June 7, 2018

• You will need your own laptop to participate in the hands-on portion of the workshop. While there will be some power distribution throuhgout the classroom, we recommend you fully charge your computer before arrival

• You do not need to insall anything prior to the Workshop. The course will be run through RStduio Cloud which runs through a web browser on all operating systems (Windows, MacOS, Linux)

• Since the hands-on portion will run through a web browser we recommend you connect to the free conference WiFi provided by ASM prior to arriving to the course

The ASM Workshop on *"Microbiome Analysis Using R" is intended to provide an overview of the basic principles underlying microbiome analysis. While the focus will be on the practical aspects of using R to complete this analysis, discussion on best practices, study design and ecological analysis will be discussed.

A significant component of the Workshop will revolve around a simple case-study which examines the time-series response of enteric bacterial communities to several different antibiotic treatments.

An overview of the analysis steps implemented:

1. Environment initiation
2. Read in your data and select samples for analysis
3. Variable examination and modification
4. Data summary and assessment
5. Taxon prevalence estimations and filtering
6. Data transformation
7. Subsetting
8. Community composition plotting
9. Alpha diversity analysis
10. Beta diversity analysis
11. Differential abundance testing

The data originate from a study on the bacterial microbiome of mice treated with or without antibiotics to test the affects of the microbiome on flavivirus infection (https://www.ncbi.nlm.nih.gov/PubMed/29590614).

• 8:00 - 8:15: Course Intro and Installation

• 8:15 - 8:30: Introduction to R and RStudio

• 8:30 - 9:30: ggplot2

• 9:30 - 10:00: R details and working with tables

• 10:00 - 11:00: Introduction to Phyloseq

• 11:00 - 12:00: Introduction to the Workshop Case Study: Antibiotic Treated Mice

• 12:00 - 1:00: Lunch Break

• 1:00 - 3:00: Working through the Case Study in R

• 3:00 - 4:00: Wrap-up and Additional Examples and Discussion