Click here for setup information

knitr::opts_chunk$set(echo = TRUE, eval = FALSE)
set.seed(119)
#library(conflicted)
#pacman::p_depends(phangorn, local = TRUE)  
#pacman::p_depends_reverse(phangorn, local = TRUE)  

library(phyloseq); packageVersion("phyloseq")
library(Biostrings); packageVersion("Biostrings")
pacman::p_load(tidyverse, patchwork,
               agricolae, labdsv, naniar, 
               microbiome, microeco, file2meco, cowplot,
               GUniFrac, ggalluvial, ggdendro, igraph, reactable,
               pheatmap, microbiomeMarker,
               captioner, downloadthis,
               install = FALSE, update = FALSE)
options(scipen=999)
knitr::opts_current$get(c(
  "cache",
  "cache.path",
  "cache.rebuild",
  "dependson",
  "autodep"
))

Synopsis

This workflow contains taxonomic diversity assessments for the 16S rRNA and ITS data sets. In order to run the workflow, you either need to first run the DADA2 Workflow, then the Data Preparation workflow, and finally the Filtering workflow or download the files linked below.

Workflow Input

All files needed to run this workflow can be downloaded from figshare.

In this workflow, we use the microeco to look at the taxonomic distribution of microbial communities.

Taxonomic Assessment

16s rRNA

Here we compare the taxonomic breakdown of the Full (unfiltered), Arbitrary filtered, PERfect filtered, and PIME filtered data sets, split by temperature treatment.

Before we plot the data, we want to separate out the Proteobacteria classes so we can plot these along with other phyla. To accomplish this we perform the following steps:

Get all Class-level Proteobacteria names

Show the code

ssu18_data_sets <- c("ssu18_ps_work",  "ssu18_ps_filt",
                     "ssu18_ps_perfect", "ssu18_ps_pime")

for (i in ssu18_data_sets) {
     tmp_name <- purrr::map_chr(i, ~paste0(., "_proteo"))
     tmp_get <- get(i)
     tmp_df <- subset_taxa(tmp_get, Phylum == "Proteobacteria")
     assign(tmp_name, tmp_df)
     print(tmp_name)
     tmp_get_taxa <- get_taxa_unique(tmp_df,
                                     taxonomic.rank = rank_names(tmp_df)[3],
                                     errorIfNULL=TRUE)
     print(tmp_get_taxa)
     rm(list = ls(pattern = "tmp_"))
     rm(list = ls(pattern = "_proteo"))
}

Replace Phylum Proteobacteria with the Class name.

Show the code

for (j in ssu18_data_sets) {
  tmp_name <- purrr::map_chr(j, ~paste0(., "_proteo_clean"))
  tmp_get <- get(j)
  tmp_clean <- data.frame(tax_table(tmp_get))

   for (i in 1:nrow(tmp_clean)){
       if (tmp_clean[i,2] == "Proteobacteria" & tmp_clean[i,3] == "Alphaproteobacteria"){
           phylum <- base::paste("Alphaproteobacteria")
           tmp_clean[i, 2] <- phylum
   }   else if (tmp_clean[i,2] == "Proteobacteria" & tmp_clean[i,3] == "Gammaproteobacteria"){
           phylum <- base::paste("Gammaproteobacteria")
           tmp_clean[i, 2] <- phylum
   }   else if (tmp_clean[i,2] == "Proteobacteria" & tmp_clean[i,3] == "Zetaproteobacteria"){
              phylum <- base::paste("Zetaproteobacteria")
           tmp_clean[i, 2] <- phylum
   }   else if (tmp_clean[i,2] == "Proteobacteria" & tmp_clean[i,3] == "p_Proteobacteria"){
           phylum <- base::paste("p_Proteobacteria")
           tmp_clean[i, 2] <- phylum
       }
     }
  tax_table(tmp_get) <- as.matrix(tmp_clean)
  rank_names(tmp_get)
  assign(tmp_name, tmp_get)
  print(c(tmp_name, tmp_get))
  print(length(get_taxa_unique(tmp_get,
                               taxonomic.rank = rank_names(tmp_get)[2],
                               errorIfNULL=TRUE)))
  tmp_path <- file.path("files/taxa/rdata/")
  saveRDS(tmp_get, paste(tmp_path, j, "_clean.rds", sep = ""))
  rm(list = ls(pattern = "tmp_"))
}
rm(class, order, phylum)
objects(pattern="_proteo_clean")

In order to use microeco, we need to add the rank designation as a prefix to each taxa. For example, Actinobacteriota is changed to p__Actinobacteriota.

Show the code

for (i in ssu18_data_sets) {
  tmp_get <- get(purrr::map_chr(i, ~paste0(., "_proteo_clean")))
  #tmp_get <- get(i)
  #tmp_path <- file.path("files/alpha/rdata/")
  #tmp_read <- readRDS(paste(tmp_path, i, ".rds", sep = ""))
  tmp_sam_data <- sample_data(tmp_get)
  tmp_tax_data <- data.frame(tax_table(tmp_get))
  tmp_tax_data$Phylum <- gsub("p_Proteobacteria", "Proteobacteria", tmp_tax_data$Phylum)

  #tmp_tax_data[,c(1:6)]
  tmp_tax_data$ASV_ID <- NULL # Some have, some do not
  tmp_tax_data$ASV_SEQ <- NULL
  
  tmp_tax_data[] <- data.frame(lapply(tmp_tax_data, gsub, 
                                      pattern = "^[k | p | c | o | f]_.*", 
                                      replacement = "", fixed = FALSE))
  tmp_tax_data$Kingdom <- paste("k__", tmp_tax_data$Kingdom, sep = "")
  tmp_tax_data$Phylum <- paste("p__", tmp_tax_data$Phylum, sep = "")
  tmp_tax_data$Class <- paste("c__", tmp_tax_data$Class, sep = "")
  tmp_tax_data$Order <- paste("o__", tmp_tax_data$Order, sep = "")
  tmp_tax_data$Family <- paste("f__", tmp_tax_data$Family, sep = "")
  tmp_tax_data$Genus <- paste("g__", tmp_tax_data$Genus, sep = "")
  tmp_tax_data <- as.matrix(tmp_tax_data)
  
  tmp_ps <- phyloseq(otu_table(tmp_get),
                     phy_tree(tmp_get),
                     tax_table(tmp_tax_data),
                     tmp_sam_data)
  assign(i, tmp_ps)
  rm(list = ls(pattern = "tmp_"))
}
rm(list = ls(pattern = "_proteo_clean"))

Next, we need to covert each phyloseq object to a microtable class. The microtable class is the basic data structure for the microeco package and designed to store basic information from all the downstream analyses (e.g, alpha diversity, beta diversity, etc.). We use the file2meco to read the phyloseq object and convert into a microtable object. We can add _me as a suffix to each object to distiguish it from its’ phyloseq counterpart.

Show the code

for (i in ssu18_data_sets) {
    tmp_get <- get(i)
    tmp_otu_table <- data.frame(t(otu_table(tmp_get)))
    tmp_sample_info <- data.frame(sample_data(tmp_get))
    tmp_taxonomy_table <- data.frame(tax_table(tmp_get))
    tmp_phylo_tree <- phy_tree(tmp_get)
    tmp_taxonomy_table %<>% tidy_taxonomy
    tmp_dataset <- microtable$new(sample_table = tmp_sample_info, 
                                  otu_table = tmp_otu_table, 
                                  tax_table = tmp_taxonomy_table, 
                                  phylo_tree = tmp_phylo_tree)
    tmp_dataset$tidy_dataset()
    print(tmp_dataset)
    tmp_dataset$tax_table %<>% base::subset(Kingdom == "k__Archaea" | Kingdom == "k__Bacteria")
    print(tmp_dataset)
    tmp_dataset$filter_pollution(taxa = c("mitochondria", "chloroplast"))
    print(tmp_dataset)
    tmp_dataset$tidy_dataset()
    print(tmp_dataset)
    tmp_name <- purrr::map_chr(i, ~paste0(., "_me")) 
    assign(tmp_name, tmp_dataset)
    rm(list = ls(pattern = "tmp_"))
}  
objects()

Here is an example of what a new microeco object looks like when you call it.

ssu18_ps_work_me

microtable class:
sample_table have 15 rows and 6 columns
otu_table have 20173 rows and 15 columns
tax_table have 20173 rows and 6 columns
phylo_tree have 20173 tips
Taxa abundance: calculated for Kingdom,Phylum,Class,Order,Family,Genus

Now, we calculate the taxa abundance at each taxonomic rank using cal_abund(). This function return a list called taxa_abund containing several data frame of the abundance information at each taxonomic rank. The list is stored in the microtable object automatically. It’s worth noting that the cal_abund() function can be used to solve some complex cases, such as supporting both the relative and absolute abundance calculation and selecting the partial taxonomic columns. More information can be found in the description of the file2meco package. In the same loop we also create a trans_abund class, which is used to transform taxonomic abundance data for plotting.

Show the code

for (i in ssu18_data_sets) {
  tmp_me <- get(purrr::map_chr(i, ~paste0(., "_me")))
  tmp_me$cal_abund()
  tmp_me_abund <- trans_abund$new(dataset = tmp_me, taxrank = "Phylum", ntaxa = 12)
  tmp_me_abund$abund_data$Abundance <- tmp_me_abund$abund_data$Abundance / 100
  tmp_me_abund_gr <- trans_abund$new(dataset = tmp_me, taxrank = "Phylum", ntaxa = 12, 
                                  groupmean = "TEMP")
  tmp_me_abund_gr$abund_data$Abundance <- tmp_me_abund_gr$abund_data$Abundance / 100
  tmp_name <- purrr::map_chr(i, ~paste0(., "_me_abund")) 
  assign(tmp_name, tmp_me_abund)  
  tmp_name_gr <- purrr::map_chr(i, ~paste0(., "_me_abund_group")) 
  assign(tmp_name_gr, tmp_me_abund_gr)  
  rm(list = ls(pattern = "tmp_"))
}
objects()

The result is stored in object$taxa_abund ...

I prefer to specify the order of taxa in these kinds of plots. We can look the top ntaxa (defined above) by accessing the use_taxanames character vector of each microtable object.

Hum. Similar but not the same. This means we need to define the order for each separately. Once we do that, we will override the use_taxanames character vectors with the reordered vectors.

Show the code

ssu18_ps_work_tax_ord <- c("Alphaproteobacteria", "Gammaproteobacteria", "Acidobacteriota", 
                           "Actinobacteriota", "Bacteroidota", "Firmicutes", "Myxococcota", 
                           "Verrucomicrobiota", "Chloroflexi", "Planctomycetota", 
                           "Methylomirabilota", "Crenarchaeota")
ssu18_ps_filt_tax_ord <- c("Alphaproteobacteria", "Gammaproteobacteria", "Acidobacteriota", 
                           "Actinobacteriota", "Bacteroidota", "Firmicutes", "Myxococcota", 
                           "Verrucomicrobiota", "RCP2-54", "Planctomycetota", 
                           "Methylomirabilota", "Crenarchaeota")
ssu18_ps_perfect_tax_ord <- c("Alphaproteobacteria", "Gammaproteobacteria", "Acidobacteriota", 
                              "Actinobacteriota", "Bacteroidota", "Firmicutes", "Myxococcota", 
                              "Verrucomicrobiota", "RCP2-54", "Planctomycetota", 
                              "Methylomirabilota", "Crenarchaeota")
ssu18_ps_pime_tax_ord <- c("Alphaproteobacteria", "Gammaproteobacteria", "Acidobacteriota", 
                           "Actinobacteriota", "Bacteroidota", "Firmicutes", "Myxococcota", 
                           "Verrucomicrobiota", "RCP2-54", "Nitrospirota", 
                           "Methylomirabilota", "Crenarchaeota")

And one more little step before plotting. Here we a) specify a custom color palette and b) specify the sample order.

Show the code

top_level <- "Phylum"
ssu18_colvec.tax <- c("#00463C","#FFD5FD","#00A51C","#C80B2A","#00C7F9","#FFA035",
                      "#ED0DFD","#0063E5","#5FFFDE","#C00B6F","#00A090","#FF95BA")
ssu18_samp_order <- c("P02_D00_010_C0A", "P04_D00_010_C0B", "P06_D00_010_C0C", "P08_D00_010_C0D", "P10_D00_010_C0E", 
                    "P01_D00_010_W3A", "P03_D00_010_W3B", "P05_D00_010_W3C", "P07_D00_010_W3D", "P09_D00_010_W3E", 
                    "P01_D00_010_W8A", "P03_D00_010_W8B", "P05_D00_010_W8C", "P07_D00_010_W8D", "P09_D00_010_W8E")

Now we can generate plots (in a loop) for each faceted data set.

Show the code

for (i in ssu18_data_sets) {
  tmp_abund <- get(purrr::map_chr(i, ~paste0(., "_me_abund")))
  tmp_tax_order <- get(purrr::map_chr(i, ~paste0(., "_tax_ord")))
  tmp_abund$use_taxanames <- tmp_tax_order
  tmp_facet_plot <- tmp_abund$plot_bar(use_colors = ssu18_colvec.tax, 
                                       others_color = "#323232", 
                                       facet = "TEMP", 
                                       xtext_keep = TRUE, 
                                       xtext_type_hor = FALSE, 
                                       legend_text_italic = FALSE, 
                                       xtext_size = 6, 
                                       facet_color = "#cccccc", 
                                       order_x = ssu18_samp_order)
  tmp_facet_name <- purrr::map_chr(i, ~paste0(., "_facet_plot")) 
  assign(tmp_facet_name, tmp_facet_plot)  
  rm(list = ls(pattern = "tmp_"))
}

Then add a little formatting to the faceted plots.

Show the code

set_to_plot <- c("ssu18_ps_work_facet_plot", "ssu18_ps_filt_facet_plot", "ssu18_ps_perfect_facet_plot", "ssu18_ps_pime_facet_plot")
for (i in  set_to_plot) {
    tmp_get <- get(i)
    tmp_get <- tmp_get +
    theme_cowplot() +
    guides(fill = guide_legend(title = top_level, reverse = FALSE,
                               keywidth = 0.7, keyheight = 0.7)) +
    ylab(NULL) + xlab("Sample") +
    theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
        panel.background = element_rect(fill = "transparent", colour = NA),
        plot.background = element_rect(fill = "transparent", colour = NA),
        panel.border = element_rect(fill = NA, color = "black"),
        legend.text = element_text(size = 7),
        legend.title = element_text(size = 10),
        legend.position = "right",
        axis.text.y = element_text(size = 8),
        axis.text.x = element_text(size = 6, angle = 90),
        strip.text = element_text(size = 8, angle = 0),
        axis.title = element_text(size = 10)) + ylab(NULL) +
      scale_y_continuous()
  assign(i, tmp_get)
  rm(list = ls(pattern = "tmp_")) 
}

And now plots for the groupmeans sets. We can use the same taxa order since that should not have changed.

Show the code

set_to_plot <- c("ssu18_ps_work_group_plot", "ssu18_ps_filt_group_plot", "ssu18_ps_perfect_group_plot", "ssu18_ps_pime_group_plot")
for (i in ssu18_data_sets) {
  tmp_abund <- get(purrr::map_chr(i, ~paste0(., "_me_abund_group")))
  tmp_tax_order <- get(purrr::map_chr(i, ~paste0(., "_tax_ord")))
  tmp_abund$use_taxanames <- tmp_tax_order
  tmp_group_plot <- tmp_abund$plot_bar(use_colors = ssu18_colvec.tax, 
                                       others_color = "#323232", 
                                       xtext_keep = TRUE, 
                                       xtext_type_hor = TRUE, 
                                       legend_text_italic = FALSE, 
                                       xtext_size = 10, 
                                       facet_color = "#cccccc")
  tmp_group_name <- purrr::map_chr(i, ~paste0(., "_group_plot")) 
  assign(tmp_group_name, tmp_group_plot)  
  rm(list = ls(pattern = "tmp_"))
}

Let’s also add a little formatting to the groupmean plots.

Show the code

for (i in  set_to_plot) {
     tmp_get <- get(i)
     tmp_get <- tmp_get +
       theme_cowplot() +
       ylab("Relative Abundance (% total reads)") + xlab("Temperature") +
       theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
             panel.background = element_rect(fill = "transparent", colour = NA),
             plot.background = element_rect(fill = "transparent", colour = NA),
             panel.border = element_rect(fill = NA, color = "black"),
             legend.position = "none",
             axis.text = element_text(size = 8),
             axis.title = element_text(size = 10)) + 
       scale_y_continuous()

     assign(i, tmp_get)
     rm(list = ls(pattern = "tmp_"))
}

Finally we use the patchwork package to combine the two plots and customize the look.

Show the code

## single index that acts as an index for referencing elements (variables) in a list
##  solution modified from this SO answerhttps://stackoverflow.com/a/54451460
var_list <- list(
  var1 = ssu18_data_sets,
  var2 = c("FULL", "Arbitrary filtered", "PERfect filtered", "PIME filtered")
)
for (j in  1:length(var_list$var1)) {
     tmp_plot_final_name <- purrr::map_chr(var_list$var1[j], ~paste0(., "_", top_level, "_plot_final"))
     tmp_set_type <- var_list$var2[j]
     tmp_p_plot <- get(purrr::map_chr(var_list$var1[j], ~paste0(., "_group_plot")))
     tmp_m_plot <- get(purrr::map_chr(var_list$var1[j], ~paste0(., "_facet_plot")))
     tmp_plot_final <- tmp_p_plot + tmp_m_plot
     tmp_plot_final <- tmp_plot_final +
         plot_annotation(tag_levels = "a", 
                         title = paste("Top", top_level, "of", tmp_set_type, "data set"), 
                         #subtitle = 'Top taxa of non-filtered data',
                         caption = "a) grouped by temperature.,
                         b) All samples, faceted by temperature.") + 
       plot_layout(widths = c(1, 2)) &
       theme(plot.title = element_text(size = 13),
             plot.subtitle = element_text(size = 10), 
             plot.tag = element_text(size = 12), 
             axis.title = element_text(size = 10), 
             axis.text = element_text(size = 8)) 
  assign(tmp_plot_final_name, tmp_plot_final)
  rm(list = ls(pattern = "tmp_"))
}

It is important to note that while most of the dominant taxa are the same across the FULL, Arbitrary, PIME, and PERfect data sets, there a few key difference:

Planctomycetota was not among the dominant taxa in the PIME data set. Nitrospirota was substituted.
Chloroflexi was not among the dominant taxa in either the Arbitrary, PERfect, or PIME data sets. RCP2-54 was substituted in both cases.

(16S rRNA) Figure 1 | Dominant taxonomic groups from the FULL (unfiltered) 16S rRNA data set. A) Grouped by temperature and B) all samples faceted by temperature treatment.

(16S rRNA) Figure 2 | Dominant taxonomic groups from the Arbitrary filtered 16S rRNA data set. A) Grouped by temperature and B) all samples faceted by temperature treatment.

(16S rRNA) Figure 3 | Dominant taxonomic groups from the PERfect filtered 16S rRNA data set. A) Grouped by temperature and B) all samples faceted by temperature treatment.

(16S rRNA) Figure 4 | Dominant taxonomic groups from the PIME filtered 16S rRNA data set. A) Grouped by temperature and B) all samples faceted by temperature treatment.

ITS

Here we compare the taxonomic breakdown of the FULL (unfiltered), PIME filtered, and PERfect filtered data sets, split by temperature treatment for the ITS data. The workflow is basically the same as the 16S rRNA data. Click the box below to see the entire workflow.

Detailed workflow for ITS taxonomic analysis.

Choose the number of taxa to display and the taxonomic level. Aggregate the rest into “Other”.

top_hits <- 14
top_level <- "Order"

As above, in order to use microeco, we need to add the rank designation as a prefix to each taxa. For example, Basidiomycota is changed to p__Basidiomycota.

for (i in its18_data_sets) {
  tmp_get <- get(i)
  tmp_sam_data <- sample_data(tmp_get)
  tmp_tax_data <- data.frame(tax_table(tmp_get))
  tmp_tax_data$ASV_ID <- NULL # Some have, some do not
  tmp_tax_data$ASV_SEQ <- NULL
  
#  tmp_tax_data[] <- data.frame(lapply(tmp_tax_data, gsub, 
#                                      pattern = "^[k | p | c | o | f]_.*", 
#                                      replacement = "", fixed = FALSE))
  tmp_tax_data$Kingdom <- paste("k__", tmp_tax_data$Kingdom, sep = "")
  tmp_tax_data$Phylum <- paste("p__", tmp_tax_data$Phylum, sep = "")
  tmp_tax_data$Class <- paste("c__", tmp_tax_data$Class, sep = "")
  tmp_tax_data$Order <- paste("o__", tmp_tax_data$Order, sep = "")
  tmp_tax_data$Family <- paste("f__", tmp_tax_data$Family, sep = "")
  tmp_tax_data$Genus <- paste("g__", tmp_tax_data$Genus, sep = "")
  tmp_tax_data <- as.matrix(tmp_tax_data)
  
  tmp_ps <- phyloseq(otu_table(tmp_get),
                     tax_table(tmp_tax_data),
                     tmp_sam_data)
  assign(i, tmp_ps)
  rm(list = ls(pattern = "tmp_"))
}
data.frame(tax_table(its18_ps_pime))

for (i in its18_data_sets) {
    tmp_get <- get(i)
    tmp_otu_table <- data.frame(t(otu_table(tmp_get)))
    tmp_sample_info <- data.frame(sample_data(tmp_get))
    tmp_taxonomy_table <- data.frame(tax_table(tmp_get))
    #tmp_taxonomy_table %<>% tidy_taxonomy
    tmp_dataset <- microtable$new(sample_table = tmp_sample_info, 
                                  otu_table = tmp_otu_table, 
                                  tax_table = tmp_taxonomy_table)
    tmp_dataset$tidy_dataset()
    print(tmp_dataset)
    tmp_dataset$tax_table %<>% base::subset(Kingdom == "k__Fungi")
    print(tmp_dataset)
    tmp_dataset$tidy_dataset()
    print(tmp_dataset)
    tmp_name <- purrr::map_chr(i, ~paste0(., "_me")) 
    assign(tmp_name, tmp_dataset)
    rm(list = ls(pattern = "tmp_"))
}  
objects()

Here is an example of what a new microeco object looks like when you call it.

its18_ps_work_me

microtable class:
sample_table have 13 rows and 6 columns
otu_table have 3355 rows and 13 columns
tax_table have 3355 rows and 6 columns
Taxa abundance: calculated for Kingdom,Phylum,Class,Order,Family,Genus

Now, we calculate the taxa abundance at each taxonomic rank using cal_abund(). This function return a list called taxa_abund containing several data frame of the abundance information at each taxonomic rank. The list is stored in the microtable object automatically. It’s worth noting that the cal_abund() function can be used to solve some complex cases, such as supporting both the relative and absolute abundance calculation and selecting the partial taxonomic columns. More information can be found in the description of the file2meco package. In the same loop we also create a trans_abund class, which is used to transform taxonomic abundance data for plotting.

for (i in its18_data_sets) {
  tmp_me <- get(purrr::map_chr(i, ~paste0(., "_me")))
  tmp_me$cal_abund()
  tmp_me_abund <- trans_abund$new(dataset = tmp_me, taxrank = top_level, ntaxa = top_hits)
  tmp_me_abund$abund_data$Abundance <- tmp_me_abund$abund_data$Abundance / 100
  tmp_me_abund_gr <- trans_abund$new(dataset = tmp_me, taxrank = top_level, ntaxa = top_hits, 
                                  groupmean = "TEMP")
  tmp_me_abund_gr$abund_data$Abundance <- tmp_me_abund_gr$abund_data$Abundance / 100
  tmp_name <- purrr::map_chr(i, ~paste0(., "_me_abund")) 
  assign(tmp_name, tmp_me_abund)  
  tmp_name_gr <- purrr::map_chr(i, ~paste0(., "_me_abund_group")) 
  assign(tmp_name_gr, tmp_me_abund_gr)  
  rm(list = ls(pattern = "tmp_"))
}

The result is stored in object$taxa_abund ...

I prefer to specify the order of taxa in these kinds of plots. We can look the top ntaxa (defined above) by accessing the use_taxanames character vector of each microtable object.

Hum. Similar but not the same. This means we need to define the order for each separately. Once we do that, we will override the use_taxanames character vectors with the reordered vectors.

Show the code

its18_ps_work_tax_ord <- rev(c(
  "k_Fungi",  "p_Ascomycota",  "c_Agaricomycetes", "Agaricales",  
  "Archaeorhizomycetales",  "Capnodiales",  "Eurotiales",  "Geastrales",  
  "Glomerales",  "Helotiales", "Hypocreales",  "Saccharomycetales",  
  "Trichosporonales",  "Xylariales"))   

its18_ps_filt_tax_ord <- rev(c(
  "k_Fungi",  "p_Ascomycota",  "Polyporales", "Agaricales",  
  "Archaeorhizomycetales",  "Capnodiales",  "Eurotiales",  "Geastrales",  
  "Glomerales",  "Mortierellales", "Hypocreales",  "Saccharomycetales",  
  "Trichosporonales",  "Xylariales"))   

its18_ps_perfect_tax_ord <- rev(c(
  "k_Fungi",  "p_Ascomycota",  "c_Agaricomycetes", "Agaricales",  
  "Archaeorhizomycetales",  "Capnodiales",  "Eurotiales",  "Geastrales",  
  "Glomerales",  "Helotiales",   "Hypocreales",  "Saccharomycetales", 
  "Trichosporonales",  "Pezizales"))   
its18_ps_pime_tax_ord <- rev(c(
  "k_Fungi",  "p_Ascomycota",  "Polyporales",  "Agaricales",  
  "Archaeorhizomycetales",  "Capnodiales",  "Eurotiales",  "Geastrales", 
  "Glomerales",  "Mortierellales",  "Hypocreales",  "Saccharomycetales",  
  "Trichosporonales",  "Pezizales"))

And one more little step before plotting. Here we a) specify a custom color palette and b) specify the sample order.

its18_colvec.tax <- rev(c("#323232", "#004949", "#924900", "#490092", 
                      "#6db6ff", "#920000", "#ffb6db", "#24ff24", 
                      "#006ddb", "#db6d00", "#b66dff", "#ffff6d", 
                      "#009292", "#b6dbff", "#ff6db6"))
its18_samp_order <- c("P02_D00_010_C0A", "P04_D00_010_C0B", "P06_D00_010_C0C", "P08_D00_010_C0D", "P10_D00_010_C0E", 
                    "P01_D00_010_W3A", "P03_D00_010_W3B", "P07_D00_010_W3D", "P09_D00_010_W3E", 
                    "P01_D00_010_W8A", "P03_D00_010_W8B", "P05_D00_010_W8C", "P07_D00_010_W8D")

Now we can generate plots (in a loop) for each faceted data set.

for (i in its18_data_sets) {
  tmp_abund <- get(purrr::map_chr(i, ~paste0(., "_me_abund")))
  tmp_tax_order <- get(purrr::map_chr(i, ~paste0(., "_tax_ord")))
  tmp_abund$use_taxanames <- tmp_tax_order
  tmp_facet_plot <- tmp_abund$plot_bar(use_colors = its18_colvec.tax, 
                                       others_color = "#323232", 
                                       facet = "TEMP", 
                                       xtext_keep = TRUE, 
                                       xtext_type_hor = FALSE, 
                                       legend_text_italic = FALSE, 
                                       xtext_size = 6, 
                                       facet_color = "#cccccc", 
                                       order_x = its18_samp_order)
  tmp_facet_name <- purrr::map_chr(i, ~paste0(., "_facet_plot")) 
  assign(tmp_facet_name, tmp_facet_plot)  
  rm(list = ls(pattern = "tmp_"))
}

Then add a little formatting to the faceted plots.

set_to_plot <- c("its18_ps_work_facet_plot", "its18_ps_filt_facet_plot", "its18_ps_perfect_facet_plot", "its18_ps_pime_facet_plot")
for (i in  set_to_plot) {
    tmp_get <- get(i)
    tmp_get <- tmp_get +
    theme_cowplot() +
    guides(fill = guide_legend(title = top_level, reverse = FALSE,
                               keywidth = 0.7, keyheight = 0.7)) +
    ylab(NULL) + xlab("Sample") +
    theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
        panel.background = element_rect(fill = "transparent", colour = NA),
        plot.background = element_rect(fill = "transparent", colour = NA),
        panel.border = element_rect(fill = NA, color = "black"),
        legend.text = element_text(size = 7),
        legend.title = element_text(size = 10),
        legend.position = "right",
        axis.text.y = element_text(size = 8),
        axis.text.x = element_text(size = 6, angle = 90),
        strip.text = element_text(size = 8, angle = 0),
        axis.title = element_text(size = 10)) + ylab(NULL) +
      scale_y_continuous()
  assign(i, tmp_get)
  rm(list = ls(pattern = "tmp_")) 
}

And now plots for the groupmeans sets. We can use the same taxa order since that should not have changed.

set_to_plot <- c("its18_ps_work_group_plot", "its18_ps_filt_group_plot", "its18_ps_perfect_group_plot", "its18_ps_pime_group_plot")
for (i in its18_data_sets) {
  tmp_abund <- get(purrr::map_chr(i, ~paste0(., "_me_abund_group")))
  tmp_tax_order <- get(purrr::map_chr(i, ~paste0(., "_tax_ord")))
  tmp_abund$use_taxanames <- tmp_tax_order
  tmp_group_plot <- tmp_abund$plot_bar(use_colors = its18_colvec.tax, 
                                       others_color = "#323232", 
                                       xtext_keep = TRUE, 
                                       xtext_type_hor = TRUE, 
                                       legend_text_italic = FALSE, 
                                       xtext_size = 10, 
                                       facet_color = "#cccccc")
  tmp_group_name <- purrr::map_chr(i, ~paste0(., "_group_plot")) 
  assign(tmp_group_name, tmp_group_plot)  
  rm(list = ls(pattern = "tmp_"))
}

Let’s also add a little formatting to the groupmean plots.

for (i in  set_to_plot) {
     tmp_get <- get(i)
     tmp_get <- tmp_get +
       theme_cowplot() +
       ylab("Relative Abundance (% total reads)") + xlab("Temperature") +
       theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
             panel.background = element_rect(fill = "transparent", colour = NA),
             plot.background = element_rect(fill = "transparent", colour = NA),
             panel.border = element_rect(fill = NA, color = "black"),
             legend.position = "none",
             axis.text = element_text(size = 8),
             axis.title = element_text(size = 10)) + 
       scale_y_continuous()

     assign(i, tmp_get)
     rm(list = ls(pattern = "tmp_"))
}

Finally we use the patchwork package to combine the two plots and customize the look.

## single index that acts as an index for referencing elements (variables) in a list
##  solution modified from this SO answerhttps://stackoverflow.com/a/54451460
var_list <- list(
  var1 = its18_data_sets,
  var2 = c("FULL", "Arbitrary filtered", "PERfect filtered", "PIME filtered")
)
for (j in  1:length(var_list$var1)) {
     tmp_plot_final_name <- purrr::map_chr(var_list$var1[j], ~paste0(., "_", top_level, "_plot_final"))
     tmp_set_type <- var_list$var2[j]
     tmp_p_plot <- get(purrr::map_chr(var_list$var1[j], ~paste0(., "_group_plot")))
     tmp_m_plot <- get(purrr::map_chr(var_list$var1[j], ~paste0(., "_facet_plot")))
     tmp_plot_final <- tmp_p_plot + tmp_m_plot
     tmp_plot_final <- tmp_plot_final +
         plot_annotation(tag_levels = "a", 
                         title = paste("Top", top_level, "of", tmp_set_type, "data set"), 
                         #subtitle = 'Top taxa of non-filtered data',
                         caption = "a) grouped by temperature.,
                         b) All samples, faceted by temperature.") + 
       plot_layout(widths = c(1, 2)) &
       theme(plot.title = element_text(size = 13),
             plot.subtitle = element_text(size = 10), 
             plot.tag = element_text(size = 12), 
             axis.title = element_text(size = 10), 
             axis.text = element_text(size = 8)) 
  assign(tmp_plot_final_name, tmp_plot_final)
  rm(list = ls(pattern = "tmp_"))
}

It is important to note that while most of the dominant taxa are the same across the FULL, Arbitrary, PIME, and PERfect data sets, there a few key difference:

c_Agaricomycetes & Helotiales were not among the dominant taxa in the Arbitrary or PIME filtered data sets. Polyporales & Mortierellales were substituted, respectively.
Xylariales was not among the dominant taxa in either the PIME or PERfect data sets. Pezizales was substituted in both cases.

(16S rRNA) Figure 5 | Dominant taxonomic groups from the FULL (unfiltered) ITS data set. A) Grouped by temperature and B) all samples faceted by temperature treatment.

(16S rRNA) Figure 6 | Dominant taxonomic groups from the Arbitrary filtered ITS data set. A) Grouped by temperature and B) all samples faceted by temperature treatment.

(16S rRNA) Figure 7 | Dominant taxonomic groups from the PERfect filtered ITS data set. A) Grouped by temperature and B) all samples faceted by temperature treatment.

(16S rRNA) Figure 8 | Dominant taxonomic groups from the PIME filtered ITS data set. A) Grouped by temperature and B) all samples faceted by temperature treatment.

Major Bacterial taxa by Family

We also look at the relative abundance of groups within dominant Phyla. Please note these analyses are for the FULL data set only, before any type of filtering. For this analysis, we use the phyloseq object created in the Data Preparation wokflow section Rename NA taxonomic ranks where unclassified ranks were renamed by the next highest rank. See step #1 below for an example of the taxa table structure.

Note

This code only works for multiple taxa from a single phyloseq object.

Detailed workflow for assessing Bacterial families.

Subset Phyla of interest.

ssu18_ps_work <- readRDS("files/data-prep/rdata/ssu18_ps_work.rds")
head(data.frame(tax_table(ssu18_ps_work)))[,1:6]

          Kingdom          Phylum               Class              Order
ASV9128  Bacteria Acidobacteriota         Subgroup_22      c_Subgroup_22
ASV7870  Bacteria Planctomycetota               OM190            c_OM190
ASV8537  Bacteria     Chloroflexi        Anaerolineae            SBR1031
ASV15357 Bacteria  Proteobacteria Gammaproteobacteria Diplorickettsiales
ASV19533 Bacteria      k_Bacteria          k_Bacteria         k_Bacteria
ASV18180 Bacteria Planctomycetota      Planctomycetes         Gemmatales
                      Family                 Genus
ASV9128        c_Subgroup_22         c_Subgroup_22
ASV7870              c_OM190               c_OM190
ASV8537                  A4b                 f_A4b
ASV15357 Diplorickettsiaceae f_Diplorickettsiaceae
ASV19533          k_Bacteria            k_Bacteria
ASV18180         Gemmataceae         f_Gemmataceae

ssu18_data_sets <- c("ssu18_ps_work")
for (i in ssu18_data_sets) {
     tmp_name <- purrr::map_chr(i, ~paste0(., "_proteo"))
     tmp_get <- get(i)
     tmp_df <- subset_taxa(tmp_get, Phylum == "Proteobacteria")
     assign(tmp_name, tmp_df)
     print(tmp_name)
     tmp_get_taxa <- get_taxa_unique(tmp_df,
                                     taxonomic.rank = rank_names(tmp_df)[3],
                                     errorIfNULL=TRUE)
     print(tmp_get_taxa)
     rm(list = ls(pattern = "tmp_"))
     rm(list = ls(pattern = "_proteo"))
}

Replace Phylum Proteobacteria with the Class name.

for (j in ssu18_data_sets) {
  tmp_name <- purrr::map_chr(j, ~paste0(., "_proteo_clean"))
  tmp_get <- get(j)
  tmp_clean <- data.frame(tax_table(tmp_get))

   for (i in 1:nrow(tmp_clean)){
       if (tmp_clean[i,2] == "Proteobacteria" & tmp_clean[i,3] == "Alphaproteobacteria"){
           phylum <- base::paste("Alphaproteobacteria")
           tmp_clean[i, 2] <- phylum
   }   else if (tmp_clean[i,2] == "Proteobacteria" & tmp_clean[i,3] == "Gammaproteobacteria"){
           phylum <- base::paste("Gammaproteobacteria")
           tmp_clean[i, 2] <- phylum
   }   else if (tmp_clean[i,2] == "Proteobacteria" & tmp_clean[i,3] == "Zetaproteobacteria"){
              phylum <- base::paste("Zetaproteobacteria")
           tmp_clean[i, 2] <- phylum
   }   else if (tmp_clean[i,2] == "Proteobacteria" & tmp_clean[i,3] == "p_Proteobacteria"){
           phylum <- base::paste("p_Proteobacteria")
           tmp_clean[i, 2] <- phylum
       }
     }
  tax_table(tmp_get) <- as.matrix(tmp_clean)
  rank_names(tmp_get)
  assign(tmp_name, tmp_get)
  print(c(tmp_name, tmp_get))
  print(length(get_taxa_unique(tmp_get,
                               taxonomic.rank = rank_names(tmp_get)[2],
                               errorIfNULL=TRUE)))
  tmp_path <- file.path("files/taxa/rdata/")
  saveRDS(tmp_get, paste(tmp_path, j, "_clean.rds", sep = ""))
  rm(list = ls(pattern = "tmp_"))
}
rm(class, order, phylum)

set_to_plot <- "ssu18_ps_work"
tax_group <- c("Alphaproteobacteria", "Gammaproteobacteria", 
               "Acidobacteriota", "Actinobacteriota", 
               "Bacteroidota", "Firmicutes", 
               "Myxococcota", "Verrucomicrobiota")
for (i in set_to_plot) {
  for (j in tax_group) {
  tmp_get <- get(purrr::map_chr(i, ~ paste0(., "_proteo_clean")))
  tmp_sub <- subset_taxa(tmp_get, Phylum == j)
  tmp_name <- purrr::map_chr(i, ~ paste0(., "_", j))
  assign(tmp_name, tmp_sub)
  rm(list = ls(pattern = "tmp_"))
  }
}

Choose the number of taxa to display and the taxonomic level. Aggregate the rest into “Other”.

top_hits <- 12
top_level <- "Family"
for (i in tax_group) {
  tmp_get <- get(purrr::map_chr(i, ~ paste0(set_to_plot, "_", i)))
  tmp_otu <- data.frame(t(otu_table(tmp_get)))
  tmp_otu[] <- lapply(tmp_otu, as.numeric)
  tmp_otu <- as.matrix(tmp_otu)
  tmp_tax <- as.matrix(data.frame(tax_table(tmp_get)))
  tmp_samples <- data.frame(sample_data(tmp_get))
  tmp_clean_df <- merge_phyloseq(otu_table(tmp_otu, taxa_are_rows = TRUE),
                          tax_table(tmp_tax),
                          sample_data(tmp_samples))
  tmp_agg_df <- microbiome::aggregate_top_taxa(tmp_clean_df,
                                                    top = top_hits,
                                                    level = top_level)
  tmp_agg_name <- purrr::map_chr(i, ~ paste0(set_to_plot, "_", i, "_agg"))
  assign(tmp_agg_name, tmp_agg_df)
  rm(list = ls(pattern = "_sep_agg"))
}

for (i in tax_group){
  tmp_data <- purrr::map_chr(i, ~ paste0(set_to_plot, "_", i, "_agg"))
  tmp_get <- get(tmp_data)
  tmp_list <- get_taxa_unique(tmp_get, taxonomic.rank = rank_names(tmp_get)[2],
                        errorIfNULL = TRUE)
  tmp_name <- purrr::map_chr(tmp_data, ~ paste0(., "_order"))
  assign(tmp_name, tmp_list)
  rm(list = ls(pattern = "tmp_"))
}

for (i in tax_group) {
  tmp_get <- get(purrr::map_chr(i, ~ paste0(set_to_plot, "_", i, "_agg_order")))
  cat("\n")
  cat("#########", i, "########", "\n")
  tmp_print <- c(tmp_get)
  cat(tmp_print, "\n")
  cat("####################################################")
  cat("\n")
  rm(list = ls(pattern = "tmp_"))
}
rm(i, j)

tmp_order <- rev(c("Other", "c_Alphaproteobacteria", "o_Elsterales", "o_Azospirillales", "Rhizobiales_Incertae_Sedis", "Xanthobacteraceae", "Sphingomonadaceae", "Rhizobiaceae", "Micropepsaceae", "Methyloligellaceae", "KF-JG30-B3", "Hyphomicrobiaceae", "Dongiaceae"))
assign(paste(set_to_plot, "_", "Alphaproteobacteria", "_agg_order", sep = ""), tmp_order) 
###################
tmp_order <- rev(c("Other", "c_Gammaproteobacteria", "o_PLTA13", "o_CCD24", "Unknown_Family", "Xanthomonadaceae", "TRA3-20", "Steroidobacteraceae", "SC-I-84", "Nitrosomonadaceae", "Comamonadaceae", "Burkholderiaceae", "B1-7BS"))
assign(paste(set_to_plot, "_", "Gammaproteobacteria", "_agg_order", sep = ""), tmp_order) 
###################
tmp_order <- rev(c("Other", "c_Subgroup_22", "c_Subgroup_25", "c_Subgroup_5", "o_Vicinamibacterales", "o_11-24", "o_Subgroup_17", "o_Subgroup_2", "o_Subgroup_7", "o_Acidobacteriales", "Vicinamibacteraceae", "Solibacteraceae", "Pyrinomonadaceae"))
assign(paste(set_to_plot, "_", "Acidobacteriota", "_agg_order", sep = ""), tmp_order) 
###################
tmp_order <- rev(c("Other", "c_MB-A2-108", "o_IMCC26256", "o_Gaiellales", "o_Frankiales", "Streptomycetaceae", "Solirubrobacteraceae", "Nocardioidaceae", "Mycobacteriaceae", "Micromonosporaceae", "Gaiellaceae", "Acidothermaceae", "67-14"))
assign(paste(set_to_plot, "_", "Actinobacteriota", "_agg_order", sep = ""), tmp_order) 
###################
tmp_order <- rev(c("Other", "c_SJA-28", "o_Chitinophagales", "Sphingobacteriaceae", "Saprospiraceae", "Microscillaceae", "Hymenobacteraceae", "Flavobacteriaceae", "Cytophagaceae", "Chitinophagaceae", "BSV26", "env.OPS_17", "AKYH767"))
assign(paste(set_to_plot, "_", "Bacteroidota", "_agg_order", sep = ""), tmp_order) 
###################
tmp_order <- rev(c("Other", "p_Firmicutes", "c_Bacilli", "o_Bacillales", "Thermoactinomycetaceae", "type_III", "Paenibacillaceae", "Lachnospiraceae", "Hungateiclostridiaceae", "Clostridiaceae", "Bacillaceae", "Alicyclobacillaceae", "Planococcaceae"))
assign(paste(set_to_plot, "_", "Firmicutes", "_agg_order", sep = ""), tmp_order) 
###################
tmp_order <- rev(c("Other", "p_Myxococcota", "c_bacteriap25", "c_Polyangia", "o_mle1-27", "o_MSB-4B10", "Sandaracinaceae", "Polyangiaceae", "Phaselicystidaceae", "Myxococcaceae", "Haliangiaceae", "BIrii41", "Anaeromyxobacteraceae"))
assign(paste(set_to_plot, "_", "Myxococcota", "_agg_order", sep = ""), tmp_order) 
###################
tmp_order <- rev(c("Other", "c_Verrucomicrobiae", "o_Chlamydiales", "o_LD1-PA32", "o_S-BQ2-57_soil_group", "Xiphinematobacteraceae", "Simkaniaceae", "Pedosphaeraceae", "Parachlamydiaceae", "Opitutaceae", "Omnitrophaceae", "cvE6", "Chthoniobacteraceae"))
assign(paste(set_to_plot, "_", "Verrucomicrobiota", "_agg_order", sep = ""), tmp_order) 
###################

Now, transform the data to relative abundance.

for (i in tax_group) {
  tmp_agg <- purrr::map_chr(i, ~ paste0(set_to_plot, "_", i, "_agg"))
  tmp_order <- purrr::map_chr(tmp_agg, ~paste0(., "_order"))
  tmp_get_agg <- get(tmp_agg)
  tmp_get_order <- get(tmp_order)
  tmp_df <- tmp_get_agg %>%
            transform_sample_counts(function(x) {x/sum(x)} ) %>%
            psmelt()
  tmp_df[[top_level]] <- gdata::reorder.factor(tmp_df[[top_level]],
                                                  new.order = rev(tmp_get_order))
  tmp_df <- tmp_df %>% dplyr::arrange(get(top_level))
  tmp_name <- purrr::map_chr(tmp_agg, ~paste0(., "_tax"))
  assign(tmp_name, tmp_df)
  #print(c(i, tmp_name, tmp_agg))
  rm(list = ls(pattern = "tmp_"))
}

for (i in tax_group) {
  tmp_get <- get(purrr::map_chr(i, ~ paste0(set_to_plot, "_", i, "_agg_tax")))
  tmp_levels <- levels(tmp_get[[top_level]])
  print(c(i, tmp_levels))
}

Plot the data for a single phyloseq object. Here you use an aggregated tax file.

for (i in tax_group) {
      tmp_get <- get(purrr::map_chr(i, ~ paste0(set_to_plot, "_", i, "_agg_tax")))
      tmp_plot <- ggplot(tmp_get, aes(x = factor(TEMP), 
                                        y = Abundance, fill = get(top_level))) +
          geom_bar(stat = "identity", position = "fill") +
          scale_fill_manual(values = ssu18_colvec.tax) +
          #scale_x_discrete("Temperature", expand = waiver(), position = "bottom", drop = FALSE) +
          theme_cowplot() +
          guides(fill = guide_legend(title = top_level)) +
          #guides(fill = guide_legend(reverse = FALSE, keywidth = 1, keyheight = 1)) +
          ylab("Relative Abundance (% total reads)") + xlab("Temperature") +
          theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
                panel.background = element_rect(fill = "transparent", colour = NA),
                plot.background = element_rect(fill = "transparent", colour = NA),
                panel.border = element_rect(fill = NA, color = "black"),
                #axis.text = element_text(size = 6),
                #axis.title = element_text(size = 8),
                legend.position = "none")
      tmp_name <- purrr::map_chr(i, ~paste0(set_to_plot, "_", ., "_plot"))
      assign(tmp_name, tmp_plot)
      rm(list = ls(pattern = "tmp_"))
}

Plot the data for multiple taxa. Here again you use an aggregated tax file. This code can be used to generate plots for multiple data frames by adding the base phyloseq names to the ssu18_split_df variable. This code will also facet the plots by a metadata variable. If you do not want to facet remove the line beginning with facet_grid.

for (i in tax_group) {
  tmp_level_get <- get(purrr::map_chr(i, ~paste0(set_to_plot, "_", .)))
  tmp_level <- data.frame(sample_data(tmp_level_get))
  tmp_level <- tmp_level[order(tmp_level$TEMP), ]
  tmp_level <- as.vector(tmp_level$SamName)

  tmp_agg_name <- purrr::map_chr(i, ~paste0(set_to_plot, "_", .,  "_agg_tax"))
  tmp_get <- get(tmp_agg_name)
  tmp_df <- reshape::melt(tmp_get, id.vars = c("Sample", "TEMP", "Abundance", "Family"))
  tmp_plot_name <- purrr::map_chr(i, ~paste0(set_to_plot, "_", ., "_plot_melt"))

  tmp_plot <- ggplot(tmp_df,
                aes(x = Sample,
                    y = Abundance, fill = get(top_level))) +
    facet_grid(. ~TEMP, scale = "free_x", space = "free_x")+
    geom_bar(stat = "identity", position = "fill")  +
    scale_fill_manual(values = ssu18_colvec.tax) +
    #scale_x_discrete("Treatment", expand = waiver(),
    #                 position = "bottom", drop = FALSE, limits = tmp_level) +
    theme_cowplot() +
    guides(fill = guide_legend(title = top_level, reverse = FALSE,
                               keywidth = 0.7, keyheight = 0.7)) +
    ylab(NULL) +
    theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
        panel.background = element_rect(fill = "transparent", colour = NA),
        plot.background = element_rect(fill = "transparent", colour = NA),
        panel.border = element_rect(fill = NA, color = "black"),
        legend.position = "right",
        #legend.text = element_text(size = 6),
        #legend.title = element_text(size = 8),
        #legend.key.size = unit(1.5, "cm"),
        #axis.text.y = element_text(size = 6),
        axis.text.x = element_text(angle = 90)
        #strip.text = element_text(size = 8, angle = 0),
        #axis.title = element_text(size = 8)
        ) + 
    ylab(NULL)
  
  assign(tmp_plot_name, tmp_plot)
  rm(list = ls(pattern = "tmp_"))
}

Finally we use the patchwork package to combine the two plots and customize the look.

for (i in tax_group) {
  tmp_plot_main <- get(purrr::map_chr(i, ~paste0(set_to_plot, "_", ., "_plot")))
  tmp_plot_melt <- get(purrr::map_chr(i, ~paste0(set_to_plot, "_", ., "_plot_melt")))
  tmp_final <- tmp_plot_main + tmp_plot_melt
  tmp_final <- tmp_final + 
    plot_annotation(tag_levels = 'a', 
                    title = i) + 
                    #subtitle = 'Top taxa of non-filtered data',
                    #caption = 'A) grouped by temperature.,
                    #B) All samples, faceted by temperature.') + 
    plot_layout(widths = c(1, 2)) &
    theme(plot.title = element_text(size = 9),
        plot.subtitle = element_text(size = 1),
        plot.tag = element_text(size = 6),
        axis.title = element_text(size = 7),
        axis.text = element_text(size = 6),
        strip.text = element_text(size = 8, angle = 0),
        legend.text = element_text(size = 7),
        legend.title = element_text(size = 9),
        )
        #legend.position = "right")
        #legend.position = "right",
        #legend.title = element_text(size = rel(1)),
        #legend.text = element_text(size = rel(1)))
  tmp_name <- purrr::map_chr(i, ~paste0(set_to_plot, "_", ., "_final_plot"))
  assign(tmp_name, tmp_final)
  rm(list = ls(pattern = "tmp_"))
}

taxa_to_plot <- c("Acidobacteriota", "Actinobacteriota", "Alphaproteobacteria", 
                  "Bacteroidota", "Firmicutes", "Gammaproteobacteria", "Myxococcota", 
                  "Verrucomicrobiota")
for (i in taxa_to_plot) {
  tmp_get <- get(purrr::map_chr(i, ~ paste0("ssu18_ps_work_", i, "_final_plot", sep = "")))
  ggplot2::ggsave(tmp_get, 
                file = paste0("files/taxa/figures/", i, "_tax_div_bar_plots.png", sep = ""), 
                height = 14, width = 21, units = 'cm', bg = "white")
  ggplot2::ggsave(tmp_get, 
                file = paste0("files/taxa/figures/", i, "_tax_div_bar_plots.pdf", sep = ""), 
                height = 14, width = 21, units = 'cm', bg = "white")
}

(16S rRNA) Figure 9 | Alphaproteobacteria family plots.

(16S rRNA) Figure 10 | Gammaproteobacteria family plots.

(16S rRNA) Figure 11 | Acidobacteriota family plots.

(16S rRNA) Figure 12 | Actinobacteriota family plots.

(16S rRNA) Figure 13 | Bacteroidota family plots.

(16S rRNA) Figure 14 | Firmicutes family plots.

(16S rRNA) Figure 15 | Myxococcota family plots.

(16S rRNA) Figure 16 | Verrucomicrobiota family plots.

Workflow Output

Data products generated in this workflow can be downloaded from figshare.

Next workflow:
5. Alpha Diversity Estimates Previous workflow:
3. Filtering

Source Code

The source code for this page can be accessed on GitHub by clicking this link.

Data Availability

Data generated in this workflow and the Rdata need to run the workflow can be accessed on figshare at 10.25573/data.16826737.

Last updated on

[1] "2025-08-23 07:32:47 PDT"

--- title: "4. Taxonomic Diversity" description: | Reproducible workflow for assessing dominant taxonomic groups in the 16S rRNA and ITS data sets. format: html: toc: true toc-depth: 3 --- ```{r} #| message: false #| results: hide #| code-fold: true #| code-summary: "Click here for setup information" knitr::opts_chunk$set(echo = TRUE, eval = FALSE) set.seed(119) #library(conflicted) #pacman::p_depends(phangorn, local = TRUE) #pacman::p_depends_reverse(phangorn, local = TRUE) library(phyloseq); packageVersion("phyloseq") library(Biostrings); packageVersion("Biostrings") pacman::p_load(tidyverse, patchwork, agricolae, labdsv, naniar, microbiome, microeco, file2meco, cowplot, GUniFrac, ggalluvial, ggdendro, igraph, reactable, pheatmap, microbiomeMarker, captioner, downloadthis, install = FALSE, update = FALSE) options(scipen=999) knitr::opts_current$get(c( "cache", "cache.path", "cache.rebuild", "dependson", "autodep" )) ``` </details> ```{r} #| echo: false #| eval: true #TEMP LOAD ONLY REMOVE WHEN WORKFLOW FINISHED remove(list = ls()) load("page_build/taxa_wf.rdata") ``` ```{r} #| echo: false #| eval: true #| results: hide # Create the caption(s) with captioner caption_tab <- captioner(prefix = "(16S rRNA) Table", suffix = " |", style = "b") caption_fig <- captioner(prefix = "(16S rRNA) Figure", suffix = " |", style = "b") # Create a function for referring to the tables in text ref <- function(x) str_extract(x, "[^|]*") %>% trimws(which = "right", whitespace = "[ ]") ``` ```{r} #| echo: false #| eval: true source("assets/captions/captions_taxa.R") ``` # Synopsis This workflow contains taxonomic diversity assessments for the 16S rRNA and ITS data sets. In order to run the workflow, you either need to first run the [DADA2 Workflow](dada2.html), then the [Data Preparation workflow](data-prep.html), and finally the [Filtering workflow](filtering.html) or download the files linked below. ## Workflow Input All files needed to run this workflow can be downloaded from figshare. <iframe src="https://widgets.figshare.com/articles/14701440/embed?show_title=1" width="100%" height="351" allowfullscreen frameborder="0"></iframe> In this workflow, we use the [microeco](https://github.com/ChiLiubio/microeco) to look at the taxonomic distribution of microbial communities. # Taxonomic Assessment ## 16s rRNA ```{r} #| echo: false ## Initial Load for ANALYSIS #1 remove(list = ls()) set.seed(119) ssu18_ps_work <- readRDS("files/data-prep/rdata/ssu18_ps_work.rds") ssu18_ps_filt <- readRDS("files/filtering/arbitrary/rdata/ssu18_ps_filt.rds") ssu18_ps_perfect <- readRDS("files/filtering/perfect/rdata/ssu18_ps_perfect.rds") ssu18_ps_pime <- readRDS("files/filtering/pime/rdata/ssu18_ps_pime.rds") objects() ``` ```{r} #| echo: false swel_col <- c("#2271B2", "#71B222", "#B22271") ``` Here we compare the taxonomic breakdown of the Full (unfiltered), Arbitrary filtered, PERfect filtered, and PIME filtered data sets, split by temperature treatment. Before we plot the data, we want to separate out the Proteobacteria classes so we can plot these along with other phyla. To accomplish this we perform the following steps: 1) Get all Class-level Proteobacteria names ```{r} #| code-fold: true ssu18_data_sets <- c("ssu18_ps_work", "ssu18_ps_filt", "ssu18_ps_perfect", "ssu18_ps_pime") for (i in ssu18_data_sets) { tmp_name <- purrr::map_chr(i, ~paste0(., "_proteo")) tmp_get <- get(i) tmp_df <- subset_taxa(tmp_get, Phylum == "Proteobacteria") assign(tmp_name, tmp_df) print(tmp_name) tmp_get_taxa <- get_taxa_unique(tmp_df, taxonomic.rank = rank_names(tmp_df)[3], errorIfNULL=TRUE) print(tmp_get_taxa) rm(list = ls(pattern = "tmp_")) rm(list = ls(pattern = "_proteo")) } ``` 2) Replace Phylum Proteobacteria with the Class name. ```{r} #| code-fold: true for (j in ssu18_data_sets) { tmp_name <- purrr::map_chr(j, ~paste0(., "_proteo_clean")) tmp_get <- get(j) tmp_clean <- data.frame(tax_table(tmp_get)) for (i in 1:nrow(tmp_clean)){ if (tmp_clean[i,2] == "Proteobacteria" & tmp_clean[i,3] == "Alphaproteobacteria"){ phylum <- base::paste("Alphaproteobacteria") tmp_clean[i, 2] <- phylum } else if (tmp_clean[i,2] == "Proteobacteria" & tmp_clean[i,3] == "Gammaproteobacteria"){ phylum <- base::paste("Gammaproteobacteria") tmp_clean[i, 2] <- phylum } else if (tmp_clean[i,2] == "Proteobacteria" & tmp_clean[i,3] == "Zetaproteobacteria"){ phylum <- base::paste("Zetaproteobacteria") tmp_clean[i, 2] <- phylum } else if (tmp_clean[i,2] == "Proteobacteria" & tmp_clean[i,3] == "p_Proteobacteria"){ phylum <- base::paste("p_Proteobacteria") tmp_clean[i, 2] <- phylum } } tax_table(tmp_get) <- as.matrix(tmp_clean) rank_names(tmp_get) assign(tmp_name, tmp_get) print(c(tmp_name, tmp_get)) print(length(get_taxa_unique(tmp_get, taxonomic.rank = rank_names(tmp_get)[2], errorIfNULL=TRUE))) tmp_path <- file.path("files/taxa/rdata/") saveRDS(tmp_get, paste(tmp_path, j, "_clean.rds", sep = "")) rm(list = ls(pattern = "tmp_")) } rm(class, order, phylum) objects(pattern="_proteo_clean") ``` 3) In order to use `microeco`, we need to add the rank designation as a prefix to each taxa. For example, `Actinobacteriota` is changed to `p__Actinobacteriota`. ```{r} #| code-fold: true for (i in ssu18_data_sets) { tmp_get <- get(purrr::map_chr(i, ~paste0(., "_proteo_clean"))) #tmp_get <- get(i) #tmp_path <- file.path("files/alpha/rdata/") #tmp_read <- readRDS(paste(tmp_path, i, ".rds", sep = "")) tmp_sam_data <- sample_data(tmp_get) tmp_tax_data <- data.frame(tax_table(tmp_get)) tmp_tax_data$Phylum <- gsub("p_Proteobacteria", "Proteobacteria", tmp_tax_data$Phylum) #tmp_tax_data[,c(1:6)] tmp_tax_data$ASV_ID <- NULL # Some have, some do not tmp_tax_data$ASV_SEQ <- NULL tmp_tax_data[] <- data.frame(lapply(tmp_tax_data, gsub, pattern = "^[k | p | c | o | f]_.*", replacement = "", fixed = FALSE)) tmp_tax_data$Kingdom <- paste("k__", tmp_tax_data$Kingdom, sep = "") tmp_tax_data$Phylum <- paste("p__", tmp_tax_data$Phylum, sep = "") tmp_tax_data$Class <- paste("c__", tmp_tax_data$Class, sep = "") tmp_tax_data$Order <- paste("o__", tmp_tax_data$Order, sep = "") tmp_tax_data$Family <- paste("f__", tmp_tax_data$Family, sep = "") tmp_tax_data$Genus <- paste("g__", tmp_tax_data$Genus, sep = "") tmp_tax_data <- as.matrix(tmp_tax_data) tmp_ps <- phyloseq(otu_table(tmp_get), phy_tree(tmp_get), tax_table(tmp_tax_data), tmp_sam_data) assign(i, tmp_ps) rm(list = ls(pattern = "tmp_")) } rm(list = ls(pattern = "_proteo_clean")) ``` 4) Next, we need to covert each `phyloseq object` to a `microtable class`. The microtable class is the basic data structure for the `microeco` package and designed to store basic information from all the downstream analyses (e.g, alpha diversity, beta diversity, etc.). We use the [file2meco](https://github.com/ChiLiubio/file2meco) to read the phyloseq object and convert into a microtable object. We can add `_me` as a suffix to each object to distiguish it from its' phyloseq counterpart. ```{r} #| code-fold: true for (i in ssu18_data_sets) { tmp_get <- get(i) tmp_otu_table <- data.frame(t(otu_table(tmp_get))) tmp_sample_info <- data.frame(sample_data(tmp_get)) tmp_taxonomy_table <- data.frame(tax_table(tmp_get)) tmp_phylo_tree <- phy_tree(tmp_get) tmp_taxonomy_table %<>% tidy_taxonomy tmp_dataset <- microtable$new(sample_table = tmp_sample_info, otu_table = tmp_otu_table, tax_table = tmp_taxonomy_table, phylo_tree = tmp_phylo_tree) tmp_dataset$tidy_dataset() print(tmp_dataset) tmp_dataset$tax_table %<>% base::subset(Kingdom == "k__Archaea" | Kingdom == "k__Bacteria") print(tmp_dataset) tmp_dataset$filter_pollution(taxa = c("mitochondria", "chloroplast")) print(tmp_dataset) tmp_dataset$tidy_dataset() print(tmp_dataset) tmp_name <- purrr::map_chr(i, ~paste0(., "_me")) assign(tmp_name, tmp_dataset) rm(list = ls(pattern = "tmp_")) } objects() ``` Here is an example of what a new microeco object looks like when you call it. ```{r} #| echo: true #| eval: true ssu18_ps_work_me ``` ```{r} #| echo: false saveRDS(ssu18_ps_work_me, "files/taxa/rdata/ssu18_ps_work_me.rds") saveRDS(ssu18_ps_filt_me, "files/taxa/rdata/ssu18_ps_filt_me.rds") saveRDS(ssu18_ps_perfect_me, "files/taxa/rdata/ssu18_ps_perfect_me.rds") saveRDS(ssu18_ps_pime_me, "files/taxa/rdata/ssu18_ps_pime_me.rds") ``` 5) Now, we calculate the taxa abundance at each taxonomic rank using `cal_abund()`. This function return a list called `taxa_abund` containing several data frame of the abundance information at each taxonomic rank. The list is stored in the microtable object automatically. It’s worth noting that the `cal_abund()` function can be used to solve some complex cases, such as supporting both the relative and absolute abundance calculation and selecting the partial taxonomic columns. More information can be found in the description of the [file2meco package](https://github.com/ChiLiubio/file2meco). In the same loop we also create a `trans_abund` class, which is used to transform taxonomic abundance data for plotting. ```{r} #| code-fold: true for (i in ssu18_data_sets) { tmp_me <- get(purrr::map_chr(i, ~paste0(., "_me"))) tmp_me$cal_abund() tmp_me_abund <- trans_abund$new(dataset = tmp_me, taxrank = "Phylum", ntaxa = 12) tmp_me_abund$abund_data$Abundance <- tmp_me_abund$abund_data$Abundance / 100 tmp_me_abund_gr <- trans_abund$new(dataset = tmp_me, taxrank = "Phylum", ntaxa = 12, groupmean = "TEMP") tmp_me_abund_gr$abund_data$Abundance <- tmp_me_abund_gr$abund_data$Abundance / 100 tmp_name <- purrr::map_chr(i, ~paste0(., "_me_abund")) assign(tmp_name, tmp_me_abund) tmp_name_gr <- purrr::map_chr(i, ~paste0(., "_me_abund_group")) assign(tmp_name_gr, tmp_me_abund_gr) rm(list = ls(pattern = "tmp_")) } objects() ``` ``` The result is stored in object$taxa_abund ... ``` 6) I prefer to specify the order of taxa in these kinds of plots. We can look the top `ntaxa` (defined above) by accessing the `use_taxanames` character vector of each microtable object. ```{r} #| echo: false print("FULL Data set (ssu18_ps_work_me)", quote = FALSE) ssu18_ps_work_me_abund$use_taxanames print("--------------------------------------------", quote = FALSE) print("Arbitrary filtered Data set (ssu18_ps_filt_me)", quote = FALSE) ssu18_ps_filt_me_abund$use_taxanames print("--------------------------------------------", quote = FALSE) print("PERfect filtered Data set (ssu18_ps_perfect)", quote = FALSE) ssu18_ps_perfect_me_abund$use_taxanames print("--------------------------------------------", quote = FALSE) print("PIME filtered Data set (ssu18_ps_pime_me)", quote = FALSE) ssu18_ps_pime_me_abund$use_taxanames print("--------------------------------------------", quote = FALSE) ``` Hum. Similar but not the same. This means we need to define the order for each separately. Once we do that, we will override the `use_taxanames` character vectors with the reordered vectors. ```{r} #| code-fold: true ssu18_ps_work_tax_ord <- c("Alphaproteobacteria", "Gammaproteobacteria", "Acidobacteriota", "Actinobacteriota", "Bacteroidota", "Firmicutes", "Myxococcota", "Verrucomicrobiota", "Chloroflexi", "Planctomycetota", "Methylomirabilota", "Crenarchaeota") ssu18_ps_filt_tax_ord <- c("Alphaproteobacteria", "Gammaproteobacteria", "Acidobacteriota", "Actinobacteriota", "Bacteroidota", "Firmicutes", "Myxococcota", "Verrucomicrobiota", "RCP2-54", "Planctomycetota", "Methylomirabilota", "Crenarchaeota") ssu18_ps_perfect_tax_ord <- c("Alphaproteobacteria", "Gammaproteobacteria", "Acidobacteriota", "Actinobacteriota", "Bacteroidota", "Firmicutes", "Myxococcota", "Verrucomicrobiota", "RCP2-54", "Planctomycetota", "Methylomirabilota", "Crenarchaeota") ssu18_ps_pime_tax_ord <- c("Alphaproteobacteria", "Gammaproteobacteria", "Acidobacteriota", "Actinobacteriota", "Bacteroidota", "Firmicutes", "Myxococcota", "Verrucomicrobiota", "RCP2-54", "Nitrospirota", "Methylomirabilota", "Crenarchaeota") ``` 7) And one more little step before plotting. Here we **a**) specify a custom color palette and **b**) specify the sample order. ```{r} #| code-fold: true top_level <- "Phylum" ssu18_colvec.tax <- c("#00463C","#FFD5FD","#00A51C","#C80B2A","#00C7F9","#FFA035", "#ED0DFD","#0063E5","#5FFFDE","#C00B6F","#00A090","#FF95BA") ssu18_samp_order <- c("P02_D00_010_C0A", "P04_D00_010_C0B", "P06_D00_010_C0C", "P08_D00_010_C0D", "P10_D00_010_C0E", "P01_D00_010_W3A", "P03_D00_010_W3B", "P05_D00_010_W3C", "P07_D00_010_W3D", "P09_D00_010_W3E", "P01_D00_010_W8A", "P03_D00_010_W8B", "P05_D00_010_W8C", "P07_D00_010_W8D", "P09_D00_010_W8E") ``` 8) Now we can generate plots (in a loop) for each faceted data set. ```{r} #| code-fold: true for (i in ssu18_data_sets) { tmp_abund <- get(purrr::map_chr(i, ~paste0(., "_me_abund"))) tmp_tax_order <- get(purrr::map_chr(i, ~paste0(., "_tax_ord"))) tmp_abund$use_taxanames <- tmp_tax_order tmp_facet_plot <- tmp_abund$plot_bar(use_colors = ssu18_colvec.tax, others_color = "#323232", facet = "TEMP", xtext_keep = TRUE, xtext_type_hor = FALSE, legend_text_italic = FALSE, xtext_size = 6, facet_color = "#cccccc", order_x = ssu18_samp_order) tmp_facet_name <- purrr::map_chr(i, ~paste0(., "_facet_plot")) assign(tmp_facet_name, tmp_facet_plot) rm(list = ls(pattern = "tmp_")) } ``` Then add a little formatting to the faceted plots. ```{r} #| code-fold: true set_to_plot <- c("ssu18_ps_work_facet_plot", "ssu18_ps_filt_facet_plot", "ssu18_ps_perfect_facet_plot", "ssu18_ps_pime_facet_plot") for (i in set_to_plot) { tmp_get <- get(i) tmp_get <- tmp_get + theme_cowplot() + guides(fill = guide_legend(title = top_level, reverse = FALSE, keywidth = 0.7, keyheight = 0.7)) + ylab(NULL) + xlab("Sample") + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.background = element_rect(fill = "transparent", colour = NA), plot.background = element_rect(fill = "transparent", colour = NA), panel.border = element_rect(fill = NA, color = "black"), legend.text = element_text(size = 7), legend.title = element_text(size = 10), legend.position = "right", axis.text.y = element_text(size = 8), axis.text.x = element_text(size = 6, angle = 90), strip.text = element_text(size = 8, angle = 0), axis.title = element_text(size = 10)) + ylab(NULL) + scale_y_continuous() assign(i, tmp_get) rm(list = ls(pattern = "tmp_")) } ``` And now plots for the groupmeans sets. We can use the same taxa order since that should not have changed. ```{r} #| code-fold: true set_to_plot <- c("ssu18_ps_work_group_plot", "ssu18_ps_filt_group_plot", "ssu18_ps_perfect_group_plot", "ssu18_ps_pime_group_plot") for (i in ssu18_data_sets) { tmp_abund <- get(purrr::map_chr(i, ~paste0(., "_me_abund_group"))) tmp_tax_order <- get(purrr::map_chr(i, ~paste0(., "_tax_ord"))) tmp_abund$use_taxanames <- tmp_tax_order tmp_group_plot <- tmp_abund$plot_bar(use_colors = ssu18_colvec.tax, others_color = "#323232", xtext_keep = TRUE, xtext_type_hor = TRUE, legend_text_italic = FALSE, xtext_size = 10, facet_color = "#cccccc") tmp_group_name <- purrr::map_chr(i, ~paste0(., "_group_plot")) assign(tmp_group_name, tmp_group_plot) rm(list = ls(pattern = "tmp_")) } ``` Let's also add a little formatting to the groupmean plots. ```{r} #| code-fold: true for (i in set_to_plot) { tmp_get <- get(i) tmp_get <- tmp_get + theme_cowplot() + ylab("Relative Abundance (% total reads)") + xlab("Temperature") + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.background = element_rect(fill = "transparent", colour = NA), plot.background = element_rect(fill = "transparent", colour = NA), panel.border = element_rect(fill = NA, color = "black"), legend.position = "none", axis.text = element_text(size = 8), axis.title = element_text(size = 10)) + scale_y_continuous() assign(i, tmp_get) rm(list = ls(pattern = "tmp_")) } ``` 10) Finally we use the `patchwork` package to combine the two plots and customize the look. ```{r} #| code-fold: true ## single index that acts as an index for referencing elements (variables) in a list ## solution modified from this SO answerhttps://stackoverflow.com/a/54451460 var_list <- list( var1 = ssu18_data_sets, var2 = c("FULL", "Arbitrary filtered", "PERfect filtered", "PIME filtered") ) for (j in 1:length(var_list$var1)) { tmp_plot_final_name <- purrr::map_chr(var_list$var1[j], ~paste0(., "_", top_level, "_plot_final")) tmp_set_type <- var_list$var2[j] tmp_p_plot <- get(purrr::map_chr(var_list$var1[j], ~paste0(., "_group_plot"))) tmp_m_plot <- get(purrr::map_chr(var_list$var1[j], ~paste0(., "_facet_plot"))) tmp_plot_final <- tmp_p_plot + tmp_m_plot tmp_plot_final <- tmp_plot_final + plot_annotation(tag_levels = "a", title = paste("Top", top_level, "of", tmp_set_type, "data set"), #subtitle = 'Top taxa of non-filtered data', caption = "a) grouped by temperature., b) All samples, faceted by temperature.") + plot_layout(widths = c(1, 2)) & theme(plot.title = element_text(size = 13), plot.subtitle = element_text(size = 10), plot.tag = element_text(size = 12), axis.title = element_text(size = 10), axis.text = element_text(size = 8)) assign(tmp_plot_final_name, tmp_plot_final) rm(list = ls(pattern = "tmp_")) } ``` ```{r} #| echo: false for (i in ssu18_data_sets) { tmp_plot_final <- get(purrr::map_chr(i, ~paste0(., "_", top_level, "_plot_final"))) ggplot2::ggsave(tmp_plot_final, file = paste0("files/taxa/figures/", i, "_tax_div_bar_plots.png", sep = ""), height = 14, width = 21, units = 'cm', bg = "white") ggplot2::ggsave(tmp_plot_final, file = paste0("files/taxa/figures/", i, "_tax_div_bar_plots.pdf", sep = ""), height = 14, width = 21, units = 'cm', bg = "white") rm(list = ls(pattern = "tmp_")) } ``` ```{r} #| echo: false system("cp files/taxa/figures/ssu18_ps_work_tax_div_bar_plots.png include/taxa/ssu18_ps_work_tax_div_bar_plots.png") system("cp files/taxa/figures/ssu18_ps_filt_tax_div_bar_plots.png include/taxa/ssu18_ps_filt_tax_div_bar_plots.png") system("cp files/taxa/figures/ssu18_ps_perfect_tax_div_bar_plots.png include/taxa/ssu18_ps_perfect_tax_div_bar_plots.png") system("cp files/taxa/figures/ssu18_ps_pime_tax_div_bar_plots.png include/taxa/ssu18_ps_pime_tax_div_bar_plots.png") ``` > It is important to note that while most of the dominant taxa are the same across the FULL, Arbitrary, PIME, and PERfect data sets, there a few key difference: 1) Planctomycetota was not among the dominant taxa in the PIME data set. Nitrospirota was substituted. 2) Chloroflexi was not among the dominant taxa in either the Arbitrary, PERfect, or PIME data sets. RCP2-54 was substituted in both cases. ::: {.column-body-outset} ::: {.panel-tabset} #### FULL (unfiltered) ```{r} #| echo: false #| eval: true #| warning: false knitr::include_graphics("include/taxa/ssu18_ps_work_tax_div_bar_plots.png") ``` `r caption_fig("major_taxa_full_ssu")` #### Arbitrary filtered ```{r} #| echo: false #| eval: true #| warning: false knitr::include_graphics("include/taxa/ssu18_ps_filt_tax_div_bar_plots.png") ``` `r caption_fig("major_taxa_filt_ssu")` #### PERFECT filtered ```{r} #| echo: false #| eval: true #| warning: false knitr::include_graphics("include/taxa/ssu18_ps_perfect_tax_div_bar_plots.png") ``` `r caption_fig("major_taxa_perfect_ssu")` #### PIME filtered ```{r} #| echo: false #| eval: true #| warning: false knitr::include_graphics("include/taxa/ssu18_ps_pime_tax_div_bar_plots.png") ``` `r caption_fig("major_taxa_pime_ssu")` ::: ::: ## ITS ```{r} #| echo: false #| eval: false ## Initial Load for ANALYSIS #1 remove(list = ls()) set.seed(119) its18_ps_work <- readRDS("files/data-prep/rdata/its18_ps_work.rds") its18_ps_filt <- readRDS("files/filtering/arbitrary/rdata/its18_ps_filt.rds") its18_ps_perfect <- readRDS("files/filtering/perfect/rdata/its18_ps_perfect.rds") its18_ps_pime <- readRDS("files/filtering/pime/rdata/its18_ps_pime.rds") ``` ```{r} #| echo: false swel_col <- c("#2271B2", "#71B222", "#B22271") ``` Here we compare the taxonomic breakdown of the FULL (unfiltered), PIME filtered, and PERfect filtered data sets, split by temperature treatment for the ITS data. The workflow is basically the same as the 16S rRNA data. Click the box below to see the entire workflow. ```{r} #| echo: false its18_data_sets <- c("its18_ps_work", "its18_ps_filt", "its18_ps_perfect", "its18_ps_pime") ``` <details> <summary>Detailed workflow for ITS taxonomic analysis.</summary> 1) Choose the **number** of taxa to display and the taxonomic **level**. Aggregate the rest into "Other". ```{r} top_hits <- 14 top_level <- "Order" ``` As above, in order to use `microeco`, we need to add the rank designation as a prefix to each taxa. For example, `Basidiomycota` is changed to `p__Basidiomycota`. ```{r} for (i in its18_data_sets) { tmp_get <- get(i) tmp_sam_data <- sample_data(tmp_get) tmp_tax_data <- data.frame(tax_table(tmp_get)) tmp_tax_data$ASV_ID <- NULL # Some have, some do not tmp_tax_data$ASV_SEQ <- NULL # tmp_tax_data[] <- data.frame(lapply(tmp_tax_data, gsub, # pattern = "^[k | p | c | o | f]_.*", # replacement = "", fixed = FALSE)) tmp_tax_data$Kingdom <- paste("k__", tmp_tax_data$Kingdom, sep = "") tmp_tax_data$Phylum <- paste("p__", tmp_tax_data$Phylum, sep = "") tmp_tax_data$Class <- paste("c__", tmp_tax_data$Class, sep = "") tmp_tax_data$Order <- paste("o__", tmp_tax_data$Order, sep = "") tmp_tax_data$Family <- paste("f__", tmp_tax_data$Family, sep = "") tmp_tax_data$Genus <- paste("g__", tmp_tax_data$Genus, sep = "") tmp_tax_data <- as.matrix(tmp_tax_data) tmp_ps <- phyloseq(otu_table(tmp_get), tax_table(tmp_tax_data), tmp_sam_data) assign(i, tmp_ps) rm(list = ls(pattern = "tmp_")) } data.frame(tax_table(its18_ps_pime)) ``` ```{r} for (i in its18_data_sets) { tmp_get <- get(i) tmp_otu_table <- data.frame(t(otu_table(tmp_get))) tmp_sample_info <- data.frame(sample_data(tmp_get)) tmp_taxonomy_table <- data.frame(tax_table(tmp_get)) #tmp_taxonomy_table %<>% tidy_taxonomy tmp_dataset <- microtable$new(sample_table = tmp_sample_info, otu_table = tmp_otu_table, tax_table = tmp_taxonomy_table) tmp_dataset$tidy_dataset() print(tmp_dataset) tmp_dataset$tax_table %<>% base::subset(Kingdom == "k__Fungi") print(tmp_dataset) tmp_dataset$tidy_dataset() print(tmp_dataset) tmp_name <- purrr::map_chr(i, ~paste0(., "_me")) assign(tmp_name, tmp_dataset) rm(list = ls(pattern = "tmp_")) } objects() ``` Here is an example of what a new microeco object looks like when you call it. ```{r} #| echo: true #| eval: true its18_ps_work_me ``` ```{r} #| echo: false saveRDS(its18_ps_work_me, "files/taxa/rdata/its18_ps_work_me.rds") saveRDS(its18_ps_filt_me, "files/taxa/rdata/its18_ps_filt_me.rds") saveRDS(its18_ps_perfect_me, "files/taxa/rdata/its18_ps_perfect_me.rds") saveRDS(its18_ps_pime_me, "files/taxa/rdata/its18_ps_pime_me.rds") ``` 5) Now, we calculate the taxa abundance at each taxonomic rank using `cal_abund()`. This function return a list called `taxa_abund` containing several data frame of the abundance information at each taxonomic rank. The list is stored in the microtable object automatically. It’s worth noting that the `cal_abund()` function can be used to solve some complex cases, such as supporting both the relative and absolute abundance calculation and selecting the partial taxonomic columns. More information can be found in the description of the [file2meco package](https://github.com/ChiLiubio/file2meco). In the same loop we also create a `trans_abund` class, which is used to transform taxonomic abundance data for plotting. ```{r} for (i in its18_data_sets) { tmp_me <- get(purrr::map_chr(i, ~paste0(., "_me"))) tmp_me$cal_abund() tmp_me_abund <- trans_abund$new(dataset = tmp_me, taxrank = top_level, ntaxa = top_hits) tmp_me_abund$abund_data$Abundance <- tmp_me_abund$abund_data$Abundance / 100 tmp_me_abund_gr <- trans_abund$new(dataset = tmp_me, taxrank = top_level, ntaxa = top_hits, groupmean = "TEMP") tmp_me_abund_gr$abund_data$Abundance <- tmp_me_abund_gr$abund_data$Abundance / 100 tmp_name <- purrr::map_chr(i, ~paste0(., "_me_abund")) assign(tmp_name, tmp_me_abund) tmp_name_gr <- purrr::map_chr(i, ~paste0(., "_me_abund_group")) assign(tmp_name_gr, tmp_me_abund_gr) rm(list = ls(pattern = "tmp_")) } ``` ``` The result is stored in object$taxa_abund ... ``` 6) I prefer to specify the order of taxa in these kinds of plots. We can look the top `ntaxa` (defined above) by accessing the `use_taxanames` character vector of each microtable object. ```{r} #| echo: false print("FULL Data set (its18_ps_work_me)", quote = FALSE) its18_ps_work_me_abund$use_taxanames print("--------------------------------------------", quote = FALSE) print("Arbitrary filtered Data set (its18_ps_filt_me)", quote = FALSE) its18_ps_filt_me_abund$use_taxanames print("--------------------------------------------", quote = FALSE) print("PERfect filtered Data set (its18_ps_perfect)", quote = FALSE) its18_ps_perfect_me_abund$use_taxanames print("--------------------------------------------", quote = FALSE) print("PIME filtered Data set (its18_ps_pime_me)", quote = FALSE) its18_ps_pime_me_abund$use_taxanames print("--------------------------------------------", quote = FALSE) ``` Hum. Similar but not the same. This means we need to define the order for each separately. Once we do that, we will override the `use_taxanames` character vectors with the reordered vectors. ```{r} #| code-fold: true its18_ps_work_tax_ord <- rev(c( "k_Fungi", "p_Ascomycota", "c_Agaricomycetes", "Agaricales", "Archaeorhizomycetales", "Capnodiales", "Eurotiales", "Geastrales", "Glomerales", "Helotiales", "Hypocreales", "Saccharomycetales", "Trichosporonales", "Xylariales")) its18_ps_filt_tax_ord <- rev(c( "k_Fungi", "p_Ascomycota", "Polyporales", "Agaricales", "Archaeorhizomycetales", "Capnodiales", "Eurotiales", "Geastrales", "Glomerales", "Mortierellales", "Hypocreales", "Saccharomycetales", "Trichosporonales", "Xylariales")) its18_ps_perfect_tax_ord <- rev(c( "k_Fungi", "p_Ascomycota", "c_Agaricomycetes", "Agaricales", "Archaeorhizomycetales", "Capnodiales", "Eurotiales", "Geastrales", "Glomerales", "Helotiales", "Hypocreales", "Saccharomycetales", "Trichosporonales", "Pezizales")) its18_ps_pime_tax_ord <- rev(c( "k_Fungi", "p_Ascomycota", "Polyporales", "Agaricales", "Archaeorhizomycetales", "Capnodiales", "Eurotiales", "Geastrales", "Glomerales", "Mortierellales", "Hypocreales", "Saccharomycetales", "Trichosporonales", "Pezizales")) ``` 7) And one more little step before plotting. Here we **a**) specify a custom color palette and **b**) specify the sample order. ```{r} its18_colvec.tax <- rev(c("#323232", "#004949", "#924900", "#490092", "#6db6ff", "#920000", "#ffb6db", "#24ff24", "#006ddb", "#db6d00", "#b66dff", "#ffff6d", "#009292", "#b6dbff", "#ff6db6")) its18_samp_order <- c("P02_D00_010_C0A", "P04_D00_010_C0B", "P06_D00_010_C0C", "P08_D00_010_C0D", "P10_D00_010_C0E", "P01_D00_010_W3A", "P03_D00_010_W3B", "P07_D00_010_W3D", "P09_D00_010_W3E", "P01_D00_010_W8A", "P03_D00_010_W8B", "P05_D00_010_W8C", "P07_D00_010_W8D") ``` 8) Now we can generate plots (in a loop) for each faceted data set. ```{r} for (i in its18_data_sets) { tmp_abund <- get(purrr::map_chr(i, ~paste0(., "_me_abund"))) tmp_tax_order <- get(purrr::map_chr(i, ~paste0(., "_tax_ord"))) tmp_abund$use_taxanames <- tmp_tax_order tmp_facet_plot <- tmp_abund$plot_bar(use_colors = its18_colvec.tax, others_color = "#323232", facet = "TEMP", xtext_keep = TRUE, xtext_type_hor = FALSE, legend_text_italic = FALSE, xtext_size = 6, facet_color = "#cccccc", order_x = its18_samp_order) tmp_facet_name <- purrr::map_chr(i, ~paste0(., "_facet_plot")) assign(tmp_facet_name, tmp_facet_plot) rm(list = ls(pattern = "tmp_")) } ``` Then add a little formatting to the faceted plots. ```{r} set_to_plot <- c("its18_ps_work_facet_plot", "its18_ps_filt_facet_plot", "its18_ps_perfect_facet_plot", "its18_ps_pime_facet_plot") for (i in set_to_plot) { tmp_get <- get(i) tmp_get <- tmp_get + theme_cowplot() + guides(fill = guide_legend(title = top_level, reverse = FALSE, keywidth = 0.7, keyheight = 0.7)) + ylab(NULL) + xlab("Sample") + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.background = element_rect(fill = "transparent", colour = NA), plot.background = element_rect(fill = "transparent", colour = NA), panel.border = element_rect(fill = NA, color = "black"), legend.text = element_text(size = 7), legend.title = element_text(size = 10), legend.position = "right", axis.text.y = element_text(size = 8), axis.text.x = element_text(size = 6, angle = 90), strip.text = element_text(size = 8, angle = 0), axis.title = element_text(size = 10)) + ylab(NULL) + scale_y_continuous() assign(i, tmp_get) rm(list = ls(pattern = "tmp_")) } ``` And now plots for the groupmeans sets. We can use the same taxa order since that should not have changed. ```{r} set_to_plot <- c("its18_ps_work_group_plot", "its18_ps_filt_group_plot", "its18_ps_perfect_group_plot", "its18_ps_pime_group_plot") for (i in its18_data_sets) { tmp_abund <- get(purrr::map_chr(i, ~paste0(., "_me_abund_group"))) tmp_tax_order <- get(purrr::map_chr(i, ~paste0(., "_tax_ord"))) tmp_abund$use_taxanames <- tmp_tax_order tmp_group_plot <- tmp_abund$plot_bar(use_colors = its18_colvec.tax, others_color = "#323232", xtext_keep = TRUE, xtext_type_hor = TRUE, legend_text_italic = FALSE, xtext_size = 10, facet_color = "#cccccc") tmp_group_name <- purrr::map_chr(i, ~paste0(., "_group_plot")) assign(tmp_group_name, tmp_group_plot) rm(list = ls(pattern = "tmp_")) } ``` Let's also add a little formatting to the groupmean plots. ```{r} for (i in set_to_plot) { tmp_get <- get(i) tmp_get <- tmp_get + theme_cowplot() + ylab("Relative Abundance (% total reads)") + xlab("Temperature") + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.background = element_rect(fill = "transparent", colour = NA), plot.background = element_rect(fill = "transparent", colour = NA), panel.border = element_rect(fill = NA, color = "black"), legend.position = "none", axis.text = element_text(size = 8), axis.title = element_text(size = 10)) + scale_y_continuous() assign(i, tmp_get) rm(list = ls(pattern = "tmp_")) } ``` 10) Finally we use the `patchwork` package to combine the two plots and customize the look. ```{r} ## single index that acts as an index for referencing elements (variables) in a list ## solution modified from this SO answerhttps://stackoverflow.com/a/54451460 var_list <- list( var1 = its18_data_sets, var2 = c("FULL", "Arbitrary filtered", "PERfect filtered", "PIME filtered") ) for (j in 1:length(var_list$var1)) { tmp_plot_final_name <- purrr::map_chr(var_list$var1[j], ~paste0(., "_", top_level, "_plot_final")) tmp_set_type <- var_list$var2[j] tmp_p_plot <- get(purrr::map_chr(var_list$var1[j], ~paste0(., "_group_plot"))) tmp_m_plot <- get(purrr::map_chr(var_list$var1[j], ~paste0(., "_facet_plot"))) tmp_plot_final <- tmp_p_plot + tmp_m_plot tmp_plot_final <- tmp_plot_final + plot_annotation(tag_levels = "a", title = paste("Top", top_level, "of", tmp_set_type, "data set"), #subtitle = 'Top taxa of non-filtered data', caption = "a) grouped by temperature., b) All samples, faceted by temperature.") + plot_layout(widths = c(1, 2)) & theme(plot.title = element_text(size = 13), plot.subtitle = element_text(size = 10), plot.tag = element_text(size = 12), axis.title = element_text(size = 10), axis.text = element_text(size = 8)) assign(tmp_plot_final_name, tmp_plot_final) rm(list = ls(pattern = "tmp_")) } ``` ```{r} #| echo: false for (i in its18_data_sets) { tmp_plot_final <- get(purrr::map_chr(i, ~paste0(., "_", top_level, "_plot_final"))) ggplot2::ggsave(tmp_plot_final, file = paste0("files/taxa/figures/", i, "_tax_div_bar_plots.png", sep = ""), height = 14, width = 21, units = 'cm', bg = "white") ggplot2::ggsave(tmp_plot_final, file = paste0("files/taxa/figures/", i, "_tax_div_bar_plots.pdf", sep = ""), height = 14, width = 21, units = 'cm', bg = "white") rm(list = ls(pattern = "tmp_")) } ``` ```{r} #| echo: false system("cp files/taxa/figures/its18_ps_work_tax_div_bar_plots.png include/taxa/its18_ps_work_tax_div_bar_plots.png") system("cp files/taxa/figures/its18_ps_filt_tax_div_bar_plots.png include/taxa/its18_ps_filt_tax_div_bar_plots.png") system("cp files/taxa/figures/its18_ps_perfect_tax_div_bar_plots.png include/taxa/its18_ps_perfect_tax_div_bar_plots.png") system("cp files/taxa/figures/its18_ps_pime_tax_div_bar_plots.png include/taxa/its18_ps_pime_tax_div_bar_plots.png") ``` </details> > It is important to note that while most of the dominant taxa are the same across the FULL, Arbitrary, PIME, and PERfect data sets, there a few key difference: 1) c_Agaricomycetes & Helotiales were not among the dominant taxa in the Arbitrary or PIME filtered data sets. Polyporales & Mortierellales were substituted, respectively. 2) Xylariales was not among the dominant taxa in either the PIME or PERfect data sets. Pezizales was substituted in both cases. ::: {.column-body-outset} ::: {.panel-tabset} #### FULL (unfiltered) ```{r} #| echo: false #| eval: true #| warning: false knitr::include_graphics("include/taxa/its18_ps_work_tax_div_bar_plots.png") ``` `r caption_fig("major_taxa_full_its")` #### Arbitrary filtered ```{r} #| echo: false #| eval: true #| warning: false knitr::include_graphics("include/taxa/its18_ps_filt_tax_div_bar_plots.png") ``` `r caption_fig("major_taxa_filt_its")` #### PERFECT filtered ```{r} #| echo: false #| eval: true #| warning: false knitr::include_graphics("include/taxa/its18_ps_perfect_tax_div_bar_plots.png") ``` `r caption_fig("major_taxa_perfect_its")` #### PIME filtered ```{r} #| echo: false #| eval: true #| warning: false knitr::include_graphics("include/taxa/its18_ps_pime_tax_div_bar_plots.png") ``` `r caption_fig("major_taxa_pime_its")` ::: ::: # Major Bacterial taxa by Family We also look at the relative abundance of groups within dominant Phyla. **Please note** these analyses are for the FULL data set only, before any type of filtering. For this analysis, we use the phyloseq object created in the Data Preparation wokflow section [Rename NA taxonomic ranks](data-prep.html#rename-na-taxonomic-ranks) where unclassified ranks were renamed by the next highest rank. See step #1 below for an example of the taxa table structure. :::{.callout-note} This code ***only*** works for multiple taxa from a *single* phyloseq object. ::: <details> <summary>Detailed workflow for assessing Bacterial families.</summary> 1) Subset Phyla of interest. ```{r} ssu18_ps_work <- readRDS("files/data-prep/rdata/ssu18_ps_work.rds") head(data.frame(tax_table(ssu18_ps_work)))[,1:6] ``` ```{r} #| echo: false #| eval: true head(data.frame(tax_table(ssu18_ps_work)))[,1:6] ``` ```{r} ssu18_data_sets <- c("ssu18_ps_work") for (i in ssu18_data_sets) { tmp_name <- purrr::map_chr(i, ~paste0(., "_proteo")) tmp_get <- get(i) tmp_df <- subset_taxa(tmp_get, Phylum == "Proteobacteria") assign(tmp_name, tmp_df) print(tmp_name) tmp_get_taxa <- get_taxa_unique(tmp_df, taxonomic.rank = rank_names(tmp_df)[3], errorIfNULL=TRUE) print(tmp_get_taxa) rm(list = ls(pattern = "tmp_")) rm(list = ls(pattern = "_proteo")) } ``` 2) Replace Phylum Proteobacteria with the Class name. ```{r} for (j in ssu18_data_sets) { tmp_name <- purrr::map_chr(j, ~paste0(., "_proteo_clean")) tmp_get <- get(j) tmp_clean <- data.frame(tax_table(tmp_get)) for (i in 1:nrow(tmp_clean)){ if (tmp_clean[i,2] == "Proteobacteria" & tmp_clean[i,3] == "Alphaproteobacteria"){ phylum <- base::paste("Alphaproteobacteria") tmp_clean[i, 2] <- phylum } else if (tmp_clean[i,2] == "Proteobacteria" & tmp_clean[i,3] == "Gammaproteobacteria"){ phylum <- base::paste("Gammaproteobacteria") tmp_clean[i, 2] <- phylum } else if (tmp_clean[i,2] == "Proteobacteria" & tmp_clean[i,3] == "Zetaproteobacteria"){ phylum <- base::paste("Zetaproteobacteria") tmp_clean[i, 2] <- phylum } else if (tmp_clean[i,2] == "Proteobacteria" & tmp_clean[i,3] == "p_Proteobacteria"){ phylum <- base::paste("p_Proteobacteria") tmp_clean[i, 2] <- phylum } } tax_table(tmp_get) <- as.matrix(tmp_clean) rank_names(tmp_get) assign(tmp_name, tmp_get) print(c(tmp_name, tmp_get)) print(length(get_taxa_unique(tmp_get, taxonomic.rank = rank_names(tmp_get)[2], errorIfNULL=TRUE))) tmp_path <- file.path("files/taxa/rdata/") saveRDS(tmp_get, paste(tmp_path, j, "_clean.rds", sep = "")) rm(list = ls(pattern = "tmp_")) } rm(class, order, phylum) ``` ```{r} set_to_plot <- "ssu18_ps_work" tax_group <- c("Alphaproteobacteria", "Gammaproteobacteria", "Acidobacteriota", "Actinobacteriota", "Bacteroidota", "Firmicutes", "Myxococcota", "Verrucomicrobiota") for (i in set_to_plot) { for (j in tax_group) { tmp_get <- get(purrr::map_chr(i, ~ paste0(., "_proteo_clean"))) tmp_sub <- subset_taxa(tmp_get, Phylum == j) tmp_name <- purrr::map_chr(i, ~ paste0(., "_", j)) assign(tmp_name, tmp_sub) rm(list = ls(pattern = "tmp_")) } } ``` ```{r} #| echo: false for (i in tax_group) { tmp_get <- get(purrr::map_chr(i, ~ paste0(set_to_plot, "_", i))) tmp_list <- get_taxa_unique(tmp_get, taxonomic.rank = rank_names(tmp_get)[5], errorIfNULL = TRUE) cat("\n") cat("####################################################", "\n") tmp_print <- c("Unique taxa:", i) cat(tmp_print, "\n") cat("####################################################") cat("\n") print(tmp_list) rm(list = ls(pattern = "tmp_")) } ``` 3) Choose the **number** of taxa to display and the taxonomic **level**. Aggregate the rest into "Other". ```{r} top_hits <- 12 top_level <- "Family" for (i in tax_group) { tmp_get <- get(purrr::map_chr(i, ~ paste0(set_to_plot, "_", i))) tmp_otu <- data.frame(t(otu_table(tmp_get))) tmp_otu[] <- lapply(tmp_otu, as.numeric) tmp_otu <- as.matrix(tmp_otu) tmp_tax <- as.matrix(data.frame(tax_table(tmp_get))) tmp_samples <- data.frame(sample_data(tmp_get)) tmp_clean_df <- merge_phyloseq(otu_table(tmp_otu, taxa_are_rows = TRUE), tax_table(tmp_tax), sample_data(tmp_samples)) tmp_agg_df <- microbiome::aggregate_top_taxa(tmp_clean_df, top = top_hits, level = top_level) tmp_agg_name <- purrr::map_chr(i, ~ paste0(set_to_plot, "_", i, "_agg")) assign(tmp_agg_name, tmp_agg_df) rm(list = ls(pattern = "_sep_agg")) } ``` ```{r} for (i in tax_group){ tmp_data <- purrr::map_chr(i, ~ paste0(set_to_plot, "_", i, "_agg")) tmp_get <- get(tmp_data) tmp_list <- get_taxa_unique(tmp_get, taxonomic.rank = rank_names(tmp_get)[2], errorIfNULL = TRUE) tmp_name <- purrr::map_chr(tmp_data, ~ paste0(., "_order")) assign(tmp_name, tmp_list) rm(list = ls(pattern = "tmp_")) } ``` ```{r} for (i in tax_group) { tmp_get <- get(purrr::map_chr(i, ~ paste0(set_to_plot, "_", i, "_agg_order"))) cat("\n") cat("#########", i, "########", "\n") tmp_print <- c(tmp_get) cat(tmp_print, "\n") cat("####################################################") cat("\n") rm(list = ls(pattern = "tmp_")) } rm(i, j) ``` ```{r} tmp_order <- rev(c("Other", "c_Alphaproteobacteria", "o_Elsterales", "o_Azospirillales", "Rhizobiales_Incertae_Sedis", "Xanthobacteraceae", "Sphingomonadaceae", "Rhizobiaceae", "Micropepsaceae", "Methyloligellaceae", "KF-JG30-B3", "Hyphomicrobiaceae", "Dongiaceae")) assign(paste(set_to_plot, "_", "Alphaproteobacteria", "_agg_order", sep = ""), tmp_order) ################### tmp_order <- rev(c("Other", "c_Gammaproteobacteria", "o_PLTA13", "o_CCD24", "Unknown_Family", "Xanthomonadaceae", "TRA3-20", "Steroidobacteraceae", "SC-I-84", "Nitrosomonadaceae", "Comamonadaceae", "Burkholderiaceae", "B1-7BS")) assign(paste(set_to_plot, "_", "Gammaproteobacteria", "_agg_order", sep = ""), tmp_order) ################### tmp_order <- rev(c("Other", "c_Subgroup_22", "c_Subgroup_25", "c_Subgroup_5", "o_Vicinamibacterales", "o_11-24", "o_Subgroup_17", "o_Subgroup_2", "o_Subgroup_7", "o_Acidobacteriales", "Vicinamibacteraceae", "Solibacteraceae", "Pyrinomonadaceae")) assign(paste(set_to_plot, "_", "Acidobacteriota", "_agg_order", sep = ""), tmp_order) ################### tmp_order <- rev(c("Other", "c_MB-A2-108", "o_IMCC26256", "o_Gaiellales", "o_Frankiales", "Streptomycetaceae", "Solirubrobacteraceae", "Nocardioidaceae", "Mycobacteriaceae", "Micromonosporaceae", "Gaiellaceae", "Acidothermaceae", "67-14")) assign(paste(set_to_plot, "_", "Actinobacteriota", "_agg_order", sep = ""), tmp_order) ################### tmp_order <- rev(c("Other", "c_SJA-28", "o_Chitinophagales", "Sphingobacteriaceae", "Saprospiraceae", "Microscillaceae", "Hymenobacteraceae", "Flavobacteriaceae", "Cytophagaceae", "Chitinophagaceae", "BSV26", "env.OPS_17", "AKYH767")) assign(paste(set_to_plot, "_", "Bacteroidota", "_agg_order", sep = ""), tmp_order) ################### tmp_order <- rev(c("Other", "p_Firmicutes", "c_Bacilli", "o_Bacillales", "Thermoactinomycetaceae", "type_III", "Paenibacillaceae", "Lachnospiraceae", "Hungateiclostridiaceae", "Clostridiaceae", "Bacillaceae", "Alicyclobacillaceae", "Planococcaceae")) assign(paste(set_to_plot, "_", "Firmicutes", "_agg_order", sep = ""), tmp_order) ################### tmp_order <- rev(c("Other", "p_Myxococcota", "c_bacteriap25", "c_Polyangia", "o_mle1-27", "o_MSB-4B10", "Sandaracinaceae", "Polyangiaceae", "Phaselicystidaceae", "Myxococcaceae", "Haliangiaceae", "BIrii41", "Anaeromyxobacteraceae")) assign(paste(set_to_plot, "_", "Myxococcota", "_agg_order", sep = ""), tmp_order) ################### tmp_order <- rev(c("Other", "c_Verrucomicrobiae", "o_Chlamydiales", "o_LD1-PA32", "o_S-BQ2-57_soil_group", "Xiphinematobacteraceae", "Simkaniaceae", "Pedosphaeraceae", "Parachlamydiaceae", "Opitutaceae", "Omnitrophaceae", "cvE6", "Chthoniobacteraceae")) assign(paste(set_to_plot, "_", "Verrucomicrobiota", "_agg_order", sep = ""), tmp_order) ################### ``` 4) Now, transform the data to relative abundance. ```{r} for (i in tax_group) { tmp_agg <- purrr::map_chr(i, ~ paste0(set_to_plot, "_", i, "_agg")) tmp_order <- purrr::map_chr(tmp_agg, ~paste0(., "_order")) tmp_get_agg <- get(tmp_agg) tmp_get_order <- get(tmp_order) tmp_df <- tmp_get_agg %>% transform_sample_counts(function(x) {x/sum(x)} ) %>% psmelt() tmp_df[[top_level]] <- gdata::reorder.factor(tmp_df[[top_level]], new.order = rev(tmp_get_order)) tmp_df <- tmp_df %>% dplyr::arrange(get(top_level)) tmp_name <- purrr::map_chr(tmp_agg, ~paste0(., "_tax")) assign(tmp_name, tmp_df) #print(c(i, tmp_name, tmp_agg)) rm(list = ls(pattern = "tmp_")) } ``` ```{r} for (i in tax_group) { tmp_get <- get(purrr::map_chr(i, ~ paste0(set_to_plot, "_", i, "_agg_tax"))) tmp_levels <- levels(tmp_get[[top_level]]) print(c(i, tmp_levels)) } ``` 5) Plot the data for a single phyloseq object. Here you use an aggregated tax file. ```{r} #| echo: false ssu18_colvec.tax <- c( "#3D3C04", "#FF95BA", "#00A090", "#C00B6F", #"#9BFF2D", #"#590A87", "#5FFFDE", "#0063E5", "#ED0DFD", "#FFA035", "#00C7F9", "#C80B2A", "#00A51C", "#FFD5FD", "#00463C" ) ``` ```{r} for (i in tax_group) { tmp_get <- get(purrr::map_chr(i, ~ paste0(set_to_plot, "_", i, "_agg_tax"))) tmp_plot <- ggplot(tmp_get, aes(x = factor(TEMP), y = Abundance, fill = get(top_level))) + geom_bar(stat = "identity", position = "fill") + scale_fill_manual(values = ssu18_colvec.tax) + #scale_x_discrete("Temperature", expand = waiver(), position = "bottom", drop = FALSE) + theme_cowplot() + guides(fill = guide_legend(title = top_level)) + #guides(fill = guide_legend(reverse = FALSE, keywidth = 1, keyheight = 1)) + ylab("Relative Abundance (% total reads)") + xlab("Temperature") + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.background = element_rect(fill = "transparent", colour = NA), plot.background = element_rect(fill = "transparent", colour = NA), panel.border = element_rect(fill = NA, color = "black"), #axis.text = element_text(size = 6), #axis.title = element_text(size = 8), legend.position = "none") tmp_name <- purrr::map_chr(i, ~paste0(set_to_plot, "_", ., "_plot")) assign(tmp_name, tmp_plot) rm(list = ls(pattern = "tmp_")) } ``` 6) Plot the data for multiple taxa. Here again you use an aggregated tax file. This code can be used to generate plots for multiple data frames by adding the base phyloseq names to the `ssu18_split_df` variable. This code will also facet the plots by a metadata variable. If you do not want to facet remove the line beginning with `facet_grid`. ```{r} for (i in tax_group) { tmp_level_get <- get(purrr::map_chr(i, ~paste0(set_to_plot, "_", .))) tmp_level <- data.frame(sample_data(tmp_level_get)) tmp_level <- tmp_level[order(tmp_level$TEMP), ] tmp_level <- as.vector(tmp_level$SamName) tmp_agg_name <- purrr::map_chr(i, ~paste0(set_to_plot, "_", ., "_agg_tax")) tmp_get <- get(tmp_agg_name) tmp_df <- reshape::melt(tmp_get, id.vars = c("Sample", "TEMP", "Abundance", "Family")) tmp_plot_name <- purrr::map_chr(i, ~paste0(set_to_plot, "_", ., "_plot_melt")) tmp_plot <- ggplot(tmp_df, aes(x = Sample, y = Abundance, fill = get(top_level))) + facet_grid(. ~TEMP, scale = "free_x", space = "free_x")+ geom_bar(stat = "identity", position = "fill") + scale_fill_manual(values = ssu18_colvec.tax) + #scale_x_discrete("Treatment", expand = waiver(), # position = "bottom", drop = FALSE, limits = tmp_level) + theme_cowplot() + guides(fill = guide_legend(title = top_level, reverse = FALSE, keywidth = 0.7, keyheight = 0.7)) + ylab(NULL) + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.background = element_rect(fill = "transparent", colour = NA), plot.background = element_rect(fill = "transparent", colour = NA), panel.border = element_rect(fill = NA, color = "black"), legend.position = "right", #legend.text = element_text(size = 6), #legend.title = element_text(size = 8), #legend.key.size = unit(1.5, "cm"), #axis.text.y = element_text(size = 6), axis.text.x = element_text(angle = 90) #strip.text = element_text(size = 8, angle = 0), #axis.title = element_text(size = 8) ) + ylab(NULL) assign(tmp_plot_name, tmp_plot) rm(list = ls(pattern = "tmp_")) } ``` 7) Finally we use the `patchwork` package to combine the two plots and customize the look. ```{r} for (i in tax_group) { tmp_plot_main <- get(purrr::map_chr(i, ~paste0(set_to_plot, "_", ., "_plot"))) tmp_plot_melt <- get(purrr::map_chr(i, ~paste0(set_to_plot, "_", ., "_plot_melt"))) tmp_final <- tmp_plot_main + tmp_plot_melt tmp_final <- tmp_final + plot_annotation(tag_levels = 'a', title = i) + #subtitle = 'Top taxa of non-filtered data', #caption = 'A) grouped by temperature., #B) All samples, faceted by temperature.') + plot_layout(widths = c(1, 2)) & theme(plot.title = element_text(size = 9), plot.subtitle = element_text(size = 1), plot.tag = element_text(size = 6), axis.title = element_text(size = 7), axis.text = element_text(size = 6), strip.text = element_text(size = 8, angle = 0), legend.text = element_text(size = 7), legend.title = element_text(size = 9), ) #legend.position = "right") #legend.position = "right", #legend.title = element_text(size = rel(1)), #legend.text = element_text(size = rel(1))) tmp_name <- purrr::map_chr(i, ~paste0(set_to_plot, "_", ., "_final_plot")) assign(tmp_name, tmp_final) rm(list = ls(pattern = "tmp_")) } ``` ```{r} taxa_to_plot <- c("Acidobacteriota", "Actinobacteriota", "Alphaproteobacteria", "Bacteroidota", "Firmicutes", "Gammaproteobacteria", "Myxococcota", "Verrucomicrobiota") for (i in taxa_to_plot) { tmp_get <- get(purrr::map_chr(i, ~ paste0("ssu18_ps_work_", i, "_final_plot", sep = ""))) ggplot2::ggsave(tmp_get, file = paste0("files/taxa/figures/", i, "_tax_div_bar_plots.png", sep = ""), height = 14, width = 21, units = 'cm', bg = "white") ggplot2::ggsave(tmp_get, file = paste0("files/taxa/figures/", i, "_tax_div_bar_plots.pdf", sep = ""), height = 14, width = 21, units = 'cm', bg = "white") } ``` </details> ::: {.column-body-outset} ::: {.panel-tabset} #### Alphaproteobacteria ```{r} #| echo: false #| eval: true #| warning: false #top_level <- "Family" #ssu18_ps_work_Alphaproteobacteria_final_plot #unique(ssu18_ps_work_Alphaproteobacteria_final_plot$data$Family) knitr::include_graphics("include/taxa/Alphaproteobacteria_tax_div_bar_plots.png") ``` `r caption_fig("alpha_ssu")` #### Gammaproteobacteria ```{r} #| echo: false #| eval: true #| warning: false #ssu18_ps_work_Gammaproteobacteria_final_plot #unique(ssu18_ps_work_Gammaproteobacteria_final_plot$data$Family) knitr::include_graphics("include/taxa/Gammaproteobacteria_tax_div_bar_plots.png") ``` `r caption_fig("gamma_ssu")` #### Acidobacteriota ```{r} #| echo: false #| eval: true #| warning: false #ssu18_ps_work_Acidobacteriota_final_plot #unique(ssu18_ps_work_Acidobacteriota_final_plot$data$Family) knitr::include_graphics("include/taxa/Acidobacteriota_tax_div_bar_plots.png") ``` `r caption_fig("acido_ssu")` #### Actinobacteriota ```{r} #| echo: false #| eval: true #| warning: false #ssu18_ps_work_Actinobacteriota_final_plot #unique(ssu18_ps_work_Actinobacteriota_final_plot$data$Family) knitr::include_graphics("include/taxa/Actinobacteriota_tax_div_bar_plots.png") ``` `r caption_fig("actino_ssu")` #### Bacteroidota ```{r} #| echo: false #| eval: true #| warning: false #ssu18_ps_work_Bacteroidota_final_plot #unique(ssu18_ps_work_Bacteroidota_final_plot$data$Family) knitr::include_graphics("include/taxa/Bacteroidota_tax_div_bar_plots.png") ``` `r caption_fig("bacteroid_ssu")` #### Firmicutes ```{r} #| echo: false #| eval: true #| warning: false #ssu18_ps_work_Firmicutes_final_plot #unique(ssu18_ps_work_Firmicutes_final_plot$data$Family) knitr::include_graphics("include/taxa/Firmicutes_tax_div_bar_plots.png") ``` `r caption_fig("firm_ssu")` #### Myxococcota ```{r} #| echo: false #| eval: true #| warning: false #ssu18_ps_work_Myxococcota_final_plot #unique(ssu18_ps_work_Myxococcota_final_plot$data$Family) knitr::include_graphics("include/taxa/Myxococcota_tax_div_bar_plots.png") ``` `r caption_fig("myxo_ssu")` #### Verrucomicrobiota ```{r} #| echo: false #| eval: true #| warning: false #ssu18_ps_work_Verrucomicrobiota_final_plot #unique(ssu18_ps_work_Verrucomicrobiota_final_plot$data$Family) knitr::include_graphics("include/taxa/Verrucomicrobiota_tax_div_bar_plots.png") ``` `r caption_fig("verruco_ssu")` ::: ::: ```{r} #| echo: false save.image("page_build/taxa_wf.rdata") ``` ```{r} #| include: false #| eval: true remove(list = ls()) ``` # Workflow Output Data products generated in this workflow can be downloaded from figshare. <iframe src="https://widgets.figshare.com/articles/16826737/embed?show_title=1" width="100%" height="221" allowfullscreen frameborder="0"></iframe> <a href="alpha.html" class="btnnav button_round" style="float: right;">Next workflow: 5. Alpha Diversity Estimates</a> <a href="filtering.html" class="btnnav button_round" style="float: left;">Previous workflow: 3. Filtering</a> ```{r} #| message: false #| results: hide #| eval: true #| echo: false remove(list = ls()) ### COmmon formatting scripts ### NOTE: captioner.R must be read BEFORE captions_XYZ.R source(file.path("assets", "functions.R")) ``` #### Source Code {.appendix} The source code for this page can be accessed on GitHub `r fa(name = "github")` by [clicking this link](`r source_code()`). #### Data Availability {.appendix} Data generated in this workflow and the Rdata need to run the workflow can be accessed on figshare at [10.25573/data.16826737](https://doi.org/10.25573/data.16826737). #### Last updated on {.appendix} ```{r} #| echo: false #| eval: true Sys.time() ```