deicode 0.1.6

Robust Aitchison compositional biplots from sparse count data

[](https://travis-ci.org/biocore/DEICODE) [](https://coveralls.io/github/biocore/DEICODE?branch=master)

DEICODE is a tool box for running Robust Aitchison PCA on sparse compositional omics datasets, linking specific features to beta-diversity ordination.

## Installation

To install the most up to date version of deicode, run the following command

# pip (only supported for QIIME2 >= 2018.8) pip install deicode

# conda (only supported for QIIME2 >= 2019.1) conda install -c conda-forge deicode

Note: that deicode is not compatible with python 2, and is compatible with Python 3.4 or later. deicode is currently in alpha. We are actively developing it, and backward-incompatible interface changes may arise.

## QIIME2 tutorial

• The QIIME2 forum tutorial can be found [here](https://forum.qiime2.org/t/robust-aitchison-pca-beta-diversity-with-deicode/8333).

• The official plugin docs and tutorial can be found [here](https://library.qiime2.org/plugins/deicode).

• The in-repo tutorial can be found [here](https://github.com/biocore/DEICODE/blob/master/ipynb/tutorials/moving-pictures.md).

## Other Resources

• [Aitchison Distance Introduction](https://github.com/biocore/DEICODE/blob/master/ipynb/introduction.ipynb)

• The code for OptSpace was translated to python from a [MATLAB package](http://swoh.web.engr.illinois.edu/software/optspace/code.html) maintained by Sewoong Oh (UIUC).

• Transforms and PCoA : [Scikit-bio](http://scikit-bio.org)

• Data For Examples : [Qiita](https://qiita.ucsd.edu/)

#### Simulation and Benchmarking

• [simulations and case studies](https://github.com/cameronmartino/deicode-benchmarking)