo

lesterrcox / gmhi_2020 Goto Github PK

View Code? Open in Web Editor NEW

This project forked from jaeyunsung/gmhi_2020

0.0 1.0 0.0 3.68 MB

Scripts for generating results in Gupta VK et al. "A Predictive Index for Health Status using Species-level Gut Microbiome Profiling". Nature Communications, 2020. DOI: 10.1038/s41467-020-18476-8.

R 100.00%

gmhi_2020's Introduction

A Predictive Index for Health Status using Species-level Gut Microbiome Profiling

Nature Communications (2020) https://www.nature.com/articles/s41467-020-18476-8

Vinod K. Gupta, Minsuk Kim, Utpal Baksh, Kevin Y. Cunningham, John M. Davis III, Konstantinos N. Lazaridis, Heidi Nelson, Nicholas Chia, & Jaeyun Sung

Description (GMHI)

GMHI is a robust index for evaluating health status based on the species-level taxonomic profile of a stool shotgun metagenome (gut microbiome) sample. GMHI is designed to evaluate the balance between two sets of microbial species associated with good and adverse health conditions, which are identified from a meta-analysis on 4,347 publicly available, human stool metagenomes integrated across multiple studies encompassing various phenotypes and geographies. GMHI denotes the degree to which a subject’s stool metagenome sample portrays microbial taxonomic properties associated with healthy (GMHI > 0) or non-healthy (GMHI < 0). A positive or negative GMHI allows the sample to be classified as healthy or non-healthy, respectively; a GMHI of 0 indicates an equal balance of Health-prevalent and Health-scarce species, and thereby classified as neither. Higher (more positive) and lower (more negative) values of GMHI reflects the dominant influence of Health-prevalent species over Health-scarce species in the healthy group, and vice versa in the non-healthy group, respectively.

Basic Usage of GMHI

Step 1: Run MetaPhlAn2 on stool metagenome(s) using the '--tax_lev s' argument. It will produce a separate output file (of clade-specific relative abundances) for each metagenome.

Step 2: If there are multiple stool metagenome samples to be processed, first merge the MetaPhlAn2 outputs using 'merge_metaphlan_tables.py' provided in the MetaPhlAn2 pipeline (follow online MetaPhlAn2 tutorial), and then save the merged file in .csv format. This file should contain species' names in the first column, and corresponding relative abundances in subsequent columns for all metagenome samples (see 'species_relative_abundances.csv').

Step 3: Open the GMHI.R script and load the merged MetaPhlAn2 output file (generated in Step 2) by providing the appropriate path. Then run GMHI.R in its entirety.

Description (Figures)

R scripts for reproducing the figures illustrated in the corresponding paper. Tested on R (3.6.1).

Scripts

GMHI (Gut Microbiome Health Index)

R script to calculate the Gut Microbiome Health Index (GMHI) for a species-level gut microbiome profile.

Rscript GMHI.R

Figure 1

Includes Figure 1 (b,c,d) generation with PERMANOVA (iterations = 999).

Rscript Fig1.R

Supplementary Table 1, Table 2, Figure 2

Related with Supplementary Table 1, Table 2, and Figure 2 (a,b) generation.

Rscript Fig2_TableS1_Table2.R

Figure 3

Related with Figure 3 (a ~ h) generation.

Rscript Fig3.R

Figure 4

Related with Figure 4 (a ~ b) generation.

Rscript Fig4.R

Figure 5

Related with Figure 5 (a ~ d) generation.

Rscript Fig5.R

Figure 6

Related with Figure 6 (a ~ b).

Rscript Fig6.R

Data

Merged version of MetaPhlAn2 output files showing only the species' relative abundances from 25 stool metagenome samples.

species_relative_abundances.csv

GMHI values (generated from GMHI.R) for all 25 stool metagenomes in 'species_relative_abundances.csv'

GMHI_output.csv

Other Meta data

validation_metadata.csv
study_wise_data.txt
MH_species.txt
MN_species.txt

gmhi_2020's People

Contributors

jaeyunsung avatar

Watchers

 avatar

Recommend Projects

  • React photo React

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

  • Vue.js photo Vue.js

    🖖 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. 📊📈🎉

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 ❤️ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.