First set of utility functions

R/app_server.R

 #' @import shiny
 app_server <- function(input, output,session) {
+  # Load package data in the session (for testing purpose)
+  # data("physeq", package = "affiliationExplorer")
+  # data("affi", package = "affiliationExplorer")
   # List the first level callModules here
 }

R/utils.R

+## Template for reading biom file 
+read_frogs_biom <- function(biomfile) {
+  phyloseq.extended::import_frogs(biom_file)
+}
+
+## Template for reading tsv files
+read_multihits <- function(multihits_file) {
+  readr::read_tsv(multihits_file)
+}
+
+## Functions to create short taxa names -------------------------------------
+long_taxa_names <- function(taxa_names, max_size = 32) {
+  ## any taxa name longer than 32 characters
+  any(taxa_names >= max_size)
+}
+
+ambiguous_taxa <- function(physeq) {
+  phyloseq::tax_table(physeq) %>% as("matrix") %>% 
+    apply(MARGIN = 1, FUN = function(x) { any(x == "Multi-affiliation")}) %>% 
+    which() %>% names()
+}
+
+create_otu_dictionary <- function(physeq) {
+  otu_dictionary <- tibble::tibble(
+    sequence  = phyloseq::taxa_names(physeq), 
+    abundance = phyloseq::taxa_sums(physeq)
+  ) %>% 
+    dplyr::arrange(desc(abundance))
+  ## If all taxa names are long, sequences are likely to have been processed by DADA
+  ## Move OTU names to sequence column and rename OTU with shorter names
+  if (long_taxa_names(otu_dictionary$OTU)) {
+   otu_dictionary <- otu_dictionary %>% dplyr::mutate(OTU = paste0("ASV", 1:n()))
+  } else {
+    otu_dictionary <- otu_dictionary %>% dplyr::mutate(OTU = sequence)
+  }
+  otu_dictionary
+}
+
+sanitize_physeq_and_affi <- function(physeq, affi) {
+  ## Create dictionary
+  otu_dictionary <- create_otu_dictionary(physeq)
+  old_to_new <- otu_dictionary %>% dplyr::select(-abundance) %>% tibble::deframe()
+  
+  ## Add abundance information to affiliation table
+  affi <- inner_join(otu_dictionary, affi, by = c("sequence" = "#observation_name")) %>% 
+    tidyr::separate(blast_taxonomy, into = rank_names(physeq), sep = ";")
+  
+  ## Rename taxa in physeq object
+  taxa_names(physeq) <- sequence_to_asv[taxa_names(physeq)] %>% unname()
+  
+  ## Remove previously curated taxa from affi
+  affi <- affi %>% filter(OTU %in% ambiguous_taxa(physeq))
+  
+  list(
+    physeq         = physeq, 
+    affi           = affi, 
+    otu_dictionary = otu_dictionary
+  )
+}
+
+## Order OTU by priority level
+sort_ambiguous_otu <- function(physeq, affi) {
+  affi %>% dplyr::arrange(desc(abundance)) %>% dplyr::pull(OTU)
+}
+
+## Extract all affiliation for a given OTU
+extract_affiliation <- function(affi, otu) {
+  affi %>% 
+    dplyr::filter(OTU == otu) %>% 
+    dplyr::select(Kingdom:Species)
+}

data-raw/create_test_data.R

@@ -2,7 +2,7 @@
 library(dplyr)
 library(phyloseq)
 physeq <- phyloseq.extended::import_frogs("data-raw/biom_16S_1M.biom")
-affi <- affi <- readr::read_tsv("data-raw/multihits_16S_1M.tsv")
+affi <- readr::read_tsv("data-raw/multihits_16S_1M.tsv")
 
 ## Sanitize taxa names (change long sequences to short ones)
 asv_dictionary <- tibble(sequence  = taxa_names(physeq), 
@@ -19,15 +19,6 @@ taxa_names(physeq) <- sequence_to_asv[taxa_names(physeq)] %>% unname()
 usethis::use_data(physeq)
 usethis::use_data(affi)
 
-## Find level of ambiguity
-find_level <- function(Kingdom, Phylum, Class, Order, Family, Genus, Species) {
-  not_consistent <- function(x) { !all(x == x[1]) }
-  rank_names <- c("Kingdom", "Phylum", "Class", "Order", "Family", "Genus", "Species")
-  for (rank in rank_names) {
-    rank_content <- get(rank)
-    if (not_consistent(rank_content)) return(rank)
-  }
-}
 
 
 ## Prioritize orphan taxa