All Projects → cduvallet → microbiomeHD

cduvallet / microbiomeHD

Licence: other
Cross-disease comparison of case-control gut microbiome studies

Programming Languages

python
139335 projects - #7 most used programming language
Makefile
30231 projects
shell
77523 projects

Projects that are alternatives of or similar to microbiomeHD

DUN
Code for "Depth Uncertainty in Neural Networks" (https://arxiv.org/abs/2006.08437)
Stars: ✭ 65 (+12.07%)
Mutual labels:  reproducible-research, reproducible-paper
Topcuoglu ML mBio 2020
Best practices for applying machine learning to bacterial 16S rRNA gene sequencing data
Stars: ✭ 21 (-63.79%)
Mutual labels:  reproducible-research, reproducible-paper
Reproducibilty-Challenge-ECANET
Unofficial Implementation of ECANets (CVPR 2020) for the Reproducibility Challenge 2020.
Stars: ✭ 27 (-53.45%)
Mutual labels:  reproducible-research, reproducible-paper
reproducibility-guide
⛔ ARCHIVED ⛔
Stars: ✭ 119 (+105.17%)
Mutual labels:  reproducible-research
software-dev
Coding Standards for the USC Biostats group
Stars: ✭ 33 (-43.1%)
Mutual labels:  reproducible-research
openscience
Empirical Software Engineering journal (EMSE) open science and reproducible research initiative
Stars: ✭ 28 (-51.72%)
Mutual labels:  reproducible-research
wrench
WRENCH: Cyberinfrastructure Simulation Workbench
Stars: ✭ 25 (-56.9%)
Mutual labels:  reproducible-research
galaksio
An easy-to-use way for running Galaxy workflows.
Stars: ✭ 19 (-67.24%)
Mutual labels:  reproducible-research
microViz
R package for microbiome data visualization and statistics. Uses phyloseq, vegan and the tidyverse. Docker image available.
Stars: ✭ 61 (+5.17%)
Mutual labels:  microbiome
bulker
Manager for multi-container computing environments
Stars: ✭ 16 (-72.41%)
Mutual labels:  reproducible-research
DRAM
Distilled and Refined Annotation of Metabolism: A tool for the annotation and curation of function for microbial and viral genomes
Stars: ✭ 159 (+174.14%)
Mutual labels:  microbiome
jupyter-guide
Guide for Reproducible Research and Data Science in Jupyter Notebooks
Stars: ✭ 111 (+91.38%)
Mutual labels:  reproducible-research
genepattern-notebook
Platform for integrating genomic analysis with Jupyter Notebooks.
Stars: ✭ 37 (-36.21%)
Mutual labels:  reproducible-research
ck-analytics
Collective Knowledge repository with actions to unify the access to different predictive analytics engines (scipy, R, DNN) from software, command line and web-services via CK JSON API:
Stars: ✭ 35 (-39.66%)
Mutual labels:  reproducible-research
OpenPlantPathology
Open Plant Pathology website
Stars: ✭ 18 (-68.97%)
Mutual labels:  reproducible-research
panoptes
Monitor computational workflows in real time
Stars: ✭ 45 (-22.41%)
Mutual labels:  reproducible-research
ANCOMBC
Differential abundance (DA) and correlation analyses for microbial absolute abundance data
Stars: ✭ 60 (+3.45%)
Mutual labels:  microbiome
RDPTools
Collection of commonly used RDP Tools for easy building
Stars: ✭ 44 (-24.14%)
Mutual labels:  microbiome
ITKPythonPackage
A setup script to generate ITK Python Wheels
Stars: ✭ 59 (+1.72%)
Mutual labels:  reproducible-research
genepattern-server
The GenePattern Server web application
Stars: ✭ 26 (-55.17%)
Mutual labels:  reproducible-research

MicrobiomeHD: the human gut microbiome in Health and Disease

This repo contains the code to reproduce all of the analyses in "Meta analysis of microbiome studies identifies shared and disease-specific patterns", Duvallet et al. 2017.

The raw data is available on Zenodo: DOI

More information on the raw data on Zenodo is in the db/ folder of this repo.

Reproducing analyses

The paper is accompanied by a Makefile, which you can use to re-make all of the analyses, figures, and tables in the paper.

The supplementary files are included in the final/supp-files/ folder of this repo. Most of the folders in this repo are currently empty, and will get populated with the results from make. The folders data/user_input and data/lit_search are the other two folders with provided information.

make will download the data from Zenodo, clean and process the raw data, perform all of the analyses in the paper, and make all of the figures and tables.

Note that make does not do any of the random forest parameter search-related analyses or figures. This part takes a very long time, and should be done as a background process.

Finally, re-building the PhyloT tree takes some time and re-orders the genera differently than in the paper (because there are multiple representations for each tree). The tree used in the paper is provided in data/analysis_results/. If you want to skip re-making the tree, you should run make tree --touch before running make.

Other things you can make separately:

  • figures: all of the figures
    • main_figures: just the figures in the main text
    • supp_figures: supplementary figures
  • tables: all of the tables, in both Markdown and tex formats
  • analysis: all of the analysis files, but none of the figures or tables
  • rf_params: the random forest parameter search analysis. Note that this is not included in any of the other make commands.
  • supp_files: the supplementary files, which are also included in the repo and don't technically need to be re-made
  • tree: the phyloT tree used to order genera in final figures.

Installing

To re-make all of the analyses, you'll first need to install the required modules.

You should probably do this in a Python 2 virtual environment. Unfortunately, many of the packages I used are no longer available in conda, so if you use anaconda you'll need to first create an empty conda environment, install pip, and then pip install the packages. If you don't use anaconda and/or have an alternative preferred way of making virtual environments, that's fine too (but I can't confirm that it will all work out). From the main directory, type:

conda create -n microbiomeHD python=2.7
source activate microbiomeHD
conda install pip
pip install -r requirements.txt

Note that all of these scripts were written in and for Python 2. Also, there have been many backward incompatible changes in some important modules used throughout, so you should install the old versions specified in the requirements.txt or else be plagued by many import errors.

You also need to install the NCBI EDirect command line tools for making the tree. Instructions on how to do that are on the NCBI documentation.

Then you just run make:

make

And voila! A paper!

Directory structure

This repo's structure follows what's recommended by Cookie Cutter Data Science. Some of the files that are made and which I think might be useful to you are already included in this repo. Other files are made by various scripts in src/.

data

All data-related files are (or will be) in data/:

  • user_input: user-inputted files, including:
    • results_folder.yaml file containing metadata on all the datasets [included]
    • list_of_tar_files.txt file that's used to download the raw data from Zenodo [included]
  • lit_search: manual curation of the results reported in the original publications [included]
  • analysis_results: files created by the analyses (e.g. q-values, random forest AUCs, etc) [made, except for the phyloT tree which is included]
  • raw_otu_tables: the raw OTU tables as downloaded from Zenodo
  • clean_tables: OTU tables and metadata in feather format, with "cleaned" data (i.e. only samples with both metadata and 16S, OTUs and samples with too few reads removed, etc)
  • tree: files associated with the phyloT tree. Note that the final tree (and its direct prerequisites) are included in this repo. If you want to re-make the tree from scratch, delete any of these files before running make. Making the tree is dicier and involves a manual step for you at http://phylot.biobyte.de/. Re-making the tree will also change the order of genera so that they no longer match the ordering in the paper exactly (since the linear order of phylogenetic groups doesn't matter).

source code

All of the code is in the src/ folder:

  • analysis: all of the code used to perform any analyses
  • data: data-related code, i.e. to download the raw data from Zenodo and to clean up the raw OTU tables and metadata files
  • final: code used to make the final figures, tables, and supplementary files
  • util: various functions and modules used in other scripts

figures, tables, and supplementary files

The Supplementary Files, Figures, and Tables are in the final/ folder. Some are made by make, others are included with this repo.

  • figures: figures [made]
  • tables: tables, in Markdown, tex, and tab-delimited formats [made]
  • supp-files: supplementary files [included]
Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].