README.md

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.

Using DEICODE as a standalone tool

$ deicode --help
Usage: deicode [OPTIONS]

  Runs RPCA with an rclr preprocessing step.

Options:
  --in-biom TEXT               Input table in biom format.  [required]
  --output-dir TEXT            Location of output files.  [required]
  --n_components INTEGER       The underlying low-rank structure (suggested: 1
                               < rank < 10) [minimum 2]  [default: 3]
  --min-sample-count INTEGER   Minimum sum cutoff of sample across all
                               features  [default: 500]
  --min-feature-count INTEGER  Minimum sum cutoff of features across all
                               samples  [default: 10]
  --max_iterations INTEGER         The number of iterations to optimize the
                               solution (suggested to be below 100; beware of
                               overfitting) [minimum 1]  [default: 5]
  --help                       Show this message and exit.

Using DEICODE inside QIIME 2

• The QIIME2 forum tutorial can be found here.
• The official plugin docs and tutorial can be found here.
• The in-repo tutorial can be found here.

Other Resources

• Aitchison Distance Introduction

• The code for OptSpace was translated to python from a MATLAB package maintained by Sewoong Oh (UIUC).
• Transforms and PCoA : Scikit-bio
• Data For Examples : Qiita

Simulation and Benchmarking

• simulations and case studies