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

posted on 02.06.2022, 16:14 authored by Jarrod ScottJarrod Scott

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:


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