Microbiome Resources

Below are some resources that I have found helpful when analyzing microbiome data

Here are some resources that I have put together to analyzing microbiome data:

Susan Holmes and Wolfgang Huber have put together a FABULOUS book on statistics that can be applied to microbiome data and is freely available here:
- web.stanford.edu/class/bios221/book/introduction.html
Working with dada2 to identify exact sequence variants, an alternative to clustering OTUs:
- https://benjjneb.github.io/dada2/tutorial.html
Using QIIME2 output files in R (if you have used QIIME2, you'll see why this is helpful):
- https://forum.qiime2.org/t/tutorial-integrating-qiime2-and-r-for-data-visualization-and-analysis-using-qiime2r/4121
Random Forest tutorial for R- very useful for using microbiome data to predict whether X, Y, and Z OTU abundances correlate to an input metadata classifier:
- https://github.com/LangilleLab/microbiome_helper/wiki/Random-Forest-Tutorial
Microbiome package in R, used to perform analyses on phyloseq objects:
- https://microbiome.github.io/microbiome/Preprocessing.html#content

Need help deciding what statistical analyses you should be using? see the links below

This website is from ECOM, which is a statistical software, but has great information about the statistics and when to perform certain analyses:
- http://www.pisces-conservation.com/ecomhelp/index.html?whatiscca.html
Michael Palmer at Oklahoma State does a great job here explaining the differences between ordination methods and how each is calculated:
- http://ordination.okstate.edu/overview.html

Below is link to Sudarshan Shetty's github, where he has a comprehensive list of microbiome related R packages:

If you are a newbie to R, Penn State has some great resources for learning basics in R:

Here is a great resource regarding data carpentry and analysis that includes r-markdown files that you can run through