(high-temp) No 8. Metadata Analysis (16S rRNA/ITS) Output

**Output files from the 8. Metadata Analysis Workflow** page of the SWELTR
high-temp study. In this workflow, we compared environmental metadata with
microbial communities. The workflow is split into two parts.

**metadata_ssu18_wf.rdata** : Part 1 contains all variables and objects for
the 16S rRNA analysis. To see the Objects, in R run
_load("metadata_ssu18_wf.rdata", verbose=TRUE)_

**metadata_its18_wf.rdata** : Part 2 contains all variables and objects for
the ITS analysis. To see the Objects, in R run
_load("metadata_its18_wf.rdata", verbose=TRUE)_
Additional files:

In both workflows, we run the following steps:

**1**) Metadata Normality Tests: Shapiro-Wilk Normality Test to test whether
each matadata parameter is normally distributed.
**2**) Normalize Parameters: R package bestNormalize to find and execute the
best normalizing transformation.
**3**) Split Metadata parameters into groups: a) Environmental and edaphic
properties, b) Microbial functional responses, and c) Temperature adaptation
properties.
**4**) Autocorrelation Tests: Test all possible pair-wise comparisons, on both
normalized and non-normalized data sets, for each group.
**5**) Remove autocorrelated parameters from each group.
**6**) Dissimilarity Correlation Tests: Use Mantel Tests to see if any on the
metadata groups are significantly correlated with the community data.
**7**) Best Subset of Variables: Determine which of the metadata parameters
from each group are the most strongly correlated with the community data. For
this we use the bioenv function from the vegan package.
**8**) Distance-based Redundancy Analysis: Ordination analysis of samples and
metadata vector overlays using capscale, also from the vegan package.

Source code for the workflow can be found here:
https://github.com/sweltr/high-temp/blob/master/metadata.Rmd