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

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