Calculating Relative Abundance

This page will instruct you in the calculation of relative abundance using the custom foodseqSetup() function and to provide background on the purpose and interpretation of relative abundance. These steps are intended to follow the calculation of diversity and precede the creation of a PCA plot.

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foodseqSetup() Function

When calculating relative abundance, feed in an unfiltered phyloseq, as opposed to the filtered phyloseq object used for calculating diversity.

foodseqSetup <- function(ps) {
  ### Relative abundance generation 
  ps.ra <- transform_sample_counts(ps, function(x){x/sum(x)})

  ### Filter and CLR transform 
  ps.filt <- ps %>%
  subset_taxa(., !is.na(superkingdom)) # foods only

  ps.filt.clr <- ps.filt %>%
  prune_samples(sample_sums(.) > 0, .) %>% # Remove samples that do not have any food reads, will mess up PCA plot
  microbiome::transform(., 'clr') # clr transform

  ### Update read counts in phyloseq object
  sample_data(ps.filt.clr)$reads <- sample_sums(ps.filt.clr)

  ### Alpha diversity metrics 
  pMR <- ifelse(subset_taxa(ps.filt.clr, !is.na(superkingdom))@otu_table>0,1,0) %>%      # Converts OTU table values to presence/absence
  rowSums()
  ps.filt.clr@sam_data$pMR <- pMR

  # This is done with raw relative abundance, not clr transformed or raw abundance data 
  shan <- estimate_richness(ps.ra, measures = "Shannon") %>%
    rownames_to_column(var = "test") 

  sample_data(ps.ra) <- ps.ra@sam_data %>%
    data.frame() %>%
    rownames_to_column(var = "test") %>%
    left_join(shan, by = "test") %>% 
    column_to_rownames(var = "test") %>%
    sample_data()

  return(list(ps.ra = ps.ra, # Relative abundance 
              ps.filt = ps.filt, # Foods only 
              ps.filt.clr = ps.filt.clr # Foods only, CLR-transformed 
              ))
}