# ============================================================================
#                         INSTALL CRAN PACKAGES
# ============================================================================

# Install missing CRAN packages
install.packages(setdiff(c("stats", "dplyr", "ggplot2", "flextable", "ggpubr", 
                           "randomForest", "ggridges", "ggalluvial", "tibble", 
                           "matrixStats", "RColorBrewer", "ape", "rlang", 
                           "scales", "magrittr", "phangorn", "igraph", "tidyr", 
                           "xml2", "data.table", "reshape2","vegan", "patchwork", "officer"), 
                         installed.packages()[,"Package"]))

# Load CRAN packages
lapply(c("stats", "dplyr", "ggplot2", "flextable", "ggpubr", "randomForest", 
         "ggridges", "ggalluvial", "tibble", "matrixStats", "RColorBrewer", 
         "ape", "rlang", "scales", "magrittr", "phangorn", "igraph", "tidyr", 
         "xml2", "data.table", "reshape2","vegan", "patchwork", "officer"), library, character.only = TRUE)

# ============================================================================
#                         INSTALL BIOCONDUCTOR PACKAGES
# ============================================================================

# Install BiocManager if not installed
if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager")

# Install missing Bioconductor packages
BiocManager::install(setdiff(c("phyloseq", "msa", "DESeq2", "ggtree", "edgeR", 
                               "Biostrings", "DECIPHER", "microbiome", "limma", 
                               "S4Vectors", "SummarizedExperiment", "TreeSummarizedExperiment"), 
                             installed.packages()[,"Package"]))

# Load Bioconductor packages
lapply(c("phyloseq", "msa", "DESeq2", "edgeR", "Biostrings", "ggtree", "DECIPHER", 
         "microbiome", "limma", "S4Vectors", "SummarizedExperiment", "TreeSummarizedExperiment"), 
       library, character.only = TRUE)

# ============================================================================
#                         INSTALL GITHUB PACKAGES
# ============================================================================

# Install remotes if not installed
if (!requireNamespace("remotes", quietly = TRUE)) install.packages("remotes")
library(remotes)

# Install missing GitHub packages
remotes::install_github("mikemc/speedyseq")
remotes::install_github("microsud/microbiomeutilities")
# Optional
#devtools::install_github("briatte/ggnet")
#devtools::install_github("zdk123/SpiecEasi")

# Load GitHub packages
library(speedyseq)
library(microbiomeutilities)
#library(SpiecEasi)
#library(ggnet)

# ============================================================================
#                         INSTALL DspikeIn FROM GITHUB
# ============================================================================

# To access the DspikeIn vignette for a detailed tutorial, use vignette("DspikeIn"), or browse all available vignettes with browseVignettes("DspikeIn").
devtools::install_github("mghotbi/DspikeIn", build_vignettes = TRUE, dependencies = TRUE)
browseVignettes("DspikeIn")
vignette("DspikeIn")

## or

if (!requireNamespace("devtools", quietly = TRUE)) install.packages("devtools")
devtools::install_github("mghotbi/DspikeIn")

# Load DspikeIn only if installed
if ("DspikeIn" %in% installed.packages()[, "Package"]) {
  library(DspikeIn)
} else {
  stop("DspikeIn installation failed. Check errors above.")
}

# =====================================================================
#             To remove strain from the taxonomic ranks
# =====================================================================

library(phyloseq)
# Function to remove strain information from taxonomy columns
remove_strain_info <- function(tax_table) {
  # Define the regex pattern for common strain identifiers
  pattern <- "Strain.*|strain.*|\\s*\\[.*\\]|\\s*\\(.*\\)"  # Adjust the regex to match specific strain formats
  # Apply the pattern to each column of the taxonomy table
  for (col in colnames(tax_table)) { 
    tax_table[, col] <- gsub(pattern, "", tax_table[, col])  # Remove strain info
    tax_table[, col] <- trimws(tax_table[, col])  # Trim trailing whitespace
  }
    return(tax_table)}
# Step 1: Extract the taxonomy table
taxonomy <- tax_table(ps)
# Step 2: Remove strain information (including the `Strain` column)
cleaned_taxonomy <- remove_strain_info(taxonomy)
# Remove the `Strain` column if it exists
if ("Strain" %in% colnames(cleaned_taxonomy)) {
  cleaned_taxonomy <- cleaned_taxonomy[, colnames(cleaned_taxonomy) != "Strain"]}
# Step 3: Update the taxonomy table in the phyloseq object
tax_table(ps) <- cleaned_taxonomy
# Step 4: Verify the changes
print(head(tax_table(ps)))  # Display the first few rows



# =====================================================================
#             To add species rank to the taxonomic ranks
# =====================================================================

library(phyloseq)
# Step 1: Extract taxonomy table safely
taxonomy <- as.data.frame(as.matrix(tax_table(ps)))
if (!"Genus" %in% colnames(taxonomy)) {
  stop("The 'Genus' column is missing in the taxonomy table")
}
# Step 2: Create a new 'species' column
taxonomy$species <- paste0(taxonomy$Genus, "_OTU", seq_len(nrow(taxonomy)))
# Step 3: Assign back to phyloseq obj
tax_table(ps) <- tax_table(as.matrix(taxonomy))

# In case there are several OTUs/ASVs resulting from the spiked species, you may want to check the phylogenetic distances.
# We first read DNA sequences from a FASTA file, to perform multiple sequence alignment and compute a distance matrix using the maximum likelihood method, then we construct a phylogenetic tree
# Use the Neighbor-Joining method  based on a Jukes-Cantor distance matrix and plot the tree with bootstrap values.
# we compare the Sanger read of Tetragenococcus halophilus with the FASTA sequence of Tetragenococcus halophilus from our phyloseq object.

library(Biostrings)
library(phyloseq)

# Get path to external data folder
extdata_path <- system.file("extdata", package = "DspikeIn")
data("physeq_ITSOTU",package = "DspikeIn")

list.files(extdata_path)

# Subset the phyloseq object to include only Tetragenococcus species first
Dekkera <- subset_taxa(physeq_ITSOTU, Genus=="Dekkera")
Dekkera <- subset_taxa(Dekkera, !is.na(taxa_names(Dekkera)) & taxa_names(Dekkera) != "")

plot_tree_nj(Dekkera, output_file = "neighbor_joining_tree_with_bootstrap.png")

# Plot phylogenetic tree
plot_tree_custom(Dekkera, output_prefix = "p0", width = 18, height = 18, layout = "circular")


# Plot the phylogenetic tree with multiple sequence alignment
# you may use the highlighted #-codes to find relevant OTUs with more than ~0.2 branch length
plot_tree_with_alignment(Dekkera, output_prefix = "tree_alignment", width = 15, height = 15)

# Plot phylogenetic tree with bootstrap values and cophenetic distances
Bootstrap_phy_tree_with_cophenetic(Dekkera, output_file = "tree_with_bootstrap_and_cophenetic.png", bootstrap_replicates = 500)

# Plot the tree with glommed OTUs at 0.3 resolution/ or modify it
plot_glommed_tree(Dekkera, resolution = 0.3, output_prefix = "top", width = 18, height = 18)

# merges ASVs/OTUs**

#The function Pre_processing_species() merges ASVs of a species using "sum" or "max" methods, preserving #taxonomic, phylogenetic, and sequencing data.

# =====================================================================
#                      Load the phyloseq objs/ TSE obj
# =====================================================================

library(phyloseq)
library(DspikeIn)
library(TreeSummarizedExperiment)
library(SummarizedExperiment)

data("physeq_ITSOTU", package = "DspikeIn")

# tse_ITSOTU <- convert_phyloseq_to_tse(physeq_ITSOTU)
# physeq_ITSOTU <- convert_tse_to_phyloseq(tse_ITSOTU)

physeq_ITSOTU <- DspikeIn::tidy_phyloseq_tse(physeq_ITSOTU)  # make it tidy 

# Check if metadata contains spiked volumes 
physeq_ITSOTU@sam_data$spiked.volume
#>   [1] 0 0 0 2 2 2 1 2 2 2 2 2 2 2 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 1 1 2 2 2 2 2
#>  [38] 2 2 2 2 2 2 2 2 2 2 2 2 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
#>  [75] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
#> [112] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 0 0 0 0 0 0 2 0 0 0 2 2 2 2 2 2 2 2
#> [149] 2 2 2 2 2 2 2 0 0 0 2 2 2 2 2 2 2 0 0 0 2 0 0 0 2 2 2 0 0 0 2 2 2 2 2 2 2
#> [186] 0 0 0 2 2 2 0 0 0 0 0 0 2 2 2 2 2 2 2 0 0 0 2 2 2 0 0 0 2 2 2 2 0 0 0 2 2
#> [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 0 0 0 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
#> [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 1 1

# =====================================================================
#                      PREREQUISITE FOR ITS & CALCULATE SPIKED %
# =====================================================================
# Define spiked species and related parameters**

library(flextable)

# Define the spiked species
spiked_cells<-733
species_name <- spiked_species<-merged_spiked_species<-"Dekkera_bruxellensis"

# Subset taxa for spiked species
Dekkera <- phyloseq::subset_taxa(
  physeq_ITSOTU, 
  Species %in% species_name)
hashcodes <- row.names(phyloseq::tax_table(Dekkera))

# Subset samples based on spiked volume 
physeq_ITSOTU_spiked <- phyloseq::subset_samples(physeq_ITSOTU, spiked.volume %in% c("2", "1"))

# if TSE format
# physeq_ITSOTU_spiked <- tse_16SOTU[, tse_16SOTU$spiked.volume %in% c("2", "1")]

# Merge OTUs derived from spiking into one
# Note: how many taxa have changed to xx 

Spiked_ITS_sum_scaled <- Pre_processing_species(
  physeq_ITSOTU_spiked, 
  species_name, 
  merge_method = "sum", 
  output_file = "merged_physeq_sum.rds"
)

# ?calculate_spike_percentage

Perc <- calculate_spike_percentage(
  Spiked_ITS_sum_scaled, 
  merged_spiked_species, 
  passed_range = c(0.1, 20)
)
#> 📂 Table saved in docx format: merged_data.docx 
#> 📂 Merged data saved as CSV: merged_data.csv

#
# =====================================================================
#                      CALCULATE SCALING FACTORS 
# =====================================================================
# Calculate scaling factors
  
result <- calculate_spikeIn_factors(Spiked_ITS_sum_scaled, spiked_cells, merged_spiked_species)
#> Extracting taxonomy and sample data...
#> Removing spiked species...
#> 🧮 Calculating total reads per sample...
#> ➖ Extracting spiked species...
#> ➕ Merging spiked species...
#> 🧮 Calculating scaling factors...
result$spiked_species_merged
#> phyloseq-class experiment-level object
#> otu_table()   OTU Table:          [ 1 taxa and 240 samples ]:
#> sample_data() Sample Data:        [ 240 samples by 32 sample variables ]:
#> tax_table()   Taxonomy Table:     [ 1 taxa by 7 taxonomic ranks ]:
#> refseq()      DNAStringSet:       [ 1 reference sequences ]
#> taxa are rows
result$spiked_species_reads
#> # A tibble: 240 × 2
#>    Sample                  Total_Reads
#>    <chr>                         <dbl>
#>  1 spiked.blank.20529_S180       51352
#>  2 spiked.blank.20913_S180       48471
#>  3 Std2uL.20721_S180             66229
#>  4 StdSwab1uL.20720_S168         53977
#>  5 STP1719.20518_S143            18165
#>  6 STP213.20519_S155              2547
#>  7 STP268.20520_S167              1332
#>  8 STP544.20515_S107             28061
#>  9 STP570.20516_S119             13722
#> 10 STP579.20517_S131             30845
#> # ℹ 230 more rows
scaling_factors <- result$scaling_factors
scaling_factors
#> spiked.blank.20529_S180 spiked.blank.20913_S180       Std2uL.20721_S180 
#>             0.014274030             0.015122444             0.011067659 
#>   StdSwab1uL.20720_S168      STP1719.20518_S143       STP213.20519_S155 
#>             0.006789929             0.040352326             0.287789556 
#>       STP268.20520_S167       STP544.20515_S107       STP570.20516_S119 
#>             0.550300300             0.026121664             0.053417869 
#>       STP579.20517_S131       STP614.20514_S190      UHM1000.20700_S118 
#>             0.023763981             0.690857681             1.559574468 
#>      UHM1001.20705_S178      UHM1007.20718_S144      UHM1009.20710_S143 
#>             0.110291905             0.640734266             0.339037928 
#>      UHM1010.20717_S132      UHM1011.20702_S142      UHM1024.20716_S120 
#>             0.060850075             0.057662052             0.028369069 
#>      UHM1026.20703_S154      UHM1028.20709_S131      UHM1032.20701_S130 
#>             0.073653537             0.348384030             0.060468570 
#>      UHM1033.20715_S108      UHM1034.20712_S167      UHM1035.20707_S107 
#>             0.096219480            18.325000000             0.087616543 
#>      UHM1036.20708_S119      UHM1052.20711_S155       UHM1060.20819_S97 
#>             0.294377510             0.083238701             0.028247717 
#>      UHM1065.20820_S109      UHM1068.20828_S110      UHM1069.20838_S135 
#>             0.047147360             0.242474363             0.211239193 
#>      UHM1070.20821_S121      UHM1071.20829_S122      UHM1072.20830_S134 
#>             0.061820022             0.331074977             0.096257387 
#>      UHM1073.20831_S146      UHM1075.20822_S133      UHM1077.20832_S158 
#>             3.683417085             0.280091708             0.049580628 
#>      UHM1078.20823_S145      UHM1080.20833_S170      UHM1081.20824_S157 
#>             1.199672668             0.955671447             0.540959410 
#>      UHM1088.20834_S182       UHM1090.20835_S99      UHM1093.20825_S169 
#>             0.460138104             0.183021223             0.453308596 
#>      UHM1095.20826_S181      UHM1097.20719_S156      UHM1099.20704_S166 
#>             0.200163845             0.027154182             0.034924719 
#>      UHM1100.20884_S117      UHM1102.20885_S129      UHM1104.20886_S141 
#>            12.423728814             2.655797101             1.692840647 
#>      UHM1105.20887_S153       UHM1109.20627_S97      UHM1110.20664_S161 
#>             1.337591241             0.179612840            28.192307692 
#>      UHM1113.20888_S165      UHM1114.20889_S177      UHM1115.20890_S189 
#>             0.495605139             0.614417435             6.787037037 
#>      UHM1117.20891_S106      UHM1118.20892_S118      UHM1120.20893_S130 
#>             5.682170543             1.385633270             1.732860520 
#>      UHM1124.20894_S142      UHM1126.20895_S154      UHM1128.20896_S166 
#>             1.846347607             1.981081081             9.644736842 
#>      UHM1140.20651_S100      UHM1145.20897_S178      UHM1163.20501_S129 
#>             1.138198758             2.188059701             0.617523168 
#>      UHM1164.20498_S188      UHM1169.20648_S159      UHM1171.20675_S103 
#>             0.079251811             0.049440173            12.216666667 
#>      UHM1176.20500_S117      UHM1177.20642_S182      UHM1182.20672_S162 
#>             1.272569444             0.028440616             1.150706436 
#>      UHM1210.20898_S190      UHM1212.20899_S107      UHM1217.20900_S119 
#>             3.646766169             1.213576159             0.667577413 
#>      UHM1218.20901_S131      UHM1219.20902_S143      UHM1220.20903_S155 
#>             1.315978456            10.471428571             3.665000000 
#>      UHM1221.20904_S167      UHM1222.20905_S179      UHM1223.20906_S191 
#>             1.918848168             0.102661064             4.639240506 
#>      UHM1225.20907_S108      UHM1227.20908_S120      UHM1228.20909_S132 
#>             2.262345679             1.227805695             6.429824561 
#>      UHM1237.20910_S144      UHM1240.20662_S137      UHM1246.20911_S156 
#>             1.240270728             0.111516811             0.530390738 
#>      UHM1247.20912_S168      UHM1248.20671_S150      UHM1256.20666_S185 
#>             1.147104851             0.025198529             0.063683753 
#>      UHM1260.20692_S117      UHM1270.20673_S174      UHM1271.20493_S128 
#>             0.771578947             0.087900228             0.538179148 
#>      UHM1272.20494_S140      UHM1274.20650_S183      UHM1275.20693_S129 
#>             0.272998138             0.255757153             1.274782609 
#>      UHM1282.20695_S153      UHM1287.20639_S146      UHM1291.20512_S166 
#>             1.074780059             0.207179197             0.289837881 
#>      UHM1296.20646_S135      UHM1319.20657_S172      UHM1324.20509_S130 
#>            11.453125000             1.586580087             0.371703854 
#>      UHM1327.20641_S170      UHM1328.20668_S114      UHM1334.20513_S178 
#>            12.016393443             0.021200289             0.925505051 
#>      UHM1338.20495_S152      UHM1341.20698_S189      UHM1356.20637_S122 
#>             0.104894104             0.014220860             0.020272139 
#>      UHM1380.20676_S115      UHM1383.20690_S188      UHM1385.20659_S101 
#>             2.863281250             1.480808081             0.048424391 
#>      UHM1399.20852_S113      UHM1400.20853_S125      UHM1401.20854_S137 
#>             0.204863052             0.090628091             0.085301990 
#>      UHM1402.20855_S149      UHM1403.20856_S161      UHM1405.20857_S173 
#>             1.000000000             0.153926921             0.603292181 
#>      UHM1406.20858_S185      UHM1414.20859_S102      UHM1419.20860_S114 
#>             0.060284563             1.440078585             0.319389978 
#>      UHM1427.20485_S127      UHM1428.20486_S139      UHM1429.20487_S151 
#>             0.536996337            22.212121212            13.830188679 
#>      UHM1430.20488_S163      UHM1432.20489_S175      UHM1435.20484_S115 
#>             5.682170543             4.524691358             1.514462810 
#>       UHM162.20656_S160       UHM198.20681_S175       UHM200.20670_S138 
#>             0.480655738             0.395148248             1.283712785 
#>       UHM203.20685_S128       UHM204.20505_S177       UHM206.20506_S189 
#>             0.607794362             0.275667544             0.053519276 
#>       UHM207.20689_S176       UHM208.20507_S106       UHM211.20502_S141 
#>             0.903822441             0.830124575             0.064913213 
#>       UHM215.20504_S165       UHM216.20525_S132       UHM219.20526_S144 
#>             0.218479881             0.044916968             0.015032814 
#>       UHM236.20527_S156       UHM238.20503_S153       UHM245.20634_S181 
#>             0.036143984             0.103239437             0.373788883 
#>       UHM252.20654_S136       UHM267.20496_S164       UHM274.20677_S127 
#>             0.859320047             0.478772044             0.393029491 
#>       UHM276.20682_S187       UHM280.20497_S176       UHM286.20521_S179 
#>             0.287450980             0.161063503             0.640734266 
#>       UHM289.20522_S191       UHM294.20523_S108       UHM298.20696_S165 
#>             0.249829584             0.408584169             5.020547945 
#>       UHM325.20644_S111       UHM337.20508_S118       UHM354.20631_S145 
#>             1.217607973             0.881009615            23.645161290 
#>       UHM356.20511_S154       UHM369.20869_S127       UHM370.20870_S139 
#>             0.213391557             1.000000000             1.377819549 
#>       UHM372.20871_S151       UHM373.20872_S163       UHM374.20873_S175 
#>             0.609310058             1.313620072             7.186274510 
#>       UHM375.20874_S187       UHM377.20875_S104       UHM382.20876_S116 
#>             0.527338129             1.539915966            10.180555556 
#>       UHM386.20877_S128       UHM387.20878_S140       UHM414.20679_S151 
#>             2.599290780             3.919786096             5.161971831 
#>       UHM418.20861_S126       UHM422.20862_S138       UHM425.20863_S150 
#>             3.739795918             0.109142347             1.163492063 
#>       UHM426.20630_S133       UHM428.20640_S158       UHM429.20655_S148 
#>             0.032558966             6.373913043             0.068755276 
#>        UHM435.20643_S99       UHM437.20864_S162       UHM439.20660_S113 
#>             0.186942107             2.227963526             1.141744548 
#>       UHM443.20524_S120       UHM445.20665_S173       UHM447.20879_S152 
#>             1.000000000            73.300000000            14.959183673 
#>       UHM448.20865_S174       UHM452.20880_S164       UHM454.20866_S186 
#>             0.330477908             0.840596330             1.665909091 
#>       UHM455.20881_S176       UHM458.20882_S188       UHM459.20883_S105 
#>             2.118497110            14.372549020             5.470149254 
#>       UHM461.20867_S103       UHM467.20868_S115       UHM470.20629_S121 
#>             0.187756148             2.341853035             0.645814978 
#>       UHM476.20510_S142       UHM478.20645_S123       UHM479.20647_S147 
#>             0.554882665             0.047244602             0.015253990 
#>       UHM481.20499_S105       UHM482.20686_S140       UHM483.20699_S106 
#>             0.085084156             0.029153243             3.646766169 
#>       UHM519.20678_S139       UHM520.20669_S126       UHM836.20481_S174 
#>             0.468969930            16.659090909             0.410414334 
#>       UHM837.20482_S186       UHM838.20483_S103       UHM891.20480_S162 
#>             0.146688013            12.216666667             7.715789474 
#>       UHM892.20628_S109       UHM893.20691_S105       UHM894.20636_S110 
#>             2.714814815            28.192307692            15.270833333 
#>       UHM895.20632_S157       UHM896.20697_S177       UHM897.20687_S152 
#>             1.800982801            15.934782609             3.898936170 
#>       UHM898.20490_S187       UHM899.20684_S116       UHM900.20491_S104 
#>             2.704797048             1.000000000            22.212121212 
#>       UHM901.20638_S134       UHM902.20680_S163       UHM903.20683_S104 
#>             1.658371041             0.371515459             0.273609556 
#>       UHM904.20663_S149       UHM905.20694_S141       UHM906.20661_S125 
#>             3.041493776             3.092827004            29.320000000 
#>       UHM907.20688_S164       UHM908.20492_S116       UHM909.20653_S124 
#>             0.241914191             0.468670077             0.047974344 
#>       UHM910.20658_S184       UHM965.20633_S169       UHM966.20839_S147 
#>             0.495270270             0.165127281             0.089412052 
#>       UHM967.20840_S159       UHM968.20667_S102       UHM969.20841_S171 
#>             1.255136986             0.012972533             5.429629630 
#>       UHM971.20842_S183       UHM973.20674_S186       UHM974.20528_S168 
#>             2.070621469             0.168235024             0.015603380 
#>       UHM975.20843_S100       UHM977.20844_S112       UHM978.20845_S124 
#>             2.468013468             0.420298165             7.479591837 
#>       UHM979.20846_S136        UHM980.20827_S98        UHM981.20635_S98 
#>             1.749403341             0.049925078             0.109305100 
#>       UHM982.20836_S111       UHM983.20652_S112       UHM984.20847_S148 
#>             4.759740260             0.442632850             5.682170543 
#>       UHM985.20848_S160       UHM988.20849_S172       UHM989.20850_S184 
#>             0.421022401             0.110474755             3.646766169 
#>       UHM991.20851_S101       UHM993.20837_S123       UHM996.20706_S190 
#>             0.174192015             3.159482759             0.025157880 
#>       UHM997.20649_S171       UHM998.20714_S191       UHM999.20713_S179 
#>             0.383970665             0.024668506             1.138198758
str(scaling_factors)
#>  Named num [1:240] 0.01427 0.01512 0.01107 0.00679 0.04035 ...
#>  - attr(*, "names")= chr [1:240] "spiked.blank.20529_S180" "spiked.blank.20913_S180" "Std2uL.20721_S180" "StdSwab1uL.20720_S168" ...

# =====================================================================
#             Convert relative counts to absolute counts
# =====================================================================
#**Absolute Read Count=Relative Read Count×Scaling Factor**

# Convert to absolute counts
absolute <- convert_to_absolute_counts(Spiked_ITS_sum_scaled, scaling_factors)

# Extract processed data
absolute_counts <- absolute$absolute_counts
physeq_absolute <- absolute$obj_adj

physeq_absolute <- tidy_phyloseq_tse(physeq_absolute)
# View absolute count data
head(absolute_counts)
#> # A tibble: 6 × 240
#>   spiked.blank.20529_S180 spiked.blank.20913_S180 Std2uL.20721_S180
#>                     <dbl>                   <dbl>             <dbl>
#> 1                       0                       0                 0
#> 2                       0                       0                 0
#> 3                       0                       0                 0
#> 4                       0                       0                 0
#> 5                       0                       0                 0
#> 6                       0                       0                 0
#> # ℹ 237 more variables: StdSwab1uL.20720_S168 <dbl>, STP1719.20518_S143 <dbl>,
#> #   STP213.20519_S155 <dbl>, STP268.20520_S167 <dbl>, STP544.20515_S107 <dbl>,
#> #   STP570.20516_S119 <dbl>, STP579.20517_S131 <dbl>, STP614.20514_S190 <dbl>,
#> #   UHM1000.20700_S118 <dbl>, UHM1001.20705_S178 <dbl>,
#> #   UHM1007.20718_S144 <dbl>, UHM1009.20710_S143 <dbl>,
#> #   UHM1010.20717_S132 <dbl>, UHM1011.20702_S142 <dbl>,
#> #   UHM1024.20716_S120 <dbl>, UHM1026.20703_S154 <dbl>, …

# =====================================================================
#                      CALCULATE SPIKE PERCENTAGE & summary stat
# =====================================================================

#**Calculate spike percentage & Generate summary statistics for absolute counts**

# Generate summary statistics for absolute counts
post_eval_summary <- calculate_summary_stats_table(absolute_counts)
#> 💾    Table saved in docx format: post_eval_summary.docx 
#> 💾    Summary statistics saved as CSV: post_eval_summary.csv
print(post_eval_summary)
#> a flextable object.
#> col_keys: `spiked.blank.20529_S180_mean`, `spiked.blank.20913_S180_mean`, `Std2uL.20721_S180_mean`, `StdSwab1uL.20720_S168_mean`, `STP1719.20518_S143_mean`, `STP213.20519_S155_mean`, `STP268.20520_S167_mean`, `STP544.20515_S107_mean`, `STP570.20516_S119_mean`, `STP579.20517_S131_mean`, `STP614.20514_S190_mean`, `UHM1000.20700_S118_mean`, `UHM1001.20705_S178_mean`, `UHM1007.20718_S144_mean`, `UHM1009.20710_S143_mean`, `UHM1010.20717_S132_mean`, `UHM1011.20702_S142_mean`, `UHM1024.20716_S120_mean`, `UHM1026.20703_S154_mean`, `UHM1028.20709_S131_mean`, `UHM1032.20701_S130_mean`, `UHM1033.20715_S108_mean`, `UHM1034.20712_S167_mean`, `UHM1035.20707_S107_mean`, `UHM1036.20708_S119_mean`, `UHM1052.20711_S155_mean`, `UHM1060.20819_S97_mean`, `UHM1065.20820_S109_mean`, `UHM1068.20828_S110_mean`, `UHM1069.20838_S135_mean`, `UHM1070.20821_S121_mean`, `UHM1071.20829_S122_mean`, `UHM1072.20830_S134_mean`, `UHM1073.20831_S146_mean`, `UHM1075.20822_S133_mean`, `UHM1077.20832_S158_mean`, `UHM1078.20823_S145_mean`, `UHM1080.20833_S170_mean`, `UHM1081.20824_S157_mean`, `UHM1088.20834_S182_mean`, `UHM1090.20835_S99_mean`, `UHM1093.20825_S169_mean`, `UHM1095.20826_S181_mean`, `UHM1097.20719_S156_mean`, `UHM1099.20704_S166_mean`, `UHM1100.20884_S117_mean`, `UHM1102.20885_S129_mean`, `UHM1104.20886_S141_mean`, `UHM1105.20887_S153_mean`, `UHM1109.20627_S97_mean`, `UHM1110.20664_S161_mean`, `UHM1113.20888_S165_mean`, `UHM1114.20889_S177_mean`, `UHM1115.20890_S189_mean`, `UHM1117.20891_S106_mean`, `UHM1118.20892_S118_mean`, `UHM1120.20893_S130_mean`, `UHM1124.20894_S142_mean`, `UHM1126.20895_S154_mean`, `UHM1128.20896_S166_mean`, `UHM1140.20651_S100_mean`, `UHM1145.20897_S178_mean`, `UHM1163.20501_S129_mean`, `UHM1164.20498_S188_mean`, `UHM1169.20648_S159_mean`, `UHM1171.20675_S103_mean`, `UHM1176.20500_S117_mean`, `UHM1177.20642_S182_mean`, `UHM1182.20672_S162_mean`, `UHM1210.20898_S190_mean`, `UHM1212.20899_S107_mean`, `UHM1217.20900_S119_mean`, `UHM1218.20901_S131_mean`, `UHM1219.20902_S143_mean`, `UHM1220.20903_S155_mean`, `UHM1221.20904_S167_mean`, `UHM1222.20905_S179_mean`, `UHM1223.20906_S191_mean`, `UHM1225.20907_S108_mean`, `UHM1227.20908_S120_mean`, `UHM1228.20909_S132_mean`, `UHM1237.20910_S144_mean`, `UHM1240.20662_S137_mean`, `UHM1246.20911_S156_mean`, `UHM1247.20912_S168_mean`, `UHM1248.20671_S150_mean`, `UHM1256.20666_S185_mean`, `UHM1260.20692_S117_mean`, `UHM1270.20673_S174_mean`, `UHM1271.20493_S128_mean`, `UHM1272.20494_S140_mean`, `UHM1274.20650_S183_mean`, `UHM1275.20693_S129_mean`, `UHM1282.20695_S153_mean`, `UHM1287.20639_S146_mean`, `UHM1291.20512_S166_mean`, `UHM1296.20646_S135_mean`, `UHM1319.20657_S172_mean`, `UHM1324.20509_S130_mean`, `UHM1327.20641_S170_mean`, `UHM1328.20668_S114_mean`, `UHM1334.20513_S178_mean`, `UHM1338.20495_S152_mean`, `UHM1341.20698_S189_mean`, `UHM1356.20637_S122_mean`, `UHM1380.20676_S115_mean`, `UHM1383.20690_S188_mean`, `UHM1385.20659_S101_mean`, `UHM1399.20852_S113_mean`, `UHM1400.20853_S125_mean`, `UHM1401.20854_S137_mean`, `UHM1402.20855_S149_mean`, `UHM1403.20856_S161_mean`, `UHM1405.20857_S173_mean`, `UHM1406.20858_S185_mean`, `UHM1414.20859_S102_mean`, `UHM1419.20860_S114_mean`, `UHM1427.20485_S127_mean`, `UHM1428.20486_S139_mean`, `UHM1429.20487_S151_mean`, `UHM1430.20488_S163_mean`, `UHM1432.20489_S175_mean`, `UHM1435.20484_S115_mean`, `UHM162.20656_S160_mean`, `UHM198.20681_S175_mean`, `UHM200.20670_S138_mean`, `UHM203.20685_S128_mean`, `UHM204.20505_S177_mean`, `UHM206.20506_S189_mean`, `UHM207.20689_S176_mean`, `UHM208.20507_S106_mean`, `UHM211.20502_S141_mean`, `UHM215.20504_S165_mean`, `UHM216.20525_S132_mean`, `UHM219.20526_S144_mean`, `UHM236.20527_S156_mean`, `UHM238.20503_S153_mean`, `UHM245.20634_S181_mean`, `UHM252.20654_S136_mean`, `UHM267.20496_S164_mean`, `UHM274.20677_S127_mean`, `UHM276.20682_S187_mean`, `UHM280.20497_S176_mean`, `UHM286.20521_S179_mean`, `UHM289.20522_S191_mean`, `UHM294.20523_S108_mean`, `UHM298.20696_S165_mean`, `UHM325.20644_S111_mean`, `UHM337.20508_S118_mean`, `UHM354.20631_S145_mean`, `UHM356.20511_S154_mean`, `UHM369.20869_S127_mean`, `UHM370.20870_S139_mean`, `UHM372.20871_S151_mean`, `UHM373.20872_S163_mean`, `UHM374.20873_S175_mean`, `UHM375.20874_S187_mean`, `UHM377.20875_S104_mean`, `UHM382.20876_S116_mean`, `UHM386.20877_S128_mean`, `UHM387.20878_S140_mean`, `UHM414.20679_S151_mean`, `UHM418.20861_S126_mean`, `UHM422.20862_S138_mean`, `UHM425.20863_S150_mean`, `UHM426.20630_S133_mean`, `UHM428.20640_S158_mean`, `UHM429.20655_S148_mean`, `UHM435.20643_S99_mean`, `UHM437.20864_S162_mean`, `UHM439.20660_S113_mean`, `UHM443.20524_S120_mean`, `UHM445.20665_S173_mean`, `UHM447.20879_S152_mean`, `UHM448.20865_S174_mean`, `UHM452.20880_S164_mean`, `UHM454.20866_S186_mean`, `UHM455.20881_S176_mean`, `UHM458.20882_S188_mean`, `UHM459.20883_S105_mean`, `UHM461.20867_S103_mean`, `UHM467.20868_S115_mean`, `UHM470.20629_S121_mean`, `UHM476.20510_S142_mean`, `UHM478.20645_S123_mean`, `UHM479.20647_S147_mean`, `UHM481.20499_S105_mean`, `UHM482.20686_S140_mean`, `UHM483.20699_S106_mean`, `UHM519.20678_S139_mean`, `UHM520.20669_S126_mean`, `UHM836.20481_S174_mean`, `UHM837.20482_S186_mean`, `UHM838.20483_S103_mean`, `UHM891.20480_S162_mean`, `UHM892.20628_S109_mean`, `UHM893.20691_S105_mean`, `UHM894.20636_S110_mean`, `UHM895.20632_S157_mean`, `UHM896.20697_S177_mean`, `UHM897.20687_S152_mean`, `UHM898.20490_S187_mean`, `UHM899.20684_S116_mean`, `UHM900.20491_S104_mean`, `UHM901.20638_S134_mean`, `UHM902.20680_S163_mean`, `UHM903.20683_S104_mean`, `UHM904.20663_S149_mean`, `UHM905.20694_S141_mean`, `UHM906.20661_S125_mean`, `UHM907.20688_S164_mean`, `UHM908.20492_S116_mean`, `UHM909.20653_S124_mean`, `UHM910.20658_S184_mean`, `UHM965.20633_S169_mean`, `UHM966.20839_S147_mean`, `UHM967.20840_S159_mean`, `UHM968.20667_S102_mean`, `UHM969.20841_S171_mean`, `UHM971.20842_S183_mean`, `UHM973.20674_S186_mean`, `UHM974.20528_S168_mean`, `UHM975.20843_S100_mean`, `UHM977.20844_S112_mean`, `UHM978.20845_S124_mean`, `UHM979.20846_S136_mean`, `UHM980.20827_S98_mean`, `UHM981.20635_S98_mean`, `UHM982.20836_S111_mean`, `UHM983.20652_S112_mean`, `UHM984.20847_S148_mean`, `UHM985.20848_S160_mean`, `UHM988.20849_S172_mean`, `UHM989.20850_S184_mean`, `UHM991.20851_S101_mean`, `UHM993.20837_S123_mean`, `UHM996.20706_S190_mean`, `UHM997.20649_S171_mean`, `UHM998.20714_S191_mean`, `UHM999.20713_S179_mean`, `spiked.blank.20529_S180_sd`, `spiked.blank.20913_S180_sd`, `Std2uL.20721_S180_sd`, `StdSwab1uL.20720_S168_sd`, `STP1719.20518_S143_sd`, `STP213.20519_S155_sd`, `STP268.20520_S167_sd`, `STP544.20515_S107_sd`, `STP570.20516_S119_sd`, `STP579.20517_S131_sd`, `STP614.20514_S190_sd`, `UHM1000.20700_S118_sd`, `UHM1001.20705_S178_sd`, `UHM1007.20718_S144_sd`, `UHM1009.20710_S143_sd`, `UHM1010.20717_S132_sd`, `UHM1011.20702_S142_sd`, `UHM1024.20716_S120_sd`, `UHM1026.20703_S154_sd`, `UHM1028.20709_S131_sd`, `UHM1032.20701_S130_sd`, `UHM1033.20715_S108_sd`, `UHM1034.20712_S167_sd`, `UHM1035.20707_S107_sd`, `UHM1036.20708_S119_sd`, `UHM1052.20711_S155_sd`, `UHM1060.20819_S97_sd`, `UHM1065.20820_S109_sd`, `UHM1068.20828_S110_sd`, `UHM1069.20838_S135_sd`, `UHM1070.20821_S121_sd`, `UHM1071.20829_S122_sd`, `UHM1072.20830_S134_sd`, `UHM1073.20831_S146_sd`, `UHM1075.20822_S133_sd`, `UHM1077.20832_S158_sd`, `UHM1078.20823_S145_sd`, `UHM1080.20833_S170_sd`, `UHM1081.20824_S157_sd`, `UHM1088.20834_S182_sd`, `UHM1090.20835_S99_sd`, `UHM1093.20825_S169_sd`, `UHM1095.20826_S181_sd`, `UHM1097.20719_S156_sd`, `UHM1099.20704_S166_sd`, `UHM1100.20884_S117_sd`, `UHM1102.20885_S129_sd`, `UHM1104.20886_S141_sd`, `UHM1105.20887_S153_sd`, `UHM1109.20627_S97_sd`, `UHM1110.20664_S161_sd`, `UHM1113.20888_S165_sd`, `UHM1114.20889_S177_sd`, `UHM1115.20890_S189_sd`, `UHM1117.20891_S106_sd`, `UHM1118.20892_S118_sd`, `UHM1120.20893_S130_sd`, `UHM1124.20894_S142_sd`, `UHM1126.20895_S154_sd`, `UHM1128.20896_S166_sd`, `UHM1140.20651_S100_sd`, `UHM1145.20897_S178_sd`, `UHM1163.20501_S129_sd`, `UHM1164.20498_S188_sd`, `UHM1169.20648_S159_sd`, `UHM1171.20675_S103_sd`, `UHM1176.20500_S117_sd`, `UHM1177.20642_S182_sd`, `UHM1182.20672_S162_sd`, `UHM1210.20898_S190_sd`, `UHM1212.20899_S107_sd`, `UHM1217.20900_S119_sd`, `UHM1218.20901_S131_sd`, `UHM1219.20902_S143_sd`, `UHM1220.20903_S155_sd`, `UHM1221.20904_S167_sd`, `UHM1222.20905_S179_sd`, `UHM1223.20906_S191_sd`, `UHM1225.20907_S108_sd`, `UHM1227.20908_S120_sd`, `UHM1228.20909_S132_sd`, `UHM1237.20910_S144_sd`, `UHM1240.20662_S137_sd`, `UHM1246.20911_S156_sd`, `UHM1247.20912_S168_sd`, `UHM1248.20671_S150_sd`, `UHM1256.20666_S185_sd`, `UHM1260.20692_S117_sd`, `UHM1270.20673_S174_sd`, `UHM1271.20493_S128_sd`, `UHM1272.20494_S140_sd`, `UHM1274.20650_S183_sd`, `UHM1275.20693_S129_sd`, `UHM1282.20695_S153_sd`, `UHM1287.20639_S146_sd`, `UHM1291.20512_S166_sd`, `UHM1296.20646_S135_sd`, `UHM1319.20657_S172_sd`, `UHM1324.20509_S130_sd`, `UHM1327.20641_S170_sd`, `UHM1328.20668_S114_sd`, `UHM1334.20513_S178_sd`, `UHM1338.20495_S152_sd`, `UHM1341.20698_S189_sd`, `UHM1356.20637_S122_sd`, `UHM1380.20676_S115_sd`, `UHM1383.20690_S188_sd`, `UHM1385.20659_S101_sd`, `UHM1399.20852_S113_sd`, `UHM1400.20853_S125_sd`, `UHM1401.20854_S137_sd`, `UHM1402.20855_S149_sd`, `UHM1403.20856_S161_sd`, `UHM1405.20857_S173_sd`, `UHM1406.20858_S185_sd`, `UHM1414.20859_S102_sd`, `UHM1419.20860_S114_sd`, `UHM1427.20485_S127_sd`, `UHM1428.20486_S139_sd`, `UHM1429.20487_S151_sd`, `UHM1430.20488_S163_sd`, `UHM1432.20489_S175_sd`, `UHM1435.20484_S115_sd`, `UHM162.20656_S160_sd`, `UHM198.20681_S175_sd`, `UHM200.20670_S138_sd`, `UHM203.20685_S128_sd`, `UHM204.20505_S177_sd`, `UHM206.20506_S189_sd`, `UHM207.20689_S176_sd`, `UHM208.20507_S106_sd`, `UHM211.20502_S141_sd`, `UHM215.20504_S165_sd`, `UHM216.20525_S132_sd`, `UHM219.20526_S144_sd`, `UHM236.20527_S156_sd`, `UHM238.20503_S153_sd`, `UHM245.20634_S181_sd`, `UHM252.20654_S136_sd`, `UHM267.20496_S164_sd`, `UHM274.20677_S127_sd`, `UHM276.20682_S187_sd`, `UHM280.20497_S176_sd`, `UHM286.20521_S179_sd`, `UHM289.20522_S191_sd`, `UHM294.20523_S108_sd`, `UHM298.20696_S165_sd`, `UHM325.20644_S111_sd`, `UHM337.20508_S118_sd`, `UHM354.20631_S145_sd`, `UHM356.20511_S154_sd`, `UHM369.20869_S127_sd`, `UHM370.20870_S139_sd`, `UHM372.20871_S151_sd`, `UHM373.20872_S163_sd`, `UHM374.20873_S175_sd`, `UHM375.20874_S187_sd`, `UHM377.20875_S104_sd`, `UHM382.20876_S116_sd`, `UHM386.20877_S128_sd`, `UHM387.20878_S140_sd`, `UHM414.20679_S151_sd`, `UHM418.20861_S126_sd`, `UHM422.20862_S138_sd`, `UHM425.20863_S150_sd`, `UHM426.20630_S133_sd`, `UHM428.20640_S158_sd`, `UHM429.20655_S148_sd`, `UHM435.20643_S99_sd`, `UHM437.20864_S162_sd`, `UHM439.20660_S113_sd`, `UHM443.20524_S120_sd`, `UHM445.20665_S173_sd`, `UHM447.20879_S152_sd`, `UHM448.20865_S174_sd`, `UHM452.20880_S164_sd`, `UHM454.20866_S186_sd`, `UHM455.20881_S176_sd`, `UHM458.20882_S188_sd`, `UHM459.20883_S105_sd`, `UHM461.20867_S103_sd`, `UHM467.20868_S115_sd`, `UHM470.20629_S121_sd`, `UHM476.20510_S142_sd`, 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`UHM1140.20651_S100_q75`, `UHM1145.20897_S178_q75`, `UHM1163.20501_S129_q75`, `UHM1164.20498_S188_q75`, `UHM1169.20648_S159_q75`, `UHM1171.20675_S103_q75`, `UHM1176.20500_S117_q75`, `UHM1177.20642_S182_q75`, `UHM1182.20672_S162_q75`, `UHM1210.20898_S190_q75`, `UHM1212.20899_S107_q75`, `UHM1217.20900_S119_q75`, `UHM1218.20901_S131_q75`, `UHM1219.20902_S143_q75`, `UHM1220.20903_S155_q75`, `UHM1221.20904_S167_q75`, `UHM1222.20905_S179_q75`, `UHM1223.20906_S191_q75`, `UHM1225.20907_S108_q75`, `UHM1227.20908_S120_q75`, `UHM1228.20909_S132_q75`, `UHM1237.20910_S144_q75`, `UHM1240.20662_S137_q75`, `UHM1246.20911_S156_q75`, `UHM1247.20912_S168_q75`, `UHM1248.20671_S150_q75`, `UHM1256.20666_S185_q75`, `UHM1260.20692_S117_q75`, `UHM1270.20673_S174_q75`, `UHM1271.20493_S128_q75`, `UHM1272.20494_S140_q75`, `UHM1274.20650_S183_q75`, `UHM1275.20693_S129_q75`, `UHM1282.20695_S153_q75`, `UHM1287.20639_S146_q75`, `UHM1291.20512_S166_q75`, `UHM1296.20646_S135_q75`, `UHM1319.20657_S172_q75`, `UHM1324.20509_S130_q75`, `UHM1327.20641_S170_q75`, `UHM1328.20668_S114_q75`, `UHM1334.20513_S178_q75`, `UHM1338.20495_S152_q75`, `UHM1341.20698_S189_q75`, `UHM1356.20637_S122_q75`, `UHM1380.20676_S115_q75`, `UHM1383.20690_S188_q75`, `UHM1385.20659_S101_q75`, `UHM1399.20852_S113_q75`, `UHM1400.20853_S125_q75`, `UHM1401.20854_S137_q75`, `UHM1402.20855_S149_q75`, `UHM1403.20856_S161_q75`, `UHM1405.20857_S173_q75`, `UHM1406.20858_S185_q75`, `UHM1414.20859_S102_q75`, `UHM1419.20860_S114_q75`, `UHM1427.20485_S127_q75`, `UHM1428.20486_S139_q75`, `UHM1429.20487_S151_q75`, `UHM1430.20488_S163_q75`, `UHM1432.20489_S175_q75`, `UHM1435.20484_S115_q75`, `UHM162.20656_S160_q75`, `UHM198.20681_S175_q75`, `UHM200.20670_S138_q75`, `UHM203.20685_S128_q75`, `UHM204.20505_S177_q75`, `UHM206.20506_S189_q75`, `UHM207.20689_S176_q75`, `UHM208.20507_S106_q75`, `UHM211.20502_S141_q75`, `UHM215.20504_S165_q75`, `UHM216.20525_S132_q75`, `UHM219.20526_S144_q75`, `UHM236.20527_S156_q75`, `UHM238.20503_S153_q75`, `UHM245.20634_S181_q75`, `UHM252.20654_S136_q75`, `UHM267.20496_S164_q75`, `UHM274.20677_S127_q75`, `UHM276.20682_S187_q75`, `UHM280.20497_S176_q75`, `UHM286.20521_S179_q75`, `UHM289.20522_S191_q75`, `UHM294.20523_S108_q75`, `UHM298.20696_S165_q75`, `UHM325.20644_S111_q75`, `UHM337.20508_S118_q75`, `UHM354.20631_S145_q75`, `UHM356.20511_S154_q75`, `UHM369.20869_S127_q75`, `UHM370.20870_S139_q75`, `UHM372.20871_S151_q75`, `UHM373.20872_S163_q75`, `UHM374.20873_S175_q75`, `UHM375.20874_S187_q75`, `UHM377.20875_S104_q75`, `UHM382.20876_S116_q75`, `UHM386.20877_S128_q75`, `UHM387.20878_S140_q75`, `UHM414.20679_S151_q75`, `UHM418.20861_S126_q75`, `UHM422.20862_S138_q75`, `UHM425.20863_S150_q75`, `UHM426.20630_S133_q75`, `UHM428.20640_S158_q75`, `UHM429.20655_S148_q75`, `UHM435.20643_S99_q75`, `UHM437.20864_S162_q75`, `UHM439.20660_S113_q75`, `UHM443.20524_S120_q75`, `UHM445.20665_S173_q75`, `UHM447.20879_S152_q75`, `UHM448.20865_S174_q75`, `UHM452.20880_S164_q75`, `UHM454.20866_S186_q75`, `UHM455.20881_S176_q75`, `UHM458.20882_S188_q75`, `UHM459.20883_S105_q75`, `UHM461.20867_S103_q75`, `UHM467.20868_S115_q75`, `UHM470.20629_S121_q75`, `UHM476.20510_S142_q75`, `UHM478.20645_S123_q75`, `UHM479.20647_S147_q75`, `UHM481.20499_S105_q75`, `UHM482.20686_S140_q75`, `UHM483.20699_S106_q75`, `UHM519.20678_S139_q75`, `UHM520.20669_S126_q75`, `UHM836.20481_S174_q75`, `UHM837.20482_S186_q75`, `UHM838.20483_S103_q75`, `UHM891.20480_S162_q75`, `UHM892.20628_S109_q75`, `UHM893.20691_S105_q75`, `UHM894.20636_S110_q75`, `UHM895.20632_S157_q75`, `UHM896.20697_S177_q75`, `UHM897.20687_S152_q75`, `UHM898.20490_S187_q75`, `UHM899.20684_S116_q75`, `UHM900.20491_S104_q75`, `UHM901.20638_S134_q75`, `UHM902.20680_S163_q75`, `UHM903.20683_S104_q75`, `UHM904.20663_S149_q75`, `UHM905.20694_S141_q75`, `UHM906.20661_S125_q75`, `UHM907.20688_S164_q75`, `UHM908.20492_S116_q75`, `UHM909.20653_S124_q75`, `UHM910.20658_S184_q75`, `UHM965.20633_S169_q75`, `UHM966.20839_S147_q75`, `UHM967.20840_S159_q75`, `UHM968.20667_S102_q75`, `UHM969.20841_S171_q75`, `UHM971.20842_S183_q75`, `UHM973.20674_S186_q75`, `UHM974.20528_S168_q75`, `UHM975.20843_S100_q75`, `UHM977.20844_S112_q75`, `UHM978.20845_S124_q75`, `UHM979.20846_S136_q75`, `UHM980.20827_S98_q75`, `UHM981.20635_S98_q75`, `UHM982.20836_S111_q75`, `UHM983.20652_S112_q75`, `UHM984.20847_S148_q75`, `UHM985.20848_S160_q75`, `UHM988.20849_S172_q75`, `UHM989.20850_S184_q75`, `UHM991.20851_S101_q75`, `UHM993.20837_S123_q75`, `UHM996.20706_S190_q75`, `UHM997.20649_S171_q75`, `UHM998.20714_S191_q75`, `UHM999.20713_S179_q75` 
#> header has 1 row(s) 
#> body has 1 row(s) 
#> original dataset sample: 
#>   spiked.blank.20529_S180_mean spiked.blank.20913_S180_mean
#> 1                   0.06339572                   0.06533468
#>   Std2uL.20721_S180_mean StdSwab1uL.20720_S168_mean STP1719.20518_S143_mean
#> 1              0.0629742                 0.03641882               0.1014163
#>   STP213.20519_S155_mean STP268.20520_S167_mean STP544.20515_S107_mean
#> 1              0.5571573               1.997049             0.07368066
#>   STP570.20516_S119_mean STP579.20517_S131_mean STP614.20514_S190_mean
#> 1              0.1730737             0.07730568               1.322627
#>   UHM1000.20700_S118_mean UHM1001.20705_S178_mean UHM1007.20718_S144_mean
#> 1                5.905665               0.2322543                2.600826
#>   UHM1009.20710_S143_mean UHM1010.20717_S132_mean UHM1011.20702_S142_mean
#> 1               0.5016861               0.1240094               0.1852976
#>   UHM1024.20716_S120_mean UHM1026.20703_S154_mean UHM1028.20709_S131_mean
#> 1              0.06457596                0.174844               0.6007419
#>   UHM1032.20701_S130_mean UHM1033.20715_S108_mean UHM1034.20712_S167_mean
#> 1               0.1490474              0.04265722                3.595178
#>   UHM1035.20707_S107_mean UHM1036.20708_S119_mean UHM1052.20711_S155_mean
#> 1               0.1572248                0.716321               0.2679987
#>   UHM1060.20819_S97_mean UHM1065.20820_S109_mean UHM1068.20828_S110_mean
#> 1              0.1106896               0.1276345               0.4419154
#>   UHM1069.20838_S135_mean UHM1070.20821_S121_mean UHM1071.20829_S122_mean
#> 1               0.4006913               0.1433148               0.6381723
#>   UHM1072.20830_S134_mean UHM1073.20831_S146_mean UHM1075.20822_S133_mean
#> 1               0.1969314                5.171978               0.6046198
#>   UHM1077.20832_S158_mean UHM1078.20823_S145_mean UHM1080.20833_S170_mean
#> 1               0.1395212                1.251644               0.9415781
#>   UHM1081.20824_S157_mean UHM1088.20834_S182_mean UHM1090.20835_S99_mean
#> 1                2.869668               0.7102512              0.2729725
#>   UHM1093.20825_S169_mean UHM1095.20826_S181_mean UHM1097.20719_S156_mean
#> 1               0.7830889               0.3325746              0.07030855
#>   UHM1099.20704_S166_mean UHM1100.20884_S117_mean UHM1102.20885_S129_mean
#> 1               0.1063902                  20.413                6.947311
#>   UHM1104.20886_S141_mean UHM1105.20887_S153_mean UHM1109.20627_S97_mean
#> 1                1.515512                2.984404               0.885011
#>   UHM1110.20664_S161_mean UHM1113.20888_S165_mean UHM1114.20889_S177_mean
#> 1                125.6893                 1.42809                1.227449
#>   UHM1115.20890_S189_mean UHM1117.20891_S106_mean UHM1118.20892_S118_mean
#> 1                13.43222                14.44099                2.121143
#>   UHM1120.20893_S130_mean UHM1124.20894_S142_mean UHM1126.20895_S154_mean
#> 1                3.313691                2.836031                 4.92438
#>   UHM1128.20896_S166_mean UHM1140.20651_S100_mean UHM1145.20897_S178_mean
#> 1                 51.5032                3.412831                5.742876
#>   UHM1163.20501_S129_mean UHM1164.20498_S188_mean UHM1169.20648_S159_mean
#> 1                1.240516               0.2037599               0.1800708
#>   UHM1171.20675_S103_mean UHM1176.20500_S117_mean UHM1177.20642_S182_mean
#> 1                27.05201                2.594503               0.1380037
#>   UHM1182.20672_S162_mean UHM1210.20898_S190_mean UHM1212.20899_S107_mean
#> 1                3.562131                5.773731                1.677036
#>   UHM1217.20900_S119_mean UHM1218.20901_S131_mean UHM1219.20902_S143_mean
#> 1                1.293795                2.708649                16.98879
#>   UHM1220.20903_S155_mean UHM1221.20904_S167_mean UHM1222.20905_S179_mean
#> 1                6.901956                2.450683               0.2677457
#>   UHM1223.20906_S191_mean UHM1225.20907_S108_mean UHM1227.20908_S120_mean
#> 1                7.764879                4.842438                2.391249
#>   UHM1228.20909_S132_mean UHM1237.20910_S144_mean UHM1240.20662_S137_mean
#> 1                9.221632                2.196678               0.2035913
#>   UHM1246.20911_S156_mean UHM1247.20912_S168_mean UHM1248.20671_S150_mean
#> 1               0.9382903                2.550919              0.09256449
#>   UHM1256.20666_S185_mean UHM1260.20692_S117_mean UHM1270.20673_S174_mean
#> 1               0.4490811                1.792362               0.2389985
#>   UHM1271.20493_S128_mean UHM1272.20494_S140_mean UHM1274.20650_S183_mean
#> 1                1.124009               0.4468892               0.6890912
#>   UHM1275.20693_S129_mean UHM1282.20695_S153_mean UHM1287.20639_S146_mean
#> 1                3.120637                3.156045               0.6135559
#>   UHM1291.20512_S166_mean UHM1296.20646_S135_mean UHM1319.20657_S172_mean
#> 1                1.185972                16.92497                4.285702
#>   UHM1324.20509_S130_mean UHM1327.20641_S170_mean UHM1328.20668_S114_mean
#> 1               0.5759568                42.77854              0.06710504
#>   UHM1334.20513_S178_mean UHM1338.20495_S152_mean UHM1341.20698_S189_mean
#> 1                 1.64812               0.2091553              0.06769516
#>   UHM1356.20637_S122_mean UHM1380.20676_S115_mean UHM1383.20690_S188_mean
#> 1              0.09113134                7.024954                3.608245
#>   UHM1385.20659_S101_mean UHM1399.20852_S113_mean UHM1400.20853_S125_mean
#> 1               0.1153263               0.3829034               0.1930534
#>   UHM1401.20854_S137_mean UHM1402.20855_S149_mean UHM1403.20856_S161_mean
#> 1               0.2014837             0.001686056               0.3522172
#>   UHM1405.20857_S173_mean UHM1406.20858_S185_mean UHM1414.20859_S102_mean
#> 1                1.180408               0.1734952                3.018041
#>   UHM1419.20860_S114_mean UHM1427.20485_S127_mean UHM1428.20486_S139_mean
#> 1               0.5883494               0.1264542                44.70722
#>   UHM1429.20487_S151_mean UHM1430.20488_S163_mean UHM1432.20489_S175_mean
#> 1                19.70637                14.34463                12.30568
#>   UHM1435.20484_S115_mean UHM162.20656_S160_mean UHM198.20681_S175_mean
#> 1                3.182937               1.424718               1.483982
#>   UHM200.20670_S138_mean UHM203.20685_S128_mean UHM204.20505_S177_mean
#> 1               3.086326               1.723993              0.3915023
#>   UHM206.20506_S189_mean UHM207.20689_S176_mean UHM208.20507_S106_mean
#> 1              0.1339572               3.116759               1.065588
#>   UHM211.20502_S141_mean UHM215.20504_S165_mean UHM216.20525_S132_mean
#> 1              0.1450851              0.2771877              0.1197943
#>   UHM219.20526_S144_mean UHM236.20527_S156_mean UHM238.20503_S153_mean
#> 1              0.0629742             0.09720115              0.2713708
#>   UHM245.20634_S181_mean UHM252.20654_S136_mean UHM267.20496_S164_mean
#> 1              0.4771539               2.573681               1.194908
#>   UHM274.20677_S127_mean UHM276.20682_S187_mean UHM280.20497_S176_mean
#> 1              0.8753161              0.6560445               0.379447
#>   UHM286.20521_S179_mean UHM289.20522_S191_mean UHM294.20523_S108_mean
#> 1               1.536925              0.4086157              0.3598044
#>   UHM298.20696_S165_mean UHM325.20644_S111_mean UHM337.20508_S118_mean
#> 1               14.45212               3.652672              0.4859214
#>   UHM354.20631_S145_mean UHM356.20511_S154_mean UHM369.20869_S127_mean
#> 1              0.1056314              0.3179902                      0
#>   UHM370.20870_S139_mean UHM372.20871_S151_mean UHM373.20872_S163_mean
#> 1               2.854072               1.383241               3.346147
#>   UHM374.20873_S175_mean UHM375.20874_S187_mean UHM377.20875_S104_mean
#> 1               14.54401               1.130585               2.368403
#>   UHM382.20876_S116_mean UHM386.20877_S128_mean UHM387.20878_S140_mean
#> 1               18.54257               6.394369               7.392092
#>   UHM414.20679_S151_mean UHM418.20861_S126_mean UHM422.20862_S138_mean
#> 1               13.20123               5.422357              0.2735626
#>   UHM425.20863_S150_mean UHM426.20630_S133_mean UHM428.20640_S158_mean
#> 1               2.641797              0.1076547                12.1294
#>   UHM429.20655_S148_mean UHM435.20643_S99_mean UHM437.20864_S162_mean
#> 1              0.1997977             0.5454392               3.985669
#>   UHM439.20660_S113_mean UHM443.20524_S120_mean UHM445.20665_S173_mean
#> 1               3.094588               1.302647               134.5565
#>   UHM447.20879_S152_mean UHM448.20865_S174_mean UHM452.20880_S164_mean
#> 1               26.25131              0.6271286               1.370258
#>   UHM454.20866_S186_mean UHM455.20881_S176_mean UHM458.20882_S188_mean
#> 1               2.675181                3.07191               17.72762
#>   UHM459.20883_S105_mean UHM461.20867_S103_mean UHM467.20868_S115_mean
#> 1               11.12789              0.5046367               4.456753
#>   UHM470.20629_S121_mean UHM476.20510_S142_mean UHM478.20645_S123_mean
#> 1               1.755016              0.8051762              0.1305851
#>   UHM479.20647_S147_mean UHM481.20499_S105_mean UHM482.20686_S140_mean
#> 1             0.06702074              0.1553701             0.08303827
#>   UHM483.20699_S106_mean UHM519.20678_S139_mean UHM520.20669_S126_mean
#> 1               15.94369               1.503372                52.4871
#>   UHM836.20481_S174_mean UHM837.20482_S186_mean UHM838.20483_S103_mean
#> 1              0.2128646             0.06870679               34.07747
#>   UHM891.20480_S162_mean UHM892.20628_S109_mean UHM893.20691_S105_mean
#> 1               16.27457                6.28115               89.09509
#>   UHM894.20636_S110_mean UHM895.20632_S157_mean UHM896.20697_S177_mean
#> 1               34.33949               3.915191               47.02816
#>   UHM897.20687_S152_mean UHM898.20490_S187_mean UHM899.20684_S116_mean
#> 1               16.51222               7.331141               2.261929
#>   UHM900.20491_S104_mean UHM901.20638_S134_mean UHM902.20680_S163_mean
#> 1               35.91544               5.474962               1.348676
#>   UHM903.20683_S104_mean UHM904.20663_S149_mean UHM905.20694_S141_mean
#> 1              0.5947564               7.609425               15.95802
#>   UHM906.20661_S125_mean UHM907.20688_S164_mean UHM908.20492_S116_mean
#> 1               62.22728              0.4893778               1.041393
#>   UHM909.20653_S124_mean UHM910.20658_S184_mean UHM965.20633_S169_mean
#> 1              0.1546957               2.278284               0.472433
#>   UHM966.20839_S147_mean UHM967.20840_S159_mean UHM968.20667_S102_mean
#> 1              0.1901028               3.151745             0.06339572
#>   UHM969.20841_S171_mean UHM971.20842_S183_mean UHM973.20674_S186_mean
#> 1               13.09695               4.165992              0.3769179
#>   UHM974.20528_S168_mean UHM975.20843_S100_mean UHM977.20844_S112_mean
#> 1             0.06373293               3.938459               1.206879
#>   UHM978.20845_S124_mean UHM979.20846_S136_mean UHM980.20827_S98_mean
#> 1               21.44613               2.467628             0.1012477
#>   UHM981.20635_S98_mean UHM982.20836_S111_mean UHM983.20652_S112_mean
#> 1             0.3614905               10.26353               1.448407
#>   UHM984.20847_S148_mean UHM985.20848_S160_mean UHM988.20849_S172_mean
#> 1               17.59105               1.269095              0.2959029
#>   UHM989.20850_S184_mean UHM991.20851_S101_mean UHM993.20837_S123_mean
#> 1               8.744141              0.3743045               5.685298
#>   UHM996.20706_S190_mean UHM997.20649_S171_mean UHM998.20714_S191_mean
#> 1             0.05791603              0.6737481             0.04375316
#>   UHM999.20713_S179_mean spiked.blank.20529_S180_sd spiked.blank.20913_S180_sd
#> 1               1.663126                   6.730747                   6.731323
#>   Std2uL.20721_S180_sd StdSwab1uL.20720_S168_sd STP1719.20518_S143_sd
#> 1             6.730294                 3.362462              7.032828
#>   STP213.20519_S155_sd STP268.20520_S167_sd STP544.20515_S107_sd
#> 1             21.25952             111.2298             6.826067
#>   STP570.20516_S119_sd STP579.20517_S131_sd STP614.20514_S190_sd
#> 1             12.37194             6.779944             68.62498
#>   UHM1000.20700_S118_sd UHM1001.20705_S178_sd UHM1007.20718_S144_sd
#> 1              463.9677              5.165837              139.2696
#>   UHM1009.20710_S143_sd UHM1010.20717_S132_sd UHM1011.20702_S142_sd
#> 1               11.3496               4.27458              8.031022
#>   UHM1024.20716_S120_sd UHM1026.20703_S154_sd UHM1028.20709_S131_sd
#> 1              3.724952              7.034125              32.60988
#>   UHM1032.20701_S130_sd UHM1033.20715_S108_sd UHM1034.20712_S167_sd
#> 1              6.534626              3.395946              313.9442
#>   UHM1035.20707_S107_sd UHM1036.20708_S119_sd UHM1052.20711_S155_sd
#> 1              4.899872              44.85895              13.91078
#>   UHM1060.20819_S97_sd UHM1065.20820_S109_sd UHM1068.20828_S110_sd
#> 1             8.119895              7.622605              17.25905
#>   UHM1069.20838_S135_sd UHM1070.20821_S121_sd UHM1071.20829_S122_sd
#> 1              14.51376              7.104436              29.52187
#>   UHM1072.20830_S134_sd UHM1073.20831_S146_sd UHM1075.20822_S133_sd
#> 1              8.023162              310.8534              29.14569
#>   UHM1077.20832_S158_sd UHM1078.20823_S145_sd UHM1080.20833_S170_sd
#> 1              7.484093              49.55108               58.6769
#>   UHM1081.20824_S157_sd UHM1088.20834_S182_sd UHM1090.20835_S99_sd
#> 1              300.8063              42.45345             10.55826
#>   UHM1093.20825_S169_sd UHM1095.20826_S181_sd UHM1097.20719_S156_sd
#> 1              42.59723               17.3579              3.437144
#>   UHM1099.20704_S166_sd UHM1100.20884_S117_sd UHM1102.20885_S129_sd
#> 1              4.582178              1654.794              535.1107
#>   UHM1104.20886_S141_sd UHM1105.20887_S153_sd UHM1109.20627_S97_sd
#> 1              49.32604              102.4097             87.97013
#>   UHM1110.20664_S161_sd UHM1113.20888_S165_sd UHM1114.20889_S177_sd
#> 1              9193.749              71.44446              46.36277
#>   UHM1115.20890_S189_sd UHM1117.20891_S106_sd UHM1118.20892_S118_sd
#> 1              1227.489              930.9339              94.68336
#>   UHM1120.20893_S130_sd UHM1124.20894_S142_sd UHM1126.20895_S154_sd
#> 1              265.6671              105.2451              314.0743
#>   UHM1128.20896_S166_sd UHM1140.20651_S100_sd UHM1145.20897_S178_sd
#> 1              5350.697              247.4484               537.039
#>   UHM1163.20501_S129_sd UHM1164.20498_S188_sd UHM1169.20648_S159_sd
#> 1              51.87968              12.45238              12.86593
#>   UHM1171.20675_S103_sd UHM1176.20500_S117_sd UHM1177.20642_S182_sd
#> 1              1363.682              83.02637              7.388534
#>   UHM1182.20672_S162_sd UHM1210.20898_S190_sd UHM1212.20899_S107_sd
#> 1              297.1795               439.591              107.1386
#>   UHM1217.20900_S119_sd UHM1218.20901_S131_sd UHM1219.20902_S143_sd
#> 1              95.23007               145.438              1209.727
#>   UHM1220.20903_S155_sd UHM1221.20904_S167_sd UHM1222.20905_S179_sd
#> 1              575.6849              192.1637              13.76585
#>   UHM1223.20906_S191_sd UHM1225.20907_S108_sd UHM1227.20908_S120_sd
#> 1              606.4634              420.8719              173.9378
#>   UHM1228.20909_S132_sd UHM1237.20910_S144_sd UHM1240.20662_S137_sd
#> 1              684.4317              160.8829              10.96837
#>   UHM1246.20911_S156_sd UHM1247.20912_S168_sd UHM1248.20671_S150_sd
#> 1              69.08158              216.7753              6.926635
#>   UHM1256.20666_S185_sd UHM1260.20692_S117_sd UHM1270.20673_S174_sd
#> 1              24.68824              141.6744              11.79198
#>   UHM1271.20493_S128_sd UHM1272.20494_S140_sd UHM1274.20650_S183_sd
#> 1              84.53201              27.15906              45.26783
#>   UHM1275.20693_S129_sd UHM1282.20695_S153_sd UHM1287.20639_S146_sd
#> 1              238.9181              284.1955              38.11322
#>   UHM1291.20512_S166_sd UHM1296.20646_S135_sd UHM1319.20657_S172_sd
#> 1              96.53267               1123.16              313.5749
#>   UHM1324.20509_S130_sd UHM1327.20641_S170_sd UHM1328.20668_S114_sd
#> 1              41.42332              4076.875              6.732564
#>   UHM1334.20513_S178_sd UHM1338.20495_S152_sd UHM1341.20698_S189_sd
#> 1              163.4727              9.969518               6.73274
#>   UHM1356.20637_S122_sd UHM1380.20676_S115_sd UHM1383.20690_S188_sd
#> 1              6.809267               462.788              274.2218
#>   UHM1385.20659_S101_sd UHM1399.20852_S113_sd UHM1400.20853_S125_sd
#> 1              7.224829              17.55122              10.89397
#>   UHM1401.20854_S137_sd UHM1402.20855_S149_sd UHM1403.20856_S161_sd
#> 1               14.2473             0.1836331               18.7312
#>   UHM1405.20857_S173_sd UHM1406.20858_S185_sd UHM1414.20859_S102_sd
#> 1              96.03448              12.17151              254.4422
#>   UHM1419.20860_S114_sd UHM1427.20485_S127_sd UHM1428.20486_S139_sd
#> 1              28.97593              8.515484              2237.697
#>   UHM1429.20487_S151_sd UHM1430.20488_S163_sd UHM1432.20489_S175_sd
#> 1               1572.78              641.2063              661.0352
#>   UHM1435.20484_S115_sd UHM162.20656_S160_sd UHM198.20681_S175_sd
#> 1              202.4216             85.05189             112.2029
#>   UHM200.20670_S138_sd UHM203.20685_S128_sd UHM204.20505_S177_sd
#> 1             191.5178             114.2623             28.92776
#>   UHM206.20506_S189_sd UHM207.20689_S176_sd UHM208.20507_S106_sd
#> 1             7.426781             153.3912               72.719
#>   UHM211.20502_S141_sd UHM215.20504_S165_sd UHM216.20525_S132_sd
#> 1             7.095001             11.29853             6.856084
#>   UHM219.20526_S144_sd UHM236.20527_S156_sd UHM238.20503_S153_sd
#> 1             6.730369             6.918625              14.4799
#>   UHM245.20634_S181_sd UHM252.20654_S136_sd UHM267.20496_S164_sd
#> 1             28.40031             158.3913              51.0037
#>   UHM274.20677_S127_sd UHM276.20682_S187_sd UHM280.20497_S176_sd
#> 1             61.12939             25.48116             18.06162
#>   UHM286.20521_S179_sd UHM289.20522_S191_sd UHM294.20523_S108_sd
#> 1             151.5534             24.91676             16.09427
#>   UHM298.20696_S165_sd UHM325.20644_S111_sd UHM337.20508_S118_sd
#> 1             1221.994             275.9052             25.51102
#>   UHM354.20631_S145_sd UHM356.20511_S154_sd UHM369.20869_S127_sd
#> 1             7.416083             20.85372                    0
#>   UHM370.20870_S139_sd UHM372.20871_S151_sd UHM373.20872_S163_sd
#> 1             186.2664             90.27269             317.6432
#>   UHM374.20873_S175_sd UHM375.20874_S187_sd UHM377.20875_S104_sd
#> 1             1004.419             84.91197             164.8947
#>   UHM382.20876_S116_sd UHM386.20877_S128_sd UHM387.20878_S140_sd
#> 1             1426.459             615.1558             461.3813
#>   UHM414.20679_S151_sd UHM418.20861_S126_sd UHM422.20862_S138_sd
#> 1              992.004              330.142             14.62342
#>   UHM425.20863_S150_sd UHM426.20630_S133_sd UHM428.20640_S158_sd
#> 1             159.3581             7.249789             800.4038
#>   UHM429.20655_S148_sd UHM435.20643_S99_sd UHM437.20864_S162_sd
#> 1             11.42767            30.93387             297.9874
#>   UHM439.20660_S113_sd UHM443.20524_S120_sd UHM445.20665_S173_sd
#> 1             254.8257             61.08636             4796.936
#>   UHM447.20879_S152_sd UHM448.20865_S174_sd UHM452.20880_S164_sd
#> 1              1986.24             52.10842             104.6164
#>   UHM454.20866_S186_sd UHM455.20881_S176_sd UHM458.20882_S188_sd
#> 1             146.6321             207.7634             1344.188
#>   UHM459.20883_S105_sd UHM461.20867_S103_sd UHM467.20868_S115_sd
#> 1             694.6036             39.92127             371.3097
#>   UHM470.20629_S121_sd UHM476.20510_S142_sd UHM478.20645_S123_sd
#> 1             121.7511             52.05833             9.908419
#>   UHM479.20647_S147_sd UHM481.20499_S105_sd UHM482.20686_S140_sd
#> 1             6.732509              7.56535              6.77745
#>   UHM483.20699_S106_sd UHM519.20678_S139_sd UHM520.20669_S126_sd
#> 1             981.3737             55.69854             5470.864
#>   UHM836.20481_S174_sd UHM837.20482_S186_sd UHM838.20483_S103_sd
#> 1             12.22542              6.74099             2148.269
#>   UHM891.20480_S162_sd UHM892.20628_S109_sd UHM893.20691_S105_sd
#> 1             1681.282             359.5898             7022.485
#>   UHM894.20636_S110_sd UHM895.20632_S157_sd UHM896.20697_S177_sd
#> 1             2681.619             170.8953             2325.908
#>   UHM897.20687_S152_sd UHM898.20490_S187_sd UHM899.20684_S116_sd
#> 1             1000.358             537.9758             141.0076
#>   UHM900.20491_S104_sd UHM901.20638_S134_sd UHM902.20680_S163_sd
#> 1             2566.026             291.9347             98.32261
#>   UHM903.20683_S104_sd UHM904.20663_S149_sd UHM905.20694_S141_sd
#> 1             50.92155             491.9152             1071.835
#>   UHM906.20661_S125_sd UHM907.20688_S164_sd UHM908.20492_S116_sd
#> 1             3285.998             28.04827             72.51777
#>   UHM909.20653_S124_sd UHM910.20658_S184_sd UHM965.20633_S169_sd
#> 1             12.11342             192.7355             12.22066
#>   UHM966.20839_S147_sd UHM967.20840_S159_sd UHM968.20667_S102_sd
#> 1              8.88396             176.9531             6.730496
#>   UHM969.20841_S171_sd UHM971.20842_S183_sd UHM973.20674_S186_sd
#> 1             1143.512             357.4598             24.11147
#>   UHM974.20528_S168_sd UHM975.20843_S100_sd UHM977.20844_S112_sd
#> 1              6.73038             190.6032             55.62855
#>   UHM978.20845_S124_sd UHM979.20846_S136_sd UHM980.20827_S98_sd
#> 1             2232.539             139.6204            6.841438
#>   UHM981.20635_S98_sd UHM982.20836_S111_sd UHM983.20652_S112_sd
#> 1            30.50647             616.0094             88.60566
#>   UHM984.20847_S148_sd UHM985.20848_S160_sd UHM988.20849_S172_sd
#> 1             1215.089              85.5511             14.13106
#>   UHM989.20850_S184_sd UHM991.20851_S101_sd UHM993.20837_S123_sd
#> 1             765.6343             19.75852             240.3693
#>   UHM996.20706_S190_sd UHM997.20649_S171_sd UHM998.20714_S191_sd
#> 1             3.404387             22.90439             3.374427
#>   UHM999.20713_S179_sd spiked.blank.20529_S180_se spiked.blank.20913_S180_se
#> 1             43.45621                 0.06179941                  0.0618047
#>   Std2uL.20721_S180_se StdSwab1uL.20720_S168_se STP1719.20518_S143_se
#> 1           0.06179524               0.03087297            0.06457301
#>   STP213.20519_S155_se STP268.20520_S167_se STP544.20515_S107_se
#> 1            0.1951976             1.021274            0.0626746
#>   STP570.20516_S119_se STP579.20517_S131_se STP614.20514_S190_se
#> 1            0.1135949           0.06225111             0.630091
#>   UHM1000.20700_S118_se UHM1001.20705_S178_se UHM1007.20718_S144_se
#> 1              4.259992            0.04743094              1.278725
#>   UHM1009.20710_S143_se UHM1010.20717_S132_se UHM1011.20702_S142_se
#> 1             0.1042081            0.03924772            0.07373809
#>   UHM1024.20716_S120_se UHM1026.20703_S154_se UHM1028.20709_S131_se
#> 1            0.03420123            0.06458492             0.2994127
#>   UHM1032.20701_S130_se UHM1033.20715_S108_se UHM1034.20712_S167_se
#> 1            0.05999869            0.03118041              2.882528
#>   UHM1035.20707_S107_se UHM1036.20708_S119_se UHM1052.20711_S155_se
#> 1            0.04498894             0.4118795              0.127724
#>   UHM1060.20819_S97_se UHM1065.20820_S109_se UHM1068.20828_S110_se
#> 1           0.07455409            0.06998814             0.1584666
#>   UHM1069.20838_S135_se UHM1070.20821_S121_se UHM1071.20829_S122_se
#> 1             0.1332604            0.06523049             0.2710597
#>   UHM1072.20830_S134_se UHM1073.20831_S146_se UHM1075.20822_S133_se
#> 1            0.07366592              2.854149             0.2676057
#>   UHM1077.20832_S158_se UHM1078.20823_S145_se UHM1080.20833_S170_se
#> 1            0.06871637              0.454961             0.5387511
#>   UHM1081.20824_S157_se UHM1088.20834_S182_se UHM1090.20835_S99_se
#> 1              2.761901              0.389793           0.09694229
#>   UHM1093.20825_S169_se UHM1095.20826_S181_se UHM1097.20719_S156_se
#> 1             0.3911131             0.1593743            0.03155868
#>   UHM1099.20704_S166_se UHM1100.20884_S117_se UHM1102.20885_S129_se
#> 1            0.04207198              15.19375              4.913202
#>   UHM1104.20886_S141_se UHM1105.20887_S153_se UHM1109.20627_S97_se
#> 1             0.4528948              0.940291            0.8077115
#>   UHM1110.20664_S161_se UHM1113.20888_S165_se UHM1114.20889_S177_se
#> 1              84.41385             0.6559785              0.425687
#>   UHM1115.20890_S189_se UHM1117.20891_S106_se UHM1118.20892_S118_se
#> 1              11.27038              8.547515             0.8693501
#>   UHM1120.20893_S130_se UHM1124.20894_S142_se UHM1126.20895_S154_se
#> 1              2.439264             0.9663243              2.883722
#>   UHM1128.20896_S166_se UHM1140.20651_S100_se UHM1145.20897_S178_se
#> 1              49.12826              2.271986              4.930907
#>   UHM1163.20501_S129_se UHM1164.20498_S188_se UHM1169.20648_S159_se
#> 1             0.4763414             0.1143334             0.1181305
#>   UHM1171.20675_S103_se UHM1176.20500_S117_se UHM1177.20642_S182_se
#> 1              12.52086             0.7623196            0.06783898
#>   UHM1182.20672_S162_se UHM1210.20898_S190_se UHM1212.20899_S107_se
#> 1                2.7286              4.036174             0.9837099
#>   UHM1217.20900_S119_se UHM1218.20901_S131_se UHM1219.20902_S143_se
#> 1             0.8743698              1.335362              11.10729
#>   UHM1220.20903_S155_se UHM1221.20904_S167_se UHM1222.20905_S179_se
#> 1              5.285741              1.764381             0.1263933
#>   UHM1223.20906_S191_se UHM1225.20907_S108_se UHM1227.20908_S120_se
#> 1              5.568339              3.864301              1.597037
#>   UHM1228.20909_S132_se UHM1237.20910_S144_se UHM1240.20662_S137_se
#> 1              6.284217              1.477172             0.1007078
#>   UHM1246.20911_S156_se UHM1247.20912_S168_se UHM1248.20671_S150_se
#> 1             0.6342834              1.990356            0.06359799
#>   UHM1256.20666_S185_se UHM1260.20692_S117_se UHM1270.20673_S174_se
#> 1             0.2266789              1.300806             0.1082699
#>   UHM1271.20493_S128_se UHM1272.20494_S140_se UHM1274.20650_S183_se
#> 1             0.7761439             0.2493652             0.4156337
#>   UHM1275.20693_S129_se UHM1282.20695_S153_se UHM1287.20639_S146_se
#> 1              2.193664              2.609385             0.3499425
#>   UHM1291.20512_S166_se UHM1296.20646_S135_se UHM1319.20657_S172_se
#> 1             0.8863299              10.31247              2.879137
#>   UHM1324.20509_S130_se UHM1327.20641_S170_se UHM1328.20668_S114_se
#> 1             0.3803347              37.43247            0.06181609
#>   UHM1334.20513_S178_se UHM1338.20495_S152_se UHM1341.20698_S189_se
#> 1               1.50095            0.09153669            0.06181771
#>   UHM1356.20637_S122_se UHM1380.20676_S115_se UHM1383.20690_S188_se
#> 1            0.06252035               4.24916               2.51781
#>   UHM1385.20659_S101_se UHM1399.20852_S113_se UHM1400.20853_S125_se
#> 1            0.06633589             0.1611493             0.1000247
#>   UHM1401.20854_S137_se UHM1402.20855_S149_se UHM1403.20856_S161_se
#> 1             0.1308138           0.001686056             0.1719835
#>   UHM1405.20857_S173_se UHM1406.20858_S185_se UHM1414.20859_S102_se
#> 1             0.8817556             0.1117547              2.336201
#>   UHM1419.20860_S114_se UHM1427.20485_S127_se UHM1428.20486_S139_se
#> 1              0.266047            0.07818625              20.54577
#>   UHM1429.20487_S151_se UHM1430.20488_S163_se UHM1432.20489_S175_se
#> 1              14.44072              5.887336              6.069398
#>   UHM1435.20484_S115_se UHM162.20656_S160_se UHM198.20681_S175_se
#> 1              1.858566            0.7809172             1.030209
#>   UHM200.20670_S138_se UHM203.20685_S128_se UHM204.20505_S177_se
#> 1              1.75845             1.049117            0.2656047
#>   UHM206.20506_S189_se UHM207.20689_S176_se UHM208.20507_S106_se
#> 1           0.06819016             1.408385            0.6676809
#>   UHM211.20502_S141_se UHM215.20504_S165_se UHM216.20525_S132_se
#> 1           0.06514386            0.1037392            0.0629502
#>   UHM219.20526_S144_se UHM236.20527_S156_se UHM238.20503_S153_se
#> 1           0.06179593           0.06352444            0.1329495
#>   UHM245.20634_S181_se UHM252.20654_S136_se UHM267.20496_S164_se
#> 1            0.2607619             1.454295            0.4682985
#>   UHM274.20677_S127_se UHM276.20682_S187_se UHM280.20497_S176_se
#> 1             0.561269            0.2339592            0.1658356
#>   UHM286.20521_S179_se UHM289.20522_S191_se UHM294.20523_S108_se
#> 1             1.391511            0.2287771             0.147772
#>   UHM298.20696_S165_se UHM325.20644_S111_se UHM337.20508_S118_se
#> 1             11.21993             2.533267            0.2342334
#>   UHM354.20631_S145_se UHM356.20511_S154_se UHM369.20869_S127_se
#> 1           0.06809192            0.1914717                    0
#>   UHM370.20870_S139_se UHM372.20871_S151_se UHM373.20872_S163_se
#> 1             1.710234            0.8288528             2.916491
#>   UHM374.20873_S175_se UHM375.20874_S187_se UHM377.20875_S104_se
#> 1             9.222234            0.7796326             1.514006
#>   UHM382.20876_S116_se UHM386.20877_S128_se UHM387.20878_S140_se
#> 1             13.09726             5.648149             4.236245
#>   UHM414.20679_S151_se UHM418.20861_S126_se UHM422.20862_S138_se
#> 1              9.10824              3.03125            0.1342672
#>   UHM425.20863_S150_se UHM426.20630_S133_se UHM428.20640_S158_se
#> 1             1.463171           0.06656507             7.349032
#>   UHM429.20655_S148_se UHM435.20643_S99_se UHM437.20864_S162_se
#> 1             0.104925           0.2840242             2.736018
#>   UHM439.20660_S113_se UHM443.20524_S120_se UHM445.20665_S173_se
#> 1             2.339722             0.560874             44.04382
#>   UHM447.20879_S152_se UHM448.20865_S174_se UHM452.20880_S164_se
#> 1             18.23697            0.4784416            0.9605517
#>   UHM454.20866_S186_se UHM455.20881_S176_se UHM458.20882_S188_se
#> 1             1.346326             1.907612             12.34188
#>   UHM459.20883_S105_se UHM461.20867_S103_se UHM467.20868_S115_se
#> 1             6.377612            0.3665434             3.409238
#>   UHM470.20629_S121_se UHM476.20510_S142_se UHM478.20645_S123_se
#> 1             1.117876            0.4779817            0.0909757
#>   UHM479.20647_S147_se UHM481.20499_S105_se UHM482.20686_S140_se
#> 1           0.06181558           0.06946245           0.06222822
#>   UHM483.20699_S106_se UHM519.20678_S139_se UHM520.20669_S126_se
#> 1             9.010636            0.5114049             50.23159
#>   UHM836.20481_S174_se UHM837.20482_S186_se UHM838.20483_S103_se
#> 1            0.1122496           0.06189345             19.72467
#>   UHM891.20480_S162_se UHM892.20628_S109_se UHM893.20691_S105_se
#> 1             15.43696              3.30163             64.47805
#>   UHM894.20636_S110_se UHM895.20632_S157_se UHM896.20697_S177_se
#> 1              24.6217             1.569102             21.35569
#>   UHM897.20687_S152_se UHM898.20490_S187_se UHM899.20684_S116_se
#> 1             9.184941             4.939509             1.294683
#>   UHM900.20491_S104_se UHM901.20638_S134_se UHM902.20680_S163_se
#> 1             23.56037             2.680444            0.9027645
#>   UHM903.20683_S104_se UHM904.20663_S149_se UHM905.20694_S141_se
#> 1            0.4675442             4.516596             9.841217
#>   UHM906.20661_S125_se UHM907.20688_S164_se UHM908.20492_S116_se
#> 1              30.1709            0.2575295            0.6658332
#>   UHM909.20653_S124_se UHM910.20658_S184_se UHM965.20633_S169_se
#> 1            0.1112213             1.769631            0.1122059
#>   UHM966.20839_S147_se UHM967.20840_S159_se UHM968.20667_S102_se
#> 1           0.08156947             1.624723           0.06179711
#>   UHM969.20841_S171_se UHM971.20842_S183_se UHM973.20674_S186_se
#> 1             10.49934             3.282073            0.2213832
#>   UHM974.20528_S168_se UHM975.20843_S100_se UHM977.20844_S112_se
#> 1           0.06179604             1.750053            0.5107622
#>   UHM978.20845_S124_se UHM979.20846_S136_se UHM980.20827_S98_se
#> 1             20.49841             1.281947          0.06281574
#>   UHM981.20635_S98_se UHM982.20836_S111_se UHM983.20652_S112_se
#> 1              0.2801             5.655986            0.8135467
#>   UHM984.20847_S148_se UHM985.20848_S160_se UHM988.20849_S172_se
#> 1             11.15653            0.7855008            0.1297465
#>   UHM989.20850_S184_se UHM991.20851_S101_se UHM993.20837_S123_se
#> 1             7.029791            0.1814159             2.206989
#>   UHM996.20706_S190_se UHM997.20649_S171_se UHM998.20714_S191_se
#> 1           0.03125791            0.2103003           0.03098282
#>   UHM999.20713_S179_se spiked.blank.20529_S180_q25 spiked.blank.20913_S180_q25
#> 1            0.3989999                           0                           0
#>   Std2uL.20721_S180_q25 StdSwab1uL.20720_S168_q25 STP1719.20518_S143_q25
#> 1                     0                         0                      0
#>   STP213.20519_S155_q25 STP268.20520_S167_q25 STP544.20515_S107_q25
#> 1                     0                     0                     0
#>   STP570.20516_S119_q25 STP579.20517_S131_q25 STP614.20514_S190_q25
#> 1                     0                     0                     0
#>   UHM1000.20700_S118_q25 UHM1001.20705_S178_q25 UHM1007.20718_S144_q25
#> 1                      0                      0                      0
#>   UHM1009.20710_S143_q25 UHM1010.20717_S132_q25 UHM1011.20702_S142_q25
#> 1                      0                      0                      0
#>   UHM1024.20716_S120_q25 UHM1026.20703_S154_q25 UHM1028.20709_S131_q25
#> 1                      0                      0                      0
#>   UHM1032.20701_S130_q25 UHM1033.20715_S108_q25 UHM1034.20712_S167_q25
#> 1                      0                      0                      0
#>   UHM1035.20707_S107_q25 UHM1036.20708_S119_q25 UHM1052.20711_S155_q25
#> 1                      0                      0                      0
#>   UHM1060.20819_S97_q25 UHM1065.20820_S109_q25 UHM1068.20828_S110_q25
#> 1                     0                      0                      0
#>   UHM1069.20838_S135_q25 UHM1070.20821_S121_q25 UHM1071.20829_S122_q25
#> 1                      0                      0                      0
#>   UHM1072.20830_S134_q25 UHM1073.20831_S146_q25 UHM1075.20822_S133_q25
#> 1                      0                      0                      0
#>   UHM1077.20832_S158_q25 UHM1078.20823_S145_q25 UHM1080.20833_S170_q25
#> 1                      0                      0                      0
#>   UHM1081.20824_S157_q25 UHM1088.20834_S182_q25 UHM1090.20835_S99_q25
#> 1                      0                      0                     0
#>   UHM1093.20825_S169_q25 UHM1095.20826_S181_q25 UHM1097.20719_S156_q25
#> 1                      0                      0                      0
#>   UHM1099.20704_S166_q25 UHM1100.20884_S117_q25 UHM1102.20885_S129_q25
#> 1                      0                      0                      0
#>   UHM1104.20886_S141_q25 UHM1105.20887_S153_q25 UHM1109.20627_S97_q25
#> 1                      0                      0                     0
#>   UHM1110.20664_S161_q25 UHM1113.20888_S165_q25 UHM1114.20889_S177_q25
#> 1                      0                      0                      0
#>   UHM1115.20890_S189_q25 UHM1117.20891_S106_q25 UHM1118.20892_S118_q25
#> 1                      0                      0                      0
#>   UHM1120.20893_S130_q25 UHM1124.20894_S142_q25 UHM1126.20895_S154_q25
#> 1                      0                      0                      0
#>   UHM1128.20896_S166_q25 UHM1140.20651_S100_q25 UHM1145.20897_S178_q25
#> 1                      0                      0                      0
#>   UHM1163.20501_S129_q25 UHM1164.20498_S188_q25 UHM1169.20648_S159_q25
#> 1                      0                      0                      0
#>   UHM1171.20675_S103_q25 UHM1176.20500_S117_q25 UHM1177.20642_S182_q25
#> 1                      0                      0                      0
#>   UHM1182.20672_S162_q25 UHM1210.20898_S190_q25 UHM1212.20899_S107_q25
#> 1                      0                      0                      0
#>   UHM1217.20900_S119_q25 UHM1218.20901_S131_q25 UHM1219.20902_S143_q25
#> 1                      0                      0                      0
#>   UHM1220.20903_S155_q25 UHM1221.20904_S167_q25 UHM1222.20905_S179_q25
#> 1                      0                      0                      0
#>   UHM1223.20906_S191_q25 UHM1225.20907_S108_q25 UHM1227.20908_S120_q25
#> 1                      0                      0                      0
#>   UHM1228.20909_S132_q25 UHM1237.20910_S144_q25 UHM1240.20662_S137_q25
#> 1                      0                      0                      0
#>   UHM1246.20911_S156_q25 UHM1247.20912_S168_q25 UHM1248.20671_S150_q25
#> 1                      0                      0                      0
#>   UHM1256.20666_S185_q25 UHM1260.20692_S117_q25 UHM1270.20673_S174_q25
#> 1                      0                      0                      0
#>   UHM1271.20493_S128_q25 UHM1272.20494_S140_q25 UHM1274.20650_S183_q25
#> 1                      0                      0                      0
#>   UHM1275.20693_S129_q25 UHM1282.20695_S153_q25 UHM1287.20639_S146_q25
#> 1                      0                      0                      0
#>   UHM1291.20512_S166_q25 UHM1296.20646_S135_q25 UHM1319.20657_S172_q25
#> 1                      0                      0                      0
#>   UHM1324.20509_S130_q25 UHM1327.20641_S170_q25 UHM1328.20668_S114_q25
#> 1                      0                      0                      0
#>   UHM1334.20513_S178_q25 UHM1338.20495_S152_q25 UHM1341.20698_S189_q25
#> 1                      0                      0                      0
#>   UHM1356.20637_S122_q25 UHM1380.20676_S115_q25 UHM1383.20690_S188_q25
#> 1                      0                      0                      0
#>   UHM1385.20659_S101_q25 UHM1399.20852_S113_q25 UHM1400.20853_S125_q25
#> 1                      0                      0                      0
#>   UHM1401.20854_S137_q25 UHM1402.20855_S149_q25 UHM1403.20856_S161_q25
#> 1                      0                      0                      0
#>   UHM1405.20857_S173_q25 UHM1406.20858_S185_q25 UHM1414.20859_S102_q25
#> 1                      0                      0                      0
#>   UHM1419.20860_S114_q25 UHM1427.20485_S127_q25 UHM1428.20486_S139_q25
#> 1                      0                      0                      0
#>   UHM1429.20487_S151_q25 UHM1430.20488_S163_q25 UHM1432.20489_S175_q25
#> 1                      0                      0                      0
#>   UHM1435.20484_S115_q25 UHM162.20656_S160_q25 UHM198.20681_S175_q25
#> 1                      0                     0                     0
#>   UHM200.20670_S138_q25 UHM203.20685_S128_q25 UHM204.20505_S177_q25
#> 1                     0                     0                     0
#>   UHM206.20506_S189_q25 UHM207.20689_S176_q25 UHM208.20507_S106_q25
#> 1                     0                     0                     0
#>   UHM211.20502_S141_q25 UHM215.20504_S165_q25 UHM216.20525_S132_q25
#> 1                     0                     0                     0
#>   UHM219.20526_S144_q25 UHM236.20527_S156_q25 UHM238.20503_S153_q25
#> 1                     0                     0                     0
#>   UHM245.20634_S181_q25 UHM252.20654_S136_q25 UHM267.20496_S164_q25
#> 1                     0                     0                     0
#>   UHM274.20677_S127_q25 UHM276.20682_S187_q25 UHM280.20497_S176_q25
#> 1                     0                     0                     0
#>   UHM286.20521_S179_q25 UHM289.20522_S191_q25 UHM294.20523_S108_q25
#> 1                     0                     0                     0
#>   UHM298.20696_S165_q25 UHM325.20644_S111_q25 UHM337.20508_S118_q25
#> 1                     0                     0                     0
#>   UHM354.20631_S145_q25 UHM356.20511_S154_q25 UHM369.20869_S127_q25
#> 1                     0                     0                     0
#>   UHM370.20870_S139_q25 UHM372.20871_S151_q25 UHM373.20872_S163_q25
#> 1                     0                     0                     0
#>   UHM374.20873_S175_q25 UHM375.20874_S187_q25 UHM377.20875_S104_q25
#> 1                     0                     0                     0
#>   UHM382.20876_S116_q25 UHM386.20877_S128_q25 UHM387.20878_S140_q25
#> 1                     0                     0                     0
#>   UHM414.20679_S151_q25 UHM418.20861_S126_q25 UHM422.20862_S138_q25
#> 1                     0                     0                     0
#>   UHM425.20863_S150_q25 UHM426.20630_S133_q25 UHM428.20640_S158_q25
#> 1                     0                     0                     0
#>   UHM429.20655_S148_q25 UHM435.20643_S99_q25 UHM437.20864_S162_q25
#> 1                     0                    0                     0
#>   UHM439.20660_S113_q25 UHM443.20524_S120_q25 UHM445.20665_S173_q25
#> 1                     0                     0                     0
#>   UHM447.20879_S152_q25 UHM448.20865_S174_q25 UHM452.20880_S164_q25
#> 1                     0                     0                     0
#>   UHM454.20866_S186_q25 UHM455.20881_S176_q25 UHM458.20882_S188_q25
#> 1                     0                     0                     0
#>   UHM459.20883_S105_q25 UHM461.20867_S103_q25 UHM467.20868_S115_q25
#> 1                     0                     0                     0
#>   UHM470.20629_S121_q25 UHM476.20510_S142_q25 UHM478.20645_S123_q25
#> 1                     0                     0                     0
#>   UHM479.20647_S147_q25 UHM481.20499_S105_q25 UHM482.20686_S140_q25
#> 1                     0                     0                     0
#>   UHM483.20699_S106_q25 UHM519.20678_S139_q25 UHM520.20669_S126_q25
#> 1                     0                     0                     0
#>   UHM836.20481_S174_q25 UHM837.20482_S186_q25 UHM838.20483_S103_q25
#> 1                     0                     0                     0
#>   UHM891.20480_S162_q25 UHM892.20628_S109_q25 UHM893.20691_S105_q25
#> 1                     0                     0                     0
#>   UHM894.20636_S110_q25 UHM895.20632_S157_q25 UHM896.20697_S177_q25
#> 1                     0                     0                     0
#>   UHM897.20687_S152_q25 UHM898.20490_S187_q25 UHM899.20684_S116_q25
#> 1                     0                     0                     0
#>   UHM900.20491_S104_q25 UHM901.20638_S134_q25 UHM902.20680_S163_q25
#> 1                     0                     0                     0
#>   UHM903.20683_S104_q25 UHM904.20663_S149_q25 UHM905.20694_S141_q25
#> 1                     0                     0                     0
#>   UHM906.20661_S125_q25 UHM907.20688_S164_q25 UHM908.20492_S116_q25
#> 1                     0                     0                     0
#>   UHM909.20653_S124_q25 UHM910.20658_S184_q25 UHM965.20633_S169_q25
#> 1                     0                     0                     0
#>   UHM966.20839_S147_q25 UHM967.20840_S159_q25 UHM968.20667_S102_q25
#> 1                     0                     0                     0
#>   UHM969.20841_S171_q25 UHM971.20842_S183_q25 UHM973.20674_S186_q25
#> 1                     0                     0                     0
#>   UHM974.20528_S168_q25 UHM975.20843_S100_q25 UHM977.20844_S112_q25
#> 1                     0                     0                     0
#>   UHM978.20845_S124_q25 UHM979.20846_S136_q25 UHM980.20827_S98_q25
#> 1                     0                     0                    0
#>   UHM981.20635_S98_q25 UHM982.20836_S111_q25 UHM983.20652_S112_q25
#> 1                    0                     0                     0
#>   UHM984.20847_S148_q25 UHM985.20848_S160_q25 UHM988.20849_S172_q25
#> 1                     0                     0                     0
#>   UHM989.20850_S184_q25 UHM991.20851_S101_q25 UHM993.20837_S123_q25
#> 1                     0                     0                     0
#>   UHM996.20706_S190_q25 UHM997.20649_S171_q25 UHM998.20714_S191_q25
#> 1                     0                     0                     0
#>   UHM999.20713_S179_q25 spiked.blank.20529_S180_median
#> 1                     0                              0
#>   spiked.blank.20913_S180_median Std2uL.20721_S180_median
#> 1                              0                        0
#>   StdSwab1uL.20720_S168_median STP1719.20518_S143_median
#> 1                            0                         0
#>   STP213.20519_S155_median STP268.20520_S167_median STP544.20515_S107_median
#> 1                        0                        0                        0
#>   STP570.20516_S119_median STP579.20517_S131_median STP614.20514_S190_median
#> 1                        0                        0                        0
#>   UHM1000.20700_S118_median UHM1001.20705_S178_median UHM1007.20718_S144_median
#> 1                         0                         0                         0
#>   UHM1009.20710_S143_median UHM1010.20717_S132_median UHM1011.20702_S142_median
#> 1                         0                         0                         0
#>   UHM1024.20716_S120_median UHM1026.20703_S154_median UHM1028.20709_S131_median
#> 1                         0                         0                         0
#>   UHM1032.20701_S130_median UHM1033.20715_S108_median UHM1034.20712_S167_median
#> 1                         0                         0                         0
#>   UHM1035.20707_S107_median UHM1036.20708_S119_median UHM1052.20711_S155_median
#> 1                         0                         0                         0
#>   UHM1060.20819_S97_median UHM1065.20820_S109_median UHM1068.20828_S110_median
#> 1                        0                         0                         0
#>   UHM1069.20838_S135_median UHM1070.20821_S121_median UHM1071.20829_S122_median
#> 1                         0                         0                         0
#>   UHM1072.20830_S134_median UHM1073.20831_S146_median UHM1075.20822_S133_median
#> 1                         0                         0                         0
#>   UHM1077.20832_S158_median UHM1078.20823_S145_median UHM1080.20833_S170_median
#> 1                         0                         0                         0
#>   UHM1081.20824_S157_median UHM1088.20834_S182_median UHM1090.20835_S99_median
#> 1                         0                         0                        0
#>   UHM1093.20825_S169_median UHM1095.20826_S181_median UHM1097.20719_S156_median
#> 1                         0                         0                         0
#>   UHM1099.20704_S166_median UHM1100.20884_S117_median UHM1102.20885_S129_median
#> 1                         0                         0                         0
#>   UHM1104.20886_S141_median UHM1105.20887_S153_median UHM1109.20627_S97_median
#> 1                         0                         0                        0
#>   UHM1110.20664_S161_median UHM1113.20888_S165_median UHM1114.20889_S177_median
#> 1                         0                         0                         0
#>   UHM1115.20890_S189_median UHM1117.20891_S106_median UHM1118.20892_S118_median
#> 1                         0                         0                         0
#>   UHM1120.20893_S130_median UHM1124.20894_S142_median UHM1126.20895_S154_median
#> 1                         0                         0                         0
#>   UHM1128.20896_S166_median UHM1140.20651_S100_median UHM1145.20897_S178_median
#> 1                         0                         0                         0
#>   UHM1163.20501_S129_median UHM1164.20498_S188_median UHM1169.20648_S159_median
#> 1                         0                         0                         0
#>   UHM1171.20675_S103_median UHM1176.20500_S117_median UHM1177.20642_S182_median
#> 1                         0                         0                         0
#>   UHM1182.20672_S162_median UHM1210.20898_S190_median UHM1212.20899_S107_median
#> 1                         0                         0                         0
#>   UHM1217.20900_S119_median UHM1218.20901_S131_median UHM1219.20902_S143_median
#> 1                         0                         0                         0
#>   UHM1220.20903_S155_median UHM1221.20904_S167_median UHM1222.20905_S179_median
#> 1                         0                         0                         0
#>   UHM1223.20906_S191_median UHM1225.20907_S108_median UHM1227.20908_S120_median
#> 1                         0                         0                         0
#>   UHM1228.20909_S132_median UHM1237.20910_S144_median UHM1240.20662_S137_median
#> 1                         0                         0                         0
#>   UHM1246.20911_S156_median UHM1247.20912_S168_median UHM1248.20671_S150_median
#> 1                         0                         0                         0
#>   UHM1256.20666_S185_median UHM1260.20692_S117_median UHM1270.20673_S174_median
#> 1                         0                         0                         0
#>   UHM1271.20493_S128_median UHM1272.20494_S140_median UHM1274.20650_S183_median
#> 1                         0                         0                         0
#>   UHM1275.20693_S129_median UHM1282.20695_S153_median UHM1287.20639_S146_median
#> 1                         0                         0                         0
#>   UHM1291.20512_S166_median UHM1296.20646_S135_median UHM1319.20657_S172_median
#> 1                         0                         0                         0
#>   UHM1324.20509_S130_median UHM1327.20641_S170_median UHM1328.20668_S114_median
#> 1                         0                         0                         0
#>   UHM1334.20513_S178_median UHM1338.20495_S152_median UHM1341.20698_S189_median
#> 1                         0                         0                         0
#>   UHM1356.20637_S122_median UHM1380.20676_S115_median UHM1383.20690_S188_median
#> 1                         0                         0                         0
#>   UHM1385.20659_S101_median UHM1399.20852_S113_median UHM1400.20853_S125_median
#> 1                         0                         0                         0
#>   UHM1401.20854_S137_median UHM1402.20855_S149_median UHM1403.20856_S161_median
#> 1                         0                         0                         0
#>   UHM1405.20857_S173_median UHM1406.20858_S185_median UHM1414.20859_S102_median
#> 1                         0                         0                         0
#>   UHM1419.20860_S114_median UHM1427.20485_S127_median UHM1428.20486_S139_median
#> 1                         0                         0                         0
#>   UHM1429.20487_S151_median UHM1430.20488_S163_median UHM1432.20489_S175_median
#> 1                         0                         0                         0
#>   UHM1435.20484_S115_median UHM162.20656_S160_median UHM198.20681_S175_median
#> 1                         0                        0                        0
#>   UHM200.20670_S138_median UHM203.20685_S128_median UHM204.20505_S177_median
#> 1                        0                        0                        0
#>   UHM206.20506_S189_median UHM207.20689_S176_median UHM208.20507_S106_median
#> 1                        0                        0                        0
#>   UHM211.20502_S141_median UHM215.20504_S165_median UHM216.20525_S132_median
#> 1                        0                        0                        0
#>   UHM219.20526_S144_median UHM236.20527_S156_median UHM238.20503_S153_median
#> 1                        0                        0                        0
#>   UHM245.20634_S181_median UHM252.20654_S136_median UHM267.20496_S164_median
#> 1                        0                        0                        0
#>   UHM274.20677_S127_median UHM276.20682_S187_median UHM280.20497_S176_median
#> 1                        0                        0                        0
#>   UHM286.20521_S179_median UHM289.20522_S191_median UHM294.20523_S108_median
#> 1                        0                        0                        0
#>   UHM298.20696_S165_median UHM325.20644_S111_median UHM337.20508_S118_median
#> 1                        0                        0                        0
#>   UHM354.20631_S145_median UHM356.20511_S154_median UHM369.20869_S127_median
#> 1                        0                        0                        0
#>   UHM370.20870_S139_median UHM372.20871_S151_median UHM373.20872_S163_median
#> 1                        0                        0                        0
#>   UHM374.20873_S175_median UHM375.20874_S187_median UHM377.20875_S104_median
#> 1                        0                        0                        0
#>   UHM382.20876_S116_median UHM386.20877_S128_median UHM387.20878_S140_median
#> 1                        0                        0                        0
#>   UHM414.20679_S151_median UHM418.20861_S126_median UHM422.20862_S138_median
#> 1                        0                        0                        0
#>   UHM425.20863_S150_median UHM426.20630_S133_median UHM428.20640_S158_median
#> 1                        0                        0                        0
#>   UHM429.20655_S148_median UHM435.20643_S99_median UHM437.20864_S162_median
#> 1                        0                       0                        0
#>   UHM439.20660_S113_median UHM443.20524_S120_median UHM445.20665_S173_median
#> 1                        0                        0                        0
#>   UHM447.20879_S152_median UHM448.20865_S174_median UHM452.20880_S164_median
#> 1                        0                        0                        0
#>   UHM454.20866_S186_median UHM455.20881_S176_median UHM458.20882_S188_median
#> 1                        0                        0                        0
#>   UHM459.20883_S105_median UHM461.20867_S103_median UHM467.20868_S115_median
#> 1                        0                        0                        0
#>   UHM470.20629_S121_median UHM476.20510_S142_median UHM478.20645_S123_median
#> 1                        0                        0                        0
#>   UHM479.20647_S147_median UHM481.20499_S105_median UHM482.20686_S140_median
#> 1                        0                        0                        0
#>   UHM483.20699_S106_median UHM519.20678_S139_median UHM520.20669_S126_median
#> 1                        0                        0                        0
#>   UHM836.20481_S174_median UHM837.20482_S186_median UHM838.20483_S103_median
#> 1                        0                        0                        0
#>   UHM891.20480_S162_median UHM892.20628_S109_median UHM893.20691_S105_median
#> 1                        0                        0                        0
#>   UHM894.20636_S110_median UHM895.20632_S157_median UHM896.20697_S177_median
#> 1                        0                        0                        0
#>   UHM897.20687_S152_median UHM898.20490_S187_median UHM899.20684_S116_median
#> 1                        0                        0                        0
#>   UHM900.20491_S104_median UHM901.20638_S134_median UHM902.20680_S163_median
#> 1                        0                        0                        0
#>   UHM903.20683_S104_median UHM904.20663_S149_median UHM905.20694_S141_median
#> 1                        0                        0                        0
#>   UHM906.20661_S125_median UHM907.20688_S164_median UHM908.20492_S116_median
#> 1                        0                        0                        0
#>   UHM909.20653_S124_median UHM910.20658_S184_median UHM965.20633_S169_median
#> 1                        0                        0                        0
#>   UHM966.20839_S147_median UHM967.20840_S159_median UHM968.20667_S102_median
#> 1                        0                        0                        0
#>   UHM969.20841_S171_median UHM971.20842_S183_median UHM973.20674_S186_median
#> 1                        0                        0                        0
#>   UHM974.20528_S168_median UHM975.20843_S100_median UHM977.20844_S112_median
#> 1                        0                        0                        0
#>   UHM978.20845_S124_median UHM979.20846_S136_median UHM980.20827_S98_median
#> 1                        0                        0                       0
#>   UHM981.20635_S98_median UHM982.20836_S111_median UHM983.20652_S112_median
#> 1                       0                        0                        0
#>   UHM984.20847_S148_median UHM985.20848_S160_median UHM988.20849_S172_median
#> 1                        0                        0                        0
#>   UHM989.20850_S184_median UHM991.20851_S101_median UHM993.20837_S123_median
#> 1                        0                        0                        0
#>   UHM996.20706_S190_median UHM997.20649_S171_median UHM998.20714_S191_median
#> 1                        0                        0                        0
#>   UHM999.20713_S179_median spiked.blank.20529_S180_q75
#> 1                        0                           0
#>   spiked.blank.20913_S180_q75 Std2uL.20721_S180_q75 StdSwab1uL.20720_S168_q75
#> 1                           0                     0                         0
#>   STP1719.20518_S143_q75 STP213.20519_S155_q75 STP268.20520_S167_q75
#> 1                      0                     0                     0
#>   STP544.20515_S107_q75 STP570.20516_S119_q75 STP579.20517_S131_q75
#> 1                     0                     0                     0
#>   STP614.20514_S190_q75 UHM1000.20700_S118_q75 UHM1001.20705_S178_q75
#> 1                     0                      0                      0
#>   UHM1007.20718_S144_q75 UHM1009.20710_S143_q75 UHM1010.20717_S132_q75
#> 1                      0                      0                      0
#>   UHM1011.20702_S142_q75 UHM1024.20716_S120_q75 UHM1026.20703_S154_q75
#> 1                      0                      0                      0
#>   UHM1028.20709_S131_q75 UHM1032.20701_S130_q75 UHM1033.20715_S108_q75
#> 1                      0                      0                      0
#>   UHM1034.20712_S167_q75 UHM1035.20707_S107_q75 UHM1036.20708_S119_q75
#> 1                      0                      0                      0
#>   UHM1052.20711_S155_q75 UHM1060.20819_S97_q75 UHM1065.20820_S109_q75
#> 1                      0                     0                      0
#>   UHM1068.20828_S110_q75 UHM1069.20838_S135_q75 UHM1070.20821_S121_q75
#> 1                      0                      0                      0
#>   UHM1071.20829_S122_q75 UHM1072.20830_S134_q75 UHM1073.20831_S146_q75
#> 1                      0                      0                      0
#>   UHM1075.20822_S133_q75 UHM1077.20832_S158_q75 UHM1078.20823_S145_q75
#> 1                      0                      0                      0
#>   UHM1080.20833_S170_q75 UHM1081.20824_S157_q75 UHM1088.20834_S182_q75
#> 1                      0                      0                      0
#>   UHM1090.20835_S99_q75 UHM1093.20825_S169_q75 UHM1095.20826_S181_q75
#> 1                     0                      0                      0
#>   UHM1097.20719_S156_q75 UHM1099.20704_S166_q75 UHM1100.20884_S117_q75
#> 1                      0                      0                      0
#>   UHM1102.20885_S129_q75 UHM1104.20886_S141_q75 UHM1105.20887_S153_q75
#> 1                      0                      0                      0
#>   UHM1109.20627_S97_q75 UHM1110.20664_S161_q75 UHM1113.20888_S165_q75
#> 1                     0                      0                      0
#>   UHM1114.20889_S177_q75 UHM1115.20890_S189_q75 UHM1117.20891_S106_q75
#> 1                      0                      0                      0
#>   UHM1118.20892_S118_q75 UHM1120.20893_S130_q75 UHM1124.20894_S142_q75
#> 1                      0                      0                      0
#>   UHM1126.20895_S154_q75 UHM1128.20896_S166_q75 UHM1140.20651_S100_q75
#> 1                      0                      0                      0
#>   UHM1145.20897_S178_q75 UHM1163.20501_S129_q75 UHM1164.20498_S188_q75
#> 1                      0                      0                      0
#>   UHM1169.20648_S159_q75 UHM1171.20675_S103_q75 UHM1176.20500_S117_q75
#> 1                      0                      0                      0
#>   UHM1177.20642_S182_q75 UHM1182.20672_S162_q75 UHM1210.20898_S190_q75
#> 1                      0                      0                      0
#>   UHM1212.20899_S107_q75 UHM1217.20900_S119_q75 UHM1218.20901_S131_q75
#> 1                      0                      0                      0
#>   UHM1219.20902_S143_q75 UHM1220.20903_S155_q75 UHM1221.20904_S167_q75
#> 1                      0                      0                      0
#>   UHM1222.20905_S179_q75 UHM1223.20906_S191_q75 UHM1225.20907_S108_q75
#> 1                      0                      0                      0
#>   UHM1227.20908_S120_q75 UHM1228.20909_S132_q75 UHM1237.20910_S144_q75
#> 1                      0                      0                      0
#>   UHM1240.20662_S137_q75 UHM1246.20911_S156_q75 UHM1247.20912_S168_q75
#> 1                      0                      0                      0
#>   UHM1248.20671_S150_q75 UHM1256.20666_S185_q75 UHM1260.20692_S117_q75
#> 1                      0                      0                      0
#>   UHM1270.20673_S174_q75 UHM1271.20493_S128_q75 UHM1272.20494_S140_q75
#> 1                      0                      0                      0
#>   UHM1274.20650_S183_q75 UHM1275.20693_S129_q75 UHM1282.20695_S153_q75
#> 1                      0                      0                      0
#>   UHM1287.20639_S146_q75 UHM1291.20512_S166_q75 UHM1296.20646_S135_q75
#> 1                      0                      0                      0
#>   UHM1319.20657_S172_q75 UHM1324.20509_S130_q75 UHM1327.20641_S170_q75
#> 1                      0                      0                      0
#>   UHM1328.20668_S114_q75 UHM1334.20513_S178_q75 UHM1338.20495_S152_q75
#> 1                      0                      0                      0
#>   UHM1341.20698_S189_q75 UHM1356.20637_S122_q75 UHM1380.20676_S115_q75
#> 1                      0                      0                      0
#>   UHM1383.20690_S188_q75 UHM1385.20659_S101_q75 UHM1399.20852_S113_q75
#> 1                      0                      0                      0
#>   UHM1400.20853_S125_q75 UHM1401.20854_S137_q75 UHM1402.20855_S149_q75
#> 1                      0                      0                      0
#>   UHM1403.20856_S161_q75 UHM1405.20857_S173_q75 UHM1406.20858_S185_q75
#> 1                      0                      0                      0
#>   UHM1414.20859_S102_q75 UHM1419.20860_S114_q75 UHM1427.20485_S127_q75
#> 1                      0                      0                      0
#>   UHM1428.20486_S139_q75 UHM1429.20487_S151_q75 UHM1430.20488_S163_q75
#> 1                      0                      0                      0
#>   UHM1432.20489_S175_q75 UHM1435.20484_S115_q75 UHM162.20656_S160_q75
#> 1                      0                      0                     0
#>   UHM198.20681_S175_q75 UHM200.20670_S138_q75 UHM203.20685_S128_q75
#> 1                     0                     0                     0
#>   UHM204.20505_S177_q75 UHM206.20506_S189_q75 UHM207.20689_S176_q75
#> 1                     0                     0                     0
#>   UHM208.20507_S106_q75 UHM211.20502_S141_q75 UHM215.20504_S165_q75
#> 1                     0                     0                     0
#>   UHM216.20525_S132_q75 UHM219.20526_S144_q75 UHM236.20527_S156_q75
#> 1                     0                     0                     0
#>   UHM238.20503_S153_q75 UHM245.20634_S181_q75 UHM252.20654_S136_q75
#> 1                     0                     0                     0
#>   UHM267.20496_S164_q75 UHM274.20677_S127_q75 UHM276.20682_S187_q75
#> 1                     0                     0                     0
#>   UHM280.20497_S176_q75 UHM286.20521_S179_q75 UHM289.20522_S191_q75
#> 1                     0                     0                     0
#>   UHM294.20523_S108_q75 UHM298.20696_S165_q75 UHM325.20644_S111_q75
#> 1                     0                     0                     0
#>   UHM337.20508_S118_q75 UHM354.20631_S145_q75 UHM356.20511_S154_q75
#> 1                     0                     0                     0
#>   UHM369.20869_S127_q75 UHM370.20870_S139_q75 UHM372.20871_S151_q75
#> 1                     0                     0                     0
#>   UHM373.20872_S163_q75 UHM374.20873_S175_q75 UHM375.20874_S187_q75
#> 1                     0                     0                     0
#>   UHM377.20875_S104_q75 UHM382.20876_S116_q75 UHM386.20877_S128_q75
#> 1                     0                     0                     0
#>   UHM387.20878_S140_q75 UHM414.20679_S151_q75 UHM418.20861_S126_q75
#> 1                     0                     0                     0
#>   UHM422.20862_S138_q75 UHM425.20863_S150_q75 UHM426.20630_S133_q75
#> 1                     0                     0                     0
#>   UHM428.20640_S158_q75 UHM429.20655_S148_q75 UHM435.20643_S99_q75
#> 1                     0                     0                    0
#>   UHM437.20864_S162_q75 UHM439.20660_S113_q75 UHM443.20524_S120_q75
#> 1                     0                     0                     0
#>   UHM445.20665_S173_q75 UHM447.20879_S152_q75 UHM448.20865_S174_q75
#> 1                     0                     0                     0
#>   UHM452.20880_S164_q75 UHM454.20866_S186_q75 UHM455.20881_S176_q75
#> 1                     0                     0                     0
#>   UHM458.20882_S188_q75 UHM459.20883_S105_q75 UHM461.20867_S103_q75
#> 1                     0                     0                     0
#>   UHM467.20868_S115_q75 UHM470.20629_S121_q75 UHM476.20510_S142_q75
#> 1                     0                     0                     0
#>   UHM478.20645_S123_q75 UHM479.20647_S147_q75 UHM481.20499_S105_q75
#> 1                     0                     0                     0
#>   UHM482.20686_S140_q75 UHM483.20699_S106_q75 UHM519.20678_S139_q75
#> 1                     0                     0                     0
#>   UHM520.20669_S126_q75 UHM836.20481_S174_q75 UHM837.20482_S186_q75
#> 1                     0                     0                     0
#>   UHM838.20483_S103_q75 UHM891.20480_S162_q75 UHM892.20628_S109_q75
#> 1                     0                     0                     0
#>   UHM893.20691_S105_q75 UHM894.20636_S110_q75 UHM895.20632_S157_q75
#> 1                     0                     0                     0
#>   UHM896.20697_S177_q75 UHM897.20687_S152_q75 UHM898.20490_S187_q75
#> 1                     0                     0                     0
#>   UHM899.20684_S116_q75 UHM900.20491_S104_q75 UHM901.20638_S134_q75
#> 1                     0                     0                     0
#>   UHM902.20680_S163_q75 UHM903.20683_S104_q75 UHM904.20663_S149_q75
#> 1                     0                     0                     0
#>   UHM905.20694_S141_q75 UHM906.20661_S125_q75 UHM907.20688_S164_q75
#> 1                     0                     0                     0
#>   UHM908.20492_S116_q75 UHM909.20653_S124_q75 UHM910.20658_S184_q75
#> 1                     0                     0                     0
#>   UHM965.20633_S169_q75 UHM966.20839_S147_q75 UHM967.20840_S159_q75
#> 1                     0                     0                     0
#>   UHM968.20667_S102_q75 UHM969.20841_S171_q75 UHM971.20842_S183_q75
#> 1                     0                     0                     0
#>   UHM973.20674_S186_q75 UHM974.20528_S168_q75 UHM975.20843_S100_q75
#> 1                     0                     0                     0
#>   UHM977.20844_S112_q75 UHM978.20845_S124_q75 UHM979.20846_S136_q75
#> 1                     0                     0                     0
#>   UHM980.20827_S98_q75 UHM981.20635_S98_q75 UHM982.20836_S111_q75
#> 1                    0                    0                     0
#>   UHM983.20652_S112_q75 UHM984.20847_S148_q75 UHM985.20848_S160_q75
#> 1                     0                     0                     0
#>   UHM988.20849_S172_q75 UHM989.20850_S184_q75 UHM991.20851_S101_q75
#> 1                     0                     0                     0
#>   UHM993.20837_S123_q75 UHM996.20706_S190_q75 UHM997.20649_S171_q75
#> 1                     0                     0                     0
#>   UHM998.20714_S191_q75 UHM999.20713_S179_q75
#> 1                     0                     0

# Back normal  
# the scaling factor was computed based on spiked species reads and fixed cell count.
# Multiplying the spiked species read count by this scaling factor restores the exact spiked cell count.
# lets check it
# BackNormal <- calculate_spike_percentage(
#  physeq_absolute, 
#  merged_spiked_species, 
#  passed_range = c(0.1, 20)
# )

#**Time to filter out unsuccessful spiked samples**

library(phyloseq)
library(dplyr)
library(tibble)
library(microbiome)

filtered_sample_data <- microbiome::meta(physeq_absolute) %>%
  as.data.frame() %>%
  tibble::rownames_to_column(var = "Sample") %>%  
  dplyr::mutate(Sample = as.character(Sample)) %>%
  dplyr::left_join(Perc, by = "Sample")

filtered_sample_data <- tibble::column_to_rownames(filtered_sample_data, "Sample")

filtered_sample_data <- sample_data(as.data.frame(filtered_sample_data))

# Assign back to phyloseq obj
sample_data(physeq_absolute) <- filtered_sample_data

# The acceptable range of spiked species retrieval is system-dependent**
# Spiked species become centroid of the community (Distance to Centroid)
# Spiked species become dominant and imbalance the community (Evenness)

# What range of spiked species retrieval is appropriate for your system?
  
# Calculate Pielou's Evenness using Shannon index and species richness (Observed)
# Load required libraries
library(vegan)

# Calculate Pielou's Evenness using Shannon index and species richness (Observed)
alphab <- estimate_richness(physeq_absolute, measures = c("Observed", "Shannon"))
alphab$Pielou_evenness <- alphab$Shannon / alphab$Observed

# Normalize values
# alphab <- alphab %>%
# mutate(across(c("Observed", "Shannon", "Pielou_evenness"), ~ as.numeric(scale(.))))

metadata <- as.data.frame(microbiome::meta(physeq_absolute))

metadata$Sample <- rownames(metadata)
alphab$Sample <- rownames(alphab)

# Merge alpha diversity metrics into metadata
metadata <- dplyr::left_join(metadata, alphab[, c("Sample", "Observed", "Shannon", "Pielou_evenness")], by = "Sample")

metadata <- metadata %>%
  column_to_rownames(var = "Sample")

# Updated metadata back to the phyloseq obj
sample_data(physeq_absolute) <- sample_data(metadata)

if (!"Spiked_Reads" %in% colnames(metadata)) {
  stop("Column 'Spiked_Reads' not found in metadata.")
}

# Generate regression plot
plot_object <- regression_plot(
  data = metadata,
  x_var = "Pielou_evenness",
  y_var = "Spiked_Reads",
  custom_range = c(0.1, 20, 30, 50, 100),
  plot_title = NULL
)

plot_object

#*Calculate the percentage of spiked species retrieval per sample***

absolute_abundance_ITS_OTU_perc <- phyloseq::subset_samples(physeq_absolute, sample.or.blank != "blank")

# Back normal to check the accuracy ## The spiked reads must be the same as the spiked cell numbers
conclusion(absolute_abundance_ITS_OTU_perc, 
  merged_spiked_species, 
  max_passed_range=20, 
  output_path)
#> 📂 Table saved in docx format: merged_data.docx 
#> 📂 Merged data saved as CSV: merged_data.csv
#> $summary_stats
#> a flextable object.
#> col_keys: `mean_total_reads_spiked`, `sd_total_reads_spiked`, `median_total_reads_spiked`, `mean_percentage`, `sd_percentage`, `median_percentage`, `passed_count`, `failed_count` 
#> header has 1 row(s) 
#> body has 1 row(s) 
#> original dataset sample: 
#>   mean_total_reads_spiked sd_total_reads_spiked median_total_reads_spiked
#> 1                75313.93                189227                   15200.5
#>   mean_percentage sd_percentage median_percentage passed_count failed_count
#> 1        15.60495      22.94053          4.581823           NA           NA
#> 
#> $full_report
#>                 Sample Total_Reads Spiked_Reads  Percentage Result
#> 1   STP1719.20518_S143        1203          733 60.93100582 failed
#> 2    STP213.20519_S155        6609          733 11.09093660 passed
#> 3    STP268.20520_S167       23689          733  3.09426316 passed
#> 4    STP544.20515_S107         874          733 83.86727689 failed
#> 5    STP570.20516_S119        2053          733 35.70384803 failed
#> 6    STP579.20517_S131         917          733 79.93456925 failed
#> 7    STP614.20514_S190       15689          733  4.67206323 passed
#> 8   UHM1000.20700_S118       70053          366  0.52246156 passed
#> 9   UHM1001.20705_S178        2755          366 13.28493648 passed
#> 10  UHM1007.20718_S144       30851          366  1.18634728 passed
#> 11  UHM1009.20710_S143        5951          366  6.15022685 passed
#> 12  UHM1010.20717_S132        1471          366 24.88103331 failed
#> 13  UHM1011.20702_S142        2198          366 16.65150136 passed
#> 14  UHM1024.20716_S120         766          366 47.78067885 failed
#> 15  UHM1026.20703_S154        2074          367 17.69527483 passed
#> 16  UHM1028.20709_S131        7126          366  5.13612125 passed
#> 17  UHM1032.20701_S130        1768          366 20.70135747 failed
#> 18  UHM1033.20715_S108         506          366 72.33201581 failed
#> 19  UHM1034.20712_S167       42646          366  0.85822820 passed
#> 20  UHM1035.20707_S107        1865          366 19.62466488 passed
#> 21  UHM1036.20708_S119        8497          366  4.30740261 passed
#> 22  UHM1052.20711_S155        3179          366 11.51305442 passed
#> 23   UHM1060.20819_S97        1313          733 55.82635187 failed
#> 24  UHM1065.20820_S109        1514          733 48.41479524 failed
#> 25  UHM1068.20828_S110        5242          733 13.98321251 passed
#> 26  UHM1069.20838_S135        4753          733 15.42183884 passed
#> 27  UHM1070.20821_S121        1700          733 43.11764706 failed
#> 28  UHM1071.20829_S122        7570          733  9.68295905 passed
#> 29  UHM1072.20830_S134        2336          733 31.37842466 failed
#> 30  UHM1073.20831_S146       61350          733  1.19478403 passed
#> 31  UHM1075.20822_S133        7172          733 10.22030117 passed
#> 32  UHM1077.20832_S158        1655          733 44.29003021 failed
#> 33  UHM1078.20823_S145       14847          733  4.93702431 passed
#> 34  UHM1080.20833_S170       11169          733  6.56280777 passed
#> 35  UHM1081.20824_S157       34040          733  2.15334900 passed
#> 36  UHM1088.20834_S182        8425          733  8.70029674 passed
#> 37   UHM1090.20835_S99        3238          733 22.63743051 failed
#> 38  UHM1093.20825_S169        9289          733  7.89105393 passed
#> 39  UHM1095.20826_S181        3945          733 18.58048162 passed
#> 40  UHM1097.20719_S156         834          366 43.88489209 failed
#> 41  UHM1099.20704_S166        1262          366 29.00158479 failed
#> 42  UHM1100.20884_S117      242139          733  0.30271869 passed
#> 43  UHM1102.20885_S129       82409          733  0.88946596 passed
#> 44  UHM1104.20886_S141       17977          733  4.07743227 passed
#> 45  UHM1105.20887_S153       35401          733  2.07056298 passed
#> 46   UHM1109.20627_S97       10498          733  6.98228234 passed
#> 47  UHM1110.20664_S161     1490927          733  0.04916404 failed
#> 48  UHM1113.20888_S165       16940          733  4.32703660 passed
#> 49  UHM1114.20889_S177       14560          733  5.03434066 passed
#> 50  UHM1115.20890_S189      159333          733  0.46004280 passed
#> 51  UHM1117.20891_S106      171299          733  0.42790676 passed
#> 52  UHM1118.20892_S118       25161          733  2.91323874 passed
#> 53  UHM1120.20893_S130       39307          733  1.86480780 passed
#> 54  UHM1124.20894_S142       33641          733  2.17888886 passed
#> 55  UHM1126.20895_S154       58413          733  1.25485765 passed
#> 56  UHM1128.20896_S166      610931          733  0.11998082 passed
#> 57  UHM1140.20651_S100       40483          733  1.81063656 passed
#> 58  UHM1145.20897_S178       68122          733  1.07601069 passed
#> 59  UHM1163.20501_S129       14715          733  4.98131159 passed
#> 60  UHM1164.20498_S188        2417          733 30.32685147 failed
#> 61  UHM1169.20648_S159        2136          733 34.31647940 failed
#> 62  UHM1171.20675_S103      320891          733  0.22842648 passed
#> 63  UHM1176.20500_S117       30776          733  2.38172602 passed
#> 64  UHM1177.20642_S182        1637          733 44.77703115 failed
#> 65  UHM1182.20672_S162       42254          733  1.73474701 passed
#> 66  UHM1210.20898_S190       68488          733  1.07026048 passed
#> 67  UHM1212.20899_S107       19893          733  3.68471322 passed
#> 68  UHM1217.20900_S119       15347          733  4.77617775 passed
#> 69  UHM1218.20901_S131       32130          733  2.28135699 passed
#> 70  UHM1219.20902_S143      201521          733  0.36373380 passed
#> 71  UHM1220.20903_S155       81871          733  0.89531092 passed
#> 72  UHM1221.20904_S167       29070          733  2.52149983 passed
#> 73  UHM1222.20905_S179        3176          733 23.07934509 failed
#> 74  UHM1223.20906_S191       92107          733  0.79581356 passed
#> 75  UHM1225.20907_S108       57441          733  1.27609199 passed
#> 76  UHM1227.20908_S120       28365          733  2.58417063 passed
#> 77  UHM1228.20909_S132      109387          733  0.67009791 passed
#> 78  UHM1237.20910_S144       26057          733  2.81306367 passed
#> 79  UHM1240.20662_S137        2415          733 30.35196687 failed
#> 80  UHM1246.20911_S156       11130          733  6.58580413 passed
#> 81  UHM1247.20912_S168       30259          733  2.42241978 passed
#> 82  UHM1248.20671_S150        1098          733 66.75774135 failed
#> 83  UHM1256.20666_S185        5327          733 13.76009011 passed
#> 84  UHM1260.20692_S117       21261          733  3.44762711 passed
#> 85  UHM1270.20673_S174        2835          733 25.85537919 failed
#> 86  UHM1271.20493_S128       13333          733  5.49763744 passed
#> 87  UHM1272.20494_S140        5301          733 13.82757970 passed
#> 88  UHM1274.20650_S183        8174          733  8.96745779 passed
#> 89  UHM1275.20693_S129       37017          733  1.98017127 passed
#> 90  UHM1282.20695_S153       37437          733  1.95795603 passed
#> 91  UHM1287.20639_S146        7278          733 10.07144820 passed
#> 92  UHM1291.20512_S166       14068          733  5.21040660 passed
#> 93  UHM1296.20646_S135      200764          733  0.36510530 passed
#> 94  UHM1319.20657_S172       50837          733  1.44186321 passed
#> 95  UHM1324.20509_S130        6832          733 10.72892272 passed
#> 96  UHM1327.20641_S170      507439          733  0.14445086 passed
#> 97  UHM1328.20668_S114         796          733 92.08542714 failed
#> 98  UHM1334.20513_S178       19550          733  3.74936061 passed
#> 99  UHM1338.20495_S152        2481          733 29.54453849 failed
#> 100 UHM1341.20698_S189         803          733 91.28268991 failed
#> 101 UHM1356.20637_S122        1081          733 67.80758557 failed
#> 102 UHM1380.20676_S115       83330          733  0.87963519 passed
#> 103 UHM1383.20690_S188       42801          733  1.71257681 passed
#> 104 UHM1385.20659_S101        1368          733 53.58187135 failed
#> 105 UHM1399.20852_S113        4542          733 16.13826508 passed
#> 106 UHM1400.20853_S125        2290          733 32.00873362 failed
#> 107 UHM1401.20854_S137        2390          733 30.66945607 failed
#> 108 UHM1402.20855_S149          20            0  0.00000000 failed
#> 109 UHM1403.20856_S161        4178          733 17.54427956 passed
#> 110 UHM1405.20857_S173       14002          733  5.23496643 passed
#> 111 UHM1406.20858_S185        2058          733 35.61710398 failed
#> 112 UHM1414.20859_S102       35800          733  2.04748603 passed
#> 113 UHM1419.20860_S114        6979          733 10.50293738 passed
#> 114 UHM1427.20485_S127        1500          733 48.86666667 failed
#> 115 UHM1428.20486_S139      530317          733  0.13821922 passed
#> 116 UHM1429.20487_S151      233757          733  0.31357350 passed
#> 117 UHM1430.20488_S163      170156          733  0.43078117 passed
#> 118 UHM1432.20489_S175      145970          733  0.50215798 passed
#> 119 UHM1435.20484_S115       37756          733  1.94141329 passed
#> 120  UHM162.20656_S160       16900          733  4.33727811 passed
#> 121  UHM198.20681_S175       17603          733  4.16406294 passed
#> 122  UHM200.20670_S138       36610          733  2.00218520 passed
#> 123  UHM203.20685_S128       20450          733  3.58435208 passed
#> 124  UHM204.20505_S177        4644          733 15.78380706 passed
#> 125  UHM206.20506_S189        1589          733 46.12964128 failed
#> 126  UHM207.20689_S176       36971          733  1.98263504 passed
#> 127  UHM208.20507_S106       12640          733  5.79905063 passed
#> 128  UHM211.20502_S141        1721          733 42.59151656 failed
#> 129  UHM215.20504_S165        3288          733 22.29318735 failed
#> 130  UHM216.20525_S132        1421          733 51.58339198 failed
#> 131  UHM219.20526_S144         747          733 98.12583668 failed
#> 132  UHM236.20527_S156        1153          733 63.57328708 failed
#> 133  UHM238.20503_S153        3219          733 22.77104691 failed
#> 134  UHM245.20634_S181        5660          733 12.95053004 passed
#> 135  UHM252.20654_S136       30529          733  2.40099577 passed
#> 136  UHM267.20496_S164       14174          733  5.17144067 passed
#> 137  UHM274.20677_S127       10383          733  7.05961668 passed
#> 138  UHM276.20682_S187        7782          733  9.41917245 passed
#> 139  UHM280.20497_S176        4501          733 16.28526994 passed
#> 140  UHM286.20521_S179       18231          733  4.02062421 passed
#> 141  UHM289.20522_S191        4847          733 15.12275634 passed
#> 142  UHM294.20523_S108        4268          733 17.17432052 passed
#> 143  UHM298.20696_S165      171431          733  0.42757728 passed
#> 144  UHM325.20644_S111       43328          733  1.69174668 passed
#> 145  UHM337.20508_S118        5764          733 12.71686329 passed
#> 146  UHM354.20631_S145        1253          733 58.49960096 failed
#> 147  UHM356.20511_S154        3772          733 19.43266172 passed
#> 148  UHM369.20869_S127           0            0         NaN   <NA>
#> 149  UHM370.20870_S139       33855          733  2.16511594 passed
#> 150  UHM372.20871_S151       16408          733  4.46733301 passed
#> 151  UHM373.20872_S163       39692          733  1.84671974 passed
#> 152  UHM374.20873_S175      172521          733  0.42487581 passed
#> 153  UHM375.20874_S187       13411          733  5.46566252 passed
#> 154  UHM377.20875_S104       28094          733  2.60909803 passed
#> 155  UHM382.20876_S116      219952          733  0.33325453 passed
#> 156  UHM386.20877_S128       75850          733  0.96638102 passed
#> 157  UHM387.20878_S140       87685          733  0.83594686 passed
#> 158  UHM414.20679_S151      156593          733  0.46809244 passed
#> 159  UHM418.20861_S126       64320          733  1.13961443 passed
#> 160  UHM422.20862_S138        3245          733 22.58859784 failed
#> 161  UHM425.20863_S150       31337          733  2.33908798 passed
#> 162  UHM426.20630_S133        1277          733 57.40015662 failed
#> 163  UHM428.20640_S158      143879          733  0.50945586 passed
#> 164  UHM429.20655_S148        2370          733 30.92827004 failed
#> 165   UHM435.20643_S99        6470          733 11.32921175 passed
#> 166  UHM437.20864_S162       47278          733  1.55040399 passed
#> 167  UHM439.20660_S113       36708          733  1.99683993 passed
#> 168  UHM443.20524_S120       15452            0  0.00000000 failed
#> 169  UHM445.20665_S173     1596109          733  0.04592418 failed
#> 170  UHM447.20879_S152      311393          733  0.23539386 passed
#> 171  UHM448.20865_S174        7439          733  9.85347493 passed
#> 172  UHM452.20880_S164       16254          733  4.50965916 passed
#> 173  UHM454.20866_S186       31733          733  2.30989821 passed
#> 174  UHM455.20881_S176       36439          733  2.01158100 passed
#> 175  UHM458.20882_S188      210285          733  0.34857455 passed
#> 176  UHM459.20883_S105      131999          733  0.55530724 passed
#> 177  UHM461.20867_S103        5986          733 12.24523889 passed
#> 178  UHM467.20868_S115       52866          733  1.38652442 passed
#> 179  UHM470.20629_S121       20818          733  3.52099145 passed
#> 180  UHM476.20510_S142        9551          733  7.67458905 passed
#> 181  UHM478.20645_S123        1549          733 47.32085216 failed
#> 182  UHM479.20647_S147         795          733 92.20125786 failed
#> 183  UHM481.20499_S105        1843          733 39.77211069 failed
#> 184  UHM482.20686_S140         985          733 74.41624365 failed
#> 185  UHM483.20699_S106      189124          733  0.38757640 passed
#> 186  UHM519.20678_S139       17833          733  4.11035720 passed
#> 187  UHM520.20669_S126      622602          733  0.11773171 passed
#> 188  UHM836.20481_S174        2525          733 29.02970297 failed
#> 189  UHM837.20482_S186         815          733 89.93865031 failed
#> 190  UHM838.20483_S103      404227          733  0.18133376 passed
#> 191  UHM891.20480_S162      193049          733  0.37969635 passed
#> 192  UHM892.20628_S109       74507          733  0.98380018 passed
#> 193  UHM893.20691_S105     1056846          733  0.06935731 failed
#> 194  UHM894.20636_S110      407335          733  0.17995016 passed
#> 195  UHM895.20632_S157       46442          733  1.57831273 passed
#> 196  UHM896.20697_S177      557848          733  0.13139780 passed
#> 197  UHM897.20687_S152      195868          733  0.37423163 passed
#> 198  UHM898.20490_S187       86962          733  0.84289690 passed
#> 199  UHM899.20684_S116       26831            0  0.00000000 failed
#> 200  UHM900.20491_S104      426029          733  0.17205402 passed
#> 201  UHM901.20638_S134       64944          733  1.12866470 passed
#> 202  UHM902.20680_S163       15998          733  4.58182273 passed
#> 203  UHM903.20683_S104        7055          733 10.38979447 passed
#> 204  UHM904.20663_S149       90263          733  0.81207139 passed
#> 205  UHM905.20694_S141      189294          733  0.38722833 passed
#> 206  UHM906.20661_S125      738140          733  0.09930366 failed
#> 207  UHM907.20688_S164        5805          733 12.62704565 passed
#> 208  UHM908.20492_S116       12353          733  5.93378127 passed
#> 209  UHM909.20653_S124        1835          733 39.94550409 failed
#> 210  UHM910.20658_S184       27025          733  2.71230342 passed
#> 211  UHM965.20633_S169        5604          733 13.07994290 passed
#> 212  UHM966.20839_S147        2255          733 32.50554324 failed
#> 213  UHM967.20840_S159       37386          733  1.96062697 passed
#> 214  UHM968.20667_S102         752          733 97.47340426 failed
#> 215  UHM969.20841_S171      155356          733  0.47181956 passed
#> 216  UHM971.20842_S183       49417          733  1.48329522 passed
#> 217  UHM973.20674_S186        4471          733 16.39454261 passed
#> 218  UHM974.20528_S168         756          733 96.95767196 failed
#> 219  UHM975.20843_S100       46718          733  1.56898840 passed
#> 220  UHM977.20844_S112       14316          733  5.12014529 passed
#> 221  UHM978.20845_S124      254394          733  0.28813573 passed
#> 222  UHM979.20846_S136       29271          733  2.50418503 passed
#> 223   UHM980.20827_S98        1201          733 61.03247294 failed
#> 224   UHM981.20635_S98        4288          733 17.09421642 passed
#> 225  UHM982.20836_S111      121746          733  0.60207317 passed
#> 226  UHM983.20652_S112       17181          733  4.26634073 passed
#> 227  UHM984.20847_S148      208665          733  0.35128076 passed
#> 228  UHM985.20848_S160       15054          733  4.86913777 passed
#> 229  UHM988.20849_S172        3510          733 20.88319088 failed
#> 230  UHM989.20850_S184      103723          733  0.70668993 passed
#> 231  UHM991.20851_S101        4440          733 16.50900901 passed
#> 232  UHM993.20837_S123       67439          733  1.08690817 passed
#> 233  UHM996.20706_S190         687          366 53.27510917 failed
#> 234  UHM997.20649_S171        7992          733  9.17167167 passed
#> 235  UHM998.20714_S191         519          366 70.52023121 failed
#> 236  UHM999.20713_S179       19728          366  1.85523114 passed
#> 
#> $phy_tree
#> 
#> Phylogenetic tree with 10451 tips and 10450 internal nodes.
#> 
#> Tip labels:
#>   b0d8709d00c52cd6f95e80208ec58bed, 8802fa20235c7208c28a77bf31b16ab0, 7f78e32ee8be7c4e84c2468a78e56ed1, e6a1c236f2da2bb42b7863b40b151857, 4bb6692c6d9f4a818a80b78eefb05ee8, 4dccbfe4f811fe0d5871482a35c0256e, ...
#> Node labels:
#>   root, 0.860, 0.301, 0.963, 0.957, 0.566, ...
#> 
#> Rooted; includes branch length(s).

 physeq_absolute <- absolute$obj_adj
 pps_Abs <- get_long_format_data(physeq_absolute)

 # calculation for relative abundance needs sum of total reads
 # total_reads <- sum(pps_Abs$Abundance)

 # Generate an alluvial plot using the extended palette
 alluvial_plot_abs <- alluvial_plot(
   data = pps_Abs,
   axes = c( "Host.genus","Ecoregion.III", "Diet"),
   abundance_threshold = 10000,
   fill_variable = "Family",
   silent = TRUE,
   abundance_type = "absolute",
   top_taxa = 15,
   text_size = 4,
   legend_ncol = 1,
   custom_colors = DspikeIn::color_palette$light_MG  # Use the extended palette from your package
 )
 alluvial_plot_abs

 ps <- physeq_ITSOTU

  # TC Normalization
  result_TC <- normalization_set(ps, method = "TC", groups = "Host.species")
  normalized_ps_TC <- result_TC$dat.normed
  scaling_factors_TC <- result_TC$scaling.factor

  # UQ Normalization
  data("physeq_ITSOTU", package = "DspikeIn")
  ps <- physeq_16SOTU
  result_UQ <- normalization_set(ps, method = "UQ", groups = "Host.species")
  normalized_ps_UQ <- result_UQ$dat.normed
  scaling_factors_UQ <- result_UQ$scaling.factor

  # Median Normalization
  data("physeq_ITSOTU", package = "DspikeIn")
  ps <- physeq_16SOTU
  result_med <- normalization_set(ps, method = "med", groups = "Host.species")
  normalized_ps_med <- result_med$dat.normed
  scaling_factors_med <- result_med$scaling.factor

  # DESeq Normalization
  data("physeq_ITSOTU", package = "DspikeIn")
  ps <- physeq_16SOTU
  ps_n <- remove_zero_negative_count_samples(ps)
  result_DESeq <- normalization_set(ps_n, method = "DESeq", groups = "Animal.type")
  normalized_ps_DESeq <- result_DESeq$dat.normed
  scaling_factors_DESeq <- result_DESeq$scaling.factor

  # Poisson Normalization
  data("physeq_ITSOTU", package = "DspikeIn")
  ps <- physeq_16SOTU
  result_Poisson <- normalization_set(ps, method = "Poisson", groups = "Host.genus")
  normalized_ps_Poisson <- result_Poisson$dat.normed
  scaling_factors_Poisson <- result_Poisson$scaling.factor

  # Quantile Normalization
  data("physeq_ITSOTU", package = "DspikeIn")
  ps <- physeq_16SOTU
  result_QN <- normalization_set(ps, method = "QN")
  normalized_ps_QN <- result_QN$dat.normed
  scaling_factors_QN <- result_QN$scaling.factor

  # TMM Normalization
  data("physeq_ITSOTU", package = "DspikeIn")
  ps <- physeq_16SOTU
  result_TMM <- normalization_set(ps, method = "TMM", groups = "Animal.type")
  normalized_ps_TMM <- result_TMM$dat.normed
  scaling_factors_TMM <- result_TMM$scaling.factor

  # CLR Normalization
  data("physeq_ITSOTU", package = "DspikeIn")
  ps <- physeq_16SOTU
  result_clr <- normalization_set(ps, method = "clr")
  normalized_ps_clr <- result_clr$dat.normed
  scaling_factors_clr <- result_clr$scaling.factor

  # Rarefying
  data("physeq_ITSOTU", package = "DspikeIn")
  ps <- physeq_16SOTU
  result_rar <- normalization_set(ps, method = "rar")
  normalized_ps_rar <- result_rar$dat.normed
  scaling_factors_rar <- result_rar$scaling.factor

  # CSS Normalization
  data("physeq_ITSOTU", package = "DspikeIn")
  ps <- physeq_16SOTU
  result_css <- normalization_set(ps, method = "css")
  normalized_ps_css <- result_css$dat.normed
  scaling_factors_css <- result_css$scaling.factor

  # TSS Normalization
  data("physeq_ITSOTU", package = "DspikeIn")
  ps <- physeq_16SOTU
  result_tss <- normalization_set(ps, method = "tss")
  normalized_ps_tss <- result_tss$dat.normed
  scaling_factors_tss <- result_tss$scaling.factor

  # RLE Normalization
  data("physeq_ITSOTU", package = "DspikeIn")
  ps <- physeq_16SOTU
  result_rle <- normalization_set(ps, method = "rle")
  normalized_ps_rle <- result_rle$dat.normed
  scaling_factors_rle <- result_rle$scaling.factor

ridge_physeq <- ridge_plot_it(physeq_ITSOTU, taxrank = "Family", top_n = 10)+ scale_fill_manual(values = DspikeIn::color_palette$cool_MG)
ridge_physeq

ps <- physeq_ITSOTU
  result_css <- normalization_set(ps, method = "css")
#> Removing 1 samples with zero, negative counts, or NA values.
  normalized_ps_css <- result_css$dat.normed
  
ridge_physeq <- ridge_plot_it(normalized_ps_css, taxrank = "Family", top_n = 10)+ scale_fill_manual(values = DspikeIn::color_palette$cool_MG)
ridge_physeq

absolute <- phyloseq::subset_taxa(physeq_absolute, Genus!="Dekkera")
Caudate_abs <- phyloseq::subset_samples(absolute, Clade.Order == "Caudate"  )
Three_Genara_abs <- phyloseq::subset_samples(Caudate_abs, Host.genus %in% c("Desmognathus", "Plethodon", "Eurycea"))
Three_Genara_abs_BlueRidge<- phyloseq::subset_samples(Three_Genara_abs,Ecoregion.III=="Blue Ridge" )
Desmog_Blue_Ins_ITS_abs<- phyloseq::subset_samples(Three_Genara_abs_BlueRidge,Host.genus=="Desmognathus")

 results_DESeq2 <- perform_and_visualize_DA(
   obj = Desmog_Blue_Ins_ITS_abs,
   method = "DESeq2",
   group_var = "Host.taxon",
   contrast = c("Desmognathus monticola", "Desmognathus imitator" ),
   output_csv_path = "DA_DESeq2.csv",
   target_glom = "Genus",
   significance_level = 0.05
 )

head(results_DESeq2$results)  
#> # A tibble: 6 × 17
#>   baseMean     logFC lfcSE      stat pvalue  padj   FDR Significance group OTU  
#>      <dbl>     <dbl> <dbl>     <dbl>  <dbl> <dbl> <dbl> <chr>        <fct> <chr>
#> 1     2     1.03e-14 0.544  1.90e-14  1.00      1     1 Not Signifi… Desm… 71c9…
#> 2     1     1.76e-23 0.748  2.35e-23  1         1     1 Not Signifi… Desm… 1bfe…
#> 3     1     1.76e-23 0.748  2.35e-23  1         1     1 Not Signifi… Desm… 9757…
#> 4     5.02 -3.56e- 2 0.357 -9.99e- 2  0.920     1     1 Not Signifi… Desm… d8b7…
#> 5     1     1.76e-23 0.748  2.35e-23  1         1     1 Not Signifi… Desm… f80a…
#> 6     3     8.42e-15 0.452  1.86e-14  1.00      1     1 Not Signifi… Desm… 097f…
#> # ℹ 7 more variables: Kingdom <chr>, Phylum <chr>, Class <chr>, Order <chr>,
#> #   Family <chr>, Genus <chr>, Species <chr>
results_DESeq2$obj_significant
#> phyloseq-class experiment-level object
#> otu_table()   OTU Table:          [ 4 taxa and 42 samples ]:
#> sample_data() Sample Data:        [ 42 samples by 39 sample variables ]:
#> tax_table()   Taxonomy Table:     [ 4 taxa by 7 taxonomic ranks ]:
#> phy_tree()    Phylogenetic Tree:  [ 4 tips and 3 internal nodes ]:
#> refseq()      DNAStringSet:       [ 4 reference sequences ]
#> taxa are rows
results_DESeq2$plot

results_DESeq2$bar_plot

# Relative abundance
# data("physeq_ITSOTU",package = "DspikeIn")
relative <- tidy_phyloseq_tse(physeq_ITSOTU)
relative <- phyloseq::subset_taxa(physeq_ITSOTU, Genus!="Dekkera")
Caudate_rel <- phyloseq::subset_samples(relative, Clade.Order == "Caudate"  )
Three_Genara_rel <- phyloseq::subset_samples(Caudate_rel, Host.genus %in% c("Desmognathus", "Plethodon", "Eurycea"))
Three_Genara_rel_BlueRidge<- phyloseq::subset_samples(Three_Genara_rel,Ecoregion.III=="Blue Ridge" )
Desmog_Blue_Ins_ITS_rel<- phyloseq::subset_samples(Three_Genara_rel_BlueRidge,Host.genus=="Desmognathus")

 results_DESeq2_rel <- perform_and_visualize_DA(
   obj = Desmog_Blue_Ins_ITS_rel,
   method = "DESeq2",
   group_var = "Host.taxon",
   contrast = c("Desmognathus monticola", "Desmognathus imitator" ),
   output_csv_path = "DA_DESeq2.csv",
   target_glom = "Genus",
   significance_level = 0.05
 )
results_DESeq2_rel$plot

results_DESeq2_rel$results  #  sig taxa
#> # A tibble: 1,077 × 17
#>    baseMean     logFC lfcSE      stat pvalue  padj   FDR Significance    group  
#>       <dbl>     <dbl> <dbl>     <dbl>  <dbl> <dbl> <dbl> <chr>           <fct>  
#>  1     3     7.47e-15 0.430  1.74e-14  1.00      1     1 Not Significant Desmog…
#>  2     1     1.73e-23 0.745  2.32e-23  1         1     1 Not Significant Desmog…
#>  3     1     1.73e-23 0.745  2.32e-23  1         1     1 Not Significant Desmog…
#>  4     1     1.73e-23 0.745  2.32e-23  1         1     1 Not Significant Desmog…
#>  5     5.02 -3.56e- 2 0.334 -1.07e- 1  0.915     1     1 Not Significant Desmog…
#>  6     1     1.73e-23 0.745  2.32e-23  1         1     1 Not Significant Desmog…
#>  7     1     1.73e-23 0.745  2.32e-23  1         1     1 Not Significant Desmog…
#>  8     3     7.47e-15 0.430  1.74e-14  1.00      1     1 Not Significant Desmog…
#>  9     3     7.47e-15 0.430  1.74e-14  1.00      1     1 Not Significant Desmog…
#> 10     4     4.87e-15 0.374  1.30e-14  1.00      1     1 Not Significant Desmog…
#> # ℹ 1,067 more rows
#> # ℹ 8 more variables: OTU <chr>, Kingdom <chr>, Phylum <chr>, Class <chr>,
#> #   Order <chr>, Family <chr>, Genus <chr>, Species <chr>
results_DESeq2_rel$bar_bplot
#> NULL

# ===========================================================
#  Visualization of community composition 
# ===========================================================


relative_Des <- physeq_ITSOTU %>%
  phyloseq::subset_taxa(Genus != "Dekkera") %>%
  phyloseq::subset_samples(Clade.Order == "Caudate") %>%
  phyloseq::subset_samples(Host.genus %in% c("Desmognathus", "Plethodon", "Eurycea")) %>%
  phyloseq::subset_samples(Ecoregion.III == "Blue Ridge") %>%
  phyloseq::subset_samples(Host.genus == "Desmognathus") 
  

taxa_barplot(
  relative_Des,
  target_glom = "Genus",
  custom_tax_names = NULL,
  normalize = TRUE,
  treatment_variable = "Animal.ecomode",
  abundance_type = "relative",
  x_angle = 25,
  fill_variable = "Genus",
  facet_variable = "Diet",
  top_n_taxa = 15,
  palette = DspikeIn::color_palette$cool_MG,
  legend_size = 11,
  legend_columns = 1,
  x_scale = "free",
  xlab = NULL
)
#> $barplot

# ===========================================================
# 1. Initialization and loading NetWorks for Comparision
# ===========================================================

#library(SpiecEasi)
#library(ggnet)
library(igraph)
library(tidyr)
library(dplyr)
library(ggpubr)

# To create a microbial co-occurrence network, you can refer to the SpiecEasi package available at:
# SpiecEasi GitHub Repository  https://github.com/zdk123/SpiecEasi 

# herp.Bas.rel.f is a merged phyloseq object for both bacterial and fungal domains
# herp.spiec <- spiec.easi(herp.Bas.rel.f, method='mb', lambda.min.ratio=1e-3, nlambda=250,pulsar.select=TRUE )
# write_graph(herp.spiec, "Complete.graphml", "graphml")

Complete <- load_graphml("Complete.graphml")
NoBasid <- load_graphml("NoBasid.graphml")
NoHubs <- load_graphml("NoHubs.graphml")

# ===========================================================
# 2. Metrics Calculation
# ===========================================================

result_Complete <- node_level_metrics(Complete)
result_NoHubs <- node_level_metrics(NoHubs)
result_NoBasid <- node_level_metrics(NoBasid)

Complete_metrics<-result_Complete$metrics
Nohub_metrics<-result_NoHubs$metrics
Nobasid_metrics<-result_NoBasid$metrics

Complete_metrics <- data.frame(lapply(Complete_metrics, as.character), stringsAsFactors = FALSE)
Nohub_metrics <- data.frame(lapply(Nohub_metrics, as.character), stringsAsFactors = FALSE)
Nobasid_metrics <- data.frame(lapply(Nobasid_metrics, as.character), stringsAsFactors = FALSE)


print(vcount(Complete))  # Number of nodes
#> [1] 308
print(ecount(Complete))  # Number of edges
#> [1] 1144

print(vcount(NoBasid))
#> [1] 307
print(ecount(NoBasid))
#> [1] 1187

print(vcount(NoHubs))
#> [1] 286
print(ecount(NoHubs))
#> [1] 916


metrics_scaled <- bind_rows(
  Complete_metrics %>% mutate(Network = "Complete"),
  Nohub_metrics %>% mutate(Network = "NoHubs"),
  Nobasid_metrics %>% mutate(Network = "NoBasid")
) %>%
dplyr::mutate(dplyr::across(where(is.numeric), scale))

metrics_long_scaled <- metrics_scaled %>%
  tidyr::pivot_longer(cols = -c(Node, Network), names_to = "Metric", values_to = "Value")

# ===========================================================
# 3. Visualization
# ===========================================================

# Remove missing values 
metrics_long_scaled <- na.omit(metrics_long_scaled)

# We visualize only six metrics 
selected_metrics <- c("Degree", "Closeness", "Betweenness", 
                      "EigenvectorCentrality", "PageRank", "Transitivity")

metrics_long_filtered <- metrics_long_scaled %>%
  filter(Metric %in% selected_metrics) %>%
  mutate(
    Value = as.numeric(as.character(Value)),
    Network = recode(Network, 
                     "Complete" = "Complete Network",
                     "NoHubs" = "Network & Module Hubs Removed",
                     "NoBasid" = "Basidiobolus Subnetwork Removed") ) %>%
  na.omit()  # Remove any NA 

metrics_long_filtered$Network <- factor(metrics_long_filtered$Network, 
                                        levels = c("Complete Network", 
                                                   "Network & Module Hubs Removed", 
                                                  "Basidiobolus Subnetwork Removed"))

# DspikeIn::color_palette$light_MG
network_colors <- c(
  "Complete Network" = "#F1E0C5", 
  "Network & Module Hubs Removed" = "#D2A5A1", 
  "Basidiobolus Subnetwork Removed" = "#B2C3A8"
)

# statistical comparisons a vs b
comparisons <- list(
  c("Complete Network", "Network & Module Hubs Removed"),
  c("Complete Network", "Basidiobolus Subnetwork Removed"),
  c("Network & Module Hubs Removed", "Basidiobolus Subnetwork Removed")
)

networks_in_data <- unique(metrics_long_filtered$Network)
comparisons <- comparisons[sapply(comparisons, function(pair) all(pair %in% networks_in_data))]

ggplot(metrics_long_filtered, aes(x = Network, y = Value, fill = Network)) +
  geom_boxplot(outlier.shape = NA) +  
  geom_jitter(aes(color = Network), 
              position = position_jitter(0.2), alpha = 0.2, size = 1.5) +  
  scale_fill_manual(values = network_colors) +
  scale_color_manual(values = network_colors) +  
  facet_wrap(~ Metric, scales = "free_y", labeller = label_wrap_gen(width = 20)) +
  ggpubr::stat_compare_means(method = "wilcox.test", label = "p.signif", comparisons = comparisons) +
  theme_minimal() +
  theme(
    axis.title.x = element_blank(),
    axis.title.y = element_blank(),
    axis.text.x = element_text(size = 10, angle = 10, hjust = 0.8),
    strip.text = element_text(size = 12, margin = margin(t = 15, b = 5)),
    legend.position = "top",
    legend.text = element_text(size = 13),
    legend.title = element_text(size = 13, face = "bold"),
    plot.title = element_text(size = 13, face = "bold")
  ) +
  labs(title = "Selected Node Metrics Across Networks", fill = "Network Type", color = "Network Type")

 Complete <- load_graphml("Complete.graphml")
 result2 <- extract_neighbors(graph = Complete,
 target_node = "OTU69:Basidiobolus_sp", mode = "all")
 print(result2$summary)
#>                Type                                           Node
#> 1    First Neighbor                            OTU8:Mortierella_sp
#> 2    First Neighbor                           OTU13:Mortierella_sp
#> 3    First Neighbor                           OTU15:Mortierella_sp
#> 4    First Neighbor                            OTU16:Ascomycota_sp
#> 5    First Neighbor                            OTU18:Helotiales_sp
#> 6    First Neighbor                 OTU19:Margaritispora_monticola
#> 7    First Neighbor                           OTU20:Scytalidium_sp
#> 8    First Neighbor                OTU27:Penicillium_glaucoalbidum
#> 9    First Neighbor                              OTU40:Tremella_sp
#> 10   First Neighbor                              OTU50:Taphrina_sp
#> 11   First Neighbor                    OTU135:uncultured_bacterium
#> 12   First Neighbor                    OTU146:uncultured_bacterium
#> 13   First Neighbor                    OTU153:uncultured_bacterium
#> 14   First Neighbor                    OTU230:uncultured_bacterium
#> 15   First Neighbor                   OTU247:uncultured_Firmicutes
#> 16   First Neighbor                    OTU287:uncultured_bacterium
#> 17   First Neighbor                OTU300:Robinsoniella_peoriensis
#> 18   First Neighbor              OTU312:uncultured_Verrucomicrobia
#> 19  Second Neighbor                     OTU4:Mortierella_pulchella
#> 20  Second Neighbor                    OTU12:Mortierella_gemmifera
#> 21  Second Neighbor                               OTU36:Inocybe_sp
#> 22  Second Neighbor                     OTU7:Mortierella_pulchella
#> 23  Second Neighbor                       OTU66:Tausonia_pullulans
#> 24  Second Neighbor                    OTU141:uncultured_bacterium
#> 25  Second Neighbor                    OTU268:uncultured_bacterium
#> 26  Second Neighbor                           OTU1:Lilapila_jurana
#> 27  Second Neighbor                           OTU11:Podila_humilis
#> 28  Second Neighbor                       OTU22:Sclerotiniaceae_sp
#> 29  Second Neighbor                           OTU51:Tremellales_sp
#> 30  Second Neighbor                      OTU42:Armillaria_borealis
#> 31  Second Neighbor                  OTU44:Kuehneromyces_rostratus
#> 32  Second Neighbor              OTU67:Rhodosporidiobolus_colostri
#> 33  Second Neighbor                                 OTU86:Fungi_sp
#> 34  Second Neighbor                OTU28:Penicillium_glaucoalbidum
#> 35  Second Neighbor                      OTU29:Cladophialophora_sp
#> 36  Second Neighbor                             OTU85:Mucorales_sp
#> 37  Second Neighbor                      OTU25:Cryptosporiopsis_sp
#> 38  Second Neighbor                OTU34:Paraboeremia_selaginellae
#> 39  Second Neighbor                         OTU61:Rozellomycota_sp
#> 40  Second Neighbor                       OTU82:Mycoacia_fuscoatra
#> 41  Second Neighbor               OTU151:Ruminococcaceae_bacterium
#> 42  Second Neighbor                    OTU198:uncultured_bacterium
#> 43  Second Neighbor                OTU289:uncultured_Bacteroidetes
#> 44  Second Neighbor                   OTU10:Dissophora_globulifera
#> 45  Second Neighbor                  OTU31:Clonostachys_krabiensis
#> 46  Second Neighbor                       OTU45:Ganoderma_carnosum
#> 47  Second Neighbor                         OTU78:Rozellomycota_sp
#> 48  Second Neighbor                         OTU79:Rozellomycota_sp
#> 49  Second Neighbor                           OTU14:Mortierella_sp
#> 50  Second Neighbor                                 OTU39:Fungi_sp
#> 51  Second Neighbor               OTU64:Curvibasidium_cygneicollum
#> 52  Second Neighbor                     OTU60:Chytriomycetaceae_sp
#> 53  Second Neighbor                                 OTU68:Fungi_sp
#> 54  Second Neighbor                         OTU70:Rozellomycota_sp
#> 55  Second Neighbor                  OTU54:Papiliotrema_flavescens
#> 56  Second Neighbor                          OTU55:Papiliotrema_sp
#> 57  Second Neighbor                           OTU62:Tremellales_sp
#> 58  Second Neighbor                       OTU73:Chytridiomycota_sp
#> 59  Second Neighbor                    OTU105:uncultured_bacterium
#> 60  Second Neighbor                    OTU200:uncultured_bacterium
#> 61  Second Neighbor                     OTU224:Breznakia_pachnodae
#> 62  Second Neighbor                    OTU310:uncultured_bacterium
#> 63  Second Neighbor              OTU100:uncultured_proteobacterium
#> 64  Second Neighbor                    OTU107:uncultured_bacterium
#> 65  Second Neighbor                      OTU123:anaerobic_digester
#> 66  Second Neighbor                    OTU124:uncultured_bacterium
#> 67  Second Neighbor                    OTU199:uncultured_bacterium
#> 68  Second Neighbor                    OTU206:uncultured_bacterium
#> 69  Second Neighbor                    OTU238:uncultured_bacterium
#> 70  Second Neighbor                    OTU261:uncultured_bacterium
#> 71  Second Neighbor                OTU269:uncultured_Bacteroidetes
#> 72  Second Neighbor                           OTU270:Alistipes_sp.
#> 73  Second Neighbor                    OTU290:uncultured_bacterium
#> 74  Second Neighbor                    OTU104:uncultured_bacterium
#> 75  Second Neighbor                    OTU112:uncultured_bacterium
#> 76  Second Neighbor                    OTU120:uncultured_bacterium
#> 77  Second Neighbor                    OTU148:uncultured_bacterium
#> 78  Second Neighbor                   OTU175:uncultured_Firmicutes
#> 79  Second Neighbor                    OTU215:uncultured_bacterium
#> 80  Second Neighbor                    OTU236:uncultured_bacterium
#> 81  Second Neighbor                    OTU239:uncultured_bacterium
#> 82  Second Neighbor                    OTU251:uncultured_bacterium
#> 83  Second Neighbor                    OTU254:uncultured_Rikenella
#> 84  Second Neighbor                OTU164:uncultured_Anaerotruncus
#> 85  Second Neighbor                    OTU165:uncultured_bacterium
#> 86  Second Neighbor                     OTU204:uncultured_organism
#> 87  Second Neighbor                    OTU205:uncultured_bacterium
#> 88  Second Neighbor                    OTU292:uncultured_bacterium
#> 89  Second Neighbor                OTU309:Akkermansia_glycaniphila
#> 90  Second Neighbor                    OTU197:uncultured_bacterium
#> 91  Second Neighbor                    OTU285:uncultured_bacterium
#> 92  Second Neighbor             OTU91:uncultured_Conexibacteraceae
#> 93  Second Neighbor                    OTU181:uncultured_bacterium
#> 94  Second Neighbor                  OTU194:Christensenella_minuta
#> 95  Second Neighbor               OTU235:Paenibacillus_taiwanensis
#> 96  Second Neighbor                         OTU243:Bacteroides_sp.
#> 97  Second Neighbor                    OTU280:uncultured_bacterium
#> 98  Second Neighbor                    OTU282:uncultured_bacterium
#> 99  Second Neighbor                    OTU291:uncultured_bacterium
#> 100 Second Neighbor                   OTU172:uncultured_Firmicutes
#> 101 Second Neighbor                OTU183:uncultured_Clostridiales
#> 102 Second Neighbor                            OTU185:unidentified
#> 103 Second Neighbor                    OTU196:uncultured_bacterium
#> 104 Second Neighbor                    OTU203:uncultured_bacterium
#> 105 Second Neighbor           OTU226:Erysipelotrichaceae_bacterium
#> 106 Second Neighbor                    OTU253:uncultured_bacterium
#> 107 Second Neighbor                  OTU277:Mucinivorans_hirudinis
#> 108 Second Neighbor                    OTU297:uncultured_bacterium
#> 109 Second Neighbor                    OTU306:uncultured_bacterium
#> 110 Second Neighbor                  OTU106:Mucinivorans_hirudinis
#> 111 Second Neighbor                    OTU111:Coprobacter_secundus
#> 112 Second Neighbor               OTU126:Actinomycetales_bacterium
#> 113 Second Neighbor OTU154:Hydrogenoanaerobacterium_saccharovorans
#> 114 Second Neighbor                    OTU166:uncultured_bacterium
#> 115 Second Neighbor                  OTU187:bacterium_endosymbiont
#> 116 Second Neighbor                    OTU208:uncultured_bacterium
#> 117 Second Neighbor                    OTU210:uncultured_bacterium
#> 118 Second Neighbor                    OTU262:uncultured_bacterium

# Load required libraries
library(igraph)
library(dplyr)
library(tidyr)
library(ggplot2)
library(ggrepel)

# Compute Node-Level Metrics
completeMetrics <- node_level_metrics(Complete)
NoHubsMetrics <- node_level_metrics(NoHubs)
NoBasidMetrics <- node_level_metrics(NoBasid)

# Ensure each dataset has a "Network" column before combining
completeMetrics$metrics <- completeMetrics$metrics %>% mutate(Network = "Complete Network")
NoHubsMetrics$metrics <- NoHubsMetrics$metrics %>% mutate(Network = "Network & Module Hubs Removed")
NoBasidMetrics$metrics <- NoBasidMetrics$metrics %>% mutate(Network = "Basidiobolus Subnetwork Removed")

# Combine All Data
combined_data <- bind_rows(
  completeMetrics$metrics, 
  NoHubsMetrics$metrics, 
  NoBasidMetrics$metrics
)


# Add Node Identifier if missing
if (!"Node" %in% colnames(combined_data)) {
  combined_data <- combined_data %>% mutate(Node = rownames(.))
}

# Convert `Network` into Factor
combined_data$Network <- factor(combined_data$Network, levels = c(
  "Complete Network", 
  "Network & Module Hubs Removed", 
  "Basidiobolus Subnetwork Removed"
))

# Convert Data to Long Format
metrics_long <- combined_data %>%
  pivot_longer(cols = c("Redundancy", "Efficiency", "Betweenness"),
               names_to = "Metric", values_to = "Value")

# Define Custom Colors and Shapes
network_colors <- c(
  "Complete Network" = "#F1E0C5",
  "Network & Module Hubs Removed" = "#D2A5A1",
  "Basidiobolus Subnetwork Removed" = "#B2C3A8"
)

network_shapes <- c(
  "Complete Network" = 21,
  "Network & Module Hubs Removed" = 22, 
  "Basidiobolus Subnetwork Removed" = 23  
)

# Determine Top 30% of Nodes to Label/Optional
metrics_long <- metrics_long %>%
  group_by(Network, Metric) %>%
  mutate(Label = ifelse(rank(-Value, ties.method = "random") / n() <= 0.3, Node, NA))

#?quadrant_plot() can creat plot for indivisual network
# plot <- quadrant_plot(metrics, x_metric = "Degree", y_metric = "Efficiency")

# Create comparision Plots  
create_metric_plot <- function(metric_name, data, title) {
  data_filtered <- data %>% filter(Metric == metric_name)
  median_degree <- median(data_filtered$Degree, na.rm = TRUE)
  median_value <- median(data_filtered$Value, na.rm = TRUE)
  
  ggplot(data_filtered, aes(x = Degree, y = Value, fill = Network)) +
    geom_point(aes(shape = Network), size = 3, stroke = 1, color = "black") +
    geom_text_repel(aes(label = Label), size = 3, max.overlaps = 50) +
    scale_fill_manual(values = network_colors) +
    scale_shape_manual(values = network_shapes) +
    geom_vline(xintercept = median_degree, linetype = "dashed", color = "black", size = 1) +
    geom_hline(yintercept = median_value, linetype = "dashed", color = "black", size = 1) +
    labs(
      title = title,
      x = "Degree",
      y = metric_name,
      fill = "Network",
      shape = "Network"
    ) +
    theme_minimal() +
    theme(
      plot.title = element_text(
        hjust = 0.5, size = 16, face = "bold",
        margin = margin(t = 10, b = 20)  # Moves the title downward
      ),
      axis.title = element_text(size = 14, face = "bold"),
      legend.position = "top",
      legend.title = element_text(size = 14, face = "bold"),
      legend.text = element_text(size = 12)
    )
}


# Generate Plots
plot_redundancy <- create_metric_plot("Redundancy", metrics_long, "Redundancy vs. Degree Across Networks")
plot_efficiency <- create_metric_plot("Efficiency", metrics_long, "Efficiency vs. Degree Across Networks")
plot_betweenness <- create_metric_plot("Betweenness", metrics_long, "Betweenness vs. Degree Across Networks")

# Save Plots
#ggsave("plot_redundancy_20percent.png", plot_redundancy, width = 8, height = 6)
#ggsave("plot_efficiency_20percent.png", plot_efficiency, width = 8, height = 6)
#ggsave("plot_betweenness_20percent.png", plot_betweenness, width = 8, height = 6)

# Print Plots
print(plot_redundancy)