QIIME2 Tutorial: q2-corncob
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 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 runconda 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
Using q2-corncob
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.
**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.