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Meta-analysis of microbiome composition measured by 16S rRNA seq in IBS case-control studies

This is the repository for the study MetaIBS - large-scale amplicon-based meta analysis of irritable bowel syndrome (link to paper). This repository contains all the data and code used for the meta-analysis, and can also be used as a template to extend this meta-analysis with your own data!

Table of contents:


Meta-analysis overview

For the sake of this study, we included 13 datasets, as summarized in this flowchart:


Structure of the repository

  • data/: data directory containing preprocessed data (phyloseq objects, count tables aggregated at different taxonomic levels, differential abundance tables, ...). You will find there the data generated by Salomé Carcy, Johannes Ostner and Viet Tran, that you can use directly to extend this meta-analysis with your own datasets;
  • data_empty/: empty data directory for users who would like to reproduce our results. It copies the directory structure of the data/ directory, containing only README.md files;
  • scripts/: code directory containing R scripts for (1) preprocessing raw fastq files into phyloseq objects; and (2) performing analyses on combined data from different datasets

ADD REPO TREE


Requirements

A few things are necessary for you to do before being able to use this repository:

  • download the data/ directory from our Zenodo link (ADD ZENODO LINK), and put it here in the root directory;
  • install git LFS to be able to clone entirely this repository;
  • install R (version 4.1.3. was used for this project);
  • install the required packages:
    • for R packages: create your R project & run renv::restore() in your console, and it will download all the packages necessary at the version used for MetaIBS;
    • for python packages: create a new python environment and install the packages in requirements.txt via pip: pip install -r requirements.txt

How to use this repository

It depends what analysis you want to do 🤔

Reproduce figures from the paper

In that case, you can:

  1. Start by downloading & preprocessing the raw data for each dataset: everything you need to know will be in the scripts/analysis-individual/ directory;
  2. Repeat the analyses performed on several datasets: everything is in the scripts/analysis-combined directory.

To note, you can go to step 2 without having to do step 1, using the data we generated that is saved in the data/ directory. Make sure to change file paths in the scripts (you can modify one file path at the top of each script) and export all of your outputs to the data_empty/ directory!

Extend the meta-analysis by adding your own datasets

In that case, you can:

  1. Take a quick look at how raw fastq files were preprocessed for the datasets already included in this meta-analysis. You will want to look at the 01_Dada2-NameDataset.Rmd files (or their HTML outputs) that are located in each subdirectory of scripts/analysis-individual/. Even though there is one preprocessing script for each dataset, they are all mostly identical (little changes for the primer sequences, etc.), so you don't need to look at all the 13 scripts (for the 13 datasets);
  2. Create a new directory within the scripts/analysis-individual/ directory, to preprocess your own data. Save your phyloseq object into the data/phyloseq-objects/ directory;
  3. Go into the scripts/analysis-combined/ directory, and you can re-run whichever analysis. Your phyloseq object should get automatically added, as we import automatically all the phyloseq objects present in the data/phyloseq-objects/ directory. Or you can of course add your own scripts to run additional analyses! 🙃