# ============================================================================
#                         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
# ============================================================================

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.")
}

# 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_16SOTU", package = "DspikeIn")
# tse_16SOTU <- convert_phyloseq_to_tse(physeq_16SOTU)
# physeq_16SOTU <- convert_tse_to_phyloseq(tse_16SOTU)

physeq_16SOTU <- DspikeIn::tidy_phyloseq_tse(physeq_16SOTU)  # make it tidy 

# Check if metadata contains spiked volumes 
physeq_16SOTU@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 2 2 0 0 0 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] 2 0 0 0 2 2 0 0 0 0 0 0 2 2 2 2 2 2 2 0 0 0 2 2 2 2 0 0 0 2 2 2 2 0 0 0 2
#> [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [260] 1 1 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 2 2 2 2 2 2 2 2 2 2
#> [297] 2 2 2 2 2 2 2 2 2 2 2 2 1 2 1 1

# =====================================================================
#                      PREREQUISITE FOR 16S & CALCULATE SPIKED %
# =====================================================================
# Define spiked species and related parameters**

library(flextable)

spiked_cells <- 1847
species_name <- spiked_species <- c("Tetragenococcus_halophilus", "Tetragenococcus_sp.")
merged_spiked_species <- "Tetragenococcus_halophilus"

 
# Subset taxa for spiked species
Tetra <- phyloseq::subset_taxa(
  physeq_16SOTU, 
  Species %in% species_name)
hashcodes <- row.names(phyloseq::tax_table(Tetra))

# Subset samples based on spiked volume 
spiked_16S_OTU_spiked <- phyloseq::subset_samples(physeq_16SOTU, spiked.volume %in% c("2", "1"))

# if TSE format
# spiked_16S_OTU_TSE <- 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 

?DspikeIn::Pre_processing_species
Spiked_16S_sum_scaled <- Pre_processing_species(
  spiked_16S_OTU_spiked, 
  species_name, 
  merge_method = "sum", 
  output_file = "merged_physeq_sum.rds"
)

# ?calculate_spike_percentage

Perc <- calculate_spike_percentage(
  Spiked_16S_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_16S_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$spiked_species_merged
#> phyloseq-class experiment-level object
#> otu_table()   OTU Table:          [ 1 taxa and 264 samples ]:
#> sample_data() Sample Data:        [ 264 samples by 34 sample variables ]:
#> tax_table()   Taxonomy Table:     [ 1 taxa by 7 taxonomic ranks ]:
#> refseq()      DNAStringSet:       [ 1 reference sequences ]
#> taxa are rows
result$spiked_species_reads
#> # A tibble: 264 × 2
#>    Sample                 Total_Reads
#>    <chr>                        <dbl>
#>  1 spiked.blank.20433_S84           8
#>  2 spiked.blank.20817_S84       47066
#>  3 Std2uL.20625_S84             62433
#>  4 StdSwab1uL.20624_S72         17639
#>  5 STP1719.20422_S47            14549
#>  6 STP213.20423_S59                83
#>  7 STP268.20424_S71                17
#>  8 STP544.20419_S11              2259
#>  9 STP570.20420_S23               822
#> 10 STP579.20421_S35              1759
#> # ℹ 254 more rows
scaling_factors <- result$scaling_factors
scaling_factors
#> spiked.blank.20433_S84 spiked.blank.20817_S84       Std2uL.20625_S84 
#>           2.308750e+02           3.924277e-02           2.958371e-02 
#>   StdSwab1uL.20624_S72      STP1719.20422_S47       STP213.20423_S59 
#>           5.235558e-02           1.269503e-01           2.225301e+01 
#>       STP268.20424_S71       STP544.20419_S11       STP570.20420_S23 
#>           1.086471e+02           8.176184e-01           2.246959e+00 
#>       STP579.20421_S35       STP614.20418_S94      UHM1000.20604_S22 
#>           1.050028e+00           1.000000e+00           7.826271e+00 
#>      UHM1001.20609_S82      UHM1007.20622_S48      UHM1009.20614_S47 
#>           9.930108e+00           7.826271e+00           7.961207e+00 
#>      UHM1010.20621_S36      UHM1011.20606_S46      UHM1024.20620_S24 
#>           9.433095e-01           7.103846e+00           9.290744e-01 
#>      UHM1026.20607_S58      UHM1028.20613_S35      UHM1032.20605_S34 
#>           2.332071e+00           1.028396e+00           5.714728e-01 
#>      UHM1033.20619_S12      UHM1034.20616_S71      UHM1035.20611_S11 
#>           2.468195e-02           2.308750e+02           4.397619e+00 
#>      UHM1036.20612_S23      UHM1052.20615_S59       UHM1060.20723_S1 
#>           3.708835e+00           6.156667e+00           7.502945e-02 
#>      UHM1065.20724_S13      UHM1068.20732_S14      UHM1069.20742_S39 
#>           2.258222e-01           7.600823e+00           5.384840e+00 
#>      UHM1070.20725_S25      UHM1071.20733_S26      UHM1072.20734_S38 
#>           1.060885e+00           3.298214e+01           1.242934e+00 
#>      UHM1073.20735_S50      UHM1075.20726_S37      UHM1077.20736_S62 
#>           4.735897e+01           1.566582e+00           2.530137e+01 
#>      UHM1078.20727_S49      UHM1080.20737_S74      UHM1081.20728_S61 
#>           1.067630e+01           2.676812e+01           1.042325e+00 
#>      UHM1088.20738_S86       UHM1090.20739_S3      UHM1093.20729_S73 
#>           7.695833e+00           3.482934e-01           5.546547e+00 
#>      UHM1095.20730_S85      UHM1097.20623_S60      UHM1099.20608_S70 
#>           2.969931e-01           1.526446e-01           3.847917e+01 
#>      UHM1100.20788_S21      UHM1102.20789_S33      UHM1104.20790_S45 
#>           2.462667e+01           1.710185e+01           2.225301e+01 
#>      UHM1105.20791_S57       UHM1109.20531_S1      UHM1110.20568_S65 
#>           1.247973e+01           1.592241e+01           3.694000e+01 
#>      UHM1113.20792_S69      UHM1114.20793_S81      UHM1115.20794_S93 
#>           2.337975e+01           5.116343e+00           1.020442e+01 
#>      UHM1117.20795_S10      UHM1118.20796_S22      UHM1120.20797_S34 
#>           3.027869e+01           7.447581e+00           2.572423e+00 
#>      UHM1124.20798_S46      UHM1126.20799_S58      UHM1128.20800_S70 
#>           3.168096e+00           9.569948e+00           1.009290e+01 
#>       UHM1140.20555_S4      UHM1145.20801_S82      UHM1163.20405_S33 
#>           1.420769e+01           4.104444e+01           1.231333e+01 
#>      UHM1164.20402_S92      UHM1169.20552_S63       UHM1171.20579_S7 
#>           6.156667e+02           2.802306e-01           1.904124e+01 
#>      UHM1176.20404_S21      UHM1177.20546_S86      UHM1182.20576_S66 
#>           2.756716e+01           1.185494e+00           7.927039e+00 
#>      UHM1210.20802_S94      UHM1212.20803_S11      UHM1217.20804_S23 
#>           1.884694e+01           6.035948e+00           9.824468e+00 
#>      UHM1218.20805_S35      UHM1219.20806_S47      UHM1220.20807_S59 
#>           1.223179e+01           3.240351e+01           2.198810e+01 
#>      UHM1221.20808_S71      UHM1222.20809_S83      UHM1223.20810_S95 
#>           1.759048e+01           6.077657e-01           2.676812e+01 
#>      UHM1225.20811_S12      UHM1227.20812_S24      UHM1228.20813_S36 
#>           8.065502e+00           2.120551e+00           2.676812e+01 
#>      UHM1237.20814_S48      UHM1240.20566_S41      UHM1246.20815_S60 
#>           4.472155e+00           2.704246e+00           3.223386e+00 
#>      UHM1247.20816_S72      UHM1248.20575_S54      UHM1256.20570_S89 
#>           7.859574e+00           5.619106e-01           2.049945e+00 
#>      UHM1260.20596_S21      UHM1270.20577_S78      UHM1271.20397_S32 
#>           3.008143e+00           8.511521e+00           2.068309e+00 
#>      UHM1272.20398_S44      UHM1274.20554_S87      UHM1275.20597_S33 
#>           1.207979e+00           3.955032e+00           1.535328e+00 
#>      UHM1282.20599_S57      UHM1287.20543_S50      UHM1291.20416_S70 
#>           4.835079e-01           9.332996e-01           7.076628e+00 
#>      UHM1296.20550_S39      UHM1319.20561_S76      UHM1324.20413_S34 
#>           2.430263e+01           1.178685e+00           4.265589e+00 
#>      UHM1327.20545_S74      UHM1328.20572_S18      UHM1334.20417_S82 
#>           2.530137e+01           5.554887e-01           1.191613e+01 
#>      UHM1338.20399_S56      UHM1341.20602_S93      UHM1356.20541_S26 
#>           2.430263e+00           2.294410e-01           2.280247e+00 
#>      UHM1380.20580_S19      UHM1383.20594_S92       UHM1385.20563_S5 
#>           5.996753e+00           4.387173e+00           9.040184e-02 
#>      UHM1399.20756_S17      UHM1400.20757_S29      UHM1401.20758_S41 
#>           2.011983e+00           5.844937e+00           7.651201e-01 
#>      UHM1402.20759_S53      UHM1403.20760_S65      UHM1405.20761_S77 
#>           1.112651e+01           6.095710e+00           8.630841e+00 
#>      UHM1406.20762_S89       UHM1414.20763_S6      UHM1419.20764_S18 
#>           8.671362e+00           1.726168e+01           3.784836e+00 
#>      UHM1427.20389_S31      UHM1428.20390_S43      UHM1429.20391_S55 
#>           1.793204e+01           1.000000e+00           6.368966e+01 
#>      UHM1430.20392_S67      UHM1432.20393_S79      UHM1435.20388_S19 
#>           1.679091e+02           1.026111e+02           1.000000e+00 
#>       UHM162.20560_S64       UHM198.20585_S79         UHM20.3314_S52 
#>           2.623580e+00           5.061661e-01           4.295349e+01 
#>         UHM20.3315_S64       UHM204.20409_S81       UHM206.20410_S93 
#>           5.292264e+00           2.798485e+01           1.000000e+00 
#>       UHM207.20593_S80       UHM208.20411_S10       UHM211.20406_S45 
#>           9.235000e+00           4.723785e+00           1.126220e+01 
#>       UHM215.20408_S69       UHM216.20429_S36       UHM219.20430_S48 
#>           2.052222e+02           2.931746e+01           8.778100e-02 
#>       UHM236.20431_S60       UHM238.20407_S57       UHM245.20538_S85 
#>           4.504878e+01           1.539167e+01           1.710185e+01 
#>       UHM252.20558_S40       UHM267.20400_S68       UHM274.20581_S31 
#>           1.694495e+00           1.454331e+01           9.120988e-01 
#>       UHM276.20586_S91       UHM280.20401_S80       UHM286.20425_S83 
#>           1.579983e+00           4.748072e+00           6.156667e+01 
#>       UHM289.20426_S95       UHM294.20427_S12       UHM298.20600_S69 
#>           4.617500e+02           3.027869e+00           4.571782e+00 
#>       UHM325.20548_S15       UHM337.20412_S22       UHM354.20535_S49 
#>           3.162671e+00           7.076628e+00           5.202817e+00 
#>       UHM356.20415_S58       UHM369.20773_S31       UHM370.20774_S43 
#>           4.493917e+00           7.927039e+00           2.098864e+01 
#>       UHM372.20775_S55       UHM373.20776_S67       UHM374.20777_S79 
#>           6.156667e+01           2.198810e+01           2.676812e+01 
#>       UHM375.20778_S91        UHM377.20779_S8         UHM38.3376_S36 
#>           2.605078e+00           3.872117e+00           1.000000e+00 
#>       UHM386.20781_S32       UHM387.20782_S44       UHM414.20583_S55 
#>           7.893162e+00           3.184483e+01           2.172941e+01 
#>       UHM418.20765_S30       UHM422.20766_S42       UHM425.20767_S54 
#>           6.840741e+00           1.614510e+00           1.565254e+01 
#>       UHM426.20534_S37       UHM428.20544_S62       UHM429.20559_S52 
#>           4.016964e-01           4.068282e+00           1.439595e+00 
#>        UHM435.20547_S3       UHM437.20768_S66       UHM439.20564_S17 
#>           1.075087e+00           3.769388e+01           2.601408e+01 
#>         UHM44.3526_S31       UHM445.20569_S77       UHM447.20783_S56 
#>           1.441842e+00           7.103846e+01           2.885938e+01 
#>       UHM448.20769_S78         UHM45.3539_S92       UHM454.20770_S90 
#>           4.356132e+00           1.000000e+00           6.643885e+00 
#>       UHM455.20785_S80       UHM458.20786_S92        UHM459.20787_S9 
#>           2.346887e+00           2.885938e+01           1.539167e+01 
#>        UHM461.20771_S7       UHM467.20772_S19       UHM470.20533_S25 
#>           7.632231e+00           3.929787e+01           1.513934e+00 
#>       UHM476.20414_S46       UHM478.20549_S27       UHM479.20551_S51 
#>           1.627313e+00           4.305361e+00           1.281571e-01 
#>        UHM481.20403_S9       UHM482.20590_S44       UHM483.20603_S10 
#>           8.136564e+00           7.927039e+00           2.029670e+01 
#>       UHM519.20582_S43       UHM520.20573_S30      UHM746.21478_S117 
#>           1.358088e+01           7.300395e+00           1.084327e-02 
#>      UHM747.21477_S106      UHM748.21467_S170      UHM748.21487_S129 
#>           1.122291e-02           1.670254e-02           2.532774e-02 
#>      UHM749.21479_S128      UHM759.21466_S159      UHM759.21486_S118 
#>           1.471010e-02           1.204010e-02           5.596970e-02 
#>      UHM775.21485_S107      UHM776.21482_S161      UHM777.21484_S183 
#>           2.145379e-02           1.587725e-02           3.932297e-02 
#>      UHM779.21468_S181      UHM779.21488_S140      UHM782.21480_S139 
#>           1.140461e-02           1.026111e+02           2.407832e-02 
#>      UHM810.21472_S138      UHM811.21471_S127      UHM813.21481_S150 
#>           9.750612e-03           3.067699e-02           1.421600e-02 
#>      UHM818.21469_S105      UHM818.21489_S151      UHM819.21473_S149 
#>           1.831723e-02           4.860526e+01           1.106996e-02 
#>      UHM820.21470_S116      UHM820.21490_S162      UHM827.21474_S160 
#>           1.397654e-02           1.793204e+00           1.106425e-02 
#>      UHM829.21476_S182      UHM832.21483_S172       UHM836.20385_S78 
#>           1.331517e-02           1.348293e-02           1.000000e+00 
#>       UHM837.20386_S90        UHM838.20387_S7       UHM891.20384_S66 
#>           1.000000e+00           1.679091e+02           2.172941e+01 
#>       UHM892.20532_S13        UHM893.20595_S9       UHM894.20540_S14 
#>           1.586770e+00           1.154375e+02           5.130556e+01 
#>       UHM895.20536_S61       UHM896.20601_S81       UHM897.20591_S56 
#>           8.728733e-01           1.000000e+00           1.000000e+00 
#>       UHM898.20394_S91       UHM899.20588_S20        UHM900.20395_S8 
#>           1.000000e+00           7.695833e+01           1.000000e+00 
#>       UHM901.20542_S38       UHM902.20584_S67        UHM903.20587_S8 
#>           1.944211e+01           1.517666e+00           3.929787e+00 
#>       UHM904.20567_S53       UHM905.20598_S45       UHM906.20565_S29 
#>           4.104444e+01           2.075281e+01           1.000000e+00 
#>       UHM907.20592_S68       UHM908.20396_S20       UHM909.20557_S28 
#>           6.156667e+01           1.000000e+00           2.122989e+01 
#>       UHM910.20562_S88       UHM965.20537_S73       UHM966.20743_S51 
#>           5.432353e+01           3.980603e+00           4.006508e+00 
#>       UHM967.20744_S63        UHM968.20571_S6       UHM969.20745_S75 
#>           1.000000e+00           5.594935e-02           6.156667e+01 
#>       UHM971.20746_S87       UHM973.20578_S90       UHM974.20432_S72 
#>           1.847000e+02           4.015217e+01           1.000000e+00 
#>        UHM975.20747_S4       UHM977.20748_S16       UHM978.20749_S28 
#>           5.596970e+01           1.847000e+01           2.029670e+01 
#>       UHM979.20750_S40        UHM980.20731_S2        UHM981.20539_S2 
#>           5.277143e+01           7.447581e+00           2.411227e+00 
#>       UHM982.20740_S15       UHM983.20556_S16       UHM984.20751_S52 
#>           1.147205e+01           1.472065e-01           7.103846e+01 
#>       UHM985.20752_S64       UHM988.20753_S76       UHM989.20754_S88 
#>           2.317440e+00           3.063018e+00           1.086471e+02 
#>        UHM991.20755_S5       UHM993.20741_S27       UHM996.20610_S94 
#>           9.908798e-01           3.551923e+01           1.000000e+00 
#>       UHM997.20553_S75       UHM998.20618_S95       UHM999.20617_S83 
#>           1.000000e+00           3.538314e-01           1.282639e+01
str(scaling_factors)
#>  Named num [1:264] 230.875 0.0392 0.0296 0.0524 0.127 ...
#>  - attr(*, "names")= chr [1:264] "spiked.blank.20433_S84" "spiked.blank.20817_S84" "Std2uL.20625_S84" "StdSwab1uL.20624_S72" ...

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

# Convert to absolute counts
absolute <- convert_to_absolute_counts(Spiked_16S_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 × 264
#>   spiked.blank.20433_S84 spiked.blank.20817_S84 Std2uL.20625_S84
#>                    <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 261 more variables: StdSwab1uL.20624_S72 <dbl>, STP1719.20422_S47 <dbl>,
#> #   STP213.20423_S59 <dbl>, STP268.20424_S71 <dbl>, STP544.20419_S11 <dbl>,
#> #   STP570.20420_S23 <dbl>, STP579.20421_S35 <dbl>, STP614.20418_S94 <dbl>,
#> #   UHM1000.20604_S22 <dbl>, UHM1001.20609_S82 <dbl>, UHM1007.20622_S48 <dbl>,
#> #   UHM1009.20614_S47 <dbl>, UHM1010.20621_S36 <dbl>, UHM1011.20606_S46 <dbl>,
#> #   UHM1024.20620_S24 <dbl>, UHM1026.20607_S58 <dbl>, UHM1028.20613_S35 <dbl>,
#> #   UHM1032.20605_S34 <dbl>, UHM1033.20619_S12 <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.20433_S84_mean`, `spiked.blank.20817_S84_mean`, `Std2uL.20625_S84_mean`, `StdSwab1uL.20624_S72_mean`, `STP1719.20422_S47_mean`, `STP213.20423_S59_mean`, `STP268.20424_S71_mean`, `STP544.20419_S11_mean`, `STP570.20420_S23_mean`, `STP579.20421_S35_mean`, `STP614.20418_S94_mean`, `UHM1000.20604_S22_mean`, `UHM1001.20609_S82_mean`, `UHM1007.20622_S48_mean`, `UHM1009.20614_S47_mean`, `UHM1010.20621_S36_mean`, `UHM1011.20606_S46_mean`, `UHM1024.20620_S24_mean`, `UHM1026.20607_S58_mean`, `UHM1028.20613_S35_mean`, `UHM1032.20605_S34_mean`, `UHM1033.20619_S12_mean`, `UHM1034.20616_S71_mean`, `UHM1035.20611_S11_mean`, `UHM1036.20612_S23_mean`, `UHM1052.20615_S59_mean`, `UHM1060.20723_S1_mean`, `UHM1065.20724_S13_mean`, `UHM1068.20732_S14_mean`, `UHM1069.20742_S39_mean`, `UHM1070.20725_S25_mean`, `UHM1071.20733_S26_mean`, `UHM1072.20734_S38_mean`, `UHM1073.20735_S50_mean`, `UHM1075.20726_S37_mean`, `UHM1077.20736_S62_mean`, `UHM1078.20727_S49_mean`, `UHM1080.20737_S74_mean`, `UHM1081.20728_S61_mean`, `UHM1088.20738_S86_mean`, `UHM1090.20739_S3_mean`, `UHM1093.20729_S73_mean`, `UHM1095.20730_S85_mean`, `UHM1097.20623_S60_mean`, `UHM1099.20608_S70_mean`, `UHM1100.20788_S21_mean`, `UHM1102.20789_S33_mean`, `UHM1104.20790_S45_mean`, `UHM1105.20791_S57_mean`, `UHM1109.20531_S1_mean`, `UHM1110.20568_S65_mean`, `UHM1113.20792_S69_mean`, `UHM1114.20793_S81_mean`, `UHM1115.20794_S93_mean`, `UHM1117.20795_S10_mean`, `UHM1118.20796_S22_mean`, `UHM1120.20797_S34_mean`, `UHM1124.20798_S46_mean`, `UHM1126.20799_S58_mean`, `UHM1128.20800_S70_mean`, `UHM1140.20555_S4_mean`, `UHM1145.20801_S82_mean`, `UHM1163.20405_S33_mean`, `UHM1164.20402_S92_mean`, `UHM1169.20552_S63_mean`, `UHM1171.20579_S7_mean`, `UHM1176.20404_S21_mean`, `UHM1177.20546_S86_mean`, `UHM1182.20576_S66_mean`, `UHM1210.20802_S94_mean`, `UHM1212.20803_S11_mean`, `UHM1217.20804_S23_mean`, `UHM1218.20805_S35_mean`, `UHM1219.20806_S47_mean`, `UHM1220.20807_S59_mean`, `UHM1221.20808_S71_mean`, `UHM1222.20809_S83_mean`, `UHM1223.20810_S95_mean`, `UHM1225.20811_S12_mean`, `UHM1227.20812_S24_mean`, `UHM1228.20813_S36_mean`, `UHM1237.20814_S48_mean`, `UHM1240.20566_S41_mean`, `UHM1246.20815_S60_mean`, `UHM1247.20816_S72_mean`, `UHM1248.20575_S54_mean`, `UHM1256.20570_S89_mean`, `UHM1260.20596_S21_mean`, `UHM1270.20577_S78_mean`, `UHM1271.20397_S32_mean`, `UHM1272.20398_S44_mean`, `UHM1274.20554_S87_mean`, `UHM1275.20597_S33_mean`, `UHM1282.20599_S57_mean`, `UHM1287.20543_S50_mean`, `UHM1291.20416_S70_mean`, `UHM1296.20550_S39_mean`, `UHM1319.20561_S76_mean`, `UHM1324.20413_S34_mean`, `UHM1327.20545_S74_mean`, `UHM1328.20572_S18_mean`, `UHM1334.20417_S82_mean`, `UHM1338.20399_S56_mean`, `UHM1341.20602_S93_mean`, `UHM1356.20541_S26_mean`, `UHM1380.20580_S19_mean`, `UHM1383.20594_S92_mean`, `UHM1385.20563_S5_mean`, `UHM1399.20756_S17_mean`, `UHM1400.20757_S29_mean`, `UHM1401.20758_S41_mean`, `UHM1402.20759_S53_mean`, `UHM1403.20760_S65_mean`, `UHM1405.20761_S77_mean`, `UHM1406.20762_S89_mean`, `UHM1414.20763_S6_mean`, `UHM1419.20764_S18_mean`, `UHM1427.20389_S31_mean`, `UHM1428.20390_S43_mean`, `UHM1429.20391_S55_mean`, `UHM1430.20392_S67_mean`, `UHM1432.20393_S79_mean`, `UHM1435.20388_S19_mean`, `UHM162.20560_S64_mean`, `UHM198.20585_S79_mean`, `UHM20.3314_S52_mean`, `UHM20.3315_S64_mean`, `UHM204.20409_S81_mean`, `UHM206.20410_S93_mean`, `UHM207.20593_S80_mean`, `UHM208.20411_S10_mean`, `UHM211.20406_S45_mean`, `UHM215.20408_S69_mean`, `UHM216.20429_S36_mean`, `UHM219.20430_S48_mean`, `UHM236.20431_S60_mean`, `UHM238.20407_S57_mean`, `UHM245.20538_S85_mean`, `UHM252.20558_S40_mean`, `UHM267.20400_S68_mean`, `UHM274.20581_S31_mean`, `UHM276.20586_S91_mean`, `UHM280.20401_S80_mean`, `UHM286.20425_S83_mean`, `UHM289.20426_S95_mean`, `UHM294.20427_S12_mean`, `UHM298.20600_S69_mean`, `UHM325.20548_S15_mean`, `UHM337.20412_S22_mean`, `UHM354.20535_S49_mean`, `UHM356.20415_S58_mean`, `UHM369.20773_S31_mean`, `UHM370.20774_S43_mean`, `UHM372.20775_S55_mean`, `UHM373.20776_S67_mean`, `UHM374.20777_S79_mean`, `UHM375.20778_S91_mean`, `UHM377.20779_S8_mean`, `UHM38.3376_S36_mean`, `UHM386.20781_S32_mean`, `UHM387.20782_S44_mean`, `UHM414.20583_S55_mean`, `UHM418.20765_S30_mean`, `UHM422.20766_S42_mean`, `UHM425.20767_S54_mean`, `UHM426.20534_S37_mean`, `UHM428.20544_S62_mean`, `UHM429.20559_S52_mean`, `UHM435.20547_S3_mean`, `UHM437.20768_S66_mean`, `UHM439.20564_S17_mean`, `UHM44.3526_S31_mean`, `UHM445.20569_S77_mean`, `UHM447.20783_S56_mean`, `UHM448.20769_S78_mean`, `UHM45.3539_S92_mean`, `UHM454.20770_S90_mean`, `UHM455.20785_S80_mean`, `UHM458.20786_S92_mean`, `UHM459.20787_S9_mean`, `UHM461.20771_S7_mean`, `UHM467.20772_S19_mean`, `UHM470.20533_S25_mean`, `UHM476.20414_S46_mean`, `UHM478.20549_S27_mean`, `UHM479.20551_S51_mean`, `UHM481.20403_S9_mean`, `UHM482.20590_S44_mean`, `UHM483.20603_S10_mean`, `UHM519.20582_S43_mean`, `UHM520.20573_S30_mean`, `UHM746.21478_S117_mean`, `UHM747.21477_S106_mean`, `UHM748.21467_S170_mean`, `UHM748.21487_S129_mean`, `UHM749.21479_S128_mean`, `UHM759.21466_S159_mean`, `UHM759.21486_S118_mean`, `UHM775.21485_S107_mean`, `UHM776.21482_S161_mean`, `UHM777.21484_S183_mean`, `UHM779.21468_S181_mean`, `UHM779.21488_S140_mean`, `UHM782.21480_S139_mean`, `UHM810.21472_S138_mean`, `UHM811.21471_S127_mean`, `UHM813.21481_S150_mean`, `UHM818.21469_S105_mean`, `UHM818.21489_S151_mean`, `UHM819.21473_S149_mean`, `UHM820.21470_S116_mean`, `UHM820.21490_S162_mean`, `UHM827.21474_S160_mean`, `UHM829.21476_S182_mean`, `UHM832.21483_S172_mean`, `UHM836.20385_S78_mean`, `UHM837.20386_S90_mean`, `UHM838.20387_S7_mean`, `UHM891.20384_S66_mean`, `UHM892.20532_S13_mean`, `UHM893.20595_S9_mean`, `UHM894.20540_S14_mean`, `UHM895.20536_S61_mean`, `UHM896.20601_S81_mean`, `UHM897.20591_S56_mean`, `UHM898.20394_S91_mean`, `UHM899.20588_S20_mean`, `UHM900.20395_S8_mean`, `UHM901.20542_S38_mean`, `UHM902.20584_S67_mean`, `UHM903.20587_S8_mean`, `UHM904.20567_S53_mean`, `UHM905.20598_S45_mean`, `UHM906.20565_S29_mean`, `UHM907.20592_S68_mean`, `UHM908.20396_S20_mean`, `UHM909.20557_S28_mean`, `UHM910.20562_S88_mean`, `UHM965.20537_S73_mean`, `UHM966.20743_S51_mean`, `UHM967.20744_S63_mean`, `UHM968.20571_S6_mean`, `UHM969.20745_S75_mean`, `UHM971.20746_S87_mean`, `UHM973.20578_S90_mean`, `UHM974.20432_S72_mean`, `UHM975.20747_S4_mean`, `UHM977.20748_S16_mean`, `UHM978.20749_S28_mean`, `UHM979.20750_S40_mean`, `UHM980.20731_S2_mean`, `UHM981.20539_S2_mean`, `UHM982.20740_S15_mean`, `UHM983.20556_S16_mean`, `UHM984.20751_S52_mean`, `UHM985.20752_S64_mean`, `UHM988.20753_S76_mean`, `UHM989.20754_S88_mean`, `UHM991.20755_S5_mean`, `UHM993.20741_S27_mean`, `UHM996.20610_S94_mean`, `UHM997.20553_S75_mean`, `UHM998.20618_S95_mean`, `UHM999.20617_S83_mean`, `spiked.blank.20433_S84_sd`, `spiked.blank.20817_S84_sd`, `Std2uL.20625_S84_sd`, `StdSwab1uL.20624_S72_sd`, `STP1719.20422_S47_sd`, `STP213.20423_S59_sd`, `STP268.20424_S71_sd`, `STP544.20419_S11_sd`, `STP570.20420_S23_sd`, `STP579.20421_S35_sd`, `STP614.20418_S94_sd`, `UHM1000.20604_S22_sd`, `UHM1001.20609_S82_sd`, `UHM1007.20622_S48_sd`, `UHM1009.20614_S47_sd`, `UHM1010.20621_S36_sd`, `UHM1011.20606_S46_sd`, `UHM1024.20620_S24_sd`, `UHM1026.20607_S58_sd`, `UHM1028.20613_S35_sd`, `UHM1032.20605_S34_sd`, `UHM1033.20619_S12_sd`, `UHM1034.20616_S71_sd`, `UHM1035.20611_S11_sd`, `UHM1036.20612_S23_sd`, `UHM1052.20615_S59_sd`, `UHM1060.20723_S1_sd`, `UHM1065.20724_S13_sd`, `UHM1068.20732_S14_sd`, `UHM1069.20742_S39_sd`, `UHM1070.20725_S25_sd`, `UHM1071.20733_S26_sd`, `UHM1072.20734_S38_sd`, `UHM1073.20735_S50_sd`, `UHM1075.20726_S37_sd`, `UHM1077.20736_S62_sd`, `UHM1078.20727_S49_sd`, `UHM1080.20737_S74_sd`, `UHM1081.20728_S61_sd`, `UHM1088.20738_S86_sd`, `UHM1090.20739_S3_sd`, `UHM1093.20729_S73_sd`, `UHM1095.20730_S85_sd`, `UHM1097.20623_S60_sd`, `UHM1099.20608_S70_sd`, `UHM1100.20788_S21_sd`, `UHM1102.20789_S33_sd`, `UHM1104.20790_S45_sd`, `UHM1105.20791_S57_sd`, `UHM1109.20531_S1_sd`, `UHM1110.20568_S65_sd`, `UHM1113.20792_S69_sd`, `UHM1114.20793_S81_sd`, `UHM1115.20794_S93_sd`, `UHM1117.20795_S10_sd`, `UHM1118.20796_S22_sd`, `UHM1120.20797_S34_sd`, `UHM1124.20798_S46_sd`, `UHM1126.20799_S58_sd`, `UHM1128.20800_S70_sd`, `UHM1140.20555_S4_sd`, `UHM1145.20801_S82_sd`, `UHM1163.20405_S33_sd`, `UHM1164.20402_S92_sd`, `UHM1169.20552_S63_sd`, `UHM1171.20579_S7_sd`, `UHM1176.20404_S21_sd`, `UHM1177.20546_S86_sd`, `UHM1182.20576_S66_sd`, `UHM1210.20802_S94_sd`, `UHM1212.20803_S11_sd`, `UHM1217.20804_S23_sd`, `UHM1218.20805_S35_sd`, `UHM1219.20806_S47_sd`, `UHM1220.20807_S59_sd`, `UHM1221.20808_S71_sd`, `UHM1222.20809_S83_sd`, `UHM1223.20810_S95_sd`, `UHM1225.20811_S12_sd`, `UHM1227.20812_S24_sd`, `UHM1228.20813_S36_sd`, `UHM1237.20814_S48_sd`, `UHM1240.20566_S41_sd`, `UHM1246.20815_S60_sd`, `UHM1247.20816_S72_sd`, `UHM1248.20575_S54_sd`, `UHM1256.20570_S89_sd`, `UHM1260.20596_S21_sd`, `UHM1270.20577_S78_sd`, `UHM1271.20397_S32_sd`, `UHM1272.20398_S44_sd`, `UHM1274.20554_S87_sd`, `UHM1275.20597_S33_sd`, `UHM1282.20599_S57_sd`, `UHM1287.20543_S50_sd`, `UHM1291.20416_S70_sd`, `UHM1296.20550_S39_sd`, `UHM1319.20561_S76_sd`, `UHM1324.20413_S34_sd`, `UHM1327.20545_S74_sd`, `UHM1328.20572_S18_sd`, `UHM1334.20417_S82_sd`, `UHM1338.20399_S56_sd`, `UHM1341.20602_S93_sd`, `UHM1356.20541_S26_sd`, `UHM1380.20580_S19_sd`, `UHM1383.20594_S92_sd`, `UHM1385.20563_S5_sd`, `UHM1399.20756_S17_sd`, `UHM1400.20757_S29_sd`, `UHM1401.20758_S41_sd`, `UHM1402.20759_S53_sd`, `UHM1403.20760_S65_sd`, `UHM1405.20761_S77_sd`, `UHM1406.20762_S89_sd`, `UHM1414.20763_S6_sd`, `UHM1419.20764_S18_sd`, `UHM1427.20389_S31_sd`, `UHM1428.20390_S43_sd`, `UHM1429.20391_S55_sd`, `UHM1430.20392_S67_sd`, `UHM1432.20393_S79_sd`, `UHM1435.20388_S19_sd`, `UHM162.20560_S64_sd`, `UHM198.20585_S79_sd`, `UHM20.3314_S52_sd`, `UHM20.3315_S64_sd`, `UHM204.20409_S81_sd`, `UHM206.20410_S93_sd`, `UHM207.20593_S80_sd`, `UHM208.20411_S10_sd`, `UHM211.20406_S45_sd`, `UHM215.20408_S69_sd`, `UHM216.20429_S36_sd`, `UHM219.20430_S48_sd`, `UHM236.20431_S60_sd`, `UHM238.20407_S57_sd`, `UHM245.20538_S85_sd`, `UHM252.20558_S40_sd`, `UHM267.20400_S68_sd`, `UHM274.20581_S31_sd`, `UHM276.20586_S91_sd`, `UHM280.20401_S80_sd`, `UHM286.20425_S83_sd`, `UHM289.20426_S95_sd`, `UHM294.20427_S12_sd`, `UHM298.20600_S69_sd`, `UHM325.20548_S15_sd`, `UHM337.20412_S22_sd`, `UHM354.20535_S49_sd`, `UHM356.20415_S58_sd`, `UHM369.20773_S31_sd`, `UHM370.20774_S43_sd`, `UHM372.20775_S55_sd`, `UHM373.20776_S67_sd`, `UHM374.20777_S79_sd`, `UHM375.20778_S91_sd`, `UHM377.20779_S8_sd`, `UHM38.3376_S36_sd`, `UHM386.20781_S32_sd`, `UHM387.20782_S44_sd`, `UHM414.20583_S55_sd`, `UHM418.20765_S30_sd`, `UHM422.20766_S42_sd`, `UHM425.20767_S54_sd`, `UHM426.20534_S37_sd`, `UHM428.20544_S62_sd`, `UHM429.20559_S52_sd`, `UHM435.20547_S3_sd`, `UHM437.20768_S66_sd`, `UHM439.20564_S17_sd`, `UHM44.3526_S31_sd`, `UHM445.20569_S77_sd`, `UHM447.20783_S56_sd`, `UHM448.20769_S78_sd`, `UHM45.3539_S92_sd`, `UHM454.20770_S90_sd`, `UHM455.20785_S80_sd`, `UHM458.20786_S92_sd`, `UHM459.20787_S9_sd`, `UHM461.20771_S7_sd`, `UHM467.20772_S19_sd`, `UHM470.20533_S25_sd`, `UHM476.20414_S46_sd`, `UHM478.20549_S27_sd`, `UHM479.20551_S51_sd`, `UHM481.20403_S9_sd`, `UHM482.20590_S44_sd`, `UHM483.20603_S10_sd`, `UHM519.20582_S43_sd`, `UHM520.20573_S30_sd`, `UHM746.21478_S117_sd`, `UHM747.21477_S106_sd`, `UHM748.21467_S170_sd`, `UHM748.21487_S129_sd`, `UHM749.21479_S128_sd`, `UHM759.21466_S159_sd`, `UHM759.21486_S118_sd`, `UHM775.21485_S107_sd`, `UHM776.21482_S161_sd`, `UHM777.21484_S183_sd`, `UHM779.21468_S181_sd`, `UHM779.21488_S140_sd`, `UHM782.21480_S139_sd`, `UHM810.21472_S138_sd`, `UHM811.21471_S127_sd`, `UHM813.21481_S150_sd`, `UHM818.21469_S105_sd`, `UHM818.21489_S151_sd`, `UHM819.21473_S149_sd`, `UHM820.21470_S116_sd`, `UHM820.21490_S162_sd`, `UHM827.21474_S160_sd`, `UHM829.21476_S182_sd`, `UHM832.21483_S172_sd`, `UHM836.20385_S78_sd`, `UHM837.20386_S90_sd`, `UHM838.20387_S7_sd`, `UHM891.20384_S66_sd`, `UHM892.20532_S13_sd`, `UHM893.20595_S9_sd`, `UHM894.20540_S14_sd`, `UHM895.20536_S61_sd`, `UHM896.20601_S81_sd`, `UHM897.20591_S56_sd`, `UHM898.20394_S91_sd`, `UHM899.20588_S20_sd`, `UHM900.20395_S8_sd`, `UHM901.20542_S38_sd`, `UHM902.20584_S67_sd`, `UHM903.20587_S8_sd`, `UHM904.20567_S53_sd`, `UHM905.20598_S45_sd`, `UHM906.20565_S29_sd`, `UHM907.20592_S68_sd`, `UHM908.20396_S20_sd`, `UHM909.20557_S28_sd`, `UHM910.20562_S88_sd`, `UHM965.20537_S73_sd`, `UHM966.20743_S51_sd`, `UHM967.20744_S63_sd`, `UHM968.20571_S6_sd`, `UHM969.20745_S75_sd`, `UHM971.20746_S87_sd`, `UHM973.20578_S90_sd`, `UHM974.20432_S72_sd`, `UHM975.20747_S4_sd`, `UHM977.20748_S16_sd`, `UHM978.20749_S28_sd`, `UHM979.20750_S40_sd`, `UHM980.20731_S2_sd`, `UHM981.20539_S2_sd`, `UHM982.20740_S15_sd`, `UHM983.20556_S16_sd`, `UHM984.20751_S52_sd`, `UHM985.20752_S64_sd`, `UHM988.20753_S76_sd`, `UHM989.20754_S88_sd`, `UHM991.20755_S5_sd`, 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`UHM747.21477_S106_q75`, `UHM748.21467_S170_q75`, `UHM748.21487_S129_q75`, `UHM749.21479_S128_q75`, `UHM759.21466_S159_q75`, `UHM759.21486_S118_q75`, `UHM775.21485_S107_q75`, `UHM776.21482_S161_q75`, `UHM777.21484_S183_q75`, `UHM779.21468_S181_q75`, `UHM779.21488_S140_q75`, `UHM782.21480_S139_q75`, `UHM810.21472_S138_q75`, `UHM811.21471_S127_q75`, `UHM813.21481_S150_q75`, `UHM818.21469_S105_q75`, `UHM818.21489_S151_q75`, `UHM819.21473_S149_q75`, `UHM820.21470_S116_q75`, `UHM820.21490_S162_q75`, `UHM827.21474_S160_q75`, `UHM829.21476_S182_q75`, `UHM832.21483_S172_q75`, `UHM836.20385_S78_q75`, `UHM837.20386_S90_q75`, `UHM838.20387_S7_q75`, `UHM891.20384_S66_q75`, `UHM892.20532_S13_q75`, `UHM893.20595_S9_q75`, `UHM894.20540_S14_q75`, `UHM895.20536_S61_q75`, `UHM896.20601_S81_q75`, `UHM897.20591_S56_q75`, `UHM898.20394_S91_q75`, `UHM899.20588_S20_q75`, `UHM900.20395_S8_q75`, `UHM901.20542_S38_q75`, `UHM902.20584_S67_q75`, `UHM903.20587_S8_q75`, `UHM904.20567_S53_q75`, `UHM905.20598_S45_q75`, `UHM906.20565_S29_q75`, `UHM907.20592_S68_q75`, `UHM908.20396_S20_q75`, `UHM909.20557_S28_q75`, `UHM910.20562_S88_q75`, `UHM965.20537_S73_q75`, `UHM966.20743_S51_q75`, `UHM967.20744_S63_q75`, `UHM968.20571_S6_q75`, `UHM969.20745_S75_q75`, `UHM971.20746_S87_q75`, `UHM973.20578_S90_q75`, `UHM974.20432_S72_q75`, `UHM975.20747_S4_q75`, `UHM977.20748_S16_q75`, `UHM978.20749_S28_q75`, `UHM979.20750_S40_q75`, `UHM980.20731_S2_q75`, `UHM981.20539_S2_q75`, `UHM982.20740_S15_q75`, `UHM983.20556_S16_q75`, `UHM984.20751_S52_q75`, `UHM985.20752_S64_q75`, `UHM988.20753_S76_q75`, `UHM989.20754_S88_q75`, `UHM991.20755_S5_q75`, `UHM993.20741_S27_q75`, `UHM996.20610_S94_q75`, `UHM997.20553_S75_q75`, `UHM998.20618_S95_q75`, `UHM999.20617_S83_q75` 
#> header has 1 row(s) 
#> body has 1 row(s) 
#> original dataset sample: 
#>   spiked.blank.20433_S84_mean spiked.blank.20817_S84_mean Std2uL.20625_S84_mean
#> 1                   0.1981972                   0.1983045             0.1981972
#>   StdSwab1uL.20624_S72_mean STP1719.20422_S47_mean STP213.20423_S59_mean
#> 1                 0.1368173              0.2609722              20.20753
#>   STP268.20424_S71_mean STP544.20419_S11_mean STP570.20420_S23_mean
#> 1              81.28426             0.2052795             0.6382659
#>   STP579.20421_S35_mean STP614.20418_S94_mean UHM1000.20604_S22_mean
#> 1             0.5850413          0.0005365383                4.67078
#>   UHM1001.20609_S82_mean UHM1007.20622_S48_mean UHM1009.20614_S47_mean
#> 1               4.220303               9.279215               19.50231
#>   UHM1010.20621_S36_mean UHM1011.20606_S46_mean UHM1024.20620_S24_mean
#> 1               0.657474               8.698358              0.2920914
#>   UHM1026.20607_S58_mean UHM1028.20613_S35_mean UHM1032.20605_S34_mean
#> 1              0.8052366              0.2815753              0.1513038
#>   UHM1033.20619_S12_mean UHM1034.20616_S71_mean UHM1035.20611_S11_mean
#> 1              0.1036592               83.96169               0.869192
#>   UHM1036.20612_S23_mean UHM1052.20615_S59_mean UHM1060.20723_S1_mean
#> 1              0.9270308               10.58322              0.222878
#>   UHM1065.20724_S13_mean UHM1068.20732_S14_mean UHM1069.20742_S39_mean
#> 1              0.2558214               2.061702               9.492649
#>   UHM1070.20725_S25_mean UHM1071.20733_S26_mean UHM1072.20734_S38_mean
#> 1               1.193154               7.178023              0.4576671
#>   UHM1073.20735_S50_mean UHM1075.20726_S37_mean UHM1077.20736_S62_mean
#> 1               13.87842               1.300676               25.76349
#>   UHM1078.20727_S49_mean UHM1080.20737_S74_mean UHM1081.20728_S61_mean
#> 1               5.243052               25.54341              0.2272776
#>   UHM1088.20738_S86_mean UHM1090.20739_S3_mean UHM1093.20729_S73_mean
#> 1               5.187681             0.4575598               1.195944
#>   UHM1095.20730_S85_mean UHM1097.20623_S60_mean UHM1099.20608_S70_mean
#> 1              0.3734306              0.1777015               15.15388
#>   UHM1100.20788_S21_mean UHM1102.20789_S33_mean UHM1104.20790_S45_mean
#> 1               12.89892               4.421826               8.144651
#>   UHM1105.20791_S57_mean UHM1109.20531_S1_mean UHM1110.20568_S65_mean
#> 1               3.167722              5.334263               27.68698
#>   UHM1113.20792_S69_mean UHM1114.20793_S81_mean UHM1115.20794_S93_mean
#> 1               40.20474               2.767357                5.75566
#>   UHM1117.20795_S10_mean UHM1118.20796_S22_mean UHM1120.20797_S34_mean
#> 1               12.93089               4.277605              0.6918124
#>   UHM1124.20798_S46_mean UHM1126.20799_S58_mean UHM1128.20800_S70_mean
#> 1               2.147333               6.110956               8.153986
#>   UHM1140.20555_S4_mean UHM1145.20801_S82_mean UHM1163.20405_S33_mean
#> 1              15.55854               14.61777               7.155918
#>   UHM1164.20402_S92_mean UHM1169.20552_S63_mean UHM1171.20579_S7_mean
#> 1               60.18596              0.2239511              10.15517
#>   UHM1176.20404_S21_mean UHM1177.20546_S86_mean UHM1182.20576_S66_mean
#> 1               16.52473              0.7512609               2.073398
#>   UHM1210.20802_S94_mean UHM1212.20803_S11_mean UHM1217.20804_S23_mean
#> 1               11.54963               3.811246               7.174268
#>   UHM1218.20805_S35_mean UHM1219.20806_S47_mean UHM1220.20807_S59_mean
#> 1               4.585041               7.914047               9.221376
#>   UHM1221.20808_S71_mean UHM1222.20809_S83_mean UHM1223.20810_S95_mean
#> 1               4.034231              0.5839682                12.2628
#>   UHM1225.20811_S12_mean UHM1227.20812_S24_mean UHM1228.20813_S36_mean
#> 1               5.180277               2.250456               15.81189
#>   UHM1237.20814_S48_mean UHM1240.20566_S41_mean UHM1246.20815_S60_mean
#> 1               2.682584               3.768215               1.731731
#>   UHM1247.20816_S72_mean UHM1248.20575_S54_mean UHM1256.20570_S89_mean
#> 1               2.849125              0.8204743               3.805022
#>   UHM1260.20596_S21_mean UHM1270.20577_S78_mean UHM1271.20397_S32_mean
#> 1                3.68076               1.307329                2.05741
#>   UHM1272.20398_S44_mean UHM1274.20554_S87_mean UHM1275.20597_S33_mean
#> 1               1.121472               3.482133               1.616161
#>   UHM1282.20599_S57_mean UHM1287.20543_S50_mean UHM1291.20416_S70_mean
#> 1              0.7013628               1.152377               7.973924
#>   UHM1296.20550_S39_mean UHM1319.20561_S76_mean UHM1324.20413_S34_mean
#> 1               22.27921               1.253246               4.852667
#>   UHM1327.20545_S74_mean UHM1328.20572_S18_mean UHM1334.20417_S82_mean
#> 1               29.27417              0.9950638               8.214508
#>   UHM1338.20399_S56_mean UHM1341.20602_S93_mean UHM1356.20541_S26_mean
#> 1               2.824767               0.518296               2.263226
#>   UHM1380.20580_S19_mean UHM1383.20594_S92_mean UHM1385.20563_S5_mean
#> 1                 7.7254               3.038523             0.2324284
#>   UHM1399.20756_S17_mean UHM1400.20757_S29_mean UHM1401.20758_S41_mean
#> 1               1.223629               5.290267              0.5350359
#>   UHM1402.20759_S53_mean UHM1403.20760_S65_mean UHM1405.20761_S77_mean
#> 1               8.466359               5.181886               5.486211
#>   UHM1406.20762_S89_mean UHM1414.20763_S6_mean UHM1419.20764_S18_mean
#> 1               9.705333              12.17684               2.381157
#>   UHM1427.20389_S31_mean UHM1428.20390_S43_mean UHM1429.20391_S55_mean
#> 1               10.06041              0.4863183               26.44221
#>   UHM1430.20392_S67_mean UHM1432.20393_S79_mean UHM1435.20388_S19_mean
#> 1               90.81082               44.62711              0.9971027
#>   UHM162.20560_S64_mean UHM198.20585_S79_mean UHM20.3314_S52_mean
#> 1              3.980685             0.6021032            143.3243
#>   UHM20.3315_S64_mean UHM204.20409_S81_mean UHM206.20410_S93_mean
#> 1            37.78399              17.18629             0.0561219
#>   UHM207.20593_S80_mean UHM208.20411_S10_mean UHM211.20406_S45_mean
#> 1              8.244232              3.658011              9.442751
#>   UHM215.20408_S69_mean UHM216.20429_S36_mean UHM219.20430_S48_mean
#> 1              423.7458              22.29606             0.2049576
#>   UHM236.20431_S60_mean UHM238.20407_S57_mean UHM245.20538_S85_mean
#> 1              21.41925              6.881318              59.54705
#>   UHM252.20558_S40_mean UHM267.20400_S68_mean UHM274.20581_S31_mean
#> 1              1.529134              19.67808              1.469578
#>   UHM276.20586_S91_mean UHM280.20401_S80_mean UHM286.20425_S83_mean
#> 1              2.314733              3.794506              14.05226
#>   UHM289.20426_S95_mean UHM294.20427_S12_mean UHM298.20600_S69_mean
#> 1             0.6441678              1.843224              10.85814
#>   UHM325.20548_S15_mean UHM337.20412_S22_mean UHM354.20535_S49_mean
#> 1              2.635691              3.501663              6.310226
#>   UHM356.20415_S58_mean UHM369.20773_S31_mean UHM370.20774_S43_mean
#> 1              4.688057              8.536431              9.491898
#>   UHM372.20775_S55_mean UHM373.20776_S67_mean UHM374.20777_S79_mean
#> 1               2.26623              12.12855              10.30604
#>   UHM375.20778_S91_mean UHM377.20779_S8_mean UHM38.3376_S36_mean
#> 1              2.042601             3.141968             3.12394
#>   UHM386.20781_S32_mean UHM387.20782_S44_mean UHM414.20583_S55_mean
#> 1              5.817792               7.34435              29.86962
#>   UHM418.20765_S30_mean UHM422.20766_S42_mean UHM425.20767_S54_mean
#> 1              5.006546               1.11836              7.104625
#>   UHM426.20534_S37_mean UHM428.20544_S62_mean UHM429.20559_S52_mean
#> 1             0.5824659              1.468934              2.276317
#>   UHM435.20547_S3_mean UHM437.20768_S66_mean UHM439.20564_S17_mean
#> 1             1.434381              15.94388              10.34456
#>   UHM44.3526_S31_mean UHM445.20569_S77_mean UHM447.20783_S56_mean
#> 1           0.5913725              9.658332              18.06524
#>   UHM448.20769_S78_mean UHM45.3539_S92_mean UHM454.20770_S90_mean
#> 1              3.022642            3.779483              4.693422
#>   UHM455.20785_S80_mean UHM458.20786_S92_mean UHM459.20787_S9_mean
#> 1              1.611868               11.0162             11.47741
#>   UHM461.20771_S7_mean UHM467.20772_S19_mean UHM470.20533_S25_mean
#> 1             8.334371              20.96727             0.9901277
#>   UHM476.20414_S46_mean UHM478.20549_S27_mean UHM479.20551_S51_mean
#> 1               2.16182              2.982294             0.2273849
#>   UHM481.20403_S9_mean UHM482.20590_S44_mean UHM483.20603_S10_mean
#> 1             1.691383             0.2161176              9.698573
#>   UHM519.20582_S43_mean UHM520.20573_S30_mean UHM746.21478_S117_mean
#> 1              23.67014              7.337804             0.09947419
#>   UHM747.21477_S106_mean UHM748.21467_S170_mean UHM748.21487_S129_mean
#> 1             0.09915227             0.09915227              0.0995815
#>   UHM749.21479_S128_mean UHM759.21466_S159_mean UHM759.21486_S118_mean
#> 1             0.09925958             0.09915227              0.1006546
#>   UHM775.21485_S107_mean UHM776.21482_S161_mean UHM777.21484_S183_mean
#> 1             0.09904496             0.09904496             0.09925958
#>   UHM779.21468_S181_mean UHM779.21488_S140_mean UHM782.21480_S139_mean
#> 1              0.0995815             0.09915227             0.09915227
#>   UHM810.21472_S138_mean UHM811.21471_S127_mean UHM813.21481_S150_mean
#> 1             0.09904496             0.09947419             0.09915227
#>   UHM818.21469_S105_mean UHM818.21489_S151_mean UHM819.21473_S149_mean
#> 1             0.09915227              0.1565619             0.09904496
#>   UHM820.21470_S116_mean UHM820.21490_S162_mean UHM827.21474_S160_mean
#> 1             0.09915227             0.09968881              0.1003327
#>   UHM829.21476_S182_mean UHM832.21483_S172_mean UHM836.20385_S78_mean
#> 1             0.09947419              0.1103123              0.324713
#>   UHM837.20386_S90_mean UHM838.20387_S7_mean UHM891.20384_S66_mean
#> 1             0.2620453             78.01781              7.550059
#>   UHM892.20532_S13_mean UHM893.20595_S9_mean UHM894.20540_S14_mean
#> 1              1.200987             123.7621              31.26033
#>   UHM895.20536_S61_mean UHM896.20601_S81_mean UHM897.20591_S56_mean
#> 1             0.6334371              1.079193               1.47269
#>   UHM898.20394_S91_mean UHM899.20588_S20_mean UHM900.20395_S8_mean
#> 1            0.06470651              87.48095            0.7078013
#>   UHM901.20542_S38_mean UHM902.20584_S67_mean UHM903.20587_S8_mean
#> 1              26.48117             0.6804378             1.985513
#>   UHM904.20567_S53_mean UHM905.20598_S45_mean UHM906.20565_S29_mean
#> 1              71.19616              7.266874              1.219015
#>   UHM907.20592_S68_mean UHM908.20396_S20_mean UHM909.20557_S28_mean
#> 1              40.66338             0.8812104              6.285331
#>   UHM910.20562_S88_mean UHM965.20537_S73_mean UHM966.20743_S51_mean
#> 1              37.22073             0.7596309              3.052581
#>   UHM967.20744_S63_mean UHM968.20571_S6_mean UHM969.20745_S75_mean
#> 1             0.4530529             0.203348              40.41346
#>   UHM971.20746_S87_mean UHM973.20578_S90_mean UHM974.20432_S72_mean
#> 1              136.9736              11.55553             0.1215796
#>   UHM975.20747_S4_mean UHM977.20748_S16_mean UHM978.20749_S28_mean
#> 1             37.14873              15.63108              13.80009
#>   UHM979.20750_S40_mean UHM980.20731_S2_mean UHM981.20539_S2_mean
#> 1              30.85621             3.059448              4.31366
#>   UHM982.20740_S15_mean UHM983.20556_S16_mean UHM984.20751_S52_mean
#> 1               7.07254             0.2022749              35.41421
#>   UHM985.20752_S64_mean UHM988.20753_S76_mean UHM989.20754_S88_mean
#> 1             0.6409486              2.547484              68.12179
#>   UHM991.20755_S5_mean UHM993.20741_S27_mean UHM996.20610_S94_mean
#> 1            0.8934435              25.48943              1.579569
#>   UHM997.20553_S75_mean UHM998.20618_S95_mean UHM999.20617_S83_mean
#> 1              1.317309             0.6405194              9.964267
#>   spiked.blank.20433_S84_sd spiked.blank.20817_S84_sd Std2uL.20625_S84_sd
#> 1                  19.13296                  19.13297            19.13296
#>   StdSwab1uL.20624_S72_sd STP1719.20422_S47_sd STP213.20423_S59_sd
#> 1                9.686204              19.2432            593.4313
#>   STP268.20424_S71_sd STP544.20419_S11_sd STP570.20420_S23_sd
#> 1            2019.324            19.14093            42.92256
#>   STP579.20421_S35_sd STP614.20418_S94_sd UHM1000.20604_S22_sd
#> 1            27.05749          0.03734784             97.60031
#>   UHM1001.20609_S82_sd UHM1007.20622_S48_sd UHM1009.20614_S47_sd
#> 1             89.28446             432.5865             1489.475
#>   UHM1010.20621_S36_sd UHM1011.20606_S46_sd UHM1024.20620_S24_sd
#> 1              14.2381             710.3862             11.73139
#>   UHM1026.20607_S58_sd UHM1028.20613_S35_sd UHM1032.20605_S34_sd
#> 1             45.38805             10.93368             10.11923
#>   UHM1033.20619_S12_sd UHM1034.20616_S71_sd UHM1035.20611_S11_sd
#> 1             9.573351             3220.642             37.08986
#>   UHM1036.20612_S23_sd UHM1052.20615_S59_sd UHM1060.20723_S1_sd
#> 1             41.95557              547.829            19.14295
#>   UHM1065.20724_S13_sd UHM1068.20732_S14_sd UHM1069.20742_S39_sd
#> 1             19.19258             44.23078             726.0189
#>   UHM1070.20725_S25_sd UHM1071.20733_S26_sd UHM1072.20734_S38_sd
#> 1             52.32642             399.3209             19.64715
#>   UHM1073.20735_S50_sd UHM1075.20726_S37_sd UHM1077.20736_S62_sd
#> 1             315.7103             47.91333             2246.436
#>   UHM1078.20727_S49_sd UHM1080.20737_S74_sd UHM1081.20728_S61_sd
#> 1             154.9411             840.4478             19.31285
#>   UHM1088.20738_S86_sd UHM1090.20739_S3_sd UHM1093.20729_S73_sd
#> 1             134.1664            19.88765             55.40129
#>   UHM1095.20730_S85_sd UHM1097.20623_S60_sd UHM1099.20608_S70_sd
#> 1             20.14066             9.957377             1448.161
#>   UHM1100.20788_S21_sd UHM1102.20789_S33_sd UHM1104.20790_S45_sd
#> 1             558.7219             92.13501             448.1184
#>   UHM1105.20791_S57_sd UHM1109.20531_S1_sd UHM1110.20568_S65_sd
#> 1             116.2209            482.5799             526.0038
#>   UHM1113.20792_S69_sd UHM1114.20793_S81_sd UHM1115.20794_S93_sd
#> 1             3277.214             123.4761             320.3602
#>   UHM1117.20795_S10_sd UHM1118.20796_S22_sd UHM1120.20797_S34_sd
#> 1             387.4926             129.4337             35.96314
#>   UHM1124.20798_S46_sd UHM1126.20799_S58_sd UHM1128.20800_S70_sd
#> 1             40.77466             142.1376             523.7844
#>   UHM1140.20555_S4_sd UHM1145.20801_S82_sd UHM1163.20405_S33_sd
#> 1            943.9256             786.7941             197.9178
#>   UHM1164.20402_S92_sd UHM1169.20552_S63_sd UHM1171.20579_S7_sd
#> 1             3288.218             19.18331            201.3854
#>   UHM1176.20404_S21_sd UHM1177.20546_S86_sd UHM1182.20576_S66_sd
#> 1             639.2037             22.65039             79.47168
#>   UHM1210.20802_S94_sd UHM1212.20803_S11_sd UHM1217.20804_S23_sd
#> 1             185.1057             105.4719             291.9855
#>   UHM1218.20805_S35_sd UHM1219.20806_S47_sd UHM1220.20807_S59_sd
#> 1             136.8927             275.4189             341.1107
#>   UHM1221.20808_S71_sd UHM1222.20809_S83_sd UHM1223.20810_S95_sd
#> 1             132.0045             25.61313             368.8874
#>   UHM1225.20811_S12_sd UHM1227.20812_S24_sd UHM1228.20813_S36_sd
#> 1              105.148             49.84736             342.4407
#>   UHM1237.20814_S48_sd UHM1240.20566_S41_sd UHM1246.20815_S60_sd
#> 1             88.53489             107.3952             43.69519
#>   UHM1247.20816_S72_sd UHM1248.20575_S54_sd UHM1256.20570_S89_sd
#> 1             93.54648             33.85625             133.8458
#>   UHM1260.20596_S21_sd UHM1270.20577_S78_sd UHM1271.20397_S32_sd
#> 1             119.9599             50.01793             68.88877
#>   UHM1272.20398_S44_sd UHM1274.20554_S87_sd UHM1275.20597_S33_sd
#> 1             49.13817             229.7002             37.93416
#>   UHM1282.20599_S57_sd UHM1287.20543_S50_sd UHM1291.20416_S70_sd
#> 1             22.26787             65.59865             196.0903
#>   UHM1296.20550_S39_sd UHM1319.20561_S76_sd UHM1324.20413_S34_sd
#> 1             1028.087             47.15055             83.69937
#>   UHM1327.20545_S74_sd UHM1328.20572_S18_sd UHM1334.20417_S82_sd
#> 1             851.0073              34.8352             297.2343
#>   UHM1338.20399_S56_sd UHM1341.20602_S93_sd UHM1356.20541_S26_sd
#> 1             124.0839             25.00839             52.47891
#>   UHM1380.20580_S19_sd UHM1383.20594_S92_sd UHM1385.20563_S5_sd
#> 1             234.9112             89.97152            19.24118
#>   UHM1399.20756_S17_sd UHM1400.20757_S29_sd UHM1401.20758_S41_sd
#> 1             36.75585             319.9697             22.30779
#>   UHM1402.20759_S53_sd UHM1403.20760_S65_sd UHM1405.20761_S77_sd
#> 1             191.0761             133.7241             125.8234
#>   UHM1406.20762_S89_sd UHM1414.20763_S6_sd UHM1419.20764_S18_sd
#> 1             176.6388            275.7595             56.67871
#>   UHM1427.20389_S31_sd UHM1428.20390_S43_sd UHM1429.20391_S55_sd
#> 1             417.0892             13.64804             1495.366
#>   UHM1430.20392_S67_sd UHM1432.20393_S79_sd UHM1435.20388_S19_sd
#> 1             3136.821             1847.109             35.92044
#>   UHM162.20560_S64_sd UHM198.20585_S79_sd UHM20.3314_S52_sd UHM20.3315_S64_sd
#> 1            138.2289            25.62098          10688.68          2104.592
#>   UHM204.20409_S81_sd UHM206.20410_S93_sd UHM207.20593_S80_sd
#> 1            757.8052            2.096145            538.2722
#>   UHM208.20411_S10_sd UHM211.20406_S45_sd UHM215.20408_S69_sd
#> 1            84.48942            700.0161            39573.74
#>   UHM216.20429_S36_sd UHM219.20430_S48_sd UHM236.20431_S60_sd
#> 1            1788.612            19.13491            1012.656
#>   UHM238.20407_S57_sd UHM245.20538_S85_sd UHM252.20558_S40_sd
#> 1            367.4016            3699.979            35.07222
#>   UHM267.20400_S68_sd UHM274.20581_S31_sd UHM276.20586_S91_sd
#> 1             922.643            42.82979            104.0571
#>   UHM280.20401_S80_sd UHM286.20425_S83_sd UHM289.20426_S95_sd
#> 1            193.8901            804.7789            47.10995
#>   UHM294.20427_S12_sd UHM298.20600_S69_sd UHM325.20548_S15_sd
#> 1            53.40157            655.8324            85.02035
#>   UHM337.20412_S22_sd UHM354.20535_S49_sd UHM356.20415_S58_sd
#> 1            69.99359            135.4667            134.8594
#>   UHM369.20773_S31_sd UHM370.20774_S43_sd UHM372.20775_S55_sd
#> 1            179.4205            269.2813            69.98738
#>   UHM373.20776_S67_sd UHM374.20777_S79_sd UHM375.20778_S91_sd
#> 1            389.8585            228.4251            49.65481
#>   UHM377.20779_S8_sd UHM38.3376_S36_sd UHM386.20781_S32_sd UHM387.20782_S44_sd
#> 1           115.8597          216.5197            133.6554            227.6595
#>   UHM414.20583_S55_sd UHM418.20765_S30_sd UHM422.20766_S42_sd
#> 1            1008.865            122.4824            31.83723
#>   UHM425.20767_S54_sd UHM426.20534_S37_sd UHM428.20544_S62_sd
#> 1            169.2548            21.08198            81.53644
#>   UHM429.20559_S52_sd UHM435.20547_S3_sd UHM437.20768_S66_sd
#> 1            49.87358            33.8204            475.3522
#>   UHM439.20564_S17_sd UHM44.3526_S31_sd UHM445.20569_S77_sd UHM447.20783_S56_sd
#> 1            347.2478          32.22866            317.3092            367.2804
#>   UHM448.20769_S78_sd UHM45.3539_S92_sd UHM454.20770_S90_sd UHM455.20785_S80_sd
#> 1            102.3915          252.4649            158.5003            45.90983
#>   UHM458.20786_S92_sd UHM459.20787_S9_sd UHM461.20771_S7_sd UHM467.20772_S19_sd
#> 1            351.8423           461.4113           230.9941            704.8089
#>   UHM470.20533_S25_sd UHM476.20414_S46_sd UHM478.20549_S27_sd
#> 1            54.77053            88.39812            232.8045
#>   UHM479.20551_S51_sd UHM481.20403_S9_sd UHM482.20590_S44_sd
#> 1            19.27435           41.84811            19.16044
#>   UHM483.20603_S10_sd UHM519.20582_S43_sd UHM520.20573_S30_sd
#> 1            496.7822            1148.985            217.3543
#>   UHM746.21478_S117_sd UHM747.21477_S106_sd UHM748.21467_S170_sd
#> 1             9.571675             9.571661             9.571661
#>   UHM748.21487_S129_sd UHM749.21479_S128_sd UHM759.21466_S159_sd
#> 1              9.57169             9.571666             9.571661
#>   UHM759.21486_S118_sd UHM775.21485_S107_sd UHM776.21482_S161_sd
#> 1             9.571802             9.561302             9.561302
#>   UHM777.21484_S183_sd UHM779.21468_S181_sd UHM779.21488_S140_sd
#> 1             9.571666             9.571679             9.571661
#>   UHM782.21480_S139_sd UHM810.21472_S138_sd UHM811.21471_S127_sd
#> 1             9.571661             9.561302             9.571686
#>   UHM813.21481_S150_sd UHM818.21469_S105_sd UHM818.21489_S151_sd
#> 1             9.571661             9.571661             9.973697
#>   UHM819.21473_S149_sd UHM820.21470_S116_sd UHM820.21490_S162_sd
#> 1             9.561302             9.571661             9.571796
#>   UHM827.21474_S160_sd UHM829.21476_S182_sd UHM832.21483_S172_sd
#> 1             9.572024             9.571675             9.631984
#>   UHM836.20385_S78_sd UHM837.20386_S90_sd UHM838.20387_S7_sd
#> 1            15.39253            11.95027           2404.314
#>   UHM891.20384_S66_sd UHM892.20532_S13_sd UHM893.20595_S9_sd
#> 1            229.0652            54.37307           4917.516
#>   UHM894.20540_S14_sd UHM895.20536_S61_sd UHM896.20601_S81_sd
#> 1            1660.413            24.94491            30.44526
#>   UHM897.20591_S56_sd UHM898.20394_S91_sd UHM899.20588_S20_sd
#> 1            73.43847            2.858507             1962.12
#>   UHM900.20395_S8_sd UHM901.20542_S38_sd UHM902.20584_S67_sd UHM903.20587_S8_sd
#> 1           20.02182            910.6987            30.14459           90.61732
#>   UHM904.20567_S53_sd UHM905.20598_S45_sd UHM906.20565_S29_sd
#> 1            4545.268            169.3084              48.374
#>   UHM907.20592_S68_sd UHM908.20396_S20_sd UHM909.20557_S28_sd
#> 1            1852.122            30.94928            300.3736
#>   UHM910.20562_S88_sd UHM965.20537_S73_sd UHM966.20743_S51_sd
#> 1            1237.545            26.14523             82.9602
#>   UHM967.20744_S63_sd UHM968.20571_S6_sd UHM969.20745_S75_sd
#> 1            11.91557           19.13539            1222.199
#>   UHM971.20746_S87_sd UHM973.20578_S90_sd UHM974.20432_S72_sd
#> 1            7852.579            464.5197            7.369624
#>   UHM975.20747_S4_sd UHM977.20748_S16_sd UHM978.20749_S28_sd
#> 1           918.8867            397.3849            501.1021
#>   UHM979.20750_S40_sd UHM980.20731_S2_sd UHM981.20539_S2_sd UHM982.20740_S15_sd
#> 1            680.8112           182.0777           203.6353            226.6291
#>   UHM983.20556_S16_sd UHM984.20751_S52_sd UHM985.20752_S64_sd
#> 1            19.13322             906.354             23.1853
#>   UHM988.20753_S76_sd UHM989.20754_S88_sd UHM991.20755_S5_sd
#> 1            74.59242            1255.675           23.54237
#>   UHM993.20741_S27_sd UHM996.20610_S94_sd UHM997.20553_S75_sd
#> 1             640.533             114.962            101.8932
#>   UHM998.20618_S95_sd UHM999.20617_S83_sd spiked.blank.20433_S84_se
#> 1            16.04605            366.2543                 0.1981972
#>   spiked.blank.20817_S84_se Std2uL.20625_S84_se StdSwab1uL.20624_S72_se
#> 1                 0.1981972           0.1981972               0.1003388
#>   STP1719.20422_S47_se STP213.20423_S59_se STP268.20424_S71_se
#> 1            0.1993392            6.147319            20.91806
#>   STP544.20419_S11_se STP570.20420_S23_se STP579.20421_S35_se
#> 1           0.1982798           0.4446323            0.280287
#>   STP614.20418_S94_se UHM1000.20604_S22_se UHM1001.20609_S82_se
#> 1        0.0003868841             1.011036            0.9248924
#>   UHM1007.20622_S48_se UHM1009.20614_S47_se UHM1010.20621_S36_se
#> 1             4.481138             15.42938            0.1474916
#>   UHM1011.20606_S46_se UHM1024.20620_S24_se UHM1026.20607_S58_se
#> 1             7.358849            0.1215248            0.4701721
#>   UHM1028.20613_S35_se UHM1032.20605_S34_se UHM1033.20619_S12_se
#> 1            0.1132613            0.1048245           0.09916978
#>   UHM1034.20616_S71_se UHM1035.20611_S11_se UHM1036.20612_S23_se
#> 1             33.36244            0.3842116            0.4346153
#>   UHM1052.20615_S59_se UHM1060.20723_S1_se UHM1065.20724_S13_se
#> 1             5.674928           0.1983007            0.1988148
#>   UHM1068.20732_S14_se UHM1069.20742_S39_se UHM1070.20725_S25_se
#> 1             0.458184             7.520787            0.5420463
#>   UHM1071.20733_S26_se UHM1072.20734_S38_se UHM1073.20735_S50_se
#> 1             4.136542            0.2035237             3.270424
#>   UHM1075.20726_S37_se UHM1077.20736_S62_se UHM1078.20727_S49_se
#> 1            0.4963314              23.2707             1.605026
#>   UHM1080.20737_S74_se UHM1081.20728_S61_se UHM1088.20738_S86_se
#> 1             8.706148            0.2000607             1.389822
#>   UHM1090.20739_S3_se UHM1093.20729_S73_se UHM1095.20730_S85_se
#> 1           0.2060149            0.5738986            0.2086359
#>   UHM1097.20623_S60_se UHM1099.20608_S70_se UHM1100.20788_S21_se
#> 1            0.1031479             15.00141             5.787767
#>   UHM1102.20789_S33_se UHM1104.20790_S45_se UHM1105.20791_S57_se
#> 1            0.9544211             4.642032             1.203926
#>   UHM1109.20531_S1_se UHM1110.20568_S65_se UHM1113.20792_S69_se
#> 1            4.999016             5.448842             33.94847
#>   UHM1114.20793_S81_se UHM1115.20794_S93_se UHM1117.20795_S10_se
#> 1             1.279081             3.318593             4.014013
#>   UHM1118.20796_S22_se UHM1120.20797_S34_se UHM1124.20798_S46_se
#> 1             1.340796              0.37254            0.4223823
#>   UHM1126.20799_S58_se UHM1128.20800_S70_se UHM1140.20555_S4_se
#> 1             1.472395             5.425852             9.77807
#>   UHM1145.20801_S82_se UHM1163.20405_S33_se UHM1164.20402_S92_se
#> 1             8.150354             2.050219             34.06245
#>   UHM1169.20552_S63_se UHM1171.20579_S7_se UHM1176.20404_S21_se
#> 1            0.1987188            2.086139             6.621474
#>   UHM1177.20546_S86_se UHM1182.20576_S66_se UHM1210.20802_S94_se
#> 1            0.2346341            0.8232424             1.917499
#>   UHM1212.20803_S11_se UHM1217.20804_S23_se UHM1218.20805_S35_se
#> 1             1.092577              3.02466             1.418063
#>   UHM1219.20806_S47_se UHM1220.20807_S59_se UHM1221.20808_S71_se
#> 1             2.853048             3.533546             1.367427
#>   UHM1222.20809_S83_se UHM1223.20810_S95_se UHM1225.20811_S12_se
#> 1            0.2653249             3.821282             1.089221
#>   UHM1227.20812_S24_se UHM1228.20813_S36_se UHM1237.20814_S48_se
#> 1            0.5163658             3.547323            0.9171277
#>   UHM1240.20566_S41_se UHM1246.20815_S60_se UHM1247.20816_S72_se
#> 1             1.112501            0.4526359            0.9690424
#>   UHM1248.20575_S54_se UHM1256.20570_S89_se UHM1260.20596_S21_se
#> 1            0.3507149             1.386501             1.242658
#>   UHM1270.20577_S78_se UHM1271.20397_S32_se UHM1272.20398_S44_se
#> 1            0.5181327            0.7136147            0.5090194
#>   UHM1274.20554_S87_se UHM1275.20597_S33_se UHM1282.20599_S57_se
#> 1              2.37945            0.3929578            0.2306715
#>   UHM1287.20543_S50_se UHM1291.20416_S70_se UHM1296.20550_S39_se
#> 1            0.6795325             2.031288             10.64989
#>   UHM1319.20561_S76_se UHM1324.20413_S34_se UHM1327.20545_S74_se
#> 1            0.4884298            0.8670368             8.815534
#>   UHM1328.20572_S18_se UHM1334.20417_S82_se UHM1338.20399_S56_se
#> 1            0.3608558             3.079033             1.285378
#>   UHM1341.20602_S93_se UHM1356.20541_S26_se UHM1380.20580_S19_se
#> 1            0.2590604            0.5436259             2.433431
#>   UHM1383.20594_S92_se UHM1385.20563_S5_se UHM1399.20756_S17_se
#> 1            0.9320096           0.1993182            0.3807517
#>   UHM1400.20757_S29_se UHM1401.20758_S41_se UHM1402.20759_S53_se
#> 1             3.314547            0.2310851             1.979346
#>   UHM1403.20760_S65_se UHM1405.20761_S77_se UHM1406.20762_S89_se
#> 1             1.385241             1.303397             1.829791
#>   UHM1414.20763_S6_se UHM1419.20764_S18_se UHM1427.20389_S31_se
#> 1            2.856576            0.5871314             4.320603
#>   UHM1428.20390_S43_se UHM1429.20391_S55_se UHM1430.20392_S67_se
#> 1            0.1413792              15.4904             32.49415
#>   UHM1432.20393_S79_se UHM1435.20388_S19_se UHM162.20560_S64_se
#> 1             19.13409            0.3720977            1.431905
#>   UHM198.20585_S79_se UHM20.3314_S52_se UHM20.3315_S64_se UHM204.20409_S81_se
#> 1           0.2654062          110.7234          21.80134            7.850059
#>   UHM206.20410_S93_se UHM207.20593_S80_se UHM208.20411_S10_se
#> 1          0.02171384             5.57593           0.8752209
#>   UHM211.20406_S45_se UHM215.20408_S69_se UHM216.20429_S36_se
#> 1            7.251425             409.942            18.52813
#>   UHM219.20430_S48_se UHM236.20431_S60_se UHM238.20407_S57_se
#> 1           0.1982174            10.49004            3.805892
#>   UHM245.20538_S85_se UHM252.20558_S40_se UHM267.20400_S68_se
#> 1            38.32786           0.3633111            9.557605
#>   UHM274.20581_S31_se UHM276.20586_S91_se UHM280.20401_S80_se
#> 1           0.4436713            1.077922            2.008496
#>   UHM286.20425_S83_se UHM289.20426_S95_se UHM294.20427_S12_se
#> 1            8.336656           0.4880092           0.5531837
#>   UHM298.20600_S69_se UHM325.20548_S15_se UHM337.20412_S22_se
#> 1            6.793729           0.8807207           0.7250595
#>   UHM354.20535_S49_se UHM356.20415_S58_se UHM369.20773_S31_se
#> 1            1.403291            1.397001            1.858607
#>   UHM370.20774_S43_se UHM372.20775_S55_se UHM373.20776_S67_se
#> 1            2.789469           0.7249951            4.038521
#>   UHM374.20777_S79_se UHM375.20778_S91_se UHM377.20779_S8_se UHM38.3376_S36_se
#> 1            2.366242           0.5143713           1.200184          2.242914
#>   UHM386.20781_S32_se UHM387.20782_S44_se UHM414.20583_S55_se
#> 1            1.384529            2.358311            10.45078
#>   UHM418.20765_S30_se UHM422.20766_S42_se UHM425.20767_S54_se
#> 1            1.268788              0.3298            1.753301
#>   UHM426.20534_S37_se UHM428.20544_S62_se UHM429.20559_S52_se
#> 1            0.218387           0.8446311           0.5166375
#>   UHM435.20547_S3_se UHM437.20768_S66_se UHM439.20564_S17_se UHM44.3526_S31_se
#> 1          0.3503435            4.924145             3.59712         0.3338548
#>   UHM445.20569_S77_se UHM447.20783_S56_se UHM448.20769_S78_se UHM45.3539_S92_se
#> 1            3.286987            3.804636            1.060668          2.615269
#>   UHM454.20770_S90_se UHM455.20785_S80_se UHM458.20786_S92_se
#> 1            1.641896           0.4755773            3.644714
#>   UHM459.20787_S9_se UHM461.20771_S7_se UHM467.20772_S19_se UHM470.20533_S25_se
#> 1           4.779732           2.392855            7.301074           0.5673647
#>   UHM476.20414_S46_se UHM478.20549_S27_se UHM479.20551_S51_se
#> 1           0.9157109            2.411608           0.1996618
#>   UHM481.20403_S9_se UHM482.20590_S44_se UHM483.20603_S10_se
#> 1          0.4335021           0.1984819            5.146137
#>   UHM519.20582_S43_se UHM520.20573_S30_se UHM746.21478_S117_se
#> 1            11.90226             2.25156           0.09915241
#>   UHM747.21477_S106_se UHM748.21467_S170_se UHM748.21487_S129_se
#> 1           0.09915227           0.09915227           0.09915257
#>   UHM749.21479_S128_se UHM759.21466_S159_se UHM759.21486_S118_se
#> 1           0.09915232           0.09915227           0.09915373
#>   UHM775.21485_S107_se UHM776.21482_S161_se UHM777.21484_S183_se
#> 1           0.09904496           0.09904496           0.09915232
#>   UHM779.21468_S181_se UHM779.21488_S140_se UHM782.21480_S139_se
#> 1           0.09915246           0.09915227           0.09915227
#>   UHM810.21472_S138_se UHM811.21471_S127_se UHM813.21481_S150_se
#> 1           0.09904496           0.09915253           0.09915227
#>   UHM818.21469_S105_se UHM818.21489_S151_se UHM819.21473_S149_se
#> 1           0.09915227            0.1033169           0.09904496
#>   UHM820.21470_S116_se UHM820.21490_S162_se UHM827.21474_S160_se
#> 1           0.09915227           0.09915366           0.09915603
#>   UHM829.21476_S182_se UHM832.21483_S172_se UHM836.20385_S78_se
#> 1           0.09915241           0.09977715           0.1594503
#>   UHM837.20386_S90_se UHM838.20387_S7_se UHM891.20384_S66_se
#> 1           0.1237921           24.90614            2.372873
#>   UHM892.20532_S13_se UHM893.20595_S9_se UHM894.20540_S14_se
#> 1           0.5632474           50.94025            17.20012
#>   UHM895.20536_S61_se UHM896.20601_S81_se UHM897.20591_S56_se
#> 1           0.2584028           0.3153806           0.7607448
#>   UHM898.20394_S91_se UHM899.20588_S20_se UHM900.20395_S8_se
#> 1          0.02961111            20.32548          0.2074048
#>   UHM901.20542_S38_se UHM902.20584_S67_se UHM903.20587_S8_se
#> 1            9.433874            0.312266          0.9386994
#>   UHM904.20567_S53_se UHM905.20598_S45_se UHM906.20565_S29_se
#> 1            47.08416            1.753855           0.5011034
#>   UHM907.20592_S68_se UHM908.20396_S20_se UHM909.20557_S28_se
#> 1            19.18602           0.3206017            3.111553
#>   UHM910.20562_S88_se UHM965.20537_S73_se UHM966.20743_S51_se
#> 1            12.81966           0.2708369           0.8593798
#>   UHM967.20744_S63_se UHM968.20571_S6_se UHM969.20745_S75_se
#> 1           0.1234327          0.1982224            12.66069
#>   UHM971.20746_S87_se UHM973.20578_S90_se UHM974.20432_S72_se
#> 1             81.3444            4.811932           0.0763415
#>   UHM975.20747_S4_se UHM977.20748_S16_se UHM978.20749_S28_se
#> 1           9.518693            4.116487            5.190887
#>   UHM979.20750_S40_se UHM980.20731_S2_se UHM981.20539_S2_se UHM982.20740_S15_se
#> 1            7.052483           1.886132           2.109446            2.347637
#>   UHM983.20556_S16_se UHM984.20751_S52_se UHM985.20752_S64_se
#> 1           0.1981999            9.388868           0.2401751
#>   UHM988.20753_S76_se UHM989.20754_S88_se UHM991.20755_S5_se
#> 1           0.7726984            13.00746           0.243874
#>   UHM993.20741_S27_se UHM996.20610_S94_se UHM997.20553_S75_se
#> 1            6.635243            1.190885            1.055506
#>   UHM998.20618_S95_se UHM999.20617_S83_se spiked.blank.20433_S84_q25
#> 1             0.16622            3.794007                          0
#>   spiked.blank.20817_S84_q25 Std2uL.20625_S84_q25 StdSwab1uL.20624_S72_q25
#> 1                          0                    0                        0
#>   STP1719.20422_S47_q25 STP213.20423_S59_q25 STP268.20424_S71_q25
#> 1                     0                    0                    0
#>   STP544.20419_S11_q25 STP570.20420_S23_q25 STP579.20421_S35_q25
#> 1                    0                    0                    0
#>   STP614.20418_S94_q25 UHM1000.20604_S22_q25 UHM1001.20609_S82_q25
#> 1                    0                     0                     0
#>   UHM1007.20622_S48_q25 UHM1009.20614_S47_q25 UHM1010.20621_S36_q25
#> 1                     0                     0                     0
#>   UHM1011.20606_S46_q25 UHM1024.20620_S24_q25 UHM1026.20607_S58_q25
#> 1                     0                     0                     0
#>   UHM1028.20613_S35_q25 UHM1032.20605_S34_q25 UHM1033.20619_S12_q25
#> 1                     0                     0                     0
#>   UHM1034.20616_S71_q25 UHM1035.20611_S11_q25 UHM1036.20612_S23_q25
#> 1                     0                     0                     0
#>   UHM1052.20615_S59_q25 UHM1060.20723_S1_q25 UHM1065.20724_S13_q25
#> 1                     0                    0                     0
#>   UHM1068.20732_S14_q25 UHM1069.20742_S39_q25 UHM1070.20725_S25_q25
#> 1                     0                     0                     0
#>   UHM1071.20733_S26_q25 UHM1072.20734_S38_q25 UHM1073.20735_S50_q25
#> 1                     0                     0                     0
#>   UHM1075.20726_S37_q25 UHM1077.20736_S62_q25 UHM1078.20727_S49_q25
#> 1                     0                     0                     0
#>   UHM1080.20737_S74_q25 UHM1081.20728_S61_q25 UHM1088.20738_S86_q25
#> 1                     0                     0                     0
#>   UHM1090.20739_S3_q25 UHM1093.20729_S73_q25 UHM1095.20730_S85_q25
#> 1                    0                     0                     0
#>   UHM1097.20623_S60_q25 UHM1099.20608_S70_q25 UHM1100.20788_S21_q25
#> 1                     0                     0                     0
#>   UHM1102.20789_S33_q25 UHM1104.20790_S45_q25 UHM1105.20791_S57_q25
#> 1                     0                     0                     0
#>   UHM1109.20531_S1_q25 UHM1110.20568_S65_q25 UHM1113.20792_S69_q25
#> 1                    0                     0                     0
#>   UHM1114.20793_S81_q25 UHM1115.20794_S93_q25 UHM1117.20795_S10_q25
#> 1                     0                     0                     0
#>   UHM1118.20796_S22_q25 UHM1120.20797_S34_q25 UHM1124.20798_S46_q25
#> 1                     0                     0                     0
#>   UHM1126.20799_S58_q25 UHM1128.20800_S70_q25 UHM1140.20555_S4_q25
#> 1                     0                     0                    0
#>   UHM1145.20801_S82_q25 UHM1163.20405_S33_q25 UHM1164.20402_S92_q25
#> 1                     0                     0                     0
#>   UHM1169.20552_S63_q25 UHM1171.20579_S7_q25 UHM1176.20404_S21_q25
#> 1                     0                    0                     0
#>   UHM1177.20546_S86_q25 UHM1182.20576_S66_q25 UHM1210.20802_S94_q25
#> 1                     0                     0                     0
#>   UHM1212.20803_S11_q25 UHM1217.20804_S23_q25 UHM1218.20805_S35_q25
#> 1                     0                     0                     0
#>   UHM1219.20806_S47_q25 UHM1220.20807_S59_q25 UHM1221.20808_S71_q25
#> 1                     0                     0                     0
#>   UHM1222.20809_S83_q25 UHM1223.20810_S95_q25 UHM1225.20811_S12_q25
#> 1                     0                     0                     0
#>   UHM1227.20812_S24_q25 UHM1228.20813_S36_q25 UHM1237.20814_S48_q25
#> 1                     0                     0                     0
#>   UHM1240.20566_S41_q25 UHM1246.20815_S60_q25 UHM1247.20816_S72_q25
#> 1                     0                     0                     0
#>   UHM1248.20575_S54_q25 UHM1256.20570_S89_q25 UHM1260.20596_S21_q25
#> 1                     0                     0                     0
#>   UHM1270.20577_S78_q25 UHM1271.20397_S32_q25 UHM1272.20398_S44_q25
#> 1                     0                     0                     0
#>   UHM1274.20554_S87_q25 UHM1275.20597_S33_q25 UHM1282.20599_S57_q25
#> 1                     0                     0                     0
#>   UHM1287.20543_S50_q25 UHM1291.20416_S70_q25 UHM1296.20550_S39_q25
#> 1                     0                     0                     0
#>   UHM1319.20561_S76_q25 UHM1324.20413_S34_q25 UHM1327.20545_S74_q25
#> 1                     0                     0                     0
#>   UHM1328.20572_S18_q25 UHM1334.20417_S82_q25 UHM1338.20399_S56_q25
#> 1                     0                     0                     0
#>   UHM1341.20602_S93_q25 UHM1356.20541_S26_q25 UHM1380.20580_S19_q25
#> 1                     0                     0                     0
#>   UHM1383.20594_S92_q25 UHM1385.20563_S5_q25 UHM1399.20756_S17_q25
#> 1                     0                    0                     0
#>   UHM1400.20757_S29_q25 UHM1401.20758_S41_q25 UHM1402.20759_S53_q25
#> 1                     0                     0                     0
#>   UHM1403.20760_S65_q25 UHM1405.20761_S77_q25 UHM1406.20762_S89_q25
#> 1                     0                     0                     0
#>   UHM1414.20763_S6_q25 UHM1419.20764_S18_q25 UHM1427.20389_S31_q25
#> 1                    0                     0                     0
#>   UHM1428.20390_S43_q25 UHM1429.20391_S55_q25 UHM1430.20392_S67_q25
#> 1                     0                     0                     0
#>   UHM1432.20393_S79_q25 UHM1435.20388_S19_q25 UHM162.20560_S64_q25
#> 1                     0                     0                    0
#>   UHM198.20585_S79_q25 UHM20.3314_S52_q25 UHM20.3315_S64_q25
#> 1                    0                  0                  0
#>   UHM204.20409_S81_q25 UHM206.20410_S93_q25 UHM207.20593_S80_q25
#> 1                    0                    0                    0
#>   UHM208.20411_S10_q25 UHM211.20406_S45_q25 UHM215.20408_S69_q25
#> 1                    0                    0                    0
#>   UHM216.20429_S36_q25 UHM219.20430_S48_q25 UHM236.20431_S60_q25
#> 1                    0                    0                    0
#>   UHM238.20407_S57_q25 UHM245.20538_S85_q25 UHM252.20558_S40_q25
#> 1                    0                    0                    0
#>   UHM267.20400_S68_q25 UHM274.20581_S31_q25 UHM276.20586_S91_q25
#> 1                    0                    0                    0
#>   UHM280.20401_S80_q25 UHM286.20425_S83_q25 UHM289.20426_S95_q25
#> 1                    0                    0                    0
#>   UHM294.20427_S12_q25 UHM298.20600_S69_q25 UHM325.20548_S15_q25
#> 1                    0                    0                    0
#>   UHM337.20412_S22_q25 UHM354.20535_S49_q25 UHM356.20415_S58_q25
#> 1                    0                    0                    0
#>   UHM369.20773_S31_q25 UHM370.20774_S43_q25 UHM372.20775_S55_q25
#> 1                    0                    0                    0
#>   UHM373.20776_S67_q25 UHM374.20777_S79_q25 UHM375.20778_S91_q25
#> 1                    0                    0                    0
#>   UHM377.20779_S8_q25 UHM38.3376_S36_q25 UHM386.20781_S32_q25
#> 1                   0                  0                    0
#>   UHM387.20782_S44_q25 UHM414.20583_S55_q25 UHM418.20765_S30_q25
#> 1                    0                    0                    0
#>   UHM422.20766_S42_q25 UHM425.20767_S54_q25 UHM426.20534_S37_q25
#> 1                    0                    0                    0
#>   UHM428.20544_S62_q25 UHM429.20559_S52_q25 UHM435.20547_S3_q25
#> 1                    0                    0                   0
#>   UHM437.20768_S66_q25 UHM439.20564_S17_q25 UHM44.3526_S31_q25
#> 1                    0                    0                  0
#>   UHM445.20569_S77_q25 UHM447.20783_S56_q25 UHM448.20769_S78_q25
#> 1                    0                    0                    0
#>   UHM45.3539_S92_q25 UHM454.20770_S90_q25 UHM455.20785_S80_q25
#> 1                  0                    0                    0
#>   UHM458.20786_S92_q25 UHM459.20787_S9_q25 UHM461.20771_S7_q25
#> 1                    0                   0                   0
#>   UHM467.20772_S19_q25 UHM470.20533_S25_q25 UHM476.20414_S46_q25
#> 1                    0                    0                    0
#>   UHM478.20549_S27_q25 UHM479.20551_S51_q25 UHM481.20403_S9_q25
#> 1                    0                    0                   0
#>   UHM482.20590_S44_q25 UHM483.20603_S10_q25 UHM519.20582_S43_q25
#> 1                    0                    0                    0
#>   UHM520.20573_S30_q25 UHM746.21478_S117_q25 UHM747.21477_S106_q25
#> 1                    0                     0                     0
#>   UHM748.21467_S170_q25 UHM748.21487_S129_q25 UHM749.21479_S128_q25
#> 1                     0                     0                     0
#>   UHM759.21466_S159_q25 UHM759.21486_S118_q25 UHM775.21485_S107_q25
#> 1                     0                     0                     0
#>   UHM776.21482_S161_q25 UHM777.21484_S183_q25 UHM779.21468_S181_q25
#> 1                     0                     0                     0
#>   UHM779.21488_S140_q25 UHM782.21480_S139_q25 UHM810.21472_S138_q25
#> 1                     0                     0                     0
#>   UHM811.21471_S127_q25 UHM813.21481_S150_q25 UHM818.21469_S105_q25
#> 1                     0                     0                     0
#>   UHM818.21489_S151_q25 UHM819.21473_S149_q25 UHM820.21470_S116_q25
#> 1                     0                     0                     0
#>   UHM820.21490_S162_q25 UHM827.21474_S160_q25 UHM829.21476_S182_q25
#> 1                     0                     0                     0
#>   UHM832.21483_S172_q25 UHM836.20385_S78_q25 UHM837.20386_S90_q25
#> 1                     0                    0                    0
#>   UHM838.20387_S7_q25 UHM891.20384_S66_q25 UHM892.20532_S13_q25
#> 1                   0                    0                    0
#>   UHM893.20595_S9_q25 UHM894.20540_S14_q25 UHM895.20536_S61_q25
#> 1                   0                    0                    0
#>   UHM896.20601_S81_q25 UHM897.20591_S56_q25 UHM898.20394_S91_q25
#> 1                    0                    0                    0
#>   UHM899.20588_S20_q25 UHM900.20395_S8_q25 UHM901.20542_S38_q25
#> 1                    0                   0                    0
#>   UHM902.20584_S67_q25 UHM903.20587_S8_q25 UHM904.20567_S53_q25
#> 1                    0                   0                    0
#>   UHM905.20598_S45_q25 UHM906.20565_S29_q25 UHM907.20592_S68_q25
#> 1                    0                    0                    0
#>   UHM908.20396_S20_q25 UHM909.20557_S28_q25 UHM910.20562_S88_q25
#> 1                    0                    0                    0
#>   UHM965.20537_S73_q25 UHM966.20743_S51_q25 UHM967.20744_S63_q25
#> 1                    0                    0                    0
#>   UHM968.20571_S6_q25 UHM969.20745_S75_q25 UHM971.20746_S87_q25
#> 1                   0                    0                    0
#>   UHM973.20578_S90_q25 UHM974.20432_S72_q25 UHM975.20747_S4_q25
#> 1                    0                    0                   0
#>   UHM977.20748_S16_q25 UHM978.20749_S28_q25 UHM979.20750_S40_q25
#> 1                    0                    0                    0
#>   UHM980.20731_S2_q25 UHM981.20539_S2_q25 UHM982.20740_S15_q25
#> 1                   0                   0                    0
#>   UHM983.20556_S16_q25 UHM984.20751_S52_q25 UHM985.20752_S64_q25
#> 1                    0                    0                    0
#>   UHM988.20753_S76_q25 UHM989.20754_S88_q25 UHM991.20755_S5_q25
#> 1                    0                    0                   0
#>   UHM993.20741_S27_q25 UHM996.20610_S94_q25 UHM997.20553_S75_q25
#> 1                    0                    0                    0
#>   UHM998.20618_S95_q25 UHM999.20617_S83_q25 spiked.blank.20433_S84_median
#> 1                    0                    0                             0
#>   spiked.blank.20817_S84_median Std2uL.20625_S84_median
#> 1                             0                       0
#>   StdSwab1uL.20624_S72_median STP1719.20422_S47_median STP213.20423_S59_median
#> 1                           0                        0                       0
#>   STP268.20424_S71_median STP544.20419_S11_median STP570.20420_S23_median
#> 1                       0                       0                       0
#>   STP579.20421_S35_median STP614.20418_S94_median UHM1000.20604_S22_median
#> 1                       0                       0                        0
#>   UHM1001.20609_S82_median UHM1007.20622_S48_median UHM1009.20614_S47_median
#> 1                        0                        0                        0
#>   UHM1010.20621_S36_median UHM1011.20606_S46_median UHM1024.20620_S24_median
#> 1                        0                        0                        0
#>   UHM1026.20607_S58_median UHM1028.20613_S35_median UHM1032.20605_S34_median
#> 1                        0                        0                        0
#>   UHM1033.20619_S12_median UHM1034.20616_S71_median UHM1035.20611_S11_median
#> 1                        0                        0                        0
#>   UHM1036.20612_S23_median UHM1052.20615_S59_median UHM1060.20723_S1_median
#> 1                        0                        0                       0
#>   UHM1065.20724_S13_median UHM1068.20732_S14_median UHM1069.20742_S39_median
#> 1                        0                        0                        0
#>   UHM1070.20725_S25_median UHM1071.20733_S26_median UHM1072.20734_S38_median
#> 1                        0                        0                        0
#>   UHM1073.20735_S50_median UHM1075.20726_S37_median UHM1077.20736_S62_median
#> 1                        0                        0                        0
#>   UHM1078.20727_S49_median UHM1080.20737_S74_median UHM1081.20728_S61_median
#> 1                        0                        0                        0
#>   UHM1088.20738_S86_median UHM1090.20739_S3_median UHM1093.20729_S73_median
#> 1                        0                       0                        0
#>   UHM1095.20730_S85_median UHM1097.20623_S60_median UHM1099.20608_S70_median
#> 1                        0                        0                        0
#>   UHM1100.20788_S21_median UHM1102.20789_S33_median UHM1104.20790_S45_median
#> 1                        0                        0                        0
#>   UHM1105.20791_S57_median UHM1109.20531_S1_median UHM1110.20568_S65_median
#> 1                        0                       0                        0
#>   UHM1113.20792_S69_median UHM1114.20793_S81_median UHM1115.20794_S93_median
#> 1                        0                        0                        0
#>   UHM1117.20795_S10_median UHM1118.20796_S22_median UHM1120.20797_S34_median
#> 1                        0                        0                        0
#>   UHM1124.20798_S46_median UHM1126.20799_S58_median UHM1128.20800_S70_median
#> 1                        0                        0                        0
#>   UHM1140.20555_S4_median UHM1145.20801_S82_median UHM1163.20405_S33_median
#> 1                       0                        0                        0
#>   UHM1164.20402_S92_median UHM1169.20552_S63_median UHM1171.20579_S7_median
#> 1                        0                        0                       0
#>   UHM1176.20404_S21_median UHM1177.20546_S86_median UHM1182.20576_S66_median
#> 1                        0                        0                        0
#>   UHM1210.20802_S94_median UHM1212.20803_S11_median UHM1217.20804_S23_median
#> 1                        0                        0                        0
#>   UHM1218.20805_S35_median UHM1219.20806_S47_median UHM1220.20807_S59_median
#> 1                        0                        0                        0
#>   UHM1221.20808_S71_median UHM1222.20809_S83_median UHM1223.20810_S95_median
#> 1                        0                        0                        0
#>   UHM1225.20811_S12_median UHM1227.20812_S24_median UHM1228.20813_S36_median
#> 1                        0                        0                        0
#>   UHM1237.20814_S48_median UHM1240.20566_S41_median UHM1246.20815_S60_median
#> 1                        0                        0                        0
#>   UHM1247.20816_S72_median UHM1248.20575_S54_median UHM1256.20570_S89_median
#> 1                        0                        0                        0
#>   UHM1260.20596_S21_median UHM1270.20577_S78_median UHM1271.20397_S32_median
#> 1                        0                        0                        0
#>   UHM1272.20398_S44_median UHM1274.20554_S87_median UHM1275.20597_S33_median
#> 1                        0                        0                        0
#>   UHM1282.20599_S57_median UHM1287.20543_S50_median UHM1291.20416_S70_median
#> 1                        0                        0                        0
#>   UHM1296.20550_S39_median UHM1319.20561_S76_median UHM1324.20413_S34_median
#> 1                        0                        0                        0
#>   UHM1327.20545_S74_median UHM1328.20572_S18_median UHM1334.20417_S82_median
#> 1                        0                        0                        0
#>   UHM1338.20399_S56_median UHM1341.20602_S93_median UHM1356.20541_S26_median
#> 1                        0                        0                        0
#>   UHM1380.20580_S19_median UHM1383.20594_S92_median UHM1385.20563_S5_median
#> 1                        0                        0                       0
#>   UHM1399.20756_S17_median UHM1400.20757_S29_median UHM1401.20758_S41_median
#> 1                        0                        0                        0
#>   UHM1402.20759_S53_median UHM1403.20760_S65_median UHM1405.20761_S77_median
#> 1                        0                        0                        0
#>   UHM1406.20762_S89_median UHM1414.20763_S6_median UHM1419.20764_S18_median
#> 1                        0                       0                        0
#>   UHM1427.20389_S31_median UHM1428.20390_S43_median UHM1429.20391_S55_median
#> 1                        0                        0                        0
#>   UHM1430.20392_S67_median UHM1432.20393_S79_median UHM1435.20388_S19_median
#> 1                        0                        0                        0
#>   UHM162.20560_S64_median UHM198.20585_S79_median UHM20.3314_S52_median
#> 1                       0                       0                     0
#>   UHM20.3315_S64_median UHM204.20409_S81_median UHM206.20410_S93_median
#> 1                     0                       0                       0
#>   UHM207.20593_S80_median UHM208.20411_S10_median UHM211.20406_S45_median
#> 1                       0                       0                       0
#>   UHM215.20408_S69_median UHM216.20429_S36_median UHM219.20430_S48_median
#> 1                       0                       0                       0
#>   UHM236.20431_S60_median UHM238.20407_S57_median UHM245.20538_S85_median
#> 1                       0                       0                       0
#>   UHM252.20558_S40_median UHM267.20400_S68_median UHM274.20581_S31_median
#> 1                       0                       0                       0
#>   UHM276.20586_S91_median UHM280.20401_S80_median UHM286.20425_S83_median
#> 1                       0                       0                       0
#>   UHM289.20426_S95_median UHM294.20427_S12_median UHM298.20600_S69_median
#> 1                       0                       0                       0
#>   UHM325.20548_S15_median UHM337.20412_S22_median UHM354.20535_S49_median
#> 1                       0                       0                       0
#>   UHM356.20415_S58_median UHM369.20773_S31_median UHM370.20774_S43_median
#> 1                       0                       0                       0
#>   UHM372.20775_S55_median UHM373.20776_S67_median UHM374.20777_S79_median
#> 1                       0                       0                       0
#>   UHM375.20778_S91_median UHM377.20779_S8_median UHM38.3376_S36_median
#> 1                       0                      0                     0
#>   UHM386.20781_S32_median UHM387.20782_S44_median UHM414.20583_S55_median
#> 1                       0                       0                       0
#>   UHM418.20765_S30_median UHM422.20766_S42_median UHM425.20767_S54_median
#> 1                       0                       0                       0
#>   UHM426.20534_S37_median UHM428.20544_S62_median UHM429.20559_S52_median
#> 1                       0                       0                       0
#>   UHM435.20547_S3_median UHM437.20768_S66_median UHM439.20564_S17_median
#> 1                      0                       0                       0
#>   UHM44.3526_S31_median UHM445.20569_S77_median UHM447.20783_S56_median
#> 1                     0                       0                       0
#>   UHM448.20769_S78_median UHM45.3539_S92_median UHM454.20770_S90_median
#> 1                       0                     0                       0
#>   UHM455.20785_S80_median UHM458.20786_S92_median UHM459.20787_S9_median
#> 1                       0                       0                      0
#>   UHM461.20771_S7_median UHM467.20772_S19_median UHM470.20533_S25_median
#> 1                      0                       0                       0
#>   UHM476.20414_S46_median UHM478.20549_S27_median UHM479.20551_S51_median
#> 1                       0                       0                       0
#>   UHM481.20403_S9_median UHM482.20590_S44_median UHM483.20603_S10_median
#> 1                      0                       0                       0
#>   UHM519.20582_S43_median UHM520.20573_S30_median UHM746.21478_S117_median
#> 1                       0                       0                        0
#>   UHM747.21477_S106_median UHM748.21467_S170_median UHM748.21487_S129_median
#> 1                        0                        0                        0
#>   UHM749.21479_S128_median UHM759.21466_S159_median UHM759.21486_S118_median
#> 1                        0                        0                        0
#>   UHM775.21485_S107_median UHM776.21482_S161_median UHM777.21484_S183_median
#> 1                        0                        0                        0
#>   UHM779.21468_S181_median UHM779.21488_S140_median UHM782.21480_S139_median
#> 1                        0                        0                        0
#>   UHM810.21472_S138_median UHM811.21471_S127_median UHM813.21481_S150_median
#> 1                        0                        0                        0
#>   UHM818.21469_S105_median UHM818.21489_S151_median UHM819.21473_S149_median
#> 1                        0                        0                        0
#>   UHM820.21470_S116_median UHM820.21490_S162_median UHM827.21474_S160_median
#> 1                        0                        0                        0
#>   UHM829.21476_S182_median UHM832.21483_S172_median UHM836.20385_S78_median
#> 1                        0                        0                       0
#>   UHM837.20386_S90_median UHM838.20387_S7_median UHM891.20384_S66_median
#> 1                       0                      0                       0
#>   UHM892.20532_S13_median UHM893.20595_S9_median UHM894.20540_S14_median
#> 1                       0                      0                       0
#>   UHM895.20536_S61_median UHM896.20601_S81_median UHM897.20591_S56_median
#> 1                       0                       0                       0
#>   UHM898.20394_S91_median UHM899.20588_S20_median UHM900.20395_S8_median
#> 1                       0                       0                      0
#>   UHM901.20542_S38_median UHM902.20584_S67_median UHM903.20587_S8_median
#> 1                       0                       0                      0
#>   UHM904.20567_S53_median UHM905.20598_S45_median UHM906.20565_S29_median
#> 1                       0                       0                       0
#>   UHM907.20592_S68_median UHM908.20396_S20_median UHM909.20557_S28_median
#> 1                       0                       0                       0
#>   UHM910.20562_S88_median UHM965.20537_S73_median UHM966.20743_S51_median
#> 1                       0                       0                       0
#>   UHM967.20744_S63_median UHM968.20571_S6_median UHM969.20745_S75_median
#> 1                       0                      0                       0
#>   UHM971.20746_S87_median UHM973.20578_S90_median UHM974.20432_S72_median
#> 1                       0                       0                       0
#>   UHM975.20747_S4_median UHM977.20748_S16_median UHM978.20749_S28_median
#> 1                      0                       0                       0
#>   UHM979.20750_S40_median UHM980.20731_S2_median UHM981.20539_S2_median
#> 1                       0                      0                      0
#>   UHM982.20740_S15_median UHM983.20556_S16_median UHM984.20751_S52_median
#> 1                       0                       0                       0
#>   UHM985.20752_S64_median UHM988.20753_S76_median UHM989.20754_S88_median
#> 1                       0                       0                       0
#>   UHM991.20755_S5_median UHM993.20741_S27_median UHM996.20610_S94_median
#> 1                      0                       0                       0
#>   UHM997.20553_S75_median UHM998.20618_S95_median UHM999.20617_S83_median
#> 1                       0                       0                       0
#>   spiked.blank.20433_S84_q75 spiked.blank.20817_S84_q75 Std2uL.20625_S84_q75
#> 1                          0                          0                    0
#>   StdSwab1uL.20624_S72_q75 STP1719.20422_S47_q75 STP213.20423_S59_q75
#> 1                        0                     0                    0
#>   STP268.20424_S71_q75 STP544.20419_S11_q75 STP570.20420_S23_q75
#> 1                    0                    0                    0
#>   STP579.20421_S35_q75 STP614.20418_S94_q75 UHM1000.20604_S22_q75
#> 1                    0                    0                     0
#>   UHM1001.20609_S82_q75 UHM1007.20622_S48_q75 UHM1009.20614_S47_q75
#> 1                     0                     0                     0
#>   UHM1010.20621_S36_q75 UHM1011.20606_S46_q75 UHM1024.20620_S24_q75
#> 1                     0                     0                     0
#>   UHM1026.20607_S58_q75 UHM1028.20613_S35_q75 UHM1032.20605_S34_q75
#> 1                     0                     0                     0
#>   UHM1033.20619_S12_q75 UHM1034.20616_S71_q75 UHM1035.20611_S11_q75
#> 1                     0                     0                     0
#>   UHM1036.20612_S23_q75 UHM1052.20615_S59_q75 UHM1060.20723_S1_q75
#> 1                     0                     0                    0
#>   UHM1065.20724_S13_q75 UHM1068.20732_S14_q75 UHM1069.20742_S39_q75
#> 1                     0                     0                     0
#>   UHM1070.20725_S25_q75 UHM1071.20733_S26_q75 UHM1072.20734_S38_q75
#> 1                     0                     0                     0
#>   UHM1073.20735_S50_q75 UHM1075.20726_S37_q75 UHM1077.20736_S62_q75
#> 1                     0                     0                     0
#>   UHM1078.20727_S49_q75 UHM1080.20737_S74_q75 UHM1081.20728_S61_q75
#> 1                     0                     0                     0
#>   UHM1088.20738_S86_q75 UHM1090.20739_S3_q75 UHM1093.20729_S73_q75
#> 1                     0                    0                     0
#>   UHM1095.20730_S85_q75 UHM1097.20623_S60_q75 UHM1099.20608_S70_q75
#> 1                     0                     0                     0
#>   UHM1100.20788_S21_q75 UHM1102.20789_S33_q75 UHM1104.20790_S45_q75
#> 1                     0                     0                     0
#>   UHM1105.20791_S57_q75 UHM1109.20531_S1_q75 UHM1110.20568_S65_q75
#> 1                     0                    0                     0
#>   UHM1113.20792_S69_q75 UHM1114.20793_S81_q75 UHM1115.20794_S93_q75
#> 1                     0                     0                     0
#>   UHM1117.20795_S10_q75 UHM1118.20796_S22_q75 UHM1120.20797_S34_q75
#> 1                     0                     0                     0
#>   UHM1124.20798_S46_q75 UHM1126.20799_S58_q75 UHM1128.20800_S70_q75
#> 1                     0                     0                     0
#>   UHM1140.20555_S4_q75 UHM1145.20801_S82_q75 UHM1163.20405_S33_q75
#> 1                    0                     0                     0
#>   UHM1164.20402_S92_q75 UHM1169.20552_S63_q75 UHM1171.20579_S7_q75
#> 1                     0                     0                    0
#>   UHM1176.20404_S21_q75 UHM1177.20546_S86_q75 UHM1182.20576_S66_q75
#> 1                     0                     0                     0
#>   UHM1210.20802_S94_q75 UHM1212.20803_S11_q75 UHM1217.20804_S23_q75
#> 1                     0                     0                     0
#>   UHM1218.20805_S35_q75 UHM1219.20806_S47_q75 UHM1220.20807_S59_q75
#> 1                     0                     0                     0
#>   UHM1221.20808_S71_q75 UHM1222.20809_S83_q75 UHM1223.20810_S95_q75
#> 1                     0                     0                     0
#>   UHM1225.20811_S12_q75 UHM1227.20812_S24_q75 UHM1228.20813_S36_q75
#> 1                     0                     0                     0
#>   UHM1237.20814_S48_q75 UHM1240.20566_S41_q75 UHM1246.20815_S60_q75
#> 1                     0                     0                     0
#>   UHM1247.20816_S72_q75 UHM1248.20575_S54_q75 UHM1256.20570_S89_q75
#> 1                     0                     0                     0
#>   UHM1260.20596_S21_q75 UHM1270.20577_S78_q75 UHM1271.20397_S32_q75
#> 1                     0                     0                     0
#>   UHM1272.20398_S44_q75 UHM1274.20554_S87_q75 UHM1275.20597_S33_q75
#> 1                     0                     0                     0
#>   UHM1282.20599_S57_q75 UHM1287.20543_S50_q75 UHM1291.20416_S70_q75
#> 1                     0                     0                     0
#>   UHM1296.20550_S39_q75 UHM1319.20561_S76_q75 UHM1324.20413_S34_q75
#> 1                     0                     0                     0
#>   UHM1327.20545_S74_q75 UHM1328.20572_S18_q75 UHM1334.20417_S82_q75
#> 1                     0                     0                     0
#>   UHM1338.20399_S56_q75 UHM1341.20602_S93_q75 UHM1356.20541_S26_q75
#> 1                     0                     0                     0
#>   UHM1380.20580_S19_q75 UHM1383.20594_S92_q75 UHM1385.20563_S5_q75
#> 1                     0                     0                    0
#>   UHM1399.20756_S17_q75 UHM1400.20757_S29_q75 UHM1401.20758_S41_q75
#> 1                     0                     0                     0
#>   UHM1402.20759_S53_q75 UHM1403.20760_S65_q75 UHM1405.20761_S77_q75
#> 1                     0                     0                     0
#>   UHM1406.20762_S89_q75 UHM1414.20763_S6_q75 UHM1419.20764_S18_q75
#> 1                     0                    0                     0
#>   UHM1427.20389_S31_q75 UHM1428.20390_S43_q75 UHM1429.20391_S55_q75
#> 1                     0                     0                     0
#>   UHM1430.20392_S67_q75 UHM1432.20393_S79_q75 UHM1435.20388_S19_q75
#> 1                     0                     0                     0
#>   UHM162.20560_S64_q75 UHM198.20585_S79_q75 UHM20.3314_S52_q75
#> 1                    0                    0                  0
#>   UHM20.3315_S64_q75 UHM204.20409_S81_q75 UHM206.20410_S93_q75
#> 1                  0                    0                    0
#>   UHM207.20593_S80_q75 UHM208.20411_S10_q75 UHM211.20406_S45_q75
#> 1                    0                    0                    0
#>   UHM215.20408_S69_q75 UHM216.20429_S36_q75 UHM219.20430_S48_q75
#> 1                    0                    0                    0
#>   UHM236.20431_S60_q75 UHM238.20407_S57_q75 UHM245.20538_S85_q75
#> 1                    0                    0                    0
#>   UHM252.20558_S40_q75 UHM267.20400_S68_q75 UHM274.20581_S31_q75
#> 1                    0                    0                    0
#>   UHM276.20586_S91_q75 UHM280.20401_S80_q75 UHM286.20425_S83_q75
#> 1                    0                    0                    0
#>   UHM289.20426_S95_q75 UHM294.20427_S12_q75 UHM298.20600_S69_q75
#> 1                    0                    0                    0
#>   UHM325.20548_S15_q75 UHM337.20412_S22_q75 UHM354.20535_S49_q75
#> 1                    0                    0                    0
#>   UHM356.20415_S58_q75 UHM369.20773_S31_q75 UHM370.20774_S43_q75
#> 1                    0                    0                    0
#>   UHM372.20775_S55_q75 UHM373.20776_S67_q75 UHM374.20777_S79_q75
#> 1                    0                    0                    0
#>   UHM375.20778_S91_q75 UHM377.20779_S8_q75 UHM38.3376_S36_q75
#> 1                    0                   0                  0
#>   UHM386.20781_S32_q75 UHM387.20782_S44_q75 UHM414.20583_S55_q75
#> 1                    0                    0                    0
#>   UHM418.20765_S30_q75 UHM422.20766_S42_q75 UHM425.20767_S54_q75
#> 1                    0                    0                    0
#>   UHM426.20534_S37_q75 UHM428.20544_S62_q75 UHM429.20559_S52_q75
#> 1                    0                    0                    0
#>   UHM435.20547_S3_q75 UHM437.20768_S66_q75 UHM439.20564_S17_q75
#> 1                   0                    0                    0
#>   UHM44.3526_S31_q75 UHM445.20569_S77_q75 UHM447.20783_S56_q75
#> 1                  0                    0                    0
#>   UHM448.20769_S78_q75 UHM45.3539_S92_q75 UHM454.20770_S90_q75
#> 1                    0                  0                    0
#>   UHM455.20785_S80_q75 UHM458.20786_S92_q75 UHM459.20787_S9_q75
#> 1                    0                    0                   0
#>   UHM461.20771_S7_q75 UHM467.20772_S19_q75 UHM470.20533_S25_q75
#> 1                   0                    0                    0
#>   UHM476.20414_S46_q75 UHM478.20549_S27_q75 UHM479.20551_S51_q75
#> 1                    0                    0                    0
#>   UHM481.20403_S9_q75 UHM482.20590_S44_q75 UHM483.20603_S10_q75
#> 1                   0                    0                    0
#>   UHM519.20582_S43_q75 UHM520.20573_S30_q75 UHM746.21478_S117_q75
#> 1                    0                    0                     0
#>   UHM747.21477_S106_q75 UHM748.21467_S170_q75 UHM748.21487_S129_q75
#> 1                     0                     0                     0
#>   UHM749.21479_S128_q75 UHM759.21466_S159_q75 UHM759.21486_S118_q75
#> 1                     0                     0                     0
#>   UHM775.21485_S107_q75 UHM776.21482_S161_q75 UHM777.21484_S183_q75
#> 1                     0                     0                     0
#>   UHM779.21468_S181_q75 UHM779.21488_S140_q75 UHM782.21480_S139_q75
#> 1                     0                     0                     0
#>   UHM810.21472_S138_q75 UHM811.21471_S127_q75 UHM813.21481_S150_q75
#> 1                     0                     0                     0
#>   UHM818.21469_S105_q75 UHM818.21489_S151_q75 UHM819.21473_S149_q75
#> 1                     0                     0                     0
#>   UHM820.21470_S116_q75 UHM820.21490_S162_q75 UHM827.21474_S160_q75
#> 1                     0                     0                     0
#>   UHM829.21476_S182_q75 UHM832.21483_S172_q75 UHM836.20385_S78_q75
#> 1                     0                     0                    0
#>   UHM837.20386_S90_q75 UHM838.20387_S7_q75 UHM891.20384_S66_q75
#> 1                    0                   0                    0
#>   UHM892.20532_S13_q75 UHM893.20595_S9_q75 UHM894.20540_S14_q75
#> 1                    0                   0                    0
#>   UHM895.20536_S61_q75 UHM896.20601_S81_q75 UHM897.20591_S56_q75
#> 1                    0                    0                    0
#>   UHM898.20394_S91_q75 UHM899.20588_S20_q75 UHM900.20395_S8_q75
#> 1                    0                    0                   0
#>   UHM901.20542_S38_q75 UHM902.20584_S67_q75 UHM903.20587_S8_q75
#> 1                    0                    0                   0
#>   UHM904.20567_S53_q75 UHM905.20598_S45_q75 UHM906.20565_S29_q75
#> 1                    0                    0                    0
#>   UHM907.20592_S68_q75 UHM908.20396_S20_q75 UHM909.20557_S28_q75
#> 1                    0                    0                    0
#>   UHM910.20562_S88_q75 UHM965.20537_S73_q75 UHM966.20743_S51_q75
#> 1                    0                    0                    0
#>   UHM967.20744_S63_q75 UHM968.20571_S6_q75 UHM969.20745_S75_q75
#> 1                    0                   0                    0
#>   UHM971.20746_S87_q75 UHM973.20578_S90_q75 UHM974.20432_S72_q75
#> 1                    0                    0                    0
#>   UHM975.20747_S4_q75 UHM977.20748_S16_q75 UHM978.20749_S28_q75
#> 1                   0                    0                    0
#>   UHM979.20750_S40_q75 UHM980.20731_S2_q75 UHM981.20539_S2_q75
#> 1                    0                   0                   0
#>   UHM982.20740_S15_q75 UHM983.20556_S16_q75 UHM984.20751_S52_q75
#> 1                    0                    0                    0
#>   UHM985.20752_S64_q75 UHM988.20753_S76_q75 UHM989.20754_S88_q75
#> 1                    0                    0                    0
#>   UHM991.20755_S5_q75 UHM993.20741_S27_q75 UHM996.20610_S94_q75
#> 1                   0                    0                    0
#>   UHM997.20553_S75_q75 UHM998.20618_S95_q75 UHM999.20617_S83_q75
#> 1                    0                    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_16S_OTU_perc <- phyloseq::subset_samples(physeq_absolute, sample.or.blank != "blank")

# Adjust the threshold range as needed based on system specifications

result_perc <- calculate_spike_percentage(
  absolute_abundance_16S_OTU_perc, 
  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

conclusion(absolute_abundance_16S_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                113318.1                306390                     29400
#>   mean_percentage sd_percentage median_percentage passed_count failed_count
#> 1        20.20095      32.41997          4.153584          178           82
#> 
#> $full_report
#>                Sample Total_Reads Spiked_Reads   Percentage Result
#> 1   STP1719.20422_S47        2432         1847  75.94572368 failed
#> 2    STP213.20423_S59      188314         1847   0.98080865 passed
#> 3    STP268.20424_S71      757488         1847   0.24383225 passed
#> 4    STP544.20419_S11        1913         1847  96.54992159 failed
#> 5    STP570.20420_S23        5948         1847  31.05245461 failed
#> 6    STP579.20421_S35        5452         1847  33.87747616 failed
#> 7    STP614.20418_S94           5            0   0.00000000 failed
#> 8   UHM1000.20604_S22       43527          924   2.12282032 passed
#> 9   UHM1001.20609_S82       39329          924   2.34941138 passed
#> 10  UHM1007.20622_S48       86473          924   1.06854163 passed
#> 11  UHM1009.20614_S47      181742          924   0.50841303 passed
#> 12  UHM1010.20621_S36        6127          924  15.08078995 passed
#> 13  UHM1011.20606_S46       81060          924   1.13989637 passed
#> 14  UHM1024.20620_S24        2722          924  33.94562821 failed
#> 15  UHM1026.20607_S58        7504          924  12.31343284 passed
#> 16  UHM1028.20613_S35        2624          924  35.21341463 failed
#> 17  UHM1032.20605_S34        1410          924  65.53191489 failed
#> 18  UHM1033.20619_S12         966          924  95.65217391 failed
#> 19  UHM1034.20616_S71      782439          924   0.11809227 passed
#> 20  UHM1035.20611_S11        8100          924  11.40740741 passed
#> 21  UHM1036.20612_S23        8639          924  10.69568237 passed
#> 22  UHM1052.20615_S59       98625          924   0.93688213 passed
#> 23   UHM1060.20723_S1        2077         1847  88.92633606 failed
#> 24  UHM1065.20724_S13        2384         1847  77.47483221 failed
#> 25  UHM1068.20732_S14       19213         1847   9.61328267 passed
#> 26  UHM1069.20742_S39       88462         1847   2.08790215 passed
#> 27  UHM1070.20725_S25       11119         1847  16.61120604 passed
#> 28  UHM1071.20733_S26       66892         1847   2.76116725 passed
#> 29  UHM1072.20734_S38        4265         1847  43.30597890 failed
#> 30  UHM1073.20735_S50      129333         1847   1.42809646 passed
#> 31  UHM1075.20726_S37       12121         1847  15.23801667 passed
#> 32  UHM1077.20736_S62      240090         1847   0.76929485 passed
#> 33  UHM1078.20727_S49       48860         1847   3.78018829 passed
#> 34  UHM1080.20737_S74      238039         1847   0.77592327 passed
#> 35  UHM1081.20728_S61        2118         1847  87.20491029 failed
#> 36  UHM1088.20738_S86       48344         1847   3.82053616 passed
#> 37   UHM1090.20739_S3        4264         1847  43.31613508 failed
#> 38  UHM1093.20729_S73       11145         1847  16.57245402 passed
#> 39  UHM1095.20730_S85        3480         1847  53.07471264 failed
#> 40  UHM1097.20623_S60        1656          924  55.79710145 failed
#> 41  UHM1099.20608_S70      141219          924   0.65430289 passed
#> 42  UHM1100.20788_S21      120205         1847   1.53654174 passed
#> 43  UHM1102.20789_S33       41207         1847   4.48224816 passed
#> 44  UHM1104.20790_S45       75900         1847   2.43346509 passed
#> 45  UHM1105.20791_S57       29520         1847   6.25677507 passed
#> 46   UHM1109.20531_S1       49710         1847   3.71555019 passed
#> 47  UHM1110.20568_S65      258015         1847   0.71584985 passed
#> 48  UHM1113.20792_S69      374668         1847   0.49296978 passed
#> 49  UHM1114.20793_S81       25789         1847   7.16196828 passed
#> 50  UHM1115.20794_S93       53637         1847   3.44351847 passed
#> 51  UHM1117.20795_S10      120503         1847   1.53274192 passed
#> 52  UHM1118.20796_S22       39863         1847   4.63336929 passed
#> 53  UHM1120.20797_S34        6447         1847  28.64898402 failed
#> 54  UHM1124.20798_S46       20011         1847   9.22992354 passed
#> 55  UHM1126.20799_S58       56948         1847   3.24330969 passed
#> 56  UHM1128.20800_S70       75987         1847   2.43067893 passed
#> 57   UHM1140.20555_S4      144990         1847   1.27388096 passed
#> 58  UHM1145.20801_S82      136223         1847   1.35586502 passed
#> 59  UHM1163.20405_S33       66686         1847   2.76969679 passed
#> 60  UHM1164.20402_S92      560873         1847   0.32930806 passed
#> 61  UHM1169.20552_S63        2087         1847  88.50023958 failed
#> 62   UHM1171.20579_S7       94636         1847   1.95168858 passed
#> 63  UHM1176.20404_S21      153994         1847   1.19939738 passed
#> 64  UHM1177.20546_S86        7001         1847  26.38194544 failed
#> 65  UHM1182.20576_S66       19322         1847   9.55905186 passed
#> 66  UHM1210.20802_S94      107631         1847   1.71604835 passed
#> 67  UHM1212.20803_S11       35517         1847   5.20032660 passed
#> 68  UHM1217.20804_S23       66857         1847   2.76261274 passed
#> 69  UHM1218.20805_S35       42728         1847   4.32269238 passed
#> 70  UHM1219.20806_S47       73751         1847   2.50437282 passed
#> 71  UHM1220.20807_S59       85934         1847   2.14932390 passed
#> 72  UHM1221.20808_S71       37595         1847   4.91288735 passed
#> 73  UHM1222.20809_S83        5442         1847  33.93972804 failed
#> 74  UHM1223.20810_S95      114277         1847   1.61624824 passed
#> 75  UHM1225.20811_S12       48275         1847   3.82599689 passed
#> 76  UHM1227.20812_S24       20972         1847   8.80698074 passed
#> 77  UHM1228.20813_S36      147351         1847   1.25346961 passed
#> 78  UHM1237.20814_S48       24999         1847   7.38829553 passed
#> 79  UHM1240.20566_S41       35116         1847   5.25971067 passed
#> 80  UHM1246.20815_S60       16138         1847  11.44503656 passed
#> 81  UHM1247.20816_S72       26551         1847   6.95642349 passed
#> 82  UHM1248.20575_S54        7646         1847  24.15642166 failed
#> 83  UHM1256.20570_S89       35459         1847   5.20883274 passed
#> 84  UHM1260.20596_S21       34301         1847   5.38468266 passed
#> 85  UHM1270.20577_S78       12183         1847  15.16046951 passed
#> 86  UHM1271.20397_S32       19173         1847   9.63333855 passed
#> 87  UHM1272.20398_S44       10451         1847  17.67294996 passed
#> 88  UHM1274.20554_S87       32450         1847   5.69183359 passed
#> 89  UHM1275.20597_S33       15061         1847  12.26346192 passed
#> 90  UHM1282.20599_S57        6536         1847  28.25887393 failed
#> 91  UHM1287.20543_S50       10739         1847  17.19899432 passed
#> 92  UHM1291.20416_S70       74309         1847   2.48556702 passed
#> 93  UHM1296.20550_S39      207620         1847   0.88960601 passed
#> 94  UHM1319.20561_S76       11679         1847  15.81471016 passed
#> 95  UHM1324.20413_S34       45222         1847   4.08429525 passed
#> 96  UHM1327.20545_S74      272806         1847   0.67703790 passed
#> 97  UHM1328.20572_S18        9273         1847  19.91804163 passed
#> 98  UHM1334.20417_S82       76551         1847   2.41277057 passed
#> 99  UHM1338.20399_S56       26324         1847   7.01641088 passed
#> 100 UHM1341.20602_S93        4830         1847  38.24016563 failed
#> 101 UHM1356.20541_S26       21091         1847   8.75728984 passed
#> 102 UHM1380.20580_S19       71993         1847   2.56552720 passed
#> 103 UHM1383.20594_S92       28316         1847   6.52281396 passed
#> 104  UHM1385.20563_S5        2166         1847  85.27239151 failed
#> 105 UHM1399.20756_S17       11403         1847  16.19749189 passed
#> 106 UHM1400.20757_S29       49300         1847   3.74645030 passed
#> 107 UHM1401.20758_S41        4986         1847  37.04372242 failed
#> 108 UHM1402.20759_S53       78898         1847   2.34099724 passed
#> 109 UHM1403.20760_S65       48290         1847   3.82480845 passed
#> 110 UHM1405.20761_S77       51126         1847   3.61264327 passed
#> 111 UHM1406.20762_S89       90444         1847   2.04214763 passed
#> 112  UHM1414.20763_S6      113476         1847   1.62765695 passed
#> 113 UHM1419.20764_S18       22190         1847   8.32356918 passed
#> 114 UHM1427.20389_S31       93753         1847   1.97007029 passed
#> 115 UHM1428.20390_S43        4532            0   0.00000000 failed
#> 116 UHM1429.20391_S55      246415         1847   0.74954853 passed
#> 117 UHM1430.20392_S67      846266         1847   0.21825289 passed
#> 118 UHM1432.20393_S79      415880         1847   0.44411850 passed
#> 119 UHM1435.20388_S19        9292            0   0.00000000 failed
#> 120  UHM162.20560_S64       37096         1847   4.97897347 passed
#> 121  UHM198.20585_S79        5611         1847  32.91748351 failed
#> 122    UHM20.3314_S52     1335639         1847   0.13828587 passed
#> 123    UHM20.3315_S64      352109         1847   0.52455348 passed
#> 124  UHM204.20409_S81      160159         1847   1.15322898 passed
#> 125  UHM206.20410_S93         523            0   0.00000000 failed
#> 126  UHM207.20593_S80       76828         1847   2.40407143 passed
#> 127  UHM208.20411_S10       34089         1847   5.41817008 passed
#> 128  UHM211.20406_S45       87997         1847   2.09893519 passed
#> 129  UHM215.20408_S69     3948887         1847   0.04677267 failed
#> 130  UHM216.20429_S36      207777         1847   0.88893381 passed
#> 131  UHM219.20430_S48        1910         1847  96.70157068 failed
#> 132  UHM236.20431_S60      199606         1847   0.92532289 passed
#> 133  UHM238.20407_S57       64127         1847   2.88022206 passed
#> 134  UHM245.20538_S85      554919         1847   0.33284137 passed
#> 135  UHM252.20558_S40       14250         1847  12.96140351 passed
#> 136  UHM267.20400_S68      183380         1847   1.00719817 passed
#> 137  UHM274.20581_S31       13695         1847  13.48667397 passed
#> 138  UHM276.20586_S91       21571         1847   8.56242177 passed
#> 139  UHM280.20401_S80       35361         1847   5.22326857 passed
#> 140  UHM286.20425_S83      130953         1847   1.41042970 passed
#> 141  UHM289.20426_S95        6003         1847  30.76794936 failed
#> 142  UHM294.20427_S12       17177         1847  10.75275077 passed
#> 143  UHM298.20600_S69      101187         1847   1.82533329 passed
#> 144  UHM325.20548_S15       24562         1847   7.51974595 passed
#> 145  UHM337.20412_S22       32632         1847   5.66008826 passed
#> 146  UHM354.20535_S49       58805         1847   3.14088938 passed
#> 147  UHM356.20415_S58       43688         1847   4.22770555 passed
#> 148  UHM369.20773_S31       79551         1847   2.32178100 passed
#> 149  UHM370.20774_S43       88455         1847   2.08806738 passed
#> 150  UHM372.20775_S55       21119         1847   8.74567925 passed
#> 151  UHM373.20776_S67      113026         1847   1.63413728 passed
#> 152  UHM374.20777_S79       96042         1847   1.92311697 passed
#> 153  UHM375.20778_S91       19035         1847   9.70317836 passed
#> 154   UHM377.20779_S8       29280         1847   6.30806011 passed
#> 155    UHM38.3376_S36       29112            0   0.00000000 failed
#> 156  UHM386.20781_S32       54216         1847   3.40674340 passed
#> 157  UHM387.20782_S44       68442         1847   2.69863534 passed
#> 158  UHM414.20583_S55      278355         1847   0.66354116 passed
#> 159  UHM418.20765_S30       46656         1847   3.95876200 passed
#> 160  UHM422.20766_S42       10422         1847  17.72212627 passed
#> 161  UHM425.20767_S54       66208         1847   2.78969309 passed
#> 162  UHM426.20534_S37        5428         1847  34.02726603 failed
#> 163  UHM428.20544_S62       13689         1847  13.49258529 passed
#> 164  UHM429.20559_S52       21213         1847   8.70692500 passed
#> 165   UHM435.20547_S3       13367         1847  13.81761053 passed
#> 166  UHM437.20768_S66      148581         1847   1.24309299 passed
#> 167  UHM439.20564_S17       96401         1847   1.91595523 passed
#> 168    UHM44.3526_S31        5511         1847  33.51478860 failed
#> 169  UHM445.20569_S77       90006         1847   2.05208542 passed
#> 170  UHM447.20783_S56      168350         1847   1.09711910 passed
#> 171  UHM448.20769_S78       28168         1847   6.55708606 passed
#> 172    UHM45.3539_S92       35221            0   0.00000000 failed
#> 173  UHM454.20770_S90       43738         1847   4.22287256 passed
#> 174  UHM455.20785_S80       15021         1847  12.29611877 passed
#> 175  UHM458.20786_S92      102660         1847   1.79914280 passed
#> 176   UHM459.20787_S9      106958         1847   1.72684605 passed
#> 177   UHM461.20771_S7       77668         1847   2.37807076 passed
#> 178  UHM467.20772_S19      195394         1847   0.94526956 passed
#> 179  UHM470.20533_S25        9227         1847  20.01734041 failed
#> 180  UHM476.20414_S46       20146         1847   9.16807307 passed
#> 181  UHM478.20549_S27       27792         1847   6.64579735 passed
#> 182  UHM479.20551_S51        2119         1847  87.16375649 failed
#> 183   UHM481.20403_S9       15762         1847  11.71805608 passed
#> 184  UHM482.20590_S44        2014         1847  91.70804369 failed
#> 185  UHM483.20603_S10       90381         1847   2.04357110 passed
#> 186  UHM519.20582_S43      220582         1847   0.83733034 passed
#> 187  UHM520.20573_S30       68381         1847   2.70104269 passed
#> 188 UHM746.21478_S117         927          924  99.67637540 failed
#> 189 UHM747.21477_S106         924          924 100.00000000 failed
#> 190 UHM748.21467_S170         924          924 100.00000000 failed
#> 191 UHM748.21487_S129         928          924  99.56896552 failed
#> 192 UHM749.21479_S128         925          924  99.89189189 failed
#> 193 UHM759.21466_S159         924          924 100.00000000 failed
#> 194 UHM759.21486_S118         938          924  98.50746269 failed
#> 195 UHM775.21485_S107         923          923 100.00000000 failed
#> 196 UHM776.21482_S161         923          923 100.00000000 failed
#> 197 UHM777.21484_S183         925          924  99.89189189 failed
#> 198 UHM779.21468_S181         928          924  99.56896552 failed
#> 199 UHM779.21488_S140         924          924 100.00000000 failed
#> 200 UHM782.21480_S139         924          924 100.00000000 failed
#> 201 UHM810.21472_S138         923          923 100.00000000 failed
#> 202 UHM811.21471_S127         927          924  99.67637540 failed
#> 203 UHM813.21481_S150         924          924 100.00000000 failed
#> 204 UHM818.21469_S105         924          924 100.00000000 failed
#> 205 UHM818.21489_S151        1459          924  63.33104866 failed
#> 206 UHM819.21473_S149         923          923 100.00000000 failed
#> 207 UHM820.21470_S116         924          924 100.00000000 failed
#> 208 UHM820.21490_S162         929          924  99.46178687 failed
#> 209 UHM827.21474_S160         935          924  98.82352941 failed
#> 210 UHM829.21476_S182         927          924  99.67637540 failed
#> 211 UHM832.21483_S172        1028          924  89.88326848 failed
#> 212  UHM836.20385_S78        3026            0   0.00000000 failed
#> 213  UHM837.20386_S90        2442            0   0.00000000 failed
#> 214   UHM838.20387_S7      727048         1847   0.25404100 passed
#> 215  UHM891.20384_S66       70359         1847   2.62510837 passed
#> 216  UHM892.20532_S13       11192         1847  16.50285919 passed
#> 217   UHM893.20595_S9     1153339         1847   0.16014372 passed
#> 218  UHM894.20540_S14      291315         1847   0.63402159 passed
#> 219  UHM895.20536_S61        5903         1847  31.28917500 failed
#> 220  UHM896.20601_S81       10057            0   0.00000000 failed
#> 221  UHM897.20591_S56       13724            0   0.00000000 failed
#> 222  UHM898.20394_S91         603            0   0.00000000 failed
#> 223  UHM899.20588_S20      815235         1847   0.22656044 passed
#> 224   UHM900.20395_S8        6596            0   0.00000000 failed
#> 225  UHM901.20542_S38      246778         1847   0.74844597 passed
#> 226  UHM902.20584_S67        6341         1847  29.12789781 failed
#> 227   UHM903.20587_S8       18503         1847   9.98216505 passed
#> 228  UHM904.20567_S53      663477         1847   0.27838192 passed
#> 229  UHM905.20598_S45       67720         1847   2.72740697 passed
#> 230  UHM906.20565_S29       11360            0   0.00000000 failed
#> 231  UHM907.20592_S68      378942         1847   0.48740968 passed
#> 232  UHM908.20396_S20        8212            0   0.00000000 failed
#> 233  UHM909.20557_S28       58573         1847   3.15333003 passed
#> 234  UHM910.20562_S88      346860         1847   0.53249150 passed
#> 235  UHM965.20537_S73        7079         1847  26.09125583 failed
#> 236  UHM966.20743_S51       28447         1847   6.49277604 passed
#> 237  UHM967.20744_S63        4222            0   0.00000000 failed
#> 238   UHM968.20571_S6        1895         1847  97.46701847 failed
#> 239  UHM969.20745_S75      376613         1847   0.49042386 passed
#> 240  UHM971.20746_S87     1276457         1847   0.14469739 passed
#> 241  UHM973.20578_S90      107686         1847   1.71517189 passed
#> 242  UHM974.20432_S72        1133            0   0.00000000 failed
#> 243   UHM975.20747_S4      346189         1847   0.53352360 passed
#> 244  UHM977.20748_S16      145666         1847   1.26796919 passed
#> 245  UHM978.20749_S28      128603         1847   1.43620289 passed
#> 246  UHM979.20750_S40      287549         1847   0.64232531 passed
#> 247   UHM980.20731_S2       28511         1847   6.47820140 passed
#> 248   UHM981.20539_S2       40199         1847   4.59464166 passed
#> 249  UHM982.20740_S15       65909         1847   2.80234869 passed
#> 250  UHM983.20556_S16        1885         1847  97.98408488 failed
#> 251  UHM984.20751_S52      330025         1847   0.55965457 passed
#> 252  UHM985.20752_S64        5973         1847  30.92248451 failed
#> 253  UHM988.20753_S76       23740         1847   7.78011794 passed
#> 254  UHM989.20754_S88      634827         1847   0.29094541 passed
#> 255   UHM991.20755_S5        8326         1847  22.18352150 failed
#> 256  UHM993.20741_S27      237536         1847   0.77756635 passed
#> 257  UHM996.20610_S94       14720            0   0.00000000 failed
#> 258  UHM997.20553_S75       12276            0   0.00000000 failed
#> 259  UHM998.20618_S95        5969          924  15.47997990 passed
#> 260  UHM999.20617_S83       92857          924   0.99507845 passed
#> 
#> $phy_tree
#> 
#> Phylogenetic tree with 8831 tips and 8830 internal nodes.
#> 
#> Tip labels:
#>   020e00d90ba97c5898944ab6f7b1b7c9, b00466354053c9065c8aa3d6fbb33eaa, f872c4bf84bcf44434fa2023788f6517, df13f71584d4a579c81d909eaba11a74, ed285eb1aac505a1f062b482300b69f7, 63f5509575600a9e7afb6847d6296976, ...
#> Node labels:
#>   0.782, 0.000, 0.773, 0.922, 0.211, 0.617, ...
#> 
#> 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_16SOTU

  # 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_16SOTU", 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_16SOTU", 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_16SOTU", 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_16SOTU", 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_16SOTU", 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_16SOTU", 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_16SOTU", 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_16SOTU", 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_16SOTU", 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_16SOTU", 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_16SOTU", 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_16SOTU, taxrank = "Family", top_n = 10)
ridge_physeq

ps <- physeq_16SOTU
  result_css <- normalization_set(ps, method = "css")
  normalized_ps_css <- result_css$dat.normed
  
ridge_physeq <- ridge_plot_it(normalized_ps_css, taxrank = "Family", top_n = 10)
ridge_physeq

absolute <- phyloseq::subset_taxa(physeq_absolute, Genus!="Tetragenococcus")
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_16_abs<- phyloseq::subset_samples(Three_Genara_abs_BlueRidge,Host.genus=="Desmognathus")

 results_DESeq2 <- perform_and_visualize_DA(
   obj = Desmog_Blue_Ins_16_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
 )

results_DESeq2$plot

head(results_DESeq2$results)  
#> # A tibble: 6 × 17
#>   baseMean     logFC lfcSE      stat pvalue   padj   FDR Significance    group  
#>      <dbl>     <dbl> <dbl>     <dbl>  <dbl>  <dbl> <dbl> <chr>           <fct>  
#> 1     9     2.32e-15 0.256  9.05e-15  1.00   1.00  1     Not Significant Desmog…
#> 2     2     9.76e-15 0.536  1.82e-14  1.00   1.00  1     Not Significant Desmog…
#> 3     1.36 -1.60e-13 0.752 -2.13e-13  1.00  NA     1     Not Significant Desmog…
#> 4     2     9.76e-15 0.536  1.82e-14  1.00   1.00  1     Not Significant Desmog…
#> 5    10     1.15e-15 0.244  4.71e-15  1.00   1.00  1     Not Significant Desmog…
#> 6    10.8   4.62e- 1 0.284  1.63e+ 0  0.104  0.563 0.874 Not Significant Desmog…
#> # ℹ 8 more variables: OTU <chr>, Kingdom <chr>, Phylum <chr>, Class <chr>,
#> #   Order <chr>, Family <chr>, Genus <chr>, Species <chr>
results_DESeq2$obj_significant
#> phyloseq-class experiment-level object
#> otu_table()   OTU Table:          [ 28 taxa and 42 samples ]:
#> sample_data() Sample Data:        [ 42 samples by 41 sample variables ]:
#> tax_table()   Taxonomy Table:     [ 28 taxa by 7 taxonomic ranks ]:
#> phy_tree()    Phylogenetic Tree:  [ 28 tips and 27 internal nodes ]:
#> refseq()      DNAStringSet:       [ 28 reference sequences ]
#> taxa are rows


# Relative abundance
data("physeq_16SOTU",package = "DspikeIn")
relative <- phyloseq::subset_taxa(physeq_16SOTU, Genus!="Tetragenococcus")
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_16_rel<- phyloseq::subset_samples(Three_Genara_rel_BlueRidge,Host.genus=="Desmognathus")

 results_DESeq2_rel <- perform_and_visualize_DA(
   obj = Desmog_Blue_Ins_16_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
 )
print(results_DESeq2_rel$plot)

head(results_DESeq2_rel$results)  #  sig taxa
#> # A tibble: 6 × 17
#>   baseMean     logFC lfcSE      stat pvalue  padj   FDR Significance group OTU  
#>      <dbl>     <dbl> <dbl>     <dbl>  <dbl> <dbl> <dbl> <chr>        <fct> <chr>
#> 1    10     3.15e-15 0.237  1.33e-14  1.00  NA        1 Not Signifi… Desm… b004…
#> 2     2     9.18e-15 0.522  1.76e-14  1.00  NA        1 Not Signifi… Desm… f872…
#> 3     1.05 -5.74e-14 0.735 -7.81e-14  1.00  NA        1 Not Signifi… Desm… df13…
#> 4     2     9.18e-15 0.522  1.76e-14  1.00  NA        1 Not Signifi… Desm… ed28…
#> 5    12    -4.74e-16 0.217 -2.18e-15  1.00   1.00     1 Not Signifi… Desm… 63f5…
#> 6    13.0   1.23e- 2 0.209  5.89e- 2  0.953  1.00     1 Not Signifi… Desm… fea9…
#> # ℹ 7 more variables: Kingdom <chr>, Phylum <chr>, Class <chr>, Order <chr>,
#> #   Family <chr>, Genus <chr>, Species <chr>
results_DESeq2_rel$obj_significant
#> phyloseq-class experiment-level object
#> otu_table()   OTU Table:          [ 4 taxa and 42 samples ]:
#> sample_data() Sample Data:        [ 42 samples by 34 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

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


Rel <- phyloseq::subset_taxa(physeq_16SOTU, Genus!="Tetragenococcus")
Prok_OTU_spiked <- phyloseq::subset_samples(Rel, spiked.volume %in% c("2", "1"))
Prok_OTU_spiked <- phyloseq::subset_samples(Prok_OTU_spiked, sample.or.blank != "blank")
Prok_OTU_sal <- phyloseq::subset_samples(Prok_OTU_spiked, Animal.type == "Salamander")

Prok_OTU_sal<-tidy_phyloseq_tse(Prok_OTU_sal)

taxa_barplot(
  Prok_OTU_sal,
  target_glom = "Genus",
  custom_tax_names = NULL,
  normalize = TRUE,
  treatment_variable = "Habitat",
  abundance_type = "relative",
  x_angle = 25,
  fill_variable = "Family",
  facet_variable = "Diet",
  top_n_taxa = 20,
  palette = DspikeIn::color_palette$MG,
  legend_size = 11,
  legend_columns = 1,
  x_scale = "free",
  xlab = NULL
)
#> $barplot

custom_detections <- list(
    prevalences = seq(0.03, 1, 0.01),
    thresholds = 10^seq(log10(0.03), log10(1), length = 10),
    min_prevalence = 0.3,
    taxa_order = "decending"
  )


Prok_OTU_sal<- tidy_phyloseq_tse(Prok_OTU_sal)

plot_result <- plot_core_microbiome_custom(
    obj = Prok_OTU_sal,
    detections = custom_detections,
    taxrank = "Phylum",
    output_core_rds = "core_microbiome.rds",
    output_core_csv = "core_microbiome.csv"
  )
#> Core microbiome saved as CSV to: core_microbiome.csv 
#> Core microbiome saved as RDS to: core_microbiome.rds

plot_result

# ===========================================================
# 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
# ===========================================================
? node_level_metrics

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 = 25, hjust = 0.9),
    strip.text = element_text(size = 12),
    legend.position = "top",
    legend.text = element_text(size = 14),
    legend.title = element_text(size = 14, face = "bold"),
    plot.title = element_text(size = 14, 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,  # Circle
  "Network & Module Hubs Removed" = 22,  # Square
  "Basidiobolus Subnetwork Removed" = 23  # Diamond
)

# 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"),
      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)