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