breakaway QIIME2 plugin

This repository contains the breakaway QIIME 2 plugin. breakaway is in active development and is available in R (https://github.com/adw96/breakaway) or as a QIIME2 plugin (q2-breakaway).

breakaway is based in R and requires installation of dependencies phyloseq, devtools, ggplot2,magrittr, tibble, dplyr,withr,testthat, and praise into your conda environment before installing breakaway. Please refer to the following instructions on how to install breakaway 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. Note: q2-breakaway is compatible only with version qiime2-2018.8 and on.

source activate qiime2-2018.8

Install breakaway dependencies

(Expected installation time ~3-5 minutes)

conda install -c bioconda -c conda-forge bioconductor-phyloseq r-devtools r-tibble r-magrittr r-dplyr r-withr r-testthat r-praise unzip

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

Install breakaway and q2-breakaway for OS X Users

pip install git+https://github.com/statdivlab/q2-breakaway.git
R -e 'library(devtools); devtools::install_github("adw96/breakaway")'
qiime dev refresh-cache

Install breakaway and q2-breakaway for Linux Users

pip install git+https://github.com/statdivlab/q2-breakaway.git
R -e 'Sys.setenv(TAR = "/bin/tar"); library(devtools); devtools::install_github("adw96/breakaway")' 
qiime dev refresh-cache

Check that breakaway is installed

qiime breakaway --help

QIIME2 Tutorial: Using q2-breakaway

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

breakaway is the premier package for statistical analysis of microbial diversity. breakaway implements the latest and greatest estimates of richness, as well as the most commonly used estimates. The breakaway philosophy is to estimate diversity, to put error bars on diversity estimates, and to perform hypothesis tests for diversity that use those error bars.

Citing breakaway

The R package breakaway implements a number of different richness estimates. Please cite the following if you use them:

• breakaway(): Willis and Bunge (2015). Estimating diversity via frequency ratios. Biometrics.

How to use q2-breakaway

• For this tutorial we will be using data from the "Moving Pictures" data. q2-breakaway requires input of a FeatureTable of frequency counts. We recommend using a FeatureTable that has been generated from deblur/vsearch or dada2 in R with pool = TRUE to make sure that singletons have not been completely filtered out.

table-deblur.qza

qiime breakaway alpha \
--i-table table-deblur.qza \
--o-alpha-diversity richness-better.qza

You can export the results out of QIIME2 to see the richness estimates, confidence intervals, and model used by breakaway.

qiime tools export \
richness-better.qza \
--output-dir richness

alpha-diversity.tsv

We see that Kemp, Poisson, and Negative Binomial models were used to generate our confidence intervals! Let's visualize our estimates and their error bars.

qiime breakaway plot \
--i-alpha-diversity richness-better.qza \
--o-visualization richness-better-plot

To view...

qiime tools view richness-better-plot.qzv

And now there are error bars around our estimates! Note that some error bars are smaller than others. This is because those samples had few rare taxa, and so low uncertainty in estimating the number of missing taxa.

Future Functionality (things to look forward to!)

• kemp(): Willis, A. & Bunge, J. (2015). Estimating diversity via frequency ratios. Biometrics.
• betta(): Willis, A., Bunge, J., & Whitman, T. (2017). Improved detection of changes in species richness in high diversity microbial communities. JRSS-C.
• breakaway_nof1(): Willis, A. (2016+). Species richness estimation with high diversity but spurious singletons. arXiv.
• objective_bayes_*(): Barger, K. & Bunge, J. (2010). Objective Bayesian estimation for the number of species. Bayesian Analysis.