This is a community tutorial for
q2-corncob version 1.0.
corncob is in active development and is available in
R (https://github.com/bryandmartin/corncob) or as a QIIME 2 plugin (https://github.com/statdivlab/q2-corncob).
corncob is based in
R and requires installation of dependencies
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 listin the command line to see a list of installed QIIME environments.
source activate qiime2-2018.8
(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
yto proceed with installation when prompted.
pip install git+https://github.com/statdivlab/q2-corncob.git qiime dev refresh-cache
corncob is installed
qiime corncob --help
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)
hypothesis testing with categorical and continuous covariates
A vignette on how to use
R can be found here.
q2-corncob has made available the following functions within
The manuscript for
corncob is currently In Prep.
How to use
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.
**Note: In q2-corncob v1.0 we require input of a taxonomy file, however, q2-corncob v2.0 will remove the necessity for a taxonomy file.
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.