orig_phylo_w_MDM_names: Create final phyloseq object with ambiguous, unassigned, and...
In ConesaLab/MDM: Complete MDM Network Analysis for 16S rRNA Data

Description Usage Arguments Value Examples

Before beginning network analysis, we reassign any OTUs designated as ambiguous, unassigned or uncultured at each rank to MDM. Rank names are changed as well from Rank1 –> Kingdom, Rank2 –> Phylum, and so forth for Kingdom to Species.

1	orig_phylo_w_MDM_names(phylo)

phylo

Original OTU table in phyloseq format

Returns

##---- Should be DIRECTLY executable !! ----
##-- ==>  Define data, use random,
##--	or do  help(data=index)  for the standard data sets.

## The function is currently defined as
function (phylo, mapping)
{
    unassignedOTUs_MDM <- c("NA", "D_1__uncultured", "D_1__uncultured bacterium",
        "D_1__Unknown Phylum", "D_2__uncultured", "D_2__uncultured bacterium",
        "D_2__Unknown Class", "Ambiguous_taxa", "Unassigned",
        "D_3__uncultured", "D_3__uncultured bacterium", "D_3__Unknown Order",
        "D_4__uncultured", "D_4__uncultured bacterium", "D_4__Unknown Family",
        "D_5__uncultured", "D_5__uncultured bacterium", "D_5__Unknown Genus",
        "D_6__uncultured", "D_6__uncultured bacterium", "D_6__Unknown Species")
    silva_otus = phyloseq::otu_table(phylo)
    silva_taxa = phyloseq::tax_table(phylo)
    silva_taxa_df <- as.data.frame(silva_taxa)
    colnames(silva_taxa_df) = c("Kingdom", "Phylum", "Class",
        "Order", "Family", "Genus", "Species")
    silva_taxa_df$Phylum[silva_taxa_df$Phylum %in% unassignedOTUs_MDM] <- "<NA>"
    silva_taxa_df$Class[silva_taxa_df$Class %in% unassignedOTUs_MDM] <- "<NA>"
    silva_taxa_df$Order[silva_taxa_df$Order %in% unassignedOTUs_MDM] <- "<NA>"
    silva_taxa_df$Family[silva_taxa_df$Family %in% unassignedOTUs_MDM] <- "<NA>"
    silva_taxa_df$Genus[silva_taxa_df$Genus %in% unassignedOTUs_MDM] <- "<NA>"
    silva_taxa_df$Species[silva_taxa_df$Species %in% unassignedOTUs_MDM] <- "<NA>"
    silva_taxa_df[] <- lapply(silva_taxa_df, as.character)
    silva_taxa_df[] <- lapply(silva_taxa_df, function(x) {
        stringr::str_replace_all(x, "uncultured", NA_character_)
    })
    newsilvataxtable2_m <- as.matrix(silva_taxa_df)
    newsilva_taxa <- tax_table(newsilvataxtable2_m)
    new_silva_phylo = phyloseq(silva_otus, newsilva_taxa, mapping)
  }