corncob QIIME2 plugin

This repository contains the corncob QIIME 2 plugin. corncob is in active development and is available in R (https://github.com/bryandmartin/corncob) or as a QIIME 2 plugin (q2-corncob).

corncob is based in R and requires installation of dependencies VGAM, devtools, magrittr, phyloseq, and dplyr into your conda environment before installing corncob. Please refer to the following instructions on how to install corncob and its dependencies.

Activate your QIIME Environment

• Here we activate our example version of QIIME, qiime2-2018.8. If you're not sure what your current version of QIIME is you can run conda env list in the command line to see a list of installed QIIME environments.

source activate qiime2-2018.8

Install corncob dependencies

(Expected installation time ~3-5 minutes)

conda install -c bioconda -c conda-forge bioconductor-phyloseq r-devtools r-magrittr r-dplyr r-vgam unzip

• Note: When installing select y to proceed with installation when prompted.

Install corncob and q2-corncob

pip install git+https://github.com/statdivlab/q2-corncob.git
qiime dev refresh-cache

Check that corncob is installed

qiime corncob --help

QIIME 2 Tutorial: Using q2-corncob

This is a Community Tutorial for q2-corncob within the qiime2-2018.8 release.

corncob is an individual taxon regression model that uses abundance tables and sample data. corncob is able to model differential abundance and differential variability and address the following statistical challenges with modeling micriobial relative abundance:

• different sequencing depth
• excessive zeros from unobserved taxa
• high variability of empirical relative abundance (overdispersion)
• within-taxon correlation
• hypothesis testing with categorical and continuous covariates

A vignette on how to use corncob in R can be found [here]().

Currently, q2-corncob has made available the following functions within corncob:

• differentialtest()

Citing corncob

The manuscript for corncob is currently In Prep.

How to use q2-corncob

For this tutorial we will be using data from the "Moving Pictures" tutorial. q2-corncob requires input of a FeatureTable, Metadata, Taxonomy, and a covariate of interest.

table.qza
taxonomy.qza
metadata.tsv

Let's say that we are interested in seeing if there are ASV's that are differentially abundant or differentially variable across groups of ReportedAntibioticUsage.

qiime corncob differentialtest \
--i-table table.qza \
--m-metadata-file metadata.tsv \
--p-variable ReportedAntibioticUsage \
--i-taxonomy taxonomy.qza \
--o-output corncobresults

Our results show a table of features, taxonomic assignment, and fdr controlled p-values for differential abundance and differential variance.

results