MicrobiotaProcess Group2 vs Group6

https://bioconductor.org/packages/release/bioc/vignettes/MicrobiotaProcess/inst/doc//MicrobiotaProcess.html

1. code of MicrobiotaProcess_Group2_vs_Group6.R

   # https://bioconductor.org/packages/release/bioc/vignettes/MicrobiotaProcess/inst/doc//MicrobiotaProcess.html
   
   # -----------------------------------
   # ---- prepare the R environment ----
   #Rscript MicrobiotaProcess.R
   #NOTE: exit R script, then login again R-environment; rm -rf Phyloseq*_cache
   rmarkdown::render('Phyloseq.Rmd',output_file='Phyloseq.html')
   
   # -----------------------------
   # ---- 3.1. bridges other tools
   ##https://github.com/YuLab-SMU/MicrobiotaProcess
   ##https://www.bioconductor.org/packages/release/bioc/vignettes/MicrobiotaProcess/inst/doc/MicrobiotaProcess.html
   ##https://chiliubio.github.io/microeco_tutorial/intro.html#framework
   ##https://yiluheihei.github.io/microbiomeMarker/reference/plot_cladogram.html
   #BiocManager::install("MicrobiotaProcess")
   #install.packages("microeco")
   #install.packages("ggalluvial")
   #install.packages("ggh4x")
   
   library(MicrobiotaProcess)
   library(microeco)
   library(ggalluvial)
   library(ggh4x)
   library(gghalves)
   
   ## Convert the phyloseq object to a MicrobiotaProcess object
   #mp <- as.MicrobiotaProcess(ps.ng.tax)
   
   #mt <- phyloseq2microeco(ps.ng.tax) #--> ERROR
   #abundance_table <- mt$abun_table
   #taxonomy_table <- mt$tax_table
   
   #ps.ng.tax_abund <- phyloseq::filter_taxa(ps.ng.tax, function(x) sum(x > total*0.01) > 0, TRUE)
   #ps.ng.tax_most = phyloseq::filter_taxa(ps.ng.tax_rel, function(x) mean(x) > 0.001, TRUE)
   
   ##OPTION1 (NOT_USED): take all samples, prepare ps.ng.tax_abund --> mpse_abund
   ##mpse <- ps.ng.tax %>% as.MPSE()
   #mpse_abund <- ps.ng.tax_abund %>% as.MPSE()
   
   ##OPTION2 (USED!): take partial samples, prepare ps.ng.tax or ps.ng.tax_abund (2 replacements!)--> ps.ng.tax_sel --> mpse_abund
   ps.ng.tax_sel <- ps.ng.tax_abund
   ##otu_table(ps.ng.tax_sel) <- otu_table(ps.ng.tax)[,c("1","2","5","6","7",  "15","16","17","18","19","20",  "29","30","31","32",  "40","41","42","43","44","46")]
   ##NOTE: Only choose Group2, Group4, Group6, Group8
   #> ps.ng.tax_sel
   #otu_table()   OTU Table:         [ 37465 taxa and 29 samples ]
   #sample_data() Sample Data:       [ 29 samples by 10 sample variables ]
   #tax_table()   Taxonomy Table:    [ 37465 taxa by 7 taxonomic ranks ]
   #phy_tree()    Phylogenetic Tree: [ 37465 tips and 37461 internal nodes ]
   #-Group4: "21","22","23","24","25","26","27","28",
   #-Group8: ,  "47","48","49","50","52","53","55"
   otu_table(ps.ng.tax_sel) <- otu_table(ps.ng.tax_abund)[,c("8","9","10","12","13","14",  "33","34","35","36","37","38","39","51")]
   mpse_abund <- ps.ng.tax_sel %>% as.MPSE()
   # A MPSE-tibble (MPSE object) abstraction: 2,352 × 20
   # NOTE mpse_abund contains 20 variables: OTU, Sample, Abundance, BarcodeSequence, LinkerPrimerSequence, FileInput, Group,
   #   Sex_age <chr>, pre_post_stroke <chr>, Conc <dbl>, Vol_50ng <dbl>, Vol_PCR <dbl>, Description <chr>,
   #   Domain <chr>, Phylum <chr>, Class <chr>, Order <chr>, Family <chr>, Genus <chr>, Species <chr>
   
   # -----------------------------------
   # ---- 3.2. alpha diversity analysis
   # Rarefied species richness + RareAbundance
   mpse_abund %<>% mp_rrarefy()
   # 'chunks' represent the split number of each sample to calculate alpha
   # diversity, default is 400. e.g. If a sample has total 40000
   # reads, if chunks is 400, it will be split to 100 sub-samples
   # (100, 200, 300,..., 40000), then alpha diversity index was
   # calculated based on the sub-samples.
   # '.abundance' the column name of abundance, if the '.abundance' is not be
   # rarefied calculate rarecurve, user can specific 'force=TRUE'.
   mpse_abund %<>%
       mp_cal_rarecurve(
           .abundance = RareAbundance,
           chunks = 400
       )
   # The RareAbundanceRarecurve column will be added the colData slot
   # automatically (default action="add")
   #NOTE mpse_abund contains 22 varibles = 20 varibles + RareAbundance <dbl> + RareAbundanceRarecurve <list>
   
   # default will display the confidence interval around smooth.
   # se=TRUE
   # NOTE that two colors #c("#00A087FF", "#3C5488FF") for .group = pre_post_stroke; four colors c("#1f78b4", "#33a02c", "#e31a1c", "#6a3d9a") for .group = Group;
   p1 <- mpse_abund %>%
         mp_plot_rarecurve(
           .rare = RareAbundanceRarecurve,
           .alpha = Observe,
         )
   
   p2 <- mpse_abund %>%
         mp_plot_rarecurve(
           .rare = RareAbundanceRarecurve,
           .alpha = Observe,
           .group = Group
         ) +
         scale_color_manual(values=c("#1f78b4", "#e31a1c")) +
         scale_fill_manual(values=c("#1f78b4", "#e31a1c"), guide="none")
   
   # combine the samples belong to the same groups if plot.group=TRUE
   p3 <- mpse_abund %>%
         mp_plot_rarecurve(
           .rare = RareAbundanceRarecurve,
           .alpha = "Observe",
           .group = Group,
           plot.group = TRUE
         ) +
         scale_color_manual(values=c("#1f78b4", "#e31a1c")) +
         scale_fill_manual(values=c("#1f78b4", "#e31a1c"),guide="none")
   
   png("rarefaction_of_samples_or_groups.png", width=1080, height=600)
   p1 + p2 + p3
   dev.off()
   
   # ------------------------------------------
   # 3.3. calculate alpha index and visualization
   library(ggplot2)
   library(MicrobiotaProcess)
   mpse_abund %<>%
       mp_cal_alpha(.abundance=RareAbundance)
   mpse_abund
   #NOTE mpse_abund contains 28 varibles = 22 varibles + Observe <dbl>, Chao1 <dbl>, ACE <dbl>, Shannon <dbl>, Simpson <dbl>, Pielou <dbl>
   
   f1 <- mpse_abund %>%
         mp_plot_alpha(
           .group=Group,
           .alpha=c(Observe, Chao1, ACE, Shannon, Simpson, Pielou)
         ) +
         scale_fill_manual(values=c("#1f78b4", "#e31a1c"), guide="none") +
         scale_color_manual(values=c("#1f78b4", "#e31a1c"), guide="none")
   
   f2 <- mpse_abund %>%
         mp_plot_alpha(
           .alpha=c(Observe, Chao1, ACE, Shannon, Simpson, Pielou)
         )
   
   #ps.ng.tax_sel contais only pre samples --> f1 cannot be generated!
   png("alpha_diversity_comparison.png", width=1400, height=600)
   f1 / f2
   dev.off()
   
   # -------------------------------------------
   # 3.4. The visualization of taxonomy abundance (Class)
   mpse_abund %<>%
       mp_cal_abundance( # for each samples
         .abundance = RareAbundance
       ) %>%
       mp_cal_abundance( # for each groups
         .abundance=RareAbundance,
         .group=Group
       )
   mpse_abund
   #NOTE mpse_abund contains 29 varibles = 28 varibles + RelRareAbundanceBySample <dbl>
   
   # visualize the relative abundance of top 20 phyla for each sample.
   #           .group=time,
   p1 <- mpse_abund %>%
           mp_plot_abundance(
             .abundance=RareAbundance,
             taxa.class = Class,
             topn = 20,
             relative = TRUE
           )
   # visualize the abundance (rarefied) of top 20 phyla for each sample.
   #            .group=time,
   p2 <- mpse_abund %>%
             mp_plot_abundance(
               .abundance=RareAbundance,
               taxa.class = Class,
               topn = 20,
               relative = FALSE
             )
   png("relative_abundance_and_abundance.png", width= 1200, height=600) #NOT PRODUCED!
   p1 / p2
   dev.off()
   
   #----
   h1 <- mpse_abund %>%
           mp_plot_abundance(
             .abundance = RareAbundance,
             .group = Group,
             taxa.class = Class,
             relative = TRUE,
             topn = 20,
             geom = 'heatmap',
             features.dist = 'euclidean',
             features.hclust = 'average',
             sample.dist = 'bray',
             sample.hclust = 'average'
           )
   
   h2 <- mpse_abund %>%
             mp_plot_abundance(
               .abundance = RareAbundance,
               .group = Group,
               taxa.class = Class,
               relative = FALSE,
               topn = 20,
               geom = 'heatmap',
               features.dist = 'euclidean',
               features.hclust = 'average',
               sample.dist = 'bray',
               sample.hclust = 'average'
             )
   # the character (scale or theme) of figure can be adjusted by set_scale_theme
   # refer to the mp_plot_dist
   png("relative_abundance_and_abundance_heatmap.png", width= 1200, height=600)
   aplot::plot_list(gglist=list(h1, h2), tag_levels="A")
   dev.off()
   
   # visualize the relative abundance of top 20 class for each .group (Group)
   p3 <- mpse_abund %>%
           mp_plot_abundance(
               .abundance=RareAbundance,
               .group=Group,
               taxa.class = Class,
               topn = 20,
               plot.group = TRUE
             )
   
   # visualize the abundance of top 20 phyla for each .group (time)
   p4 <- mpse_abund %>%
             mp_plot_abundance(
               .abundance=RareAbundance,
               .group= Group,
               taxa.class = Class,
               topn = 20,
               relative = FALSE,
               plot.group = TRUE
             )
   png("relative_abundance_and_abundance_groups.png", width= 1000, height=1000)
   p3 / p4
   dev.off()
   
   # ---------------------------
   # 3.5. Beta diversity analysis
   
   # ---------------------------------------------
   # 3.5.1 The distance between samples or groups
   # standardization
   # mp_decostand wraps the decostand of vegan, which provides
   # many standardization methods for community ecology.
   # default is hellinger, then the abundance processed will
   # be stored to the assays slot.
   mpse_abund %<>%
       mp_decostand(.abundance=Abundance)
   mpse_abund
   #NOTE mpse_abund contains 30 varibles = 29 varibles + hellinger <dbl>
   
   # calculate the distance between the samples.
   # the distance will be generated a nested tibble and added to the
   # colData slot.
   mpse_abund %<>% mp_cal_dist(.abundance=hellinger, distmethod="bray")
   mpse_abund
   #NOTE mpse_abund contains 31 varibles = 30 varibles + bray <list>
   
   # mp_plot_dist provides there methods to visualize the distance between the samples or groups
   # when .group is not provided, the dot heatmap plot will be return
   p1 <- mpse_abund %>% mp_plot_dist(.distmethod = bray)
   png("distance_between_samples.png", width= 1000, height=1000)
   p1
   dev.off()
   
   # when .group is provided, the dot heatmap plot with group information will be return.
   p2 <- mpse_abund %>% mp_plot_dist(.distmethod = bray, .group = Group)
   # The scale or theme of dot heatmap plot can be adjusted using set_scale_theme function.
   p2 %>% set_scale_theme(
             x = scale_fill_manual(
                   values=c("#1f78b4", "#e31a1c"),  #c("orange", "deepskyblue"),
                   guide = guide_legend(
                               keywidth = 1,
                               keyheight = 0.5,
                               title.theme = element_text(size=8),
                               label.theme = element_text(size=6)
                   )
                 ),
             aes_var = Group # specific the name of variable
         ) %>%
         set_scale_theme(
             x = scale_color_gradient(
                   guide = guide_legend(keywidth = 0.5, keyheight = 0.5)
                 ),
             aes_var = bray
         ) %>%
         set_scale_theme(
             x = scale_size_continuous(
                   range = c(0.1, 3),
                   guide = guide_legend(keywidth = 0.5, keyheight = 0.5)
                 ),
             aes_var = bray
         )
   png("distance_between_samples_with_group_info.png", width= 1000, height=1000)
   p2
   dev.off()
   
   # when .group is provided and group.test is TRUE, the comparison of different groups will be returned
   # Assuming p3 is a ggplot object after mp_plot_dist call
   p3 <- mpse_abund %>%
         mp_plot_dist(.distmethod = bray, .group = Group, group.test = TRUE, textsize = 6) +
         theme(
           axis.title.x = element_text(size = 14),  # Customize x-axis label  face = "bold"
           axis.title.y = element_text(size = 14),  # Customize y-axis label
           axis.text.x = element_text(size = 14),  # Customize x-axis ticks
           axis.text.y = element_text(size = 14)   # Customize y-axis ticks
         )
   
   # Save the plot with the new theme settings
   png("Comparison_of_Bray_Distances.png", width = 1000, height = 1000)
   print(p3)  # Ensure that p3 is explicitly printed in the device
   dev.off()
   
   # Extract Bray-Curtis Distance Values and save them in a Excel-table.
   library(dplyr)
   library(tidyr)
   library(openxlsx)
   
   # Define the sample numbers vector
   sample_numbers <- c("8", "9", "10", "12", "13", "14", "33", "34", "35", "36", "37", "38", "39", "51")
   
   # Consolidate the list of tibbles using the actual sample numbers
   bray_data <- bind_rows(
     lapply(seq_along(mpse_abund$bray), function(i) {
       tibble(
         Sample1 = sample_numbers[i],  # Use actual sample number
         Sample2 = mpse_abund$bray[[i]]$braySampley,
         BrayDistance = mpse_abund$bray[[i]]$bray
       )
     }),
     .id = "PairID"
   )
   
   # Print the data frame to check the output
   print(bray_data)
   
   # Write the data frame to an Excel file
   write.xlsx(bray_data, file = "Bray_Curtis_Distances.xlsx")
   #DELETE the column "PairID" in Excel file
   
   # -----------------------
   # 3.5.2 The PCoA analysis
   
   #install.packages("corrr")
   library(corrr)
   #install.packages("ggside")
   library(ggside)
   mpse_abund %<>%
       mp_cal_pcoa(.abundance=hellinger, distmethod="bray")
   # The dimensions of ordination analysis will be added the colData slot (default).
   mpse_abund
   mpse_abund %>% print(width=380, n=2)
   #NOTE mpse_abund contains 34 varibles = 31 varibles + `PCo1 (30.16%)` <dbl>, `PCo2 (15.75%)` <dbl>, `PCo3 (10.53%)` <dbl>
   #BUG why 36 variables in mpse_abund %>% print(width=380, n=1) [RareAbundanceBySample <list>, RareAbundanceByGroup <list>]
   
   #> methods(class=class(mpse_abund))
   # [1] [                        [[<-                     [<-
   # [4] $                        $<-                      arrange
   # [7] as_tibble                as.data.frame            as.phyloseq
   #[10] coerce                   coerce<-                 colData<-
   #[13] distinct                 filter                   group_by
   #[16] left_join                mp_adonis                mp_aggregate_clade
   #[19] mp_aggregate             mp_anosim                mp_balance_clade
   #[22] mp_cal_abundance         mp_cal_alpha             mp_cal_cca
   #[25] mp_cal_clust             mp_cal_dca               mp_cal_dist
   #[28] mp_cal_nmds              mp_cal_pca               mp_cal_pcoa
   #[31] mp_cal_pd_metric         mp_cal_rarecurve         mp_cal_rda
   #[34] mp_cal_upset             mp_cal_venn              mp_decostand
   #[37] mp_diff_analysis         mp_diff_clade            mp_envfit
   #[40] mp_extract_abundance     mp_extract_assays        mp_extract_dist
   #[43] mp_extract_feature       mp_extract_internal_attr mp_extract_rarecurve
   #[46] mp_extract_refseq        mp_extract_sample        mp_extract_taxonomy
   #[49] mp_extract_tree          mp_filter_taxa           mp_mantel
   #[52] mp_mrpp                  mp_plot_abundance        mp_plot_alpha
   #[55] mp_plot_diff_boxplot     mp_plot_diff_res         mp_plot_dist
   #[58] mp_plot_ord              mp_plot_rarecurve        mp_plot_upset
   #[61] mp_plot_venn             mp_rrarefy               mp_select_as_tip
   #[64] mp_stat_taxa             mutate                   otutree
   #[67] otutree<-                print                    pull
   #[70] refsequence              refsequence<-            rename
   #[73] rownames<-               select                   show
   # [ reached getOption("max.print") -- omitted 6 entries ]
   #see '?methods' for accessing help and source code
   
   # We also can perform adonis or anosim to check whether it is significant to the dissimilarities of groups.
   mpse_abund %<>%
       mp_adonis(.abundance=hellinger, .formula=~Group, distmethod="bray", permutations=9999, action="add")
   mpse_abund %>% mp_extract_internal_attr(name=adonis)
   #NOTE mpse_abund contains 34 varibles, no new variable, it has been saved in mpse_abund and can be extracted with "mpse_abund %>% mp_extract_internal_attr(name='adonis')"
   
   #The result of adonis has been saved to the internal attribute !
   #It can be extracted using this-object %>% mp_extract_internal_attr(name='adonis')
   #The object contained internal attribute: PCoA ADONIS
   #Permutation test for adonis under reduced model
   #Terms added sequentially (first to last)
   #Permutation: free
   #Number of permutations: 9999
   #
   #vegan::adonis2(formula = .formula, data = sampleda, permutations = permutations, method = distmethod)
   #         Df SumOfSqs      R2      F Pr(>F)
   #Group     1  0.23448 0.22659 3.5158  5e-04 ***
   #Residual 12  0.80032 0.77341
   #Total    13  1.03480 1.00000
   #---
   #Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
   
   # ("1","2","5","6","7",  "15","16","17","18","19","20",  "29","30","31","32",  "40","41","42","43","44","46")
   #div.df2[div.df2 == "Group1"] <- "aged.post"
   #div.df2[div.df2 == "Group3"] <- "young.post"
   #div.df2[div.df2 == "Group5"] <- "aged.post"
   #div.df2[div.df2 == "Group7"] <- "young.post"
   
   # ("8","9","10","12","13","14",  "21","22","23","24","25","26","27","28",  "33","34","35","36","37","38","39","51",  "47","48","49","50","52","53","55")
   #div.df2[div.df2 == "Group2"] <- "aged.pre"
   #div.df2[div.df2 == "Group4"] <- "young.pre"
   #div.df2[div.df2 == "Group6"] <- "aged.pre"
   #div.df2[div.df2 == "Group8"] <- "young.pre"
   
   #Group1: f.aged and post
   #Group2: f.aged and pre
   #Group3: f.young and post
   #Group4: f.young and pre
   #Group5: m.aged and post
   #Group6: m.aged and pre
   #Group7: m.young and post
   #Group8: m.young and pre
   
   #[,c("1","2","5","6","7",                "8","9","10","12","13","14")]
   #[,c("15","16","17","18","19","20",      "21","22","23","24","25","26","27","28")]
   #[,c("29","30","31","32",                "33","34","35","36","37","38","39","51")]
   #[,c("40","41","42","43","44","46",      "47","48","49","50","52","53","55")]
   
   #For the first set:
       #a6cee3: This is a light blue color, somewhat pastel and soft.
       #b2df8a: A soft, pale green, similar to a light lime.
       #fb9a99: A soft pink, slightly peachy or salmon-like.
       #cab2d6: A pale purple, reminiscent of lavender or a light mauve.
   #For the second set:
       #1f78b4: This is a strong, vivid blue, close to cobalt or a medium-dark blue.
       #33a02c: A medium forest green, vibrant and leafy.
       #e31a1c: A bright red, very vivid, similar to fire engine red.
       #6a3d9a: This would be described as a deep purple, akin to a dark lavender or plum.
   p1 <- mpse_abund %>%
           mp_plot_ord(
             .ord = pcoa,
             .group = Group,
             .color = Group,
             .size = 2.4,
             .alpha = 1,
             ellipse = TRUE,
             show.legend = FALSE # don't display the legend of stat_ellipse
           ) +
           scale_fill_manual(
             #values = c("#a6cee3", "#1f78b4", "#b2df8a", "#33a02c", "#fb9a99", "#e31a1c", "#cab2d6", "#6a3d9a"),
             #values = c("#a6cee3", "#b2df8a", "#fb9a99", "#cab2d6"),
             values = c("#1f78b4", "#e31a1c"),
             guide = guide_legend(keywidth=1.6, keyheight=1.6, label.theme=element_text(size=12))
           ) +
           scale_color_manual(
             #values=c("#a6cee3", "#1f78b4", "#b2df8a", "#33a02c", "#fb9a99", "#e31a1c", "#cab2d6", "#6a3d9a"),
             #values = c("#a6cee3", "#b2df8a", "#fb9a99", "#cab2d6"),
             values = c("#1f78b4", "#e31a1c"),
             guide = guide_legend(keywidth=1.6, keyheight=1.6, label.theme=element_text(size=12))
           )
           #scale_fill_manual(values=c("#FF0000", "#000000", "#0000FF", "#C0C0C0", "#00FF00", "#FFFF00", "#00FFFF", "#FFA500")) +
           #scale_color_manual(values=c("#FF0000", "#000000", "#0000FF", "#C0C0C0", "#00FF00", "#FFFF00", "#00FFFF", "#FFA500"))
           #scale_fill_manual(values=c("#00A087FF", "#3C5488FF")) +
           #scale_color_manual(values=c("#00A087FF", "#3C5488FF"))
   #png("PCoA.png", width= 1000, height=1000)
   #svg("PCoA.svg", width= 11, height=10)
   #svg("PCoA_.svg", width=10, height=10)
   #svg("PCoA.svg")
   pdf("PCoA_Group2_vs_Group6.pdf")
   p1
   dev.off()
   
       #FF0000: Red
       #000000: Black
       #0000FF: Blue
       #C0C0C0: Silver
       #00FF00: Lime (often referred to simply as Green in web colors)
       #FFFF00: Yellow
       #00FFFF: Aqua (also known as Cyan)
       #FFA500: Orange
   # The size of point also can be mapped to other variables such as Observe, or Shannon
   # Then the alpha diversity and beta diversity will be displayed simultaneously.
   p2 <- mpse_abund %>%
           mp_plot_ord(
             .ord = pcoa,
             .group = Group,
             .color = Group,
             .size = Shannon,
             .alpha = Observe,
             ellipse = TRUE,
             show.legend = FALSE # don't display the legend of stat_ellipse
           ) +
           scale_fill_manual(
             values = c("#1f78b4", "#e31a1c"),  #only needs four colors.
             #values = c("#FF0000", "#000000", "#0000FF", "#C0C0C0", "#00FF00", "#FFFF00", "#00FFFF", "#FFA500"),
             guide = guide_legend(keywidth=0.6, keyheight=0.6, label.theme=element_text(size=8))
           ) +
           scale_color_manual(
             values = c("#1f78b4", "#e31a1c"),  #only needs four colors.
             #values=c("#FF0000", "#000000", "#0000FF", "#C0C0C0", "#00FF00", "#FFFF00", "#00FFFF", "#FFA500"),
             guide = guide_legend(keywidth=0.6, keyheight=0.6, label.theme=element_text(size=8))
           ) +
           scale_size_continuous(
             range=c(0.5, 3),
             guide = guide_legend(keywidth=0.6, keyheight=0.6, label.theme=element_text(size=8))
           )
   pdf("PCoA2_Group2_vs_Group6.pdf")
   p2
   dev.off()
   
   # ------------------------------------------
   # 3.5.3 Hierarchical cluster (tree) analysis
   #input should contain hellinger!
   mpse_abund %<>%
         mp_cal_clust(
           .abundance = hellinger,
           distmethod = "bray",
           hclustmethod = "average", # (UPGAE)
           action = "add" # action is used to control which result will be returned
         )
   mpse_abund
   mpse_abund %>% print(width=380, n=2)
   #NOTE mpse_abund contains 34 varibles, no new variable, the column bray has been new calculated!
   
   # if action = 'add', the result of hierarchical cluster will be added to the MPSE object
   # mp_extract_internal_attr can extract it. It is a treedata object, so it can be visualized
   # by ggtree.
   sample.clust <- mpse_abund %>% mp_extract_internal_attr(name='SampleClust')
   #The object contained internal attribute: PCoA ADONIS SampleClust
   sample.clust
   #--> The associated data tibble abstraction: 27 × 30
   
   library(ggtree)
   p <- ggtree(sample.clust) +
         geom_tippoint(aes(color=Group)) +
         geom_tiplab(as_ylab = TRUE) +
         ggplot2::scale_x_continuous(expand=c(0, 0.01))
   png("hierarchical_cluster1.png", width= 1000, height=800)
   p
   dev.off()
   
   #https://bioconductor.org/packages/release/bioc/vignettes/MicrobiotaProcess/inst/doc//MicrobiotaProcess.html
   #         mapping = aes(x = RelRareAbundanceBySample-->Group,
   #                       y = Sample-->Group,
   #                       fill = Phyla
   #                 ),
   library(ggtreeExtra)
   library(ggplot2)
   # Extract relative abundance of phyla
   phyla.tb <- mpse_abund %>%
               mp_extract_abundance(taxa.class=Phylum, topn=30)
   # The abundance of each samples is nested, it can be flatted using the unnest of tidyr.
   phyla.tb %<>% tidyr::unnest(cols=RareAbundanceBySample) %>% dplyr::rename(Phyla="label")
   phyla.tb
   phyla.tb %>% print(width=380, n=10)
   
   p1 <- p +
         geom_fruit(
           data=phyla.tb,
           geom=geom_col,
           mapping = aes(x = RelRareAbundanceBySample,
                         y = Sample,
                         fill = Phyla
                   ),
           orientation = "y",
           #offset = 0.4,
           pwidth = 3,
           axis.params = list(axis = "x",
                               title = "The relative abundance of phyla (%)",
                               title.size = 4,
                               text.size = 2,
                               vjust = 1),
           grid.params = list()
         )
   png("hierarchical_cluster2_Phyla.png", width = 1000, height = 800)
   p1
   dev.off()
   
   # Extract relative abundance of classes
   class.tb <- mpse_abund %>%
               mp_extract_abundance(taxa.class = Class, topn = 30)
   # Flatten and rename the columns
   class.tb %<>% tidyr::unnest(cols = RareAbundanceBySample) %>% dplyr::rename(Class = "label")
   # View the data frame
   class.tb
   
   # Create the plot
   p1 <- p +
         geom_fruit(
           data = class.tb,
           geom = geom_col,
           mapping = aes(x = RelRareAbundanceBySample,
                         y = Sample,
                         fill = Class
                   ),
           orientation = "y",
           pwidth = 3,
           axis.params = list(axis = "x",
                               title = "The relative abundance of classes (%)",
                               title.size = 4,
                               text.size = 2,
                               vjust = 1),
           grid.params = list()
         )
   
   # Save the plot to a file  #ERROR-->NEED to be DEBUGGED!
   png("hierarchical_cluster2_Class.png", width = 1000, height = 800)
   print(p1)
   dev.off()
   
   # -----------------------
   # 3.6 Biomarker discovery
   library(ggtree)
   library(ggtreeExtra)
   library(ggplot2)
   library(MicrobiotaProcess)
   library(tidytree)
   library(ggstar)
   library(forcats)
   mpse_abund %>% print(width=150)
   
   #mpse_abund %<>%
   #    mp_cal_abundance( # for each samples
   #      .abundance = RareAbundance
   #    ) %>%
   #    mp_cal_abundance( # for each groups
   #      .abundance=RareAbundance,
   #      .group=Group
   #    )
   #mpse_abund
   
   mpse_abund %<>%
       mp_diff_analysis(
         .abundance = RelRareAbundanceBySample,
         .group = Group,
         first.test.alpha = 0.01, filter.p="pvalue"
       )
   # The result is stored to the taxatree or otutree slot, you can use mp_extract_tree to extract the specific slot.
   taxa.tree <- mpse_abund %>%
                 mp_extract_tree(type="taxatree")
   taxa.tree
   
   ## And the result tibble of different analysis can also be extracted
   ## with tidytree (>=0.3.5)
   taxa.tree %>% select(label, nodeClass, LDAupper, LDAmean, LDAlower, Sign_Group, pvalue, fdr) %>% dplyr::filter(!is.na(fdr))
   taxa.tree %>% print(width=150, n=1)
   
   # Since taxa.tree is treedata object, it can be visualized by ggtree and ggtreeExtra
   p1 <- ggtree(
           taxa.tree,
           layout="radial",
           size = 0.3
         ) +
         geom_point(
           data = td_filter(!isTip),
           fill="white",
           size=1,
           shape=21
         )
   # display the high light of phylum clade.
   p2 <- p1 +
         geom_hilight(
           data = td_filter(nodeClass == "Phylum"),
           mapping = aes(node = node, fill = label)
         )
   # display the relative abundance of features(OTU)
   p3 <- p2 +
         ggnewscale::new_scale("fill") +
         geom_fruit(
           data = td_unnest(RareAbundanceBySample),
           geom = geom_star,
           mapping = aes(
                         x = fct_reorder(Sample, Group, .fun=min),
                         size = RelRareAbundanceBySample,
                         fill = Group,
                         subset = RelRareAbundanceBySample > 0
                     ),
           starshape = 13,
           starstroke = 0.25,
           offset = 0.03,
           pwidth = 0.4,
           grid.params = list(linetype=2)
         ) +
         scale_size_continuous(
           name="Relative Abundance (%)",
           range = c(.5, 3)
         ) +
         scale_fill_manual(values=c("#1B9E77", "#D95F02"))
   
   # display the tip labels of taxa tree
   p4 <- p3 + geom_tiplab(size=6, offset=4.0)
   # display the LDA of significant OTU.
   #p5 <- p4 +
   #      ggnewscale::new_scale("fill") +
   #      geom_fruit(
   #         geom = geom_col,
   #         mapping = aes(
   #                       x = LDAmean,
   #                       fill = Sign_Group,
   #                       subset = !is.na(LDAmean)
   #                       ),
   #         orientation = "y",
   #         offset = 0.3,
   #         pwidth = 0.5,
   #         axis.params = list(axis = "x",
   #                            title = "Log10(LDA)",
   #                            title.height = 0.01,
   #                            title.size = 2,
   #                            text.size = 1.8,
   #                            vjust = 1),
   #         grid.params = list(linetype = 2)
   #      )
   
   # display the significant (FDR-->pvalue) taxonomy after kruskal.test (default)
   #shape = 21,
   #scale_size_continuous(range=c(1, 3)) +
   p6 <- p4 +
         ggnewscale::new_scale("size") +
         geom_point(
           data=td_filter(!is.na(Sign_Group)),
           mapping = aes(size = -log10(pvalue),
                         fill = Sign_Group,
                         ),
           shape = 21,
         ) +
         scale_size_continuous(range=c(1, 4)) +
         scale_fill_manual(values=c("#1B9E77", "#D95F02"))
   
   svg("diff_analysis_Group2_vs_Group6.svg",width=22, height=22)
   #png("differently_expressed_otu.png", width=2000, height=2000)
   p6 + theme(
             legend.key.height = unit(1.0, "cm"),
             legend.key.width = unit(1.0, "cm"),
             legend.spacing.y = unit(0.01, "cm"),
             legend.text = element_text(size = 20),
             legend.title = element_text(size = 20)
             #legend.position = c(0.99, 0.01)
             )
   dev.off()
   
   #-- save the data in an Excel-file --
   
   #install.packages("tidyverse")
   #library(tidyverse)
   install.packages("dplyr")
   install.packages("readr")
   library(dplyr)
   library(readr)
   
   # Extracting different parts of mpse_abund, assuming these functions are applicable:
   abundance_data <- as.data.frame(mp_extract_abundance(mpse_abund))
   taxonomy_data <- as.data.frame(mp_extract_taxonomy(mpse_abund))
   
   # Joining the data together by a common identifier, assuming 'OTU' is present in both
   combined_data <- left_join(abundance_data, taxonomy_data, by = "OTU")
   
   # Install and load openxlsx package if not already installed
   if (!require(openxlsx)) {
     install.packages("openxlsx")
     library(openxlsx)
   }
   
   # Assuming `abundance_data` has lists that need flattening; example for one column:
   abundance_data <- abundance_data %>%
     mutate(RareAbundanceBySample = sapply(RareAbundanceBySample, function(x) paste(x, collapse = ", "))) %>%
     mutate(RareAbundanceByGroup = sapply(RareAbundanceByGroup, function(x) paste(x, collapse = ", ")))
   
   # Check the structure now
   str(abundance_data)
   
   # Rename the 'label' column in abundance_data to 'OTU'
   colnames(abundance_data)[colnames(abundance_data) == "label"] <- "OTU"
   
   # Merge the cleaned abundance data with taxonomy data
   combined_data <- left_join(abundance_data, taxonomy_data, by = "OTU")
   
   # Check the resulting data frame
   str(combined_data)
   
   library(openxlsx)
   
   # Write the data frame to an Excel file
   write.xlsx(combined_data, "diff_analysis_Group2_vs_Group6.xlsx")
   
   #delete RareAbundanceBySample!
   
   # ------ Deprecated ------
   
   #.data, layout, tree.type, .taxa.class, tiplab.size, offset.abun, pwidth.abun, offset.effsize, pwidth.effsize, group.abun, tiplab.linetype
   p <- mpse_abund %>%
         mp_plot_diff_res(
           group.abun = FALSE,
           pwidth.abun=0.01,
           offset.abun=0.2,
           pwidth.effsize=0.01,
           offset.effsize=0.2,
           tiplab.size = 5.0,
           tiplab.linetype = 3
         ) +
         scale_fill_manual(values=c("deepskyblue", "orange")) +
         scale_fill_manual(
           aesthetics = "fill_new", # The fill aes was renamed to "fill_new" for the abundance dotplot layer
           values = c("deepskyblue", "orange")
         ) +
         scale_fill_manual(
           aesthetics = "fill_new_new", # The fill aes for hight light layer of tree was renamed to 'fill_new_new'
           values = c("#E41A1C", "#377EB8", "#4DAF4A",
                       "#984EA3", "#FF7F00", "#FFFF33",
                       "#A65628", "#F781BF", "#00FFFF", "#999999"
                     )
         ) +
         theme(
       axis.title = element_text(size = 28),        # Font size for axis titles
       axis.text = element_text(size = 28),          # Font size for axis text
       plot.title = element_text(size = 16),         # Font size for plot title
       legend.title = element_text(size = 16),       # Font size for legend title
       legend.text = element_text(size = 14)         # Font size for legend text
     )
   #p$layers[[2]]$geom <- geom_tiplab(fontsize = 22)  # Change 12 to the desired font size
   
   p <- mpse_abund %>%
         mp_plot_diff_res(
           group.abun = TRUE,
           pwidth.abun=0.05,
           offset.abun=0.04,
           pwidth.effsize=0.3,
           offset.effsize=0.6,
           tiplab.size = 4.9,
           barplot.x = -log10(pvalue)
         ) +
         scale_fill_manual(values=c("deepskyblue", "orange")) +
         scale_fill_manual(
           aesthetics = "fill_new", # The fill aes was renamed to "fill_new" for the abundance dotplot layer
           values = c("deepskyblue", "orange")
         ) +
         scale_fill_manual(
           aesthetics = "fill_new_new", # The fill aes for hight light layer of tree was renamed to 'fill_new_new'
           values = c("#E41A1C", "#377EB8", "#4DAF4A",
                       "#984EA3", "#FF7F00", "#FFFF33",
                       "#A65628", "#F781BF", "#00FFFF", "#999999"
                     )
         ) +
         theme(
       axis.title = element_text(size = 28),        # Font size for axis titles
       axis.text = element_text(size = 28),          # Font size for axis text
       plot.title = element_text(size = 28),         # Font size for plot title
       legend.title = element_text(size = 16),       # Font size for legend title
       legend.text = element_text(size = 14)         # Font size for legend text
     )
   #png("differently_expressed_otu.png", width=2000, height=2000)
   svg("p7.svg",width=22, height=22)
   p
   dev.off()
   
   p <- mpse_abund %>%
         mp_plot_diff_res(
           group.abun = FALSE,
           pwidth.abun=0.1,
           tiplab.size = 0.1,
           offset.effsize=0.1,
           pwidth.effsize=0.2
         ) +
         scale_fill_manual(values=c("deepskyblue", "orange")) +
         scale_fill_manual(
           aesthetics = "fill_new", # The fill aes was renamed to "fill_new" for the abundance dotplot layer
           values = c("deepskyblue", "orange")
         ) +
         scale_fill_manual(
           aesthetics = "fill_new_new", # The fill aes for hight light layer of tree was renamed to 'fill_new_new'
           values = c("#E41A1C", "#377EB8", "#4DAF4A",
                       "#984EA3", "#FF7F00", "#FFFF33",
                       "#A65628", "#F781BF", "#00FFFF", "#999999"
                     )
         )
   #svg("p8.svg",width=22, height=22)
   png("differently_expressed_otu.png", width=2000, height=2000)
   p
   dev.off()
   
   f <- mpse_abund %>%
       mp_plot_diff_cladogram(
         label.size = 2.5,
         hilight.alpha = .3,
         bg.tree.size = .5,
         bg.point.size = 2,
         bg.point.stroke = .25
       ) +
       scale_fill_diff_cladogram( # set the color of different group.
         values = c('deepskyblue', 'orange')
       ) +
       scale_size_continuous(range = c(1, 4))
   #png("cladogram.png", width=1000, height=1000)
   svg("cladogram.svg", width=10, height=10)
   f
   dev.off()
   
   ## Extract the OTU table and taxonomy table from the phyloseq object
   otu_table <- phyloseq::otu_table(ps.ng.tax_abund) %>% as.data.frame() %>% as.matrix()
   tax_table <- phyloseq::tax_table(ps.ng.tax_abund) %>% as.data.frame() %>% as.matrix()
   write.csv(otu_table, file="otu_table.csv")
   write.csv(tax_table, file="tax_table.csv")
   #~/Tools/csv2xls-0.4/csv_to_xls.py otu_table.csv tax_table.csv -d',' -o otu_tax.xls
   

2. code of Phyloseq.Rmd

   author: ""
   date: '`r format(Sys.time(), "%d %m %Y")`'
   header-includes:
     - \usepackage{color, fancyvrb}
   output:
     rmdformats::readthedown:
       highlight: kate
       number_sections : yes
     pdf_document:
       toc: yes
       toc_depth: 2
       number_sections : yes
   ---
   
   ```{r, echo=FALSE, warning=FALSE}
   ## Global options
   # TODO: reproduce the html with the additional figure/SVN-files for editing.
   # IMPORTANT NOTE: needs before "mkdir figures"
   #rmarkdown::render('Phyloseq.Rmd',output_file='Phyloseq.html')
   ```
   
   ```{r load-packages, include=FALSE}
   library(knitr)
   library(rmdformats)
   library(readxl)
   library(dplyr)
   library(kableExtra)
   
   options(max.print="75")
   knitr::opts_chunk$set(fig.width=8,
                         fig.height=6,
                         eval=TRUE,
                         cache=TRUE,
                         echo=TRUE,
                         prompt=FALSE,
                         tidy=TRUE,
                         comment=NA,
                         message=FALSE,
                         warning=FALSE)
   opts_knit$set(width=85)
   # Phyloseq R library
   #* Phyloseq web site : https://joey711.github.io/phyloseq/index.html
   #* See in particular tutorials for
   #    - importing data: https://joey711.github.io/phyloseq/import-data.html
   #    - heat maps: https://joey711.github.io/phyloseq/plot_heatmap-examples.html
   ```
   
   # Data
   
   Import raw data and assign sample key:
   
   ```{r, echo=TRUE, warning=FALSE}
   #extend map_corrected.txt with Diet and Flora
   #setwd("~/DATA/Data_Laura_16S_2/core_diversity_e4753")
   map_corrected <- read.csv("map_corrected.txt", sep="\t", row.names=1)
   knitr::kable(map_corrected) %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))
   ```
   
   # Prerequisites to be installed
   
   * R : https://pbil.univ-lyon1.fr/CRAN/
   * R studio : https://www.rstudio.com/products/rstudio/download/#download
   
   ```R
   install.packages("dplyr")     # To manipulate dataframes
   install.packages("readxl")    # To read Excel files into R
   install.packages("ggplot2")   # for high quality graphics
   install.packages("heatmaply")
   source("https://bioconductor.org/biocLite.R")
   biocLite("phyloseq")
   ```
   
   ```{r libraries, echo=TRUE, message=FALSE}
   library("readxl") # necessary to import the data from Excel file
   library("ggplot2") # graphics
   library("picante")
   library("microbiome") # data analysis and visualisation
   library("phyloseq") # also the basis of data object. Data analysis and visualisation
   library("ggpubr") # publication quality figures, based on ggplot2
   library("dplyr") # data handling, filter and reformat data frames
   library("RColorBrewer") # nice color options
   library("heatmaply")
   library(vegan)
   library(gplots)
   ```
   
   # Read the data and create phyloseq objects
   
   Three tables are needed
   
   * OTU
   * Taxonomy
   * Samples
   
   ```{r, echo=TRUE, warning=FALSE}
       #Change your working directory to where the files are located
       ps.ng.tax <- import_biom("./table_even42369.biom", "./clustering/rep_set.tre")
       sample <- read.csv("./map_corrected.txt", sep="\t", row.names=1)
       SAM = sample_data(sample, errorIfNULL = T)
       rownames(SAM) <-
       c("1","2","3","5","6","7","8","9","10","12","13","14","15","16","17","18","19","20","21","22","23","24","25","26","27","28","29","30","31","32","33","34","35","36","37","38","39","40","41","42","43","44","46","47","48","49","50","51","52","53","55")
       ps.ng.tax <- merge_phyloseq(ps.ng.tax, SAM)
       print(ps.ng.tax)
       colnames(tax_table(ps.ng.tax)) <- c("Domain","Phylum","Class","Order","Family","Genus","Species")
       saveRDS(ps.ng.tax, "./ps.ng.tax.rds")
   ```
   
   Visualize data
   ```{r, echo=TRUE, warning=FALSE}
     sample_names(ps.ng.tax)
     rank_names(ps.ng.tax)
     sample_variables(ps.ng.tax)
   ```
   
   Normalize number of reads in each sample using median sequencing depth.
   ```{r, echo=TRUE, warning=FALSE}
   # RAREFACTION
   #set.seed(9242)  # This will help in reproducing the filtering and nomalisation.
   #ps.ng.tax <- rarefy_even_depth(ps.ng.tax, sample.size = 42369)
   #total <- 42369
   
   # NORMALIZE number of reads in each sample using median sequencing depth.
   total = median(sample_sums(ps.ng.tax))
   #> total
   #[1] 42369
   standf = function(x, t=total) round(t * (x / sum(x)))
   ps.ng.tax = transform_sample_counts(ps.ng.tax, standf)
   ps.ng.tax_rel <- microbiome::transform(ps.ng.tax, "compositional")
   
   saveRDS(ps.ng.tax, "./ps.ng.tax.rds")
   hmp.meta <- meta(ps.ng.tax)
   hmp.meta$sam_name <- rownames(hmp.meta)
   ```
   
   # Heatmaps
   
   ```{r, echo=TRUE, warning=FALSE}
   #MOVE_FROM_ABOVE: The number of reads used for normalization is **`r sprintf("%.0f", total)`**.
   #A basic heatmap using the default parameters.
   #  plot_heatmap(ps.ng.tax, method = "NMDS", distance = "bray")
   #NOTE that giving the correct OTU numbers in the text (1%, 0.5%, ...)!!!
   ```
   
   We consider the most abundant OTUs for heatmaps. For example one can only take OTUs that represent at least 1% of reads in at least one sample. Remember we normalized all the sampples to median number of reads (total).  We are left with only 168 OTUS which makes the reading much more easy.
   ```{r, echo=TRUE, warning=FALSE}
   
   # Custom function to plot a heatmap with the specified sample order
   #plot_heatmap_custom <- function(ps, sample_order, method = "NMDS", distance = "bray") {
   ps.ng.tax_abund <- phyloseq::filter_taxa(ps.ng.tax, function(x) sum(x > total*0.01) > 0, TRUE)
   kable(otu_table(ps.ng.tax_abund)) %>%
   kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))
   # Calculate the relative abundance for each sample
   ps.ng.tax_abund_rel <- transform_sample_counts(ps.ng.tax_abund, function(x) x / sum(x))
   
   datamat_ = as.data.frame(otu_table(ps.ng.tax_abund))
   #datamat <- datamat_[c("1","2","5","6","7",  "8","9","10","12","13","14",    "15","16","17","18","19","20",  "21","22","23","24","25","26","27","28",    "29","30","31","32",  "33","34","35","36","37","38","39","51",    "40","41","42","43","44","46",  "47","48","49","50","52","53","55")]
   datamat <- datamat_[c("8","9","10","12","13","14",    "21","22","23","24","25","26","27","28",    "33","34","35","36","37","38","39","51",    "47","48","49","50","52","53","55")]
   hr <- hclust(as.dist(1-cor(t(datamat), method="pearson")), method="complete")
   hc <- hclust(as.dist(1-cor(datamat, method="spearman")), method="complete")
   mycl = cutree(hr, h=max(hr$height)/1.08)
   mycol = c("YELLOW", "DARKBLUE", "DARKORANGE", "DARKMAGENTA", "DARKCYAN", "DARKRED",  "MAROON", "DARKGREEN", "LIGHTBLUE", "PINK", "MAGENTA", "LIGHTCYAN","LIGHTGREEN", "BLUE", "ORANGE", "CYAN", "RED", "GREEN");
   
   mycol = mycol[as.vector(mycl)]
   sampleCols <- rep('GREY',ncol(datamat))
   #names(sampleCols) <- c("Group1", "Group1", "Group1", "Group1", "Group1",   "Group2", "Group2", "Group2", "Group2", "Group2","Group2",    "Group3", "Group3", "Group3", "Group3",   "Group1", "Group1", "Group1", "Group1", "Group1",   "Group1", "Group1", "Group1", "Group1", "Group1",  "Group1", "Group1", "Group1", "Group1", "Group1",  "Group1", "Group1", "Group1", "Group1", "Group1",  "Group1", "Group1", "Group1", "Group1", "Group1",    "Group1", "Group1", "Group1", "Group1", "Group1",  "Group1", "Group1", "Group1", "Group1", "Group1")
   
   #sampleCols[colnames(datamat)=='1'] <- '#a6cee3'
   #sampleCols[colnames(datamat)=='2'] <- '#a6cee3'
   #sampleCols[colnames(datamat)=='5'] <- '#a6cee3'
   #sampleCols[colnames(datamat)=='6'] <- '#a6cee3'
   #sampleCols[colnames(datamat)=='7'] <- '#a6cee3'
   
   sampleCols[colnames(datamat)=='8'] <- '#1f78b4'
   sampleCols[colnames(datamat)=='9'] <- '#1f78b4'
   sampleCols[colnames(datamat)=='10'] <- '#1f78b4'
   sampleCols[colnames(datamat)=='12'] <- '#1f78b4'
   sampleCols[colnames(datamat)=='13'] <- '#1f78b4'
   sampleCols[colnames(datamat)=='14'] <- '#1f78b4'
   
   #sampleCols[colnames(datamat)=='15'] <- '#b2df8a'
   #sampleCols[colnames(datamat)=='16'] <- '#b2df8a'
   #sampleCols[colnames(datamat)=='17'] <- '#b2df8a'
   #sampleCols[colnames(datamat)=='18'] <- '#b2df8a'
   #sampleCols[colnames(datamat)=='19'] <- '#b2df8a'
   #sampleCols[colnames(datamat)=='20'] <- '#b2df8a'
   
   sampleCols[colnames(datamat)=='21'] <- '#33a02c'
   sampleCols[colnames(datamat)=='22'] <- '#33a02c'
   sampleCols[colnames(datamat)=='23'] <- '#33a02c'
   sampleCols[colnames(datamat)=='24'] <- '#33a02c'
   sampleCols[colnames(datamat)=='25'] <- '#33a02c'
   sampleCols[colnames(datamat)=='26'] <- '#33a02c'
   sampleCols[colnames(datamat)=='27'] <- '#33a02c'
   sampleCols[colnames(datamat)=='28'] <- '#33a02c'
   
   #sampleCols[colnames(datamat)=='29'] <- '#fb9a99'
   #sampleCols[colnames(datamat)=='30'] <- '#fb9a99'
   #sampleCols[colnames(datamat)=='31'] <- '#fb9a99'
   #sampleCols[colnames(datamat)=='32'] <- '#fb9a99'
   
   sampleCols[colnames(datamat)=='33'] <- '#e31a1c'
   sampleCols[colnames(datamat)=='34'] <- '#e31a1c'
   sampleCols[colnames(datamat)=='35'] <- '#e31a1c'
   sampleCols[colnames(datamat)=='36'] <- '#e31a1c'
   sampleCols[colnames(datamat)=='37'] <- '#e31a1c'
   sampleCols[colnames(datamat)=='38'] <- '#e31a1c'
   sampleCols[colnames(datamat)=='39'] <- '#e31a1c'
   sampleCols[colnames(datamat)=='51'] <- '#e31a1c'
   
   #sampleCols[colnames(datamat)=='40'] <- '#cab2d6'
   #sampleCols[colnames(datamat)=='41'] <- '#cab2d6'
   #sampleCols[colnames(datamat)=='42'] <- '#cab2d6'
   #sampleCols[colnames(datamat)=='43'] <- '#cab2d6'
   #sampleCols[colnames(datamat)=='44'] <- '#cab2d6'
   #sampleCols[colnames(datamat)=='46'] <- '#cab2d6'
   
   sampleCols[colnames(datamat)=='47'] <- '#6a3d9a'
   sampleCols[colnames(datamat)=='48'] <- '#6a3d9a'
   sampleCols[colnames(datamat)=='49'] <- '#6a3d9a'
   sampleCols[colnames(datamat)=='50'] <- '#6a3d9a'
   sampleCols[colnames(datamat)=='52'] <- '#6a3d9a'
   sampleCols[colnames(datamat)=='53'] <- '#6a3d9a'
   sampleCols[colnames(datamat)=='55'] <- '#6a3d9a'
   #bluered(75)
   #color_pattern <- colorRampPalette(c("blue", "white", "red"))(100)
   library(RColorBrewer)
   custom_palette <- colorRampPalette(brewer.pal(9, "Blues"))
   heatmap_colors <- custom_palette(100)
   #colors <- heatmap_color_default(100)
   png("figures/heatmap.png", width=1200, height=2400)
   #par(mar=c(2, 2, 2, 2))  , lwid=1    lhei=c(0.7, 10)) # Adjust height of color keys   keysize=0.3,
   heatmap.2(as.matrix(datamat),Rowv=as.dendrogram(hr),Colv = NA, dendrogram = 'row',
               scale='row',trace='none',col=heatmap_colors, cexRow=1.2, cexCol=1.5,
               RowSideColors = mycol, ColSideColors = sampleCols, srtCol=15, labRow=row.names(datamat), key=TRUE, margins=c(10, 15), lhei=c(0.7, 15), lwid=c(1,8))
   dev.off()
   ```
   ```{r, echo=TRUE, warning=FALSE, fig.cap="Heatmap", out.width = '100%', fig.align= "center"}
   knitr::include_graphics("./figures/heatmap.png")
   ```
   
   ```{r, echo=FALSE, warning=FALSE}
     #It is possible to use different distances and different multivaraite methods. Many different built-in distances can be used.
     #dist_methods <- unlist(distanceMethodList)
     #print(dist_methods)
   ```
   
   \pagebreak
   
   # Taxonomic summary
   
   ## Bar plots in phylum level
   
   ```{r, echo=FALSE, warning=FALSE}
   #Make the bargraph nicer by removing OTUs boundaries. This is done by adding ggplot2 modifier.
   # 1: uniform color. Color is for the border, fill is for the inside
   #ggplot(mtcars, aes(x=as.factor(cyl) )) +
   #  geom_bar(color="blue", fill=rgb(0.1,0.4,0.5,0.7) )
   # 2: Using Hue
   #ggplot(mtcars, aes(x=as.factor(cyl), fill=as.factor(cyl) )) +
   #  geom_bar( ) +
   #  scale_fill_hue(c = 40) +
   #  theme(legend.position="none")
   # 3: Using RColorBrewer
   #ggplot(mtcars, aes(x=as.factor(cyl), fill=as.factor(cyl) )) +
   #  geom_bar( ) +
   #  scale_fill_brewer(palette = "Set1") +
   #  theme(legend.position="none")
   # 4: Using greyscale:
   #ggplot(mtcars, aes(x=as.factor(cyl), fill=as.factor(cyl) )) +
   #  geom_bar( ) +
   #  scale_fill_grey(start = 0.25, end = 0.75) +
   #  theme(legend.position="none")
   # 5: Set manualy
   #ggplot(mtcars, aes(x=as.factor(cyl), fill=as.factor(cyl) )) +
   #  geom_bar( ) +
   #  scale_fill_manual(values = c("red", "green", "blue") ) +
   #  theme(legend.position="none")
   #NOT SUCCESSFUL!
   #allGroupsColors<- c(
   #  "grey0", "grey50", "dodgerblu", "deepskyblue",
   #  "red", "darkred", "green", "green4")
   #  plot_bar(ps.ng.tax_rel, fill="Phylum") +
   #  geom_bar(stat="identity", position="stack") + scale_color_manual(values = allGroupsColors) #, fill=Phylum   + scale_fill_brewer(palette = "Set1")
     # ##### Keep only the most abundant phyla and
     #ps.ng.tax_most <- subset_taxa(ps.ng.tax_rel, Phylum %in% c("D_1__Actinobacteria")) #1.57
     #ps.ng.tax_most <- subset_taxa(ps.ng.tax_rel, Phylum %in% c("D_1__Bacteroidetes"))  #27.27436
     #ps.ng.tax_most <- subset_taxa(ps.ng.tax_rel, Phylum %in% c("D_1__Cyanobacteria"))  #0.02244249
     #ps.ng.tax_most <- subset_taxa(ps.ng.tax_rel, Phylum %in% c("D_1__Epsilonbacteraeota"))  #0.01309145
     #ps.ng.tax_most <- subset_taxa(ps.ng.tax_rel, Phylum %in% c("D_1__Euryarchaeota"))  #0.1210024
     #ps.ng.tax_most <- subset_taxa(ps.ng.tax_rel, Phylum %in% c("D_1__Firmicutes"))     #32.50589
     #ps.ng.tax_most <- subset_taxa(ps.ng.tax_rel, Phylum %in% c("D_1__Lentisphaerae"))  #0.0001870208
     #ps.ng.tax_most <- subset_taxa(ps.ng.tax_rel, Phylum %in% c("D_1__Patescibacteria")) #0.008789976
     #ps.ng.tax_most <- subset_taxa(ps.ng.tax_rel, Phylum %in% c("D_1__Planctomycetes")) #0.01365252
     #ps.ng.tax_most <- subset_taxa(ps.ng.tax_rel, Phylum %in% c("D_1__Proteobacteria")) #6.769216
     #ps.ng.tax_most <- subset_taxa(ps.ng.tax_rel, Phylum %in% c("D_1__Synergistetes"))  #0.005049561
     #ps.ng.tax_most <- subset_taxa(ps.ng.tax_rel, Phylum %in% c("D_1__Tenericutes"))    #0.0005610623
     #ps.ng.tax_most <- subset_taxa(ps.ng.tax_rel, Phylum %in% c("D_1__Verrucomicrobia"))  #2.076304
     #ps.ng.tax_most <- subset_taxa(ps.ng.tax_rel, Phylum %in% c(NA))  #sum(otu_table(ps.ng.tax_most)) = 2.619413
   ```
   ```{r, echo=TRUE, warning=FALSE}
     library(ggplot2)
     geom.text.size = 6
     theme.size = 8 #(14/5) * geom.text.size
     #ps.ng.tax_most <- subset_taxa(ps.ng.tax_rel, Phylum %in% c("D_1__Actinobacteria", "D_1__Bacteroidetes", "D_1__Firmicutes", "D_1__Proteobacteria", "D_1__Verrucomicrobia", NA))
     ps.ng.tax_most = phyloseq::filter_taxa(ps.ng.tax_rel, function(x) mean(x) > 0.001, TRUE)
     #CONSOLE(OPTIONAL): for sampleid in 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73; do
     #echo "otu_table(ps.ng.tax_most)[,${sampleid}]=otu_table(ps.ng.tax_most)[,${sampleid}]/sum(otu_table(ps.ng.tax_most)[,${sampleid}])" done
     #OR
     ps.ng.tax_most_ = transform_sample_counts(ps.ng.tax_most, function(x) x / sum(x))
   ```
   
   ```{r, echo=FALSE, warning=FALSE}
     ##--Creating 100% stacked bar plots with less abundant taxa in a sub-category #901--
     ##https://github.com/joey711/phyloseq/issues/901
     ##ps.ng.tax_most_df <- psmelt(ps.ng.tax_most_)  #5986x19
     #glom <- tax_glom(ps.ng.tax_most_, taxrank = 'Phylum')
     #tax_table(glom) # should list # taxa as # phyla
     #data <- psmelt(glom) # create dataframe from phyloseq object
     #data$Phylum <- as.character(data$Phylum) #convert to character
     ##simple way to rename phyla with < 1% abundance
     #data$Phylum[data$Abundance < 0.001] <- "< 0.1% abund."
     #
     #library(plyr)
     #medians <- ddply(data, ~Phylum, function(x) c(median=median(x$Abundance)))
     #remainder <- medians[medians$median <= 0.001,]$Phylum
     #data[data$Phylum %in% remainder,]$Phylum <- "Phyla < 0.1% abund."
     #data$Phylum[data$Abundance < 0.001] <- "Phyla < 0.1% abund."
     ##--> data are not used!
     #
     ##in class level
     #glom <- tax_glom(ps.ng.tax_most_, taxrank = 'Class')
     #tax_table(glom) # should list # taxa as # phyla
     #data <- psmelt(glom) # create dataframe from phyloseq object
     #data$Class <- as.character(data$Class) #convert to character
     #
     ##simple way to rename phyla with < 1% abundance
     #data$Class[data$Abundance < 0.001] <- "< 0.1% abund."
     #Count = length(unique(data$Class))
     #
     ##unique(data$Class)
     ##data$Class <- factor(data$Class, levels = c("Bacilli", "Bacteroidia", "Verrucomicrobiae", "Clostridia", "Gammaproteobacteria", "Alphaproteobacteria", "Actinobacteria", "Negativicutes", "Erysipelotrichia", "Methanobacteria", "< 0.1% abund."))
     ##------- Creating 100% stacked bar plots END --------
   
     library(stringr)
   #FITTING1:
   # tax_table(ps.ng.tax_most_)[1,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[1,"Domain"], "__")[[1]][2]
   # ... ...
   # tax_table(ps.ng.tax_most_)[167,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[167,"Species"], "__")[[1]][2]
   #ps.ng.tax_most_
   #in total [ 89 taxa and 55 samples ]
   #otu_table()   OTU Table:         [ 166 taxa and 54 samples ]
   #otu_table()   OTU Table:         [ 168 taxa and 50 samples ]
   #for id in 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100  101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166; do
   #echo "tax_table(ps.ng.tax_most_)[${id},\"Domain\"] <- str_split(tax_table(ps.ng.tax_most_)[${id},\"Domain\"], \"__\")[[1]][2]"
   #echo "tax_table(ps.ng.tax_most_)[${id},\"Phylum\"] <- str_split(tax_table(ps.ng.tax_most_)[${id},\"Phylum\"], \"__\")[[1]][2]"
   #echo "tax_table(ps.ng.tax_most_)[${id},\"Class\"] <- str_split(tax_table(ps.ng.tax_most_)[${id},\"Class\"], \"__\")[[1]][2]"
   #echo "tax_table(ps.ng.tax_most_)[${id},\"Order\"] <- str_split(tax_table(ps.ng.tax_most_)[${id},\"Order\"], \"__\")[[1]][2]"
   #echo "tax_table(ps.ng.tax_most_)[${id},\"Family\"] <- str_split(tax_table(ps.ng.tax_most_)[${id},\"Family\"], \"__\")[[1]][2]"
   #echo "tax_table(ps.ng.tax_most_)[${id},\"Genus\"] <- str_split(tax_table(ps.ng.tax_most_)[${id},\"Genus\"], \"__\")[[1]][2]"
   #echo "tax_table(ps.ng.tax_most_)[${id},\"Species\"] <- str_split(tax_table(ps.ng.tax_most_)[${id},\"Species\"], \"__\")[[1]][2]"
   #done
   
   tax_table(ps.ng.tax_most_)[1,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[1,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[1,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[1,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[1,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[1,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[1,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[1,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[1,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[1,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[1,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[1,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[1,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[1,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[2,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[2,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[2,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[2,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[2,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[2,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[2,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[2,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[2,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[2,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[2,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[2,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[2,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[2,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[3,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[3,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[3,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[3,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[3,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[3,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[3,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[3,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[3,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[3,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[3,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[3,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[3,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[3,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[4,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[4,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[4,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[4,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[4,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[4,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[4,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[4,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[4,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[4,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[4,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[4,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[4,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[4,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[5,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[5,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[5,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[5,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[5,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[5,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[5,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[5,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[5,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[5,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[5,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[5,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[5,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[5,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[6,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[6,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[6,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[6,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[6,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[6,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[6,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[6,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[6,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[6,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[6,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[6,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[6,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[6,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[7,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[7,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[7,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[7,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[7,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[7,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[7,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[7,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[7,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[7,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[7,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[7,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[7,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[7,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[8,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[8,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[8,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[8,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[8,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[8,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[8,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[8,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[8,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[8,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[8,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[8,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[8,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[8,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[9,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[9,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[9,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[9,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[9,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[9,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[9,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[9,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[9,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[9,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[9,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[9,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[9,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[9,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[10,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[10,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[10,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[10,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[10,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[10,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[10,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[10,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[10,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[10,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[10,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[10,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[10,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[10,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[11,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[11,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[11,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[11,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[11,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[11,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[11,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[11,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[11,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[11,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[11,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[11,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[11,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[11,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[12,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[12,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[12,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[12,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[12,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[12,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[12,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[12,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[12,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[12,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[12,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[12,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[12,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[12,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[13,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[13,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[13,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[13,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[13,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[13,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[13,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[13,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[13,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[13,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[13,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[13,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[13,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[13,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[14,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[14,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[14,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[14,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[14,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[14,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[14,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[14,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[14,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[14,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[14,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[14,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[14,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[14,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[15,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[15,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[15,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[15,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[15,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[15,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[15,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[15,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[15,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[15,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[15,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[15,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[15,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[15,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[16,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[16,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[16,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[16,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[16,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[16,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[16,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[16,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[16,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[16,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[16,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[16,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[16,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[16,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[17,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[17,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[17,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[17,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[17,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[17,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[17,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[17,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[17,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[17,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[17,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[17,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[17,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[17,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[18,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[18,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[18,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[18,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[18,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[18,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[18,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[18,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[18,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[18,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[18,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[18,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[18,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[18,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[19,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[19,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[19,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[19,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[19,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[19,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[19,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[19,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[19,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[19,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[19,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[19,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[19,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[19,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[20,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[20,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[20,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[20,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[20,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[20,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[20,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[20,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[20,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[20,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[20,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[20,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[20,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[20,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[21,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[21,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[21,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[21,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[21,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[21,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[21,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[21,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[21,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[21,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[21,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[21,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[21,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[21,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[22,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[22,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[22,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[22,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[22,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[22,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[22,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[22,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[22,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[22,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[22,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[22,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[22,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[22,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[23,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[23,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[23,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[23,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[23,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[23,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[23,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[23,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[23,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[23,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[23,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[23,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[23,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[23,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[24,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[24,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[24,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[24,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[24,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[24,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[24,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[24,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[24,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[24,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[24,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[24,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[24,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[24,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[25,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[25,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[25,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[25,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[25,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[25,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[25,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[25,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[25,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[25,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[25,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[25,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[25,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[25,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[26,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[26,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[26,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[26,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[26,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[26,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[26,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[26,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[26,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[26,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[26,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[26,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[26,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[26,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[27,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[27,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[27,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[27,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[27,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[27,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[27,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[27,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[27,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[27,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[27,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[27,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[27,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[27,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[28,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[28,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[28,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[28,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[28,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[28,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[28,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[28,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[28,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[28,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[28,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[28,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[28,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[28,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[29,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[29,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[29,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[29,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[29,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[29,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[29,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[29,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[29,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[29,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[29,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[29,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[29,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[29,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[30,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[30,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[30,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[30,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[30,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[30,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[30,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[30,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[30,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[30,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[30,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[30,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[30,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[30,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[31,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[31,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[31,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[31,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[31,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[31,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[31,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[31,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[31,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[31,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[31,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[31,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[31,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[31,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[32,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[32,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[32,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[32,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[32,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[32,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[32,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[32,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[32,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[32,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[32,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[32,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[32,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[32,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[33,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[33,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[33,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[33,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[33,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[33,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[33,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[33,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[33,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[33,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[33,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[33,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[33,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[33,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[34,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[34,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[34,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[34,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[34,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[34,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[34,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[34,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[34,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[34,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[34,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[34,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[34,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[34,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[35,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[35,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[35,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[35,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[35,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[35,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[35,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[35,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[35,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[35,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[35,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[35,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[35,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[35,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[36,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[36,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[36,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[36,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[36,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[36,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[36,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[36,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[36,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[36,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[36,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[36,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[36,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[36,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[37,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[37,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[37,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[37,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[37,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[37,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[37,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[37,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[37,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[37,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[37,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[37,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[37,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[37,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[38,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[38,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[38,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[38,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[38,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[38,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[38,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[38,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[38,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[38,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[38,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[38,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[38,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[38,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[39,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[39,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[39,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[39,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[39,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[39,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[39,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[39,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[39,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[39,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[39,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[39,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[39,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[39,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[40,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[40,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[40,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[40,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[40,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[40,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[40,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[40,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[40,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[40,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[40,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[40,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[40,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[40,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[41,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[41,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[41,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[41,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[41,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[41,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[41,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[41,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[41,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[41,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[41,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[41,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[41,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[41,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[42,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[42,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[42,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[42,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[42,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[42,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[42,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[42,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[42,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[42,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[42,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[42,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[42,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[42,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[43,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[43,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[43,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[43,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[43,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[43,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[43,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[43,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[43,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[43,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[43,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[43,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[43,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[43,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[44,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[44,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[44,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[44,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[44,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[44,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[44,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[44,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[44,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[44,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[44,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[44,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[44,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[44,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[45,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[45,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[45,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[45,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[45,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[45,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[45,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[45,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[45,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[45,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[45,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[45,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[45,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[45,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[46,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[46,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[46,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[46,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[46,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[46,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[46,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[46,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[46,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[46,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[46,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[46,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[46,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[46,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[47,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[47,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[47,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[47,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[47,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[47,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[47,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[47,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[47,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[47,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[47,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[47,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[47,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[47,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[48,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[48,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[48,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[48,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[48,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[48,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[48,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[48,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[48,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[48,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[48,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[48,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[48,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[48,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[49,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[49,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[49,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[49,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[49,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[49,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[49,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[49,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[49,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[49,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[49,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[49,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[49,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[49,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[50,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[50,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[50,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[50,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[50,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[50,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[50,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[50,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[50,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[50,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[50,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[50,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[50,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[50,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[51,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[51,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[51,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[51,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[51,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[51,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[51,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[51,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[51,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[51,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[51,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[51,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[51,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[51,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[52,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[52,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[52,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[52,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[52,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[52,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[52,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[52,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[52,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[52,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[52,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[52,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[52,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[52,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[53,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[53,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[53,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[53,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[53,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[53,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[53,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[53,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[53,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[53,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[53,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[53,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[53,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[53,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[54,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[54,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[54,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[54,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[54,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[54,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[54,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[54,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[54,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[54,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[54,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[54,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[54,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[54,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[55,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[55,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[55,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[55,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[55,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[55,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[55,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[55,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[55,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[55,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[55,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[55,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[55,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[55,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[56,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[56,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[56,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[56,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[56,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[56,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[56,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[56,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[56,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[56,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[56,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[56,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[56,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[56,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[57,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[57,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[57,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[57,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[57,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[57,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[57,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[57,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[57,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[57,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[57,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[57,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[57,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[57,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[58,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[58,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[58,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[58,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[58,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[58,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[58,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[58,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[58,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[58,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[58,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[58,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[58,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[58,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[59,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[59,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[59,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[59,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[59,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[59,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[59,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[59,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[59,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[59,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[59,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[59,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[59,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[59,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[60,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[60,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[60,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[60,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[60,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[60,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[60,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[60,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[60,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[60,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[60,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[60,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[60,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[60,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[61,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[61,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[61,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[61,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[61,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[61,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[61,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[61,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[61,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[61,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[61,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[61,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[61,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[61,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[62,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[62,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[62,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[62,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[62,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[62,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[62,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[62,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[62,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[62,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[62,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[62,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[62,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[62,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[63,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[63,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[63,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[63,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[63,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[63,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[63,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[63,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[63,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[63,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[63,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[63,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[63,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[63,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[64,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[64,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[64,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[64,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[64,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[64,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[64,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[64,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[64,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[64,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[64,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[64,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[64,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[64,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[65,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[65,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[65,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[65,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[65,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[65,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[65,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[65,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[65,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[65,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[65,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[65,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[65,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[65,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[66,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[66,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[66,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[66,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[66,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[66,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[66,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[66,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[66,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[66,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[66,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[66,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[66,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[66,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[67,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[67,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[67,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[67,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[67,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[67,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[67,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[67,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[67,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[67,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[67,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[67,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[67,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[67,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[68,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[68,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[68,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[68,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[68,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[68,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[68,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[68,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[68,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[68,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[68,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[68,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[68,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[68,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[69,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[69,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[69,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[69,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[69,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[69,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[69,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[69,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[69,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[69,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[69,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[69,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[69,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[69,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[70,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[70,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[70,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[70,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[70,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[70,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[70,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[70,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[70,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[70,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[70,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[70,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[70,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[70,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[71,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[71,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[71,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[71,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[71,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[71,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[71,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[71,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[71,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[71,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[71,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[71,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[71,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[71,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[72,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[72,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[72,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[72,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[72,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[72,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[72,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[72,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[72,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[72,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[72,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[72,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[72,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[72,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[73,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[73,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[73,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[73,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[73,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[73,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[73,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[73,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[73,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[73,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[73,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[73,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[73,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[73,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[74,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[74,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[74,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[74,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[74,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[74,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[74,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[74,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[74,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[74,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[74,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[74,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[74,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[74,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[75,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[75,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[75,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[75,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[75,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[75,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[75,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[75,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[75,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[75,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[75,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[75,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[75,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[75,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[76,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[76,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[76,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[76,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[76,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[76,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[76,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[76,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[76,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[76,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[76,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[76,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[76,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[76,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[77,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[77,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[77,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[77,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[77,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[77,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[77,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[77,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[77,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[77,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[77,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[77,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[77,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[77,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[78,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[78,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[78,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[78,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[78,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[78,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[78,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[78,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[78,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[78,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[78,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[78,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[78,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[78,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[79,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[79,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[79,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[79,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[79,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[79,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[79,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[79,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[79,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[79,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[79,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[79,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[79,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[79,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[80,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[80,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[80,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[80,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[80,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[80,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[80,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[80,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[80,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[80,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[80,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[80,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[80,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[80,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[81,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[81,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[81,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[81,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[81,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[81,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[81,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[81,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[81,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[81,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[81,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[81,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[81,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[81,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[82,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[82,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[82,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[82,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[82,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[82,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[82,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[82,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[82,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[82,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[82,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[82,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[82,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[82,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[83,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[83,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[83,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[83,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[83,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[83,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[83,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[83,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[83,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[83,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[83,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[83,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[83,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[83,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[84,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[84,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[84,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[84,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[84,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[84,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[84,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[84,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[84,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[84,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[84,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[84,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[84,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[84,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[85,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[85,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[85,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[85,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[85,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[85,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[85,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[85,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[85,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[85,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[85,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[85,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[85,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[85,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[86,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[86,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[86,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[86,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[86,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[86,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[86,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[86,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[86,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[86,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[86,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[86,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[86,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[86,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[87,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[87,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[87,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[87,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[87,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[87,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[87,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[87,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[87,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[87,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[87,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[87,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[87,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[87,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[88,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[88,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[88,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[88,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[88,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[88,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[88,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[88,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[88,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[88,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[88,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[88,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[88,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[88,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[89,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[89,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[89,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[89,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[89,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[89,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[89,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[89,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[89,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[89,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[89,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[89,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[89,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[89,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[90,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[90,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[90,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[90,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[90,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[90,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[90,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[90,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[90,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[90,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[90,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[90,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[90,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[90,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[91,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[91,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[91,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[91,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[91,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[91,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[91,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[91,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[91,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[91,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[91,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[91,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[91,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[91,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[92,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[92,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[92,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[92,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[92,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[92,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[92,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[92,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[92,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[92,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[92,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[92,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[92,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[92,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[93,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[93,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[93,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[93,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[93,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[93,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[93,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[93,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[93,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[93,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[93,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[93,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[93,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[93,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[94,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[94,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[94,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[94,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[94,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[94,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[94,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[94,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[94,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[94,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[94,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[94,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[94,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[94,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[95,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[95,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[95,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[95,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[95,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[95,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[95,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[95,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[95,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[95,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[95,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[95,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[95,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[95,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[96,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[96,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[96,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[96,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[96,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[96,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[96,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[96,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[96,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[96,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[96,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[96,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[96,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[96,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[97,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[97,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[97,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[97,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[97,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[97,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[97,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[97,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[97,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[97,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[97,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[97,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[97,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[97,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[98,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[98,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[98,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[98,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[98,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[98,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[98,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[98,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[98,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[98,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[98,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[98,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[98,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[98,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[99,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[99,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[99,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[99,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[99,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[99,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[99,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[99,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[99,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[99,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[99,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[99,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[99,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[99,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[100,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[100,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[100,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[100,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[100,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[100,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[100,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[100,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[100,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[100,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[100,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[100,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[100,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[100,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[101,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[101,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[101,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[101,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[101,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[101,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[101,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[101,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[101,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[101,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[101,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[101,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[101,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[101,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[102,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[102,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[102,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[102,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[102,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[102,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[102,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[102,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[102,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[102,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[102,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[102,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[102,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[102,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[103,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[103,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[103,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[103,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[103,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[103,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[103,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[103,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[103,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[103,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[103,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[103,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[103,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[103,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[104,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[104,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[104,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[104,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[104,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[104,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[104,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[104,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[104,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[104,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[104,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[104,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[104,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[104,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[105,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[105,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[105,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[105,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[105,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[105,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[105,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[105,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[105,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[105,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[105,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[105,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[105,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[105,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[106,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[106,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[106,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[106,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[106,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[106,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[106,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[106,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[106,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[106,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[106,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[106,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[106,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[106,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[107,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[107,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[107,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[107,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[107,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[107,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[107,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[107,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[107,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[107,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[107,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[107,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[107,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[107,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[108,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[108,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[108,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[108,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[108,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[108,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[108,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[108,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[108,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[108,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[108,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[108,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[108,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[108,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[109,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[109,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[109,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[109,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[109,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[109,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[109,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[109,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[109,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[109,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[109,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[109,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[109,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[109,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[110,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[110,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[110,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[110,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[110,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[110,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[110,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[110,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[110,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[110,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[110,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[110,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[110,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[110,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[111,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[111,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[111,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[111,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[111,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[111,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[111,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[111,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[111,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[111,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[111,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[111,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[111,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[111,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[112,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[112,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[112,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[112,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[112,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[112,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[112,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[112,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[112,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[112,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[112,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[112,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[112,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[112,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[113,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[113,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[113,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[113,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[113,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[113,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[113,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[113,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[113,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[113,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[113,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[113,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[113,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[113,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[114,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[114,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[114,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[114,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[114,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[114,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[114,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[114,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[114,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[114,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[114,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[114,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[114,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[114,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[115,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[115,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[115,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[115,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[115,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[115,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[115,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[115,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[115,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[115,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[115,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[115,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[115,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[115,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[116,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[116,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[116,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[116,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[116,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[116,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[116,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[116,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[116,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[116,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[116,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[116,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[116,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[116,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[117,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[117,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[117,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[117,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[117,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[117,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[117,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[117,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[117,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[117,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[117,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[117,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[117,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[117,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[118,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[118,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[118,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[118,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[118,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[118,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[118,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[118,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[118,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[118,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[118,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[118,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[118,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[118,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[119,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[119,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[119,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[119,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[119,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[119,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[119,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[119,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[119,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[119,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[119,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[119,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[119,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[119,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[120,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[120,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[120,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[120,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[120,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[120,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[120,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[120,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[120,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[120,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[120,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[120,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[120,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[120,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[121,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[121,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[121,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[121,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[121,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[121,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[121,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[121,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[121,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[121,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[121,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[121,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[121,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[121,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[122,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[122,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[122,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[122,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[122,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[122,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[122,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[122,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[122,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[122,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[122,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[122,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[122,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[122,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[123,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[123,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[123,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[123,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[123,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[123,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[123,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[123,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[123,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[123,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[123,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[123,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[123,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[123,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[124,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[124,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[124,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[124,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[124,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[124,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[124,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[124,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[124,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[124,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[124,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[124,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[124,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[124,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[125,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[125,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[125,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[125,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[125,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[125,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[125,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[125,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[125,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[125,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[125,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[125,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[125,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[125,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[126,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[126,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[126,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[126,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[126,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[126,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[126,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[126,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[126,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[126,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[126,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[126,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[126,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[126,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[127,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[127,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[127,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[127,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[127,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[127,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[127,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[127,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[127,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[127,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[127,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[127,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[127,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[127,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[128,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[128,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[128,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[128,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[128,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[128,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[128,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[128,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[128,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[128,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[128,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[128,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[128,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[128,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[129,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[129,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[129,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[129,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[129,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[129,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[129,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[129,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[129,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[129,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[129,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[129,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[129,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[129,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[130,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[130,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[130,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[130,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[130,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[130,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[130,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[130,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[130,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[130,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[130,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[130,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[130,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[130,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[131,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[131,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[131,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[131,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[131,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[131,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[131,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[131,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[131,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[131,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[131,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[131,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[131,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[131,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[132,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[132,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[132,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[132,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[132,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[132,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[132,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[132,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[132,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[132,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[132,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[132,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[132,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[132,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[133,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[133,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[133,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[133,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[133,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[133,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[133,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[133,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[133,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[133,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[133,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[133,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[133,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[133,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[134,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[134,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[134,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[134,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[134,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[134,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[134,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[134,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[134,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[134,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[134,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[134,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[134,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[134,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[135,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[135,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[135,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[135,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[135,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[135,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[135,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[135,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[135,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[135,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[135,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[135,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[135,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[135,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[136,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[136,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[136,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[136,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[136,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[136,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[136,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[136,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[136,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[136,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[136,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[136,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[136,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[136,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[137,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[137,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[137,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[137,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[137,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[137,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[137,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[137,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[137,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[137,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[137,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[137,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[137,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[137,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[138,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[138,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[138,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[138,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[138,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[138,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[138,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[138,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[138,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[138,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[138,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[138,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[138,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[138,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[139,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[139,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[139,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[139,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[139,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[139,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[139,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[139,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[139,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[139,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[139,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[139,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[139,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[139,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[140,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[140,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[140,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[140,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[140,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[140,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[140,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[140,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[140,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[140,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[140,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[140,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[140,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[140,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[141,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[141,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[141,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[141,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[141,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[141,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[141,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[141,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[141,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[141,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[141,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[141,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[141,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[141,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[142,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[142,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[142,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[142,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[142,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[142,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[142,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[142,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[142,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[142,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[142,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[142,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[142,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[142,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[143,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[143,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[143,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[143,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[143,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[143,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[143,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[143,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[143,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[143,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[143,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[143,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[143,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[143,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[144,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[144,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[144,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[144,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[144,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[144,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[144,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[144,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[144,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[144,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[144,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[144,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[144,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[144,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[145,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[145,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[145,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[145,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[145,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[145,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[145,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[145,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[145,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[145,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[145,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[145,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[145,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[145,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[146,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[146,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[146,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[146,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[146,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[146,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[146,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[146,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[146,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[146,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[146,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[146,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[146,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[146,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[147,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[147,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[147,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[147,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[147,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[147,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[147,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[147,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[147,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[147,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[147,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[147,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[147,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[147,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[148,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[148,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[148,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[148,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[148,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[148,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[148,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[148,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[148,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[148,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[148,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[148,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[148,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[148,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[149,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[149,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[149,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[149,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[149,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[149,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[149,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[149,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[149,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[149,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[149,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[149,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[149,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[149,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[150,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[150,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[150,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[150,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[150,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[150,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[150,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[150,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[150,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[150,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[150,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[150,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[150,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[150,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[151,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[151,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[151,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[151,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[151,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[151,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[151,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[151,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[151,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[151,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[151,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[151,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[151,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[151,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[152,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[152,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[152,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[152,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[152,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[152,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[152,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[152,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[152,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[152,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[152,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[152,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[152,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[152,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[153,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[153,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[153,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[153,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[153,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[153,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[153,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[153,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[153,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[153,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[153,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[153,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[153,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[153,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[154,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[154,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[154,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[154,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[154,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[154,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[154,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[154,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[154,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[154,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[154,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[154,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[154,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[154,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[155,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[155,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[155,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[155,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[155,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[155,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[155,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[155,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[155,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[155,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[155,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[155,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[155,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[155,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[156,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[156,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[156,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[156,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[156,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[156,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[156,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[156,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[156,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[156,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[156,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[156,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[156,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[156,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[157,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[157,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[157,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[157,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[157,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[157,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[157,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[157,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[157,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[157,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[157,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[157,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[157,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[157,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[158,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[158,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[158,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[158,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[158,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[158,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[158,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[158,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[158,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[158,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[158,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[158,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[158,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[158,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[159,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[159,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[159,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[159,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[159,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[159,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[159,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[159,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[159,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[159,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[159,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[159,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[159,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[159,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[160,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[160,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[160,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[160,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[160,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[160,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[160,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[160,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[160,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[160,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[160,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[160,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[160,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[160,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[161,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[161,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[161,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[161,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[161,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[161,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[161,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[161,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[161,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[161,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[161,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[161,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[161,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[161,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[162,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[162,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[162,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[162,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[162,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[162,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[162,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[162,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[162,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[162,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[162,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[162,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[162,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[162,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[163,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[163,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[163,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[163,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[163,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[163,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[163,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[163,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[163,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[163,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[163,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[163,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[163,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[163,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[164,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[164,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[164,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[164,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[164,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[164,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[164,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[164,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[164,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[164,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[164,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[164,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[164,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[164,"Species"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[165,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[165,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[165,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[165,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[165,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[165,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[165,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[165,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[165,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[165,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[165,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[165,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[165,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[165,"Species"], "__")[[1]][2]
   
   tax_table(ps.ng.tax_most_)[166,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[166,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[166,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[166,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[166,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[166,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[166,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[166,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[166,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[166,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[166,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[166,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[166,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[166,"Species"], "__")[[1]][2]
   
   tax_table(ps.ng.tax_most_)[167,"Domain"] <- str_split(tax_table(ps.ng.tax_most_)[167,"Domain"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[167,"Phylum"] <- str_split(tax_table(ps.ng.tax_most_)[167,"Phylum"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[167,"Class"] <- str_split(tax_table(ps.ng.tax_most_)[167,"Class"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[167,"Order"] <- str_split(tax_table(ps.ng.tax_most_)[167,"Order"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[167,"Family"] <- str_split(tax_table(ps.ng.tax_most_)[167,"Family"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[167,"Genus"] <- str_split(tax_table(ps.ng.tax_most_)[167,"Genus"], "__")[[1]][2]
   tax_table(ps.ng.tax_most_)[167,"Species"] <- str_split(tax_table(ps.ng.tax_most_)[167,"Species"], "__")[[1]][2]
   ```
   
   ```{r, echo=TRUE, warning=FALSE}
     #aes(color="Phylum", fill="Phylum") --> aes()
     #ggplot(data=data, aes(x=Sample, y=Abundance, fill=Phylum))
     plot_bar(ps.ng.tax_most_, fill="Phylum") + geom_bar(aes(), stat="identity", position="stack") +
     scale_fill_manual(values = c("darkblue", "darkgoldenrod1", "darkseagreen", "darkorchid", "darkolivegreen1", "lightskyblue", "darkgreen", "deeppink", "khaki2", "firebrick", "brown1", "darkorange1", "cyan1", "royalblue4", "darksalmon", "darkblue","royalblue4", "dodgerblue3", "steelblue1", "lightskyblue", "darkseagreen", "darkgoldenrod1", "darkseagreen", "darkorchid", "darkolivegreen1", "brown1", "darkorange1", "cyan1", "darkgrey")) + theme(axis.text = element_text(size = 5, colour="black")) + theme(legend.position="bottom") + guides(fill=guide_legend(nrow=2))                                  #6 instead of theme.size
   ```
   ```{r, echo=FALSE, warning=FALSE}
     #png("abc.png")
     #knitr::include_graphics("./Phyloseq_files/figure-html/unnamed-chunk-7-1.png")
     #dev.off()
   ```
   
   \pagebreak
   
   Regroup together pre vs post stroke samples and normalize number of reads in each group using median sequencing depth.
   
   ```{r, echo=TRUE, warning=FALSE}
     ps.ng.tax_most_pre_post_stroke <- merge_samples(ps.ng.tax_most_, "pre_post_stroke")
     ps.ng.tax_most_pre_post_stroke_ = transform_sample_counts(ps.ng.tax_most_pre_post_stroke, function(x) x / sum(x))
     #plot_bar(ps.ng.tax_most_SampleType_, fill = "Phylum") + geom_bar(aes(color=Phylum, fill=Phylum), stat="identity", position="stack")
     plot_bar(ps.ng.tax_most_pre_post_stroke_, fill="Phylum") + geom_bar(aes(), stat="identity", position="stack") +
     scale_fill_manual(values = c("darkblue", "darkgoldenrod1", "darkseagreen", "darkorchid", "darkolivegreen1", "lightskyblue", "darkgreen", "deeppink", "khaki2", "firebrick", "brown1", "darkorange1", "cyan1", "royalblue4", "darksalmon", "darkblue","royalblue4", "dodgerblue3", "steelblue1", "lightskyblue", "darkseagreen", "darkgoldenrod1", "darkseagreen", "darkorchid", "darkolivegreen1", "brown1", "darkorange1", "cyan1", "darkgrey")) + theme(axis.text = element_text(size = theme.size, colour="black"))
   ```
   
   \pagebreak
   
   Use color according to phylum. Do separate panels Stroke and Sex_age.
   ```{r, echo=TRUE, warning=FALSE}
     ps.ng.tax_most_copied <- data.table::copy(ps.ng.tax_most_)
     #FITTING6: regulate the bar height if it has replicates: 5+6+6+8+4+8+6+7=25+25=50
     otu_table(ps.ng.tax_most_)[,c("1")] <- otu_table(ps.ng.tax_most_)[,c("1")]/5
     otu_table(ps.ng.tax_most_)[,c("2")] <- otu_table(ps.ng.tax_most_)[,c("2")]/5
     otu_table(ps.ng.tax_most_)[,c("5")] <- otu_table(ps.ng.tax_most_)[,c("5")]/5
     otu_table(ps.ng.tax_most_)[,c("6")] <- otu_table(ps.ng.tax_most_)[,c("6")]/5
     otu_table(ps.ng.tax_most_)[,c("7")] <- otu_table(ps.ng.tax_most_)[,c("7")]/5
   
     otu_table(ps.ng.tax_most_)[,c("8")] <- otu_table(ps.ng.tax_most_)[,c("8")]/6
     otu_table(ps.ng.tax_most_)[,c("9")] <- otu_table(ps.ng.tax_most_)[,c("9")]/6
     otu_table(ps.ng.tax_most_)[,c("10")] <- otu_table(ps.ng.tax_most_)[,c("10")]/6
     otu_table(ps.ng.tax_most_)[,c("12")] <- otu_table(ps.ng.tax_most_)[,c("12")]/6
     otu_table(ps.ng.tax_most_)[,c("13")] <- otu_table(ps.ng.tax_most_)[,c("13")]/6
     otu_table(ps.ng.tax_most_)[,c("14")] <- otu_table(ps.ng.tax_most_)[,c("14")]/6
   
     otu_table(ps.ng.tax_most_)[,c("15")] <- otu_table(ps.ng.tax_most_)[,c("15")]/6
     otu_table(ps.ng.tax_most_)[,c("16")] <- otu_table(ps.ng.tax_most_)[,c("16")]/6
     otu_table(ps.ng.tax_most_)[,c("17")] <- otu_table(ps.ng.tax_most_)[,c("17")]/6
     otu_table(ps.ng.tax_most_)[,c("18")] <- otu_table(ps.ng.tax_most_)[,c("18")]/6
     otu_table(ps.ng.tax_most_)[,c("19")] <- otu_table(ps.ng.tax_most_)[,c("19")]/6
     otu_table(ps.ng.tax_most_)[,c("20")] <- otu_table(ps.ng.tax_most_)[,c("20")]/6
   
     otu_table(ps.ng.tax_most_)[,c("21")] <- otu_table(ps.ng.tax_most_)[,c("21")]/8
     otu_table(ps.ng.tax_most_)[,c("22")] <- otu_table(ps.ng.tax_most_)[,c("22")]/8
     otu_table(ps.ng.tax_most_)[,c("23")] <- otu_table(ps.ng.tax_most_)[,c("23")]/8
     otu_table(ps.ng.tax_most_)[,c("24")] <- otu_table(ps.ng.tax_most_)[,c("24")]/8
     otu_table(ps.ng.tax_most_)[,c("25")] <- otu_table(ps.ng.tax_most_)[,c("25")]/8
     otu_table(ps.ng.tax_most_)[,c("26")] <- otu_table(ps.ng.tax_most_)[,c("26")]/8
     otu_table(ps.ng.tax_most_)[,c("27")] <- otu_table(ps.ng.tax_most_)[,c("27")]/8
     otu_table(ps.ng.tax_most_)[,c("28")] <- otu_table(ps.ng.tax_most_)[,c("28")]/8
   
     otu_table(ps.ng.tax_most_)[,c("29")] <- otu_table(ps.ng.tax_most_)[,c("29")]/4
     otu_table(ps.ng.tax_most_)[,c("30")] <- otu_table(ps.ng.tax_most_)[,c("30")]/4
     otu_table(ps.ng.tax_most_)[,c("31")] <- otu_table(ps.ng.tax_most_)[,c("31")]/4
     otu_table(ps.ng.tax_most_)[,c("32")] <- otu_table(ps.ng.tax_most_)[,c("32")]/4
   
     otu_table(ps.ng.tax_most_)[,c("33")] <- otu_table(ps.ng.tax_most_)[,c("33")]/8
     otu_table(ps.ng.tax_most_)[,c("34")] <- otu_table(ps.ng.tax_most_)[,c("34")]/8
     otu_table(ps.ng.tax_most_)[,c("35")] <- otu_table(ps.ng.tax_most_)[,c("35")]/8
     otu_table(ps.ng.tax_most_)[,c("36")] <- otu_table(ps.ng.tax_most_)[,c("36")]/8
     otu_table(ps.ng.tax_most_)[,c("37")] <- otu_table(ps.ng.tax_most_)[,c("37")]/8
     otu_table(ps.ng.tax_most_)[,c("38")] <- otu_table(ps.ng.tax_most_)[,c("38")]/8
     otu_table(ps.ng.tax_most_)[,c("39")] <- otu_table(ps.ng.tax_most_)[,c("39")]/8
     otu_table(ps.ng.tax_most_)[,c("51")] <- otu_table(ps.ng.tax_most_)[,c("51")]/8
   
     otu_table(ps.ng.tax_most_)[,c("40")] <- otu_table(ps.ng.tax_most_)[,c("40")]/6
     otu_table(ps.ng.tax_most_)[,c("41")] <- otu_table(ps.ng.tax_most_)[,c("41")]/6
     otu_table(ps.ng.tax_most_)[,c("42")] <- otu_table(ps.ng.tax_most_)[,c("42")]/6
     otu_table(ps.ng.tax_most_)[,c("43")] <- otu_table(ps.ng.tax_most_)[,c("43")]/6
     otu_table(ps.ng.tax_most_)[,c("44")] <- otu_table(ps.ng.tax_most_)[,c("44")]/6
     otu_table(ps.ng.tax_most_)[,c("46")] <- otu_table(ps.ng.tax_most_)[,c("46")]/6
   
     otu_table(ps.ng.tax_most_)[,c("47")] <- otu_table(ps.ng.tax_most_)[,c("47")]/7
     otu_table(ps.ng.tax_most_)[,c("48")] <- otu_table(ps.ng.tax_most_)[,c("48")]/7
     otu_table(ps.ng.tax_most_)[,c("49")] <- otu_table(ps.ng.tax_most_)[,c("49")]/7
     otu_table(ps.ng.tax_most_)[,c("50")] <- otu_table(ps.ng.tax_most_)[,c("50")]/7
     otu_table(ps.ng.tax_most_)[,c("52")] <- otu_table(ps.ng.tax_most_)[,c("52")]/7
     otu_table(ps.ng.tax_most_)[,c("53")] <- otu_table(ps.ng.tax_most_)[,c("53")]/7
     otu_table(ps.ng.tax_most_)[,c("55")] <- otu_table(ps.ng.tax_most_)[,c("55")]/7
   
     #plot_bar(ps.ng.tax_most_swab_, x="Phylum", fill = "Phylum", facet_grid = Patient~RoundDay) + geom_bar(aes(color=Phylum, fill=Phylum), stat="identity", position="stack") + theme(axis.text = element_text(size = theme.size, colour="black"))
     plot_bar(ps.ng.tax_most_, x="Phylum", fill="Phylum", facet_grid = pre_post_stroke~Sex_age) + geom_bar(aes(), stat="identity", position="stack") +
     scale_fill_manual(values = c("darkblue", "darkgoldenrod1", "darkseagreen", "darkorchid", "darkolivegreen1", "lightskyblue", "darkgreen", "deeppink", "khaki2", "firebrick", "brown1", "darkorange1", "cyan1", "royalblue4", "darksalmon", "darkblue","royalblue4", "dodgerblue3", "steelblue1", "lightskyblue", "darkseagreen", "darkgoldenrod1", "darkseagreen", "darkorchid", "darkolivegreen1", "brown1", "darkorange1", "cyan1", "darkgrey")) + theme(axis.text = element_text(size = 5, colour="black"), axis.text.x=element_blank(), axis.ticks=element_blank()) + theme(legend.position="bottom") + guides(fill=guide_legend(nrow=2))
   ```
   ```{r, echo=FALSE, warning=FALSE}
     #knitr::include_graphics("./Phyloseq_files/figure-html/unnamed-chunk-10-1.png")
     #> tax_table(carbom)
     #Taxonomy Table:     [205 taxa by 7 taxonomic ranks]:
     #     Domain      Supergroup       Division      Class
     #Otu001 "Eukaryota" "Archaeplastida" "Chlorophyta" "Mamiellophyceae"
     #       Order                      Family                 Genus
     #Otu001 "Mamiellales"              "Bathycoccaceae"       "Ostreococcus"
     #sample_data(ps.ng.tax)
   ```
   
   ## Bar plots in class level
   ```{r, echo=TRUE, warning=FALSE}
     plot_bar(ps.ng.tax_most_copied, fill="Class") + geom_bar(aes(), stat="identity", position="stack") +
     scale_fill_manual(values = c("darkblue", "darkgoldenrod1", "darkseagreen", "darkorchid", "darkolivegreen1", "lightskyblue", "darkgreen", "deeppink", "khaki2", "firebrick", "brown1", "darkorange1", "cyan1", "royalblue4", "darksalmon", "darkblue","royalblue4", "dodgerblue3", "steelblue1", "lightskyblue", "darkseagreen", "darkgoldenrod1", "darkseagreen", "darkorchid", "darkolivegreen1", "brown1", "darkorange1", "cyan1", "darkgrey")) + theme(axis.text = element_text(size = 5, colour="black")) + theme(legend.position="bottom") + guides(fill=guide_legend(nrow=3))
   ```
   
   Regroup together pre vs post stroke samples and normalize number of reads in each group using median sequencing depth.
   ```{r, echo=TRUE, warning=FALSE}
     plot_bar(ps.ng.tax_most_pre_post_stroke_, fill="Class") + geom_bar(aes(), stat="identity", position="stack") +
     scale_fill_manual(values = c("darkblue", "darkgoldenrod1", "darkseagreen", "darkorchid", "darkolivegreen1", "lightskyblue", "darkgreen", "deeppink", "khaki2", "firebrick", "brown1", "darkorange1", "cyan1", "royalblue4", "darksalmon", "darkblue","royalblue4", "dodgerblue3", "steelblue1", "lightskyblue", "darkseagreen", "darkgoldenrod1", "darkseagreen", "darkorchid", "darkolivegreen1", "brown1", "darkorange1", "cyan1", "darkgrey")) + theme(axis.text = element_text(size = theme.size, colour="black"))
   ```
   \pagebreak
   
   Use color according to class. Do separate panels Stroke and Sex_age.
   ```{r, echo=TRUE, warning=FALSE}
     #-- If existing replicates, to be processed as follows --
     plot_bar(ps.ng.tax_most_, x="Class", fill="Class", facet_grid = pre_post_stroke~Sex_age) + geom_bar(aes(), stat="identity", position="stack") +
     scale_fill_manual(values = c("darkblue", "darkgoldenrod1", "darkseagreen", "darkorchid", "darkolivegreen1", "lightskyblue", "darkgreen", "deeppink", "khaki2", "firebrick", "brown1", "darkorange1", "cyan1", "royalblue4", "darksalmon", "darkblue","royalblue4", "dodgerblue3", "steelblue1", "lightskyblue", "darkseagreen", "darkgoldenrod1", "darkseagreen", "darkorchid", "darkolivegreen1", "brown1", "darkorange1", "cyan1", "darkgrey")) + theme(axis.text = element_text(size = 5, colour="black"), axis.text.x=element_blank(), axis.ticks=element_blank()) + theme(legend.position="bottom") + guides(fill=guide_legend(nrow=3))
   ```
   
   ## Bar plots in order level
   
   ```{r, echo=TRUE, warning=FALSE}
     plot_bar(ps.ng.tax_most_copied, fill="Order") + geom_bar(aes(), stat="identity", position="stack") +
     scale_fill_manual(values = c("darkblue", "darkgoldenrod1", "darkseagreen", "darkorchid", "darkolivegreen1", "lightskyblue", "darkgreen", "deeppink", "khaki2", "firebrick", "brown1", "darkorange1", "cyan1", "royalblue4", "darksalmon", "darkblue","royalblue4", "dodgerblue3", "steelblue1", "lightskyblue", "darkseagreen", "darkgoldenrod1", "darkseagreen", "darkorchid", "darkolivegreen1", "brown1", "darkorange1", "cyan1", "darkgrey")) + theme(axis.text = element_text(size = 5, colour="black")) + theme(legend.position="bottom") + guides(fill=guide_legend(nrow=4))
   ```
   
   Regroup together pre vs post stroke and normalize number of reads in each group using median sequencing depth.
   ```{r, echo=TRUE, warning=FALSE}
     plot_bar(ps.ng.tax_most_pre_post_stroke_, fill="Order") + geom_bar(aes(), stat="identity", position="stack") +
     scale_fill_manual(values = c("darkblue", "darkgoldenrod1", "darkseagreen", "darkorchid", "darkolivegreen1", "lightskyblue", "darkgreen", "deeppink", "khaki2", "firebrick", "brown1", "darkorange1", "cyan1", "royalblue4", "darksalmon", "darkblue","royalblue4", "dodgerblue3", "steelblue1", "lightskyblue", "darkseagreen", "darkgoldenrod1", "darkseagreen", "darkorchid", "darkolivegreen1", "brown1", "darkorange1", "cyan1", "darkgrey")) + theme(axis.text = element_text(size = theme.size, colour="black")) + theme(legend.position="bottom") + guides(fill=guide_legend(nrow=4))
   ```
   \pagebreak
   
   Use color according to order. Do separate panels Stroke and Sex_age.
   ```{r, echo=TRUE, warning=FALSE}
     #FITTING7: regulate the bar height if it has replicates
     plot_bar(ps.ng.tax_most_, x="Order", fill="Order", facet_grid = pre_post_stroke~Sex_age) + geom_bar(aes(), stat="identity", position="stack") +
     scale_fill_manual(values = c("darkblue", "darkgoldenrod1", "darkseagreen", "darkorchid", "darkolivegreen1", "lightskyblue", "darkgreen", "deeppink", "khaki2", "firebrick", "brown1", "darkorange1", "cyan1", "royalblue4", "darksalmon", "darkblue","royalblue4", "dodgerblue3", "steelblue1", "lightskyblue", "darkseagreen", "darkgoldenrod1", "darkseagreen", "darkorchid", "darkolivegreen1", "brown1", "darkorange1", "cyan1", "darkgrey")) + theme(axis.text = element_text(size = 5, colour="black"), axis.text.x=element_blank(), axis.ticks=element_blank()) + theme(legend.position="bottom") + guides(fill=guide_legend(nrow=4))
   ```
   
   ## Bar plots in family level
   
   ```{r, echo=TRUE, warning=FALSE}
     plot_bar(ps.ng.tax_most_copied, fill="Family") + geom_bar(aes(), stat="identity", position="stack") +
     scale_fill_manual(values = c("#FF0000", "#000000", "#0000FF", "#C0C0C0", "#FFFFFF", "#FFFF00", "#00FFFF", "#FFA500", "#00FF00", "#808080", "#FF00FF", "#800080", "#FDD017", "#0000A0", "#3BB9FF", "#008000", "#800000", "#ADD8E6", "#F778A1", "#800517", "#736F6E", "#F52887", "#C11B17", "#5CB3FF", "#A52A2A", "#FF8040", "#2B60DE", "#736AFF", "#1589FF", "#98AFC7", "#8D38C9", "#307D7E", "#F6358A", "#151B54", "#6D7B8D", "#FDEEF4", "#FF0080", "#F88017", "#2554C7", "#FFF8C6", "#D4A017", "#306EFF", "#151B8D", "#9E7BFF", "#EAC117", "#E0FFFF", "#15317E", "#6C2DC7", "#FBB917", "#FCDFFF", "#15317E", "#254117", "#FAAFBE", "#357EC7")) + theme(axis.text = element_text(size = 5, colour="black")) + theme(legend.position="bottom") + guides(fill=guide_legend(nrow=8))
   ```
   
   Regroup together pre vs post stroke samples and normalize number of reads in each group using median sequencing depth.
   ```{r, echo=TRUE, warning=FALSE}
     plot_bar(ps.ng.tax_most_pre_post_stroke_, fill="Family") + geom_bar(aes(), stat="identity", position="stack") +
     scale_fill_manual(values = c("#FF0000", "#000000", "#0000FF", "#C0C0C0", "#FFFFFF", "#FFFF00", "#00FFFF", "#FFA500", "#00FF00", "#808080", "#FF00FF", "#800080", "#FDD017", "#0000A0", "#3BB9FF", "#008000", "#800000", "#ADD8E6", "#F778A1", "#800517", "#736F6E", "#F52887", "#C11B17", "#5CB3FF", "#A52A2A", "#FF8040", "#2B60DE", "#736AFF", "#1589FF", "#98AFC7", "#8D38C9", "#307D7E", "#F6358A", "#151B54", "#6D7B8D", "#FDEEF4", "#FF0080", "#F88017", "#2554C7", "#FFF8C6", "#D4A017", "#306EFF", "#151B8D", "#9E7BFF", "#EAC117", "#E0FFFF", "#15317E", "#6C2DC7", "#FBB917", "#FCDFFF", "#15317E", "#254117", "#FAAFBE", "#357EC7")) + theme(axis.text = element_text(size = theme.size, colour="black")) + theme(legend.position="bottom") + guides(fill=guide_legend(nrow=8))
   ```
   \pagebreak
   
   Use color according to family. Do separate panels Stroke and Sex_age.
   ```{r, echo=TRUE, warning=FALSE}
     #-- If existing replicates, to be processed as follows --
     plot_bar(ps.ng.tax_most_, x="Family", fill="Family", facet_grid = pre_post_stroke~Sex_age) + geom_bar(aes(), stat="identity", position="stack") +
     scale_fill_manual(values = c("#FF0000", "#000000", "#0000FF", "#C0C0C0", "#FFFFFF", "#FFFF00", "#00FFFF", "#FFA500", "#00FF00", "#808080", "#FF00FF", "#800080", "#FDD017", "#0000A0", "#3BB9FF", "#008000", "#800000", "#ADD8E6", "#F778A1", "#800517", "#736F6E", "#F52887", "#C11B17", "#5CB3FF", "#A52A2A", "#FF8040", "#2B60DE", "#736AFF", "#1589FF", "#98AFC7", "#8D38C9", "#307D7E", "#F6358A", "#151B54", "#6D7B8D", "#FDEEF4", "#FF0080", "#F88017", "#2554C7", "#FFF8C6", "#D4A017", "#306EFF", "#151B8D", "#9E7BFF", "#EAC117", "#E0FFFF", "#15317E", "#6C2DC7", "#FBB917", "#FCDFFF", "#15317E", "#254117", "#FAAFBE", "#357EC7")) + theme(axis.text = element_text(size = 5, colour="black"), axis.text.x=element_blank(), axis.ticks=element_blank()) + theme(legend.position="bottom") + guides(fill=guide_legend(nrow=8))
   ```
   
   ```{r, echo=FALSE, warning=FALSE}
     #MOVE_FROM_ABOVE: ## Bar plots in genus level
     #MOVE_FROM_ABOVE: Regroup together pre vs post stroke samples and normalize number of reads in each group using median sequencing depth.
     #plot_bar(ps.ng.tax_most_pre_post_stroke_, fill="Genus") + geom_bar(aes(), stat="identity", position="stack") +
     #scale_fill_manual(values = c("#FF0000", "#000000", "#0000FF", "#C0C0C0", "#FFFFFF", "#FFFF00", "#00FFFF", "#FFA500", "#00FF00", "#808080", "#FF00FF", "#800080", "#FDD017", "#0000A0", "#3BB9FF", "#008000", "#800000", "#ADD8E6", "#F778A1", "#800517", "#736F6E", "#F52887", "#C11B17", "#5CB3FF", "#A52A2A", "#FF8040", "#2B60DE", "#736AFF", "#1589FF", "#98AFC7", "#8D38C9", "#307D7E", "#F6358A", "#151B54", "#6D7B8D", "#FDEEF4", "#FF0080", "#F88017", "#2554C7", "#FFF8C6", "#D4A017", "#306EFF", "#151B8D", "#9E7BFF", "#EAC117", "#E0FFFF", "#15317E", "#6C2DC7", "#FBB917", "#FCDFFF", "#15317E", "#254117", "#FAAFBE", "#357EC7")) + theme(axis.text = element_text(size = theme.size, colour="black")) + theme(legend.position="bottom")
   ```
   \pagebreak
   
   ```{r, echo=FALSE, warning=FALSE}
     #MOVE_FROM_ABOVE: Use color according to genus. Do separate panels Stroke and Sex_age.
     ##-- If existing replicates, to be processed as follows --
     #plot_bar(ps.ng.tax_most_, x="Genus", fill="Genus", facet_grid = pre_post_stroke~Sex_age) + geom_bar(aes(), stat="identity", position="stack") +
     #scale_fill_manual(values = c("#FF0000", "#000000", "#0000FF", "#C0C0C0", "#FFFFFF", "#FFFF00", "#00FFFF", "#FFA500", "#00FF00", "#808080", "#FF00FF", "#800080", "#FDD017", "#0000A0", "#3BB9FF", "#008000", "#800000", "#ADD8E6", "#F778A1", "#800517", "#736F6E", "#F52887", "#C11B17", "#5CB3FF", "#A52A2A", "#FF8040", "#2B60DE", "#736AFF", "#1589FF", "#98AFC7", "#8D38C9", "#307D7E", "#F6358A", "#151B54", "#6D7B8D", "#FDEEF4", "#FF0080", "#F88017", "#2554C7", "#FFF8C6", "#D4A017", "#306EFF", "#151B8D", "#9E7BFF", "#EAC117", "#E0FFFF", "#15317E", "#6C2DC7", "#FBB917", "#FCDFFF", "#15317E", "#254117", "#FAAFBE", "#357EC7")) + theme(axis.text = element_text(size = 6, colour="black"), axis.text.x=element_blank(), axis.ticks=element_blank()) + theme(legend.position="bottom") + guides(fill=guide_legend(nrow=18))
   ```
   
   \pagebreak
   
   # Alpha diversity
   Plot Chao1 richness estimator, Observed OTUs, Shannon index, and Phylogenetic diversity.
   Regroup together samples from the same group.
   ```{r, echo=FALSE, warning=FALSE}
   # using rarefied data
   #FITTING2: CONSOLE:
   #gunzip table_even4753.biom.gz
   #alpha_diversity.py -i table_even42369.biom --metrics chao1,observed_otus,shannon,PD_whole_tree -o adiv_even.txt -t ../clustering/rep_set.tre
   #gunzip table_even4753.biom.gz
   #alpha_diversity.py -i table_even4753.biom --metrics chao1,observed_otus,shannon,PD_whole_tree -o adiv_even.txt -t ../clustering_stool/rep_set.tre
   #gunzip table_even4753.biom.gz
   #alpha_diversity.py -i table_even4753.biom --metrics chao1,observed_otus,shannon,PD_whole_tree -o adiv_even.txt -t ../clustering_swab/rep_set.tre
   ```
   
   ```{r, echo=TRUE, warning=FALSE}
   hmp.div_qiime <- read.csv("adiv_even.txt", sep="\t")
   colnames(hmp.div_qiime) <- c("sam_name", "chao1", "observed_otus", "shannon", "PD_whole_tree")
   row.names(hmp.div_qiime) <- hmp.div_qiime$sam_name
   div.df <- merge(hmp.div_qiime, hmp.meta, by = "sam_name")
   div.df2 <- div.df[, c("Group", "chao1", "shannon", "observed_otus", "PD_whole_tree")]
   colnames(div.df2) <- c("Group", "Chao-1", "Shannon", "OTU", "Phylogenetic Diversity")
   #colnames(div.df2)
   options(max.print=999999)
   #27     H47 830.5000 5.008482 319               10.60177
   #FITTING4: if occuring "Computation failed in `stat_signif()`:not enough 'y' observations"
   #means: the patient H47 contains only one sample, it should be removed for the statistical p-values calculations.
   #delete H47(1)
   #div.df2 <- div.df2[-c(3), ]
   #div.df2 <- div.df2[-c(55,54, 45,40,39,27,26,25,1), ]
   knitr::kable(div.df2) %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))
   
   #https://uc-r.github.io/t_test
   #We can perform the test with t.test and transform our data and we can also perform the nonparametric test with the wilcox.test function.
   stat.test.Shannon <- compare_means(
   Shannon ~ Group, data = div.df2,
   method = "t.test"
   )
   knitr::kable(stat.test.Shannon) %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))
   
   div_df_melt <- reshape2::melt(div.df2)
   #head(div_df_melt)
   
   #https://plot.ly/r/box-plots/#horizontal-boxplot
   #http://www.sthda.com/english/wiki/print.php?id=177
   #https://rpkgs.datanovia.com/ggpubr/reference/as_ggplot.html
   #http://www.sthda.com/english/articles/24-ggpubr-publication-ready-plots/82-ggplot2-easy-way-to-change-graphical-parameters/
   #https://plot.ly/r/box-plots/#horizontal-boxplot
   #library("gridExtra")
   #par(mfrow=c(4,1))
   p <- ggboxplot(div_df_melt, x = "Group", y = "value",
                 facet.by = "variable",
                 scales = "free",
                 width = 0.5,
                 fill = "gray", legend= "right")
   #ggpar(p, xlab = FALSE, ylab = FALSE)
   lev <- levels(factor(div_df_melt$Group)) # get the variables
   #FITTING4: delete H47(1) in lev
   #lev <- lev[-c(3)]
   # make a pairwise list that we want to compare.
   #my_stat_compare_means
   #https://stackoverflow.com/questions/47839988/indicating-significance-with-ggplot2-in-a-boxplot-with-multiple-groups
   L.pairs <- combn(seq_along(lev), 2, simplify = FALSE, FUN = function(i) lev[i]) #%>% filter(p.signif != "ns")
   my_stat_compare_means  <- function (mapping = NULL, data = NULL, method = NULL, paired = FALSE,
       method.args = list(), ref.group = NULL, comparisons = NULL,
       hide.ns = FALSE, label.sep = ", ", label = NULL, label.x.npc = "left",
       label.y.npc = "top", label.x = NULL, label.y = NULL, tip.length = 0.03,
       symnum.args = list(), geom = "text", position = "identity",
       na.rm = FALSE, show.legend = NA, inherit.aes = TRUE, ...)
   {
       if (!is.null(comparisons)) {
           method.info <- ggpubr:::.method_info(method)
           method <- method.info$method
           method.args <- ggpubr:::.add_item(method.args, paired = paired)
           if (method == "wilcox.test")
               method.args$exact <- FALSE
           pms <- list(...)
           size <- ifelse(is.null(pms$size), 0.3, pms$size)
           color <- ifelse(is.null(pms$color), "black", pms$color)
           map_signif_level <- FALSE
           if (is.null(label))
               label <- "p.format"
           if (ggpubr:::.is_p.signif_in_mapping(mapping) | (label %in% "p.signif")) {
               if (ggpubr:::.is_empty(symnum.args)) {
                   map_signif_level <- c(`****` = 1e-04, `***` = 0.001,
                     `**` = 0.01, `*` = 0.05, ns = 1)
               } else {
                 map_signif_level <- symnum.args
               }
               if (hide.ns)
                   names(map_signif_level)[5] <- " "
           }
           step_increase <- ifelse(is.null(label.y), 0.12, 0)
           ggsignif::geom_signif(comparisons = comparisons, y_position = label.y,
               test = method, test.args = method.args, step_increase = step_increase,
               size = size, color = color, map_signif_level = map_signif_level,
               tip_length = tip.length, data = data)
       } else {
           mapping <- ggpubr:::.update_mapping(mapping, label)
           layer(stat = StatCompareMeans, data = data, mapping = mapping,
               geom = geom, position = position, show.legend = show.legend,
               inherit.aes = inherit.aes, params = list(label.x.npc = label.x.npc,
                   label.y.npc = label.y.npc, label.x = label.x,
                   label.y = label.y, label.sep = label.sep, method = method,
                   method.args = method.args, paired = paired, ref.group = ref.group,
                   symnum.args = symnum.args, hide.ns = hide.ns,
                   na.rm = na.rm, ...))
       }
   }
   
   p2 <- p +
   stat_compare_means(
     method="t.test",
     comparisons = list(c("Group1", "Group2"), c("Group1", "Group3"), c("Group1", "Group4"), c("Group1", "Group6"), c("Group1", "Group8"), c("Group2", "Group5"),c("Group4", "Group5"),c("Group4", "Group6"),c("Group4", "Group7"),c("Group6", "Group7")),
     label = "p.signif",
     symnum.args <- list(cutpoints = c(0, 0.0001, 0.001, 0.01, 0.05, 1), symbols = c("****", "***", "**", "*", "ns"))
   )
   #comparisons = L.pairs,
   #symnum.args <- list(cutpoints = c(0, 0.0001, 0.001, 0.01, 0.05), symbols = c("****", "***", "**", "*")),
   #stat_pvalue_manual
   #print(p2)
   #https://stackoverflow.com/questions/20500706/saving-multiple-ggplots-from-ls-into-one-and-separate-files-in-r
   #FITTING3: mkdir figures
   ggsave("./figures/alpha_diversity_Group.png", device="png", height = 10, width = 12)
   ggsave("./figures/alpha_diversity_Group.svg", device="svg", height = 10, width = 12)
   
   p3 <- p +
   stat_compare_means(
     method="t.test",
     comparisons = list(c("Group2", "Group4"), c("Group2", "Group6"), c("Group4", "Group8"), c("Group6", "Group8")),
     label = "p.signif",
     symnum.args <- list(cutpoints = c(0, 0.0001, 0.001, 0.01, 0.05, 1), symbols = c("****", "***", "**", "*", "ns")),
   )
   #symnum.args <- list(cutpoints = c(0, 0.0001, 0.001, 0.01, 0.05), symbols = c("****", "***", "**", "*")),
   #stat_pvalue_manual
   #print(p2)
   #https://stackoverflow.com/questions/20500706/saving-multiple-ggplots-from-ls-into-one-and-separate-files-in-r
   #FITTING3: mkdir figures
   ggsave("./figures/alpha_diversity_Group2.png", device="png", height = 10, width = 12)
   ggsave("./figures/alpha_diversity_Group2.svg", device="svg", height = 10, width = 12)
   ```
   
   # Selected alpha diversity
   ```{r, echo=TRUE, warning=FALSE, fig.cap="Alpha diversity", out.width = '100%', fig.align= "center"}
   knitr::include_graphics("./figures/alpha_diversity_Group2.png")
   
   selected_alpha_diversities<-read.csv("selected_alpha_diversities.txt",sep="\t")
   knitr::kable(selected_alpha_diversities) %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))
   ```
   
   # Beta diversity
   ```{r, echo=TRUE, warning=FALSE, fig.cap="Beta diversity", out.width = '100%', fig.align= "center"}
   #file:///home/jhuang/DATA/Data_Marius_16S/core_diversity_e42369/bdiv_even42369_Group/unweighted_unifrac_boxplots/Group_Stats.txt
   beta_diversity_group_stats<-read.csv("unweighted_unifrac_boxplots_Group_Stats.txt",sep="\t")
   knitr::kable(beta_diversity_group_stats) %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))
   #NOTE: Run this Phyloseq0.Rmd, then run the code of MicrobiotaProcess.R to manually generate PCoA.png, then run this Phyloseq.Rmd!
   #NOTE: AT_FIRST_DEACTIVATE_THIS_LINE: knitr::include_graphics("./figures/PCoA.png")
   ```
   
   # Differential abundance analysis
   
   Differential abundance analysis aims to find the differences in the abundance of each taxa between two groups of samples, assigning a significance value to each comparison.
   
   ## Group2 vs Group4
   
   ```{r, echo=TRUE, warning=FALSE}
   library("DESeq2")
   #ALTERNATIVE using ps.ng.tax_most_copied: ps.ng.tax (40594) vs. ps.ng.tax_most_copied (166)
   ps.ng.tax_sel <- ps.ng.tax
   #FITTING5: correct the id of the group members, see FITTING6
   otu_table(ps.ng.tax_sel) <- otu_table(ps.ng.tax)[,c("8","9","10","12","13","14",  "21","22","23","24","25","26","27","28")]
   diagdds = phyloseq_to_deseq2(ps.ng.tax_sel, ~Group)
   diagdds$Group <- relevel(diagdds$Group, "Group4")
   diagdds = DESeq(diagdds, test="Wald", fitType="parametric")
   resultsNames(diagdds)
   
   res = results(diagdds, cooksCutoff = FALSE)
   alpha = 0.05
   sigtab = res[which(res$padj < alpha), ]
   sigtab = cbind(as(sigtab, "data.frame"), as(tax_table(ps.ng.tax_sel)[rownames(sigtab), ], "matrix"))
   sigtab <- sigtab[rownames(sigtab) %in% rownames(tax_table(ps.ng.tax_most_copied)), ]
   kable(sigtab) %>%
     kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))
   #rownames(sigtab) %in% rownames(tax_table(ps.ng.tax_most_copied))
   #subv %in% v
   ### returns a vector TRUE FALSE
   #is.element(subv, v)
   ### returns a vector TRUE FALSE
   
   library("ggplot2")
   theme_set(theme_bw())
   scale_fill_discrete <- function(palname = "Set1", ...) {
       scale_fill_brewer(palette = palname, ...)
   }
   x = tapply(sigtab$log2FoldChange, sigtab$Order, function(x) max(x))
   x = sort(x)
   sigtab$Order = factor(as.character(sigtab$Order), levels=names(x))
   x = tapply(sigtab$log2FoldChange, sigtab$Family, function(x) max(x))
   x = sort(x)
   sigtab$Family = factor(as.character(sigtab$Family), levels=names(x))
   ggplot(sigtab, aes(x=log2FoldChange, y=Family, color=Order)) + geom_point(aes(size=padj)) + scale_size_continuous(name="padj",range=c(8,4))+
     theme(axis.text.x = element_text(angle = -25, hjust = 0, vjust=0.5))
   
   #Error in checkForExperimentalReplicates(object, modelMatrix) :
   #  The design matrix has the same number of samples and coefficients to fit,
   #  so estimation of dispersion is not possible. Treating samples
   #  as replicates was deprecated in v1.20 and no longer supported since v1.22.
   ```
   
   ## Group2 vs Group6
   
   ```{r, echo=TRUE, warning=FALSE}
   ps.ng.tax_sel <- ps.ng.tax
   otu_table(ps.ng.tax_sel) <- otu_table(ps.ng.tax)[,c("8","9","10","12","13","14",    "33","34","35","36","37","38","39","51")]
   diagdds = phyloseq_to_deseq2(ps.ng.tax_sel, ~Group)
   diagdds$Group <- relevel(diagdds$Group, "Group6")
   diagdds = DESeq(diagdds, test="Wald", fitType="parametric")
   resultsNames(diagdds)
   
   res = results(diagdds, cooksCutoff = FALSE)
   alpha = 0.05
   sigtab = res[which(res$padj < alpha), ]
   sigtab = cbind(as(sigtab, "data.frame"), as(tax_table(ps.ng.tax_sel)[rownames(sigtab), ], "matrix"))
   sigtab <- sigtab[rownames(sigtab) %in% rownames(tax_table(ps.ng.tax_most_copied)), ]
   kable(sigtab) %>%
     kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))
   
   library("ggplot2")
   theme_set(theme_bw())
   scale_fill_discrete <- function(palname = "Set1", ...) {
       scale_fill_brewer(palette = palname, ...)
   }
   x = tapply(sigtab$log2FoldChange, sigtab$Order, function(x) max(x))
   x = sort(x)
   sigtab$Order = factor(as.character(sigtab$Order), levels=names(x))
   x = tapply(sigtab$log2FoldChange, sigtab$Family, function(x) max(x))
   x = sort(x)
   sigtab$Family = factor(as.character(sigtab$Family), levels=names(x))
   ggplot(sigtab, aes(x=log2FoldChange, y=Family, color=Order)) + geom_point(aes(size=padj)) + scale_size_continuous(name="padj",range=c(8,4))+
     theme(axis.text.x = element_text(angle = -25, hjust = 0, vjust=0.5))
   ```
   
   ## Group4 vs Group8
   
   ```{r, echo=TRUE, warning=FALSE}
   ps.ng.tax_sel <- ps.ng.tax
   otu_table(ps.ng.tax_sel) <- otu_table(ps.ng.tax)[,c("21","22","23","24","25","26","27","28",    "47","48","49","50","52","53","55")]
   diagdds = phyloseq_to_deseq2(ps.ng.tax_sel, ~Group)
   diagdds$Group <- relevel(diagdds$Group, "Group8")
   diagdds = DESeq(diagdds, test="Wald", fitType="parametric")
   resultsNames(diagdds)
   
   res = results(diagdds, cooksCutoff = FALSE)
   alpha = 0.05
   sigtab = res[which(res$padj < alpha), ]
   sigtab = cbind(as(sigtab, "data.frame"), as(tax_table(ps.ng.tax_sel)[rownames(sigtab), ], "matrix"))
   sigtab <- sigtab[rownames(sigtab) %in% rownames(tax_table(ps.ng.tax_most_copied)), ]
   kable(sigtab) %>%
     kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))
   
   library("ggplot2")
   theme_set(theme_bw())
   scale_fill_discrete <- function(palname = "Set1", ...) {
       scale_fill_brewer(palette = palname, ...)
   }
   x = tapply(sigtab$log2FoldChange, sigtab$Order, function(x) max(x))
   x = sort(x)
   sigtab$Order = factor(as.character(sigtab$Order), levels=names(x))
   x = tapply(sigtab$log2FoldChange, sigtab$Family, function(x) max(x))
   x = sort(x)
   sigtab$Family = factor(as.character(sigtab$Family), levels=names(x))
   ggplot(sigtab, aes(x=log2FoldChange, y=Family, color=Order)) + geom_point(aes(size=padj)) + scale_size_continuous(name="padj",range=c(8,4))+
     theme(axis.text.x = element_text(angle = -25, hjust = 0, vjust=0.5))
   ```
   
   ## Group6 vs Group8
   
   ```{r, echo=TRUE, warning=FALSE}
   ps.ng.tax_sel <- ps.ng.tax
   otu_table(ps.ng.tax_sel) <- otu_table(ps.ng.tax)[,c("33","34","35","36","37","38","39","51",    "47","48","49","50","52","53","55")]
   diagdds = phyloseq_to_deseq2(ps.ng.tax_sel, ~Group)
   diagdds$Group <- relevel(diagdds$Group, "Group8")
   diagdds = DESeq(diagdds, test="Wald", fitType="parametric")
   resultsNames(diagdds)
   
   res = results(diagdds, cooksCutoff = FALSE)
   alpha = 0.05
   sigtab = res[which(res$padj < alpha), ]
   sigtab = cbind(as(sigtab, "data.frame"), as(tax_table(ps.ng.tax_sel)[rownames(sigtab), ], "matrix"))
   sigtab <- sigtab[rownames(sigtab) %in% rownames(tax_table(ps.ng.tax_most_copied)), ]
   kable(sigtab) %>%
     kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))
   
   library("ggplot2")
   theme_set(theme_bw())
   scale_fill_discrete <- function(palname = "Set1", ...) {
       scale_fill_brewer(palette = palname, ...)
   }
   x = tapply(sigtab$log2FoldChange, sigtab$Order, function(x) max(x))
   x = sort(x)
   sigtab$Order = factor(as.character(sigtab$Order), levels=names(x))
   x = tapply(sigtab$log2FoldChange, sigtab$Family, function(x) max(x))
   x = sort(x)
   sigtab$Family = factor(as.character(sigtab$Family), levels=names(x))
   ggplot(sigtab, aes(x=log2FoldChange, y=Family, color=Order)) + geom_point(aes(size=padj)) + scale_size_continuous(name="padj",range=c(8,4))+
     theme(axis.text.x = element_text(angle = -25, hjust = 0, vjust=0.5))
   ```
   

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