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ampvisdata$metadata[[group]] <- factor(ampvisdata$metadata[[group]], levels = lev) |
5b. PCoA plots and statistics
The overview section described (with links and references) the ordination methods that can be used to estimate and plot beta diversity.
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To compare the overall differences between groups within your chosen variable, a Permutational Multivariate Analysis of Variance (PERMANOVA) test can be performed and similarly a pairwise Permutational Multivariate Analysis of Variance ( PERMANOVA ) test can be performed to compare differences between each group.
The R-squared value represents the percentage of variance explained by the examined groups. E.g. if R = 0.23 then 23% of the total diversity is explained by groupwise differences. PERMANOVA is based on groupwise differences, thus cannot be applied to continuous data.
PERMANOVA:
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# Need to remove rows (from ASV abundance table) with all 0 counts first asvmatrix <- ampvisdata$abund asvmatrix <- asvmatrix[rowSums(asvmatrix) > 0, ] # Also need to transpose (samples need to be as rows, asv's as columns) asvmatrix <- t(asvmatrix) # Then generate pairwise distance matrix sampdist <- vegdist(asvmatrix, method="bray") # Subset samples table by samples in ampvis2 object (in case some were filtered out) samples_table_filt <- samples_table[samples_table$ID %in% ampvisdata$metadata$ID, ] # Use adonis function (vegan package: "Permutational Multivariate Analysis of Variance Using Distance Matrices") to run PERMANOVA on distances pathotype.adonis <- adonis2(sampdist ~ get(group), data = samples_table_filt) # Output the r squared and p values as variables r2 <- pathotype.adonis$R2[1] pval <- pathotype.adonis$`Pr(>F)`[1] |
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write_csv(tabstat_adonis, paste0("beta_div_pairwise_PERMANOVA", group, "_", index, "_samples_.csv")) |
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