# ============================================================================
# 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
# =====================================================================
library(phyloseq)
library(DspikeIn)
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)
)
#> đź“‚ Table saved in docx format: merged_data.docx
#> đź“‚ 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...
#> âž– Extracting spiked species...
#> âž• 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 Ă— 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
#> # â„ą 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
# View absolute count data
head(absolute_counts)
#> # A tibble: 6 Ă— 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
#> # â„ą 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)
#> đź’ľ Table saved in docx format: post_eval_summary.docx
#> đź’ľ 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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`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)
)
#> đź“‚ Table saved in docx format: merged_data.docx
#> đź“‚ Merged data saved as CSV: merged_data.csv
conclusion(absolute_abundance_16S_OTU_perc,
merged_spiked_species,
max_passed_range=20,
output_path)
#> đź“‚ Table saved in docx format: merged_data.docx
#> đź“‚ 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
#>
#> $phy_tree
#>
#> Phylogenetic tree with 9319 tips and 9318 internal nodes.
#>
#> Tip labels:
#> 020e00d90ba97c5898944ab6f7b1b7c9, b00466354053c9065c8aa3d6fbb33eaa, f872c4bf84bcf44434fa2023788f6517, df13f71584d4a579c81d909eaba11a74, ed285eb1aac505a1f062b482300b69f7, 63f5509575600a9e7afb6847d6296976, ...
#> Node labels:
#> 0.782, 0.000, 0.773, 0.922, 0.211, 0.617, ...
#>
#> Rooted; includes branch length(s). physeq_absolute <- absolute$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_absyou may select to transform your data befor moving forward with Differential Abundance
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.factorremove the spike-in sp before further analysis
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)
results_DESeq2$obj_significant
# Relative abundance
data("physeq_16SOTU",package = "DspikeIn")
relative <- phyloseq::subset_taxa(physeq_16SOTU, Genus!="Tetragenococcus")
Caudate_rel <- phyloseq::subset_samples(relative, Clade.Order == "Caudate" )
Three_Genara_rel <- phyloseq::subset_samples(Caudate_rel, Host.genus %in% c("Desmognathus", "Plethodon", "Eurycea"))
Three_Genara_rel_BlueRidge<- phyloseq::subset_samples(Three_Genara_rel,Ecoregion.III=="Blue Ridge" )
Desmog_Blue_Ins_16_rel<- phyloseq::subset_samples(Three_Genara_rel_BlueRidge,Host.genus=="Desmognathus")
results_DESeq2_rel <- perform_and_visualize_DA(
obj = Desmog_Blue_Ins_16_rel,
method = "DESeq2",
group_var = "Host.taxon",
contrast = c("Desmognathus monticola", "Desmognathus imitator" ),
output_csv_path = "DA_DESeq2.csv",
target_glom = "Genus",
significance_level = 0.05
)
print(results_DESeq2_rel$plot)
head(results_DESeq2_rel$results) # sig taxa
results_DESeq2_rel$obj_significantVisualization
# ===========================================================
# 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")
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
#>
#> $taxa_data
#> phyloseq-class experiment-level object
#> otu_table() OTU Table: [ 21 taxa and 146 samples ]:
#> sample_data() Sample Data: [ 146 samples by 34 sample variables ]:
#> tax_table() Taxonomy Table: [ 21 taxa by 7 taxonomic ranks ]:
#> taxa are rows
# ===========================================================
# 1. Initialization and loading NetWorks for Comparision
# ===========================================================
#library(SpiecEasi)
#library(ggnet)
library(igraph)
library(tidyr)
library(dplyr)
library(ggpubr)
# To create a microbial co-occurrence network, you can refer to the SpiecEasi package available at:
# SpiecEasi GitHub Repository https://github.com/zdk123/SpiecEasi
# herp.Bas.rel.f is a merged phyloseq object for both bacterial and fungal domains
# herp.spiec <- spiec.easi(herp.Bas.rel.f, method='mb', lambda.min.ratio=1e-3, nlambda=250,pulsar.select=TRUE )
# write_graph(herp.spiec, "Complete.graphml", "graphml")
Complete <- load_graphml("Complete.graphml")
NoBasid <- load_graphml("NoBasid.graphml")
NoHubs <- load_graphml("NoHubs.graphml")
# ===========================================================
# 2. Metrics Calculation
# ===========================================================
? node_level_metrics
result_Complete <- node_level_metrics(Complete)
result_NoHubs <- node_level_metrics(NoHubs)
result_NoBasid <- node_level_metrics(NoBasid)
Complete_metrics<-result_Complete$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")bind the metrics to plot them
# ===========================================================
# 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")
To find your node of interest first and second neighbors
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 = 35) +
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)
metrics
# Compute degree metrics and visualize the network
# Options: `"stress"` (default), `"graphopt"`, `"fr"`
result <- degree_network(graph_path = Complete, save_metrics = TRUE)
print(result$metrics)
#> Nodes Edges Modularity Density Transitivity Diameter Avg_Path_Length
#> 1 308 1144 0.7478992 0.0241973 0.1789773 NA NA
#> Avg_Degree
#> 1 7.428571
print(result$plot)
# Compute network weights for different graph structures
NH <- weight_Network(graph_path = "NoHubs.graphml")
NB <- weight_Network(graph_path = "NoBasid.graphml")
C <- weight_Network(graph_path = "Complete.graphml")
# Extract metrics from the computed network weights
CompleteM <- C$metrics
NoHubsM <- NH$metrics
NoBasidM <- NB$metrics
# Combine metrics into a single dataframe for comparison
df <- bind_rows(
CompleteM %>% mutate(Group = "CompleteM"),
NoHubsM %>% mutate(Group = "NoHubsM"),
NoBasidM %>% mutate(Group = "NoBasidM")
) %>%
pivot_longer(cols = -Group, names_to = "Metric", values_to = "Value")
# Aggregate the total values by metric and group
df_bar <- df %>%
group_by(Metric, Group) %>%
summarise(Total_Value = sum(Value), .groups = "drop")
# Plot the metrics comparison
ggplot(df_bar, aes(x = Metric, y = log1p(Total_Value), fill = Group)) +
geom_bar(stat = "identity", position = "dodge", alpha = 0.8) +
theme_minimal(base_size = 14) +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
scale_fill_manual(values = c("#F1E0C5", "#D2A5A1", "#B2C3A8")) +
labs(title = "Total Network Metrics Comparison",
x = "Metric",
y = "Total Value (log-scaled)",
fill = "Group") 