The metadata includes our metadata (like age and ADGKG) as well as alpha-diversity metrics from .unique_list.0.03. calculated in mothur. We will also be using tab-delimited metadata and SCFA files created in Excel. .unique_list.0.03.cons.taxonomy (Taxonomy of OTUs)..unique_list.0.03.norm.shared (OTU table).We will use the following files created using the Microbiota Processing in mothur: Standard Operating Procedure (SOP).
Specifically, we will be correlating the fecal bacterial microbiota of 8 dairy calves at different ages (2 weeks, 8 weeks, 1 year) to variables like weight gain (average daily gain in kg, ADGKG) and gastrointestinal short chain fatty acids (SCFA). The full data set is in Dill-McFarland et al. The data used here were created using 2x250 bp amplicon sequencing of the bacterial V4 region of the 16S rRNA gene on the Illumina MiSeq platform. Hopefully, this is just the start for your data! Data The incorrect use of statistics is a pervasive and serious problem in the sciences so don’t become part of the problem! That said, this is an introductory tutorial and there are many, many further analyses that can be done with microbiota data. If you are not familiar with statistics at this level, we strongly recommend collaborating with someone who is. Please consider if your data fit the assumptions of each test (normality? equal sampling? Etc.). This tutorial assumes some basic statistical knowledge.
In general, this pipeline can be used for any microbiota data set that has been clustered into operational taxonomic units (OTUs). The goal of this tutorial is to demonstrate basic analyses of microbiota data to determine if and how communities differ by variables of interest. Written for R v3.4.3 in RStudio v1.1.383 Goal Matrices/data frames are designated by where it is.ORANGE boxes contain R code which you should NOT execute today, either because it takes forever or because we do not have the necessary input to run it. WHITE boxes contain sample output of this code, and nothing will happen if you try to copy it into your console. GREEN boxes contain code that you can copy and paste to run on your machine.They are also not evaluated by R and can be copied into your script with the code. These lines are not code and do not need to be entered into the console. Lines starting with # are comments that are for the reader’s benefit.If you have any issues in R, type ?command into the console where command is the function you are having issues with and a help page will come up.
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