https://github.com/earmingol/microbiome-factorization
Examples of using PhyloFactor and DEICODE to analyze microbiome datasets.
https://github.com/earmingol/microbiome-factorization
factorization factorization-methods machine-learning microbiome microbiome-datasets
Last synced: 11 months ago
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Examples of using PhyloFactor and DEICODE to analyze microbiome datasets.
- Host: GitHub
- URL: https://github.com/earmingol/microbiome-factorization
- Owner: earmingol
- Created: 2019-06-13T18:59:17.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2019-06-16T21:47:14.000Z (over 6 years ago)
- Last Synced: 2025-01-29T23:28:46.279Z (about 1 year ago)
- Topics: factorization, factorization-methods, machine-learning, microbiome, microbiome-datasets
- Language: Jupyter Notebook
- Size: 14 MB
- Stars: 1
- Watchers: 1
- Forks: 1
- Open Issues: 0
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Metadata Files:
- Readme: README.MD
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README
# Factorization of microbiome datasets
This repo was created to exemplify how to use two factorization methods on microbiome datasets. Also,
to generate a Random Forest model from them.
The codes of these examples were generated during my lab rotation at Rob Knight's Lab - Spring 2019.
**Here, you can find examples of using [PhyloFactor](https://github.com/reptalex/phylofactor) and
[DEICODE](https://github.com/biocore/DEICODE) to analyze a
[previously published study](https://aem.asm.org/content/75/15/5111).**
## Requirements
- [Follow this guide about installing Qiime2](https://docs.qiime2.org/2019.4/install/) (miniconda or anaconda are required).
- [Follow this guide about installing PhyloFactor and an environment for R](./PhyloFactor-Installation.pdf).
For more info, [check the original repo](https://github.com/reptalex/phylofactor) and
[this tutorial](https://docs.wixstatic.com/ugd/0119a1_099ae20df8424af9a38585dcebc0d45a.pdf).
- [Follow the original instructions about installing DEICODE](https://github.com/biocore/DEICODE).
- [Follow the original instructions about installing Qurro](https://github.com/fedarko/qurro).
## Examples
- The datasets used in the examples are available in the [data folder](./data/).
- Data preprocessing used before the factorization methods is available in
[this notebook](./notebooks/DataPreprocessing-pH.ipynb).
- To execute PhyloFactor and generate predictive models, see examples in the [notebooks folder](./notebooks/).
- To execute DEICODE + Qurro, [follow these instructions](./src/deicode/).