QC
Emma M Pfortmiller
Last updated: 2026-01-13
Checks: 6 1
Knit directory: Dissrt/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | e7f3a80 | Emma M. Pfortmiller | 2026-01-13 | Build site. |
| Rmd | b31faa7 | Emma M. Pfortmiller | 2026-01-13 | Initialize Analysis Website 01/13/26 |
| Rmd | 9269951 | Emma M. Pfortmiller | 2026-01-08 | updated analysis 01/08/26 |
| Rmd | 92f37b9 | Emma M. Pfortmiller | 2025-12-27 | Updated QC analysis subset data 12/27/25 EMP |
| html | 92f37b9 | Emma M. Pfortmiller | 2025-12-27 | Updated QC analysis subset data 12/27/25 EMP |
Load Libraries
Define plot theme
# Define the custom theme
# plot_theme_custom <- function() {
# theme_minimal() +
# theme(
# #line for x and y axis
# axis.line = element_line(linewidth = 1,
# color = "black"),
#
# #axis ticks only on x and y, length standard
# axis.ticks.x = element_line(color = "black",
# linewidth = 1),
# axis.ticks.y = element_line(color = "black",
# linewidth = 1),
# axis.ticks.length = unit(0.05, "in"),
#
# #text and font
# axis.text = element_text(color = "black",
# family = "Arial",
# size = 8),
# axis.title = element_text(color = "black",
# family = "Arial",
# size = 10),
# legend.text = element_text(color = "black",
# family = "Arial",
# size = 8),
# legend.title = element_text(color = "black",
# family = "Arial",
# size = 10),
# plot.title = element_text(color = "black",
# family = "Arial",
# size = 12),
#
# #blank background and border
# panel.background = element_blank(),
# panel.border = element_blank(),
#
# #gridlines for alignment
# panel.grid.major = element_line(color = "grey80", linewidth = 0.5), #grey major grid for align in illus
# panel.grid.minor = element_line(color = "grey90", linewidth = 0.5) #grey minor grid for align in illus
# )
# }
# saveRDS(plot_theme_custom, "data/plot_theme_custom.RDS")
theme_custom <- readRDS("data/plot_theme_custom.RDS")Define saving plots as pdfs
save_plot <- function(plot, filename,
folder = ".",
width = 8,
height = 6,
units = "in",
dpi = 300,
add_date = TRUE) {
if (missing(filename)) stop("Please provide a filename (without extension) for the plot.")
date_str <- if (add_date) paste0("_", format(Sys.Date(), "%y%m%d")) else ""
pdf_file <- file.path(folder, paste0(filename, date_str, ".pdf"))
png_file <- file.path(folder, paste0(filename, date_str, ".png"))
#add conditions for recorded plots and ggsave
if (inherits(plot, "recordedplot")) {
#pdf save
pdf(pdf_file, width = width, height = height, bg = "transparent")
replayPlot(plot)
dev.off()
#png save
png(png_file, width = width, height = height, units = units, res = dpi, bg = "transparent")
replayPlot(plot)
dev.off()
} else {
#save pdf
ggsave(filename = pdf_file, plot = plot, device = cairo_pdf,
width = width, height = height, units = units, bg = "transparent")
#save png
ggsave(filename = png_file, plot = plot, device = "png",
width = width, height = height, units = units, dpi = dpi, bg = "transparent")
message("Saved plot as PDF: ", pdf_file)
message("Saved plot as PNG: ", png_file)
}
}
output_folder <- "C:/Users/emmap/OneDrive/Desktop/Ward Lab/Experiments/Stressor Project/Full Set RNAseq/plots"
#save plot function created
#to use: just define the plot name, filename_base, width, heightComplex Heatmap Saving Script
save_complex_heatmap <- function(ht, filename, folder = ".", width = 6, height = 10, dpi = 300, add_date = TRUE) {
if (missing(filename)) stop("Please provide a filename (without extension) for the plot.")
date_str <- if (add_date) paste0("_", format(Sys.Date(), "%y%m%d")) else ""
pdf_file <- file.path(folder, paste0(filename, date_str, ".pdf"))
png_file <- file.path(folder, paste0(filename, date_str, ".png"))
# Save PDF
cairo_pdf(pdf_file, width = width, height = height)
draw(ht)
dev.off()
message("Saved PDF: ", pdf_file)
# Save PNG
png(png_file, width = width, height = height, units = "in", res = dpi)
draw(ht)
dev.off()
message("Saved PNG: ", png_file)
}Define colors and factors
#each of these to be used after pairwise comparison save individual
####STIMULI####
# stim_list <- list(
# "Tunicamycin" = "TUN",
# "Thapsigargin" = "THA",
# "Doxorubicin" = "DOX",
# "Nutlin-3" = "NUTL",
# "Lipopolysaccharides" = "LPS",
# "Tumor Necrosis Factor alpha" = "TNFa",
# "Bisphenol A" = "BPA",
# "Perfluorooctanoic Acid" = "PFOA"
# )
# stimuli_vec <- unlist(lapply(names(stim_list), function(cat) {
# setNames(rep(cat, length(stim_list[[cat]])), stim_list[[cat]])
# }))
# saveRDS(stimuli_vec, "data/theme/stimuli_fullname_vector.RDS")
stim_vec <- readRDS("data/theme/stimuli_fullname_vector.RDS")
####RESPONSE CATEGORY####
# resp_list <- list(
# UPR = c("TUN", "THA"),
# DDR = c("DOX", "NUTL"),
# IMR = c("LPS", "TNFa"),
# MMR = c("BPA", "PFOA")
# )
# response_vec <- unlist(lapply(names(resp_list), function(cat) {
# setNames(rep(cat, length(resp_list[[cat]])), resp_list[[cat]])
# }))
# saveRDS(response_vec, "data/theme/response_categories_vector.RDS")
response_vec <- readRDS("data/theme/response_categories_vector.RDS")
####SPECIES####
# spec_list <- list(
# Human = "H",
# Chimp = "C"
# )
# species_vec <- unlist(lapply(names(spec_list), function(cat) {
# setNames(rep(cat, length(spec_list[[cat]])), spec_list[[cat]])
# }))
# saveRDS(species_vec, "data/theme/species_vector.RDS")
species_vec <- readRDS("data/theme/species_vector.RDS")
####TIME####
# time_list <- list(
# "2hr" = "2",
# "24hr" = "24"
# )
# time_vec <- unlist(lapply(names(time_list), function(cat) {
# setNames(rep(cat, length(time_list[[cat]])), time_list[[cat]])
# }))
# saveRDS(time_vec, "data/theme/time_vector.RDS")
time_vec <- readRDS("data/theme/time_vector.RDS")
####INDIVIDUAL####
# ind_list <- list(
# H24280 = "H1",
# H28126 = "H2",
# "84-1" = "H3",
# H21792 = "H4",
# H20682 = "H5",
# H22422 = "H6",
# "78-1" = "H7",
# C3647 = "C1",
# C8861 = "C2",
# C4020 = "C3",
# C3649 = "C4",
# C3651 = "C5",
# C40280 = "C6",
# C4955 = "C7"
# )
# ind_vec <- unlist(lapply(names(ind_list), function (cat) {
# setNames(rep(cat, length(ind_list[[cat]])), ind_list[[cat]])
# }))
# saveRDS(ind_vec, "data/theme/individual_vector.RDS")
ind_vec <- readRDS("data/theme/individual_vector.RDS")
#color schemes
# ind_col <- list(
# H1 = "#264653",
# H2 = "#2A9D8F",
# H3 = "#06D6A0",
# H4 = "#68DD94",
# H5 = "#22C75E",
# H6 = "#1DC10B",
# H7 = "#2D6910",
# C1 = "#FFB347",
# C2 = "#F97316",
# C3 = "#F44E53",
# C4 = "#C6134F",
# C5 = "#F03A6E",
# C6 = "#5D0E70",
# C7 = "#A069E0"
# )
stim_col <- readRDS("data/theme/stimulus_color_palette_all.RDS")
ind_col <- readRDS("data/theme/individual_category_color_palette.RDS")
time_col <- readRDS("data/theme/time_category_color_palette.RDS")
spec_col <- readRDS("data/theme/species_category_color_palette.RDS")Import Counts Files for Both Species
#I have one counts table for chimpanzee and another for human
#read in chimpanzee counts
# c_raw_counts <- read_csv("C:/Users/emmap/OneDrive/Desktop/Ward Lab/Experiments/Stressor Project/Full Set RNAseq/featurecounts/c_samples_counts_fin.csv")
# saveRDS(c_raw_counts, "data/counts/c_raw_counts.RDS")
c_raw_counts <- readRDS("data/counts/c_raw_counts.RDS")
#read in human counts table in the same manner
# h_raw_counts <- read_csv("C:/Users/emmap/OneDrive/Desktop/Ward Lab/Experiments/Stressor Project/Full Set RNAseq/featurecounts/h_samples_counts_fin.csv")
# saveRDS(h_raw_counts, "data/counts/h_raw_counts.RDS")
h_raw_counts <- readRDS("data/counts/h_raw_counts.RDS")Import Metadata Table
#I have a metadata sheet so I can associate the column names with their sample ID
# metadata <- read_csv("C:/Users/emmap/OneDrive/Desktop/Ward Lab/Experiments/Stressor Project/Full Set RNAseq/featurecounts/dissrt_metadata.csv",
# col_types = cols(Conc = col_number()))
#save metadata sheet
# saveRDS(metadata, "data/counts/dissrt_metadata.RDS")
metadata <- readRDS("data/counts/dissrt_metadata.RDS")
#subset my metadata
human_metadata <- metadata[metadata$Species == "H", ]
chimp_metadata <- metadata[metadata$Species == "C", ]Combine my datasets
#make sure to sort them so that all of the genes match up
#
# h_counts <- h_counts[order(h_counts$Ensembl_ID), ]
# c_counts <- c_counts[order(c_counts$Ensembl_ID), ]
#
# hc_counts <- left_join(c_counts, h_counts, by = "Ensembl_ID")
#now save my combined counts table with both species
# saveRDS(hc_counts, "data/counts/combined_counts_table_h_c.RDS")
# hc_counts <- readRDS("data/counts/combined_counts_table_h_c.RDS")
#now I have a combined dataframe with the treatment names included
####Replace hc_counts with one made below that has Entrez_ID instead of Ensembl_ID
hc_counts <- readRDS("data/counts/hc_counts_entrez.RDS")#Subset Dataset to Remove Extraneous Samples
#I want to go through my metadata and my counts dataframe to put together a subset of samples
#I will be keeping all 24hr samples
#I will also be keeping TUN, THA, DMSO for 2hr
# metadata_sub <- metadata %>%
# filter(
# Time == 24 |
# (Time == 2 & Drug %in% c("TUN", "THA", "DMSO"))
# )
# saveRDS(metadata_sub, "data/counts/metadata_subset.RDS")
#I am going to go into here and change the individual names and final names to match my other sheets
#I changed the individuals as listed:
## C1 = C3647
## C2 = C8861
## C3 = C40210
## C4 = C3649
## C5 = C3651
## C6 = C40280
## C7 = C4955
## H1 = H24280
## H2 = H28126
## H3 = 84-1
## H4 = H21792
## H5 = H20682
## H6 = H22422
## H7 = 78-1
# Final sample names reflect these changes
metadata_sub <- read_csv("data/counts/metadata_subset.csv")
# saveRDS(metadata_sub, "data/counts/metadata_subset.RDS")
#Now go in and change hc_counts
#first remove extraneous samples as above
# hc_counts <- readRDS("data/counts/hc_counts_entrez.RDS")
# keep_cols <-
# grepl("_24_", colnames(hc_counts)) |
# grepl("^TUN_2_", colnames(hc_counts)) |
# grepl("^THA_2_", colnames(hc_counts)) |
# grepl("^DMSO_2_", colnames(hc_counts))
# hc_counts_sub <- hc_counts[, keep_cols]
#now that I have only the 195 columns left, match them up to my metadata table to rename all as Final_sample_name
# colnames(hc_counts_sub) <- metadata_sub$Final_sample_name
# hc_counts_entrez <- hc_counts_sub %>%
# rownames_to_column(var = "Entrez_ID")
# saveRDS(hc_counts_sub, "data/counts/hc_counts_subset_entrez.RDS")
# write_csv(hc_counts_entrez, "data/counts/hc_counts_subset_entrez.csv")
# write_csv(hc_counts_sub, "data/counts/hc_counts_subset.csv")
hc_counts_sub <- readRDS("data/counts/hc_counts_subset_entrez.RDS")QC Plots
Generate plots for the subset of samples kept for this experiment.
#read in my excel file with all of this information
# map_align_stats <- read_excel("C:/Users/emmap/OneDrive/Desktop/Ward Lab/Experiments/Stressor Project/Full Set RNAseq/featurecounts/Mapping_Alignment_Stats_Dissrt_EMP_250930.xlsx",
# sheet = "Map_Align_all", col_types = c("text",
# "text", "skip", "text", "text", "text",
# "text", "numeric", "text", "text",
# "numeric", "text", "skip", "text",
# "skip", "text", "skip", "text", "text",
# "text", "numeric", "numeric", "numeric",
# "numeric", "numeric", "numeric",
# "numeric", "numeric", "numeric",
# "numeric", "numeric", "numeric",
# "numeric", "numeric", "numeric",
# "numeric", "numeric", "numeric"))
# View(map_align_stats)
#save as a csv for later
# saveRDS(map_align_stats, "data/counts/mapping_alignment_stats_sheet.RDS")
# write.csv(map_align_stats, "data/counts/mapping_alignment_stats_sheet.csv")
map_align_stats <- readRDS("data/counts/mapping_alignment_stats_sheet.RDS")
#now I want to do some QC plots to highlight this information
Individual <- as.character(map_align_stats$Ind)
Species <- as.character(map_align_stats$Species)
Time <- as.character(map_align_stats$Time_Num)
Stimulus <- as.character(map_align_stats$Stimulus)
####Fragments by Sample####
map_align_stats$Stimulus <- factor(
map_align_stats$Stimulus,
levels = c("TUN", "THA", "DOX", "NUTL", "DMSO",
"LPS", "TNFa", "H2O", "BPA", "PFOA", "EtOH")
)
map_align_stats$Ind <- factor(
map_align_stats$Ind,
levels = c("H1", "H2", "H3", "H4", "H5",
"H6", "H7", "C1", "C2", "C3",
"C4", "C5", "C6", "C7")
)
#Total Reads per Sample
total_reads_sample_plot <- map_align_stats %>%
ggplot(., aes (x = Sample_Tx, y = Total_Reads_Paired,
fill = Stimulus,
group_by = Sample_Tx))+
geom_col()+
scale_fill_manual(values=stim_col)+
ggtitle(expression("Total fragments by sample"))+
xlab("")+
ylab(expression("RNA-sequencing fragments"))+
theme_custom()+
theme(
axis.text.x = element_text(size =10, color = "black",
angle = 90, hjust = 1, vjust = 0.2))
#Total Mapped Reads per Sample
map_reads_sample_plot <- map_align_stats %>%
ggplot(., aes (x = Sample_Tx, y = Mapped_Paired,
fill = Stimulus,
group_by = Sample_Tx))+
geom_col() +
geom_hline(aes(yintercept=20000000))+
scale_fill_manual(values = stim_col)+
ggtitle(expression("Mapped fragments by sample")) +
xlab("") +
ylab(expression("RNA-sequencing fragments")) +
theme_custom() +
theme(
axis.text.x = element_text(size =10, color = "black",
angle = 90, hjust = 1, vjust = 0.2))
####Total Reads by Sample Boxplot####
total_reads_sample_boxplot <- map_align_stats %>%
ggplot(., aes (x = Sample_Tx, y= Total_Reads_Paired, fill = Stimulus))+
geom_boxplot()+
scale_fill_manual(values = stim_col)+
ggtitle(expression("Total fragments by sample"))+
xlab("")+
ylab(expression("RNA-sequencing fragments"))+
theme_custom()+
theme(
axis.text.x = element_text(size =10, color = "black",
angle = 90, hjust = 1, vjust = 0.2))
####Total Mapped Reads by Sample Boxplot####
map_reads_sample_boxplot <- map_align_stats %>%
ggplot(., aes (x = Sample_Tx, y= Mapped_Paired, fill = Stimulus))+
geom_boxplot()+
scale_fill_manual(values= stim_col)+
ggtitle(expression("Mapped fragments by sample"))+
xlab("")+
ylab(expression("RNA-sequencing fragments"))+
theme_custom()+
theme(
axis.text.x = element_text(size =10, color = "black",
angle = 90, hjust = 1, vjust = 0.2))
####Total Reads by Stimulus####
total_reads_stimulus_plot <- map_align_stats %>%
ggplot(., aes (x = Stimulus, y= Total_Reads_Paired, fill = Stimulus))+
geom_boxplot()+
scale_fill_manual(values=stim_col)+
ggtitle(expression("Total fragments by stimulus"))+
xlab("")+
ylab(expression("RNA-sequencing fragments"))+
theme_custom()+
theme(
axis.text.x = element_text(size =10, color = "black",
angle = 90, hjust = 1, vjust = 0.2))
####Mapped Reads by Stimulus####
map_reads_stimulus_plot <- map_align_stats %>%
ggplot(., aes (x = Stimulus, y= Mapped_Paired, fill = Stimulus))+
geom_boxplot()+
scale_fill_manual(values=stim_col)+
ggtitle(expression("Mapped fragments by stimulus"))+
xlab("")+
ylab(expression("RNA-sequencing fragments"))+
theme_custom()+
theme(
axis.text.x = element_text(size =10, color = "black",
angle = 90, hjust = 1, vjust = 0.2))
####Total Reads Per Individual####
total_reads_ind_plot <- map_align_stats %>%
ggplot(., aes (x =as.factor(Ind), y = Total_Reads_Paired))+
geom_boxplot(aes(fill= Ind))+
scale_fill_manual(values = ind_col)+
ggtitle(expression("Total fragments by individual"))+
xlab("")+
ylab(expression("RNA-sequencing fragments"))+
theme_custom()+
theme(
axis.text.x = element_text(size =10, color = "black",
angle = 90, hjust = 1, vjust = 0.2))
####Total Mapped Reads Per Individual####
map_reads_ind_plot <- map_align_stats %>%
ggplot(., aes (x =as.factor(Ind), y = Mapped_Paired))+
geom_boxplot(aes(fill=Ind))+
scale_fill_manual(values = c(ind_col))+
ggtitle(expression("Mapped fragments by individual"))+
xlab("")+
ylab(expression("RNA-sequencing fragments"))+
theme_custom()+
theme(
axis.text.x = element_text(size =10, color = "black",
angle = 90, hjust = 1, vjust = 0.2))
####Total Reads Per Timepoint####
map_align_stats$Time_Num <- factor(map_align_stats$Time_Num,
levels = c("2", "24"))
reads_by_time <- c("2" = "#392344", "24" = "#457291")
total_reads_time_plot <- map_align_stats %>%
ggplot(., aes (x = Sample_ID, y = Total_Reads_Paired, fill = Time_Num, group_by = Ind))+
geom_col()+
# geom_hline(aes(yintercept=20000000))+
scale_fill_manual(values = reads_by_time)+
ggtitle(expression("Total fragments by timepoint"))+
xlab("")+
ylab(expression("RNA-sequencing fragments"))+
theme_custom()+
theme(
axis.text.x = element_text(size =10, color = "black",
angle = 90, hjust = 1, vjust = 0.2))
####Mapped Reads Per Timepoint####
map_reads_time_plot <- map_align_stats %>%
ggplot(., aes (x = Sample_ID, y = Mapped_Paired, fill = Time_Num, group_by = Ind))+
geom_col()+
# geom_hline(aes(yintercept=20000000))+
scale_fill_manual(values = reads_by_time)+
ggtitle(expression("Mapped fragments by timepoint"))+
xlab("")+
ylab(expression("RNA-sequencing fragments"))+
theme_custom()+
theme(
axis.text.x = element_text(size =10, color = "black",
angle = 90, hjust = 1, vjust = 0.2))
####Total Reads Per Timepoint Boxplot####
total_reads_time_boxplot <- map_align_stats %>%
ggplot(., aes (x = Time_Num, y= Total_Reads_Paired, fill = Time_Num))+
geom_boxplot()+
scale_fill_manual(values=reads_by_time)+
ggtitle(expression("Total fragments by timepoint"))+
xlab("")+
ylab(expression("RNA-sequencing fragments"))+
theme_custom()+
theme(
axis.text.x = element_text(size =10, color = "black",
angle = 90, hjust = 1, vjust = 0.2))
####Total Mapped Reads Per Timepoint####
map_reads_time_boxplot <- map_align_stats %>%
ggplot(., aes (x = Time_Num, y= Mapped_Paired, fill = Time_Num))+
geom_boxplot()+
scale_fill_manual(values=reads_by_time)+
ggtitle(expression("Mapped fragments by timepoint"))+
xlab("")+
ylab(expression("RNA-sequencing fragments"))+
theme_custom()+
theme(
axis.text.x = element_text(size =10, color = "black",
angle = 90, hjust = 1, vjust = 0.2))
####Total Reads Per Species####
reads_by_species <- c("H" = "#E56473", "C" = "#EE4902")
total_reads_species_plot <- map_align_stats %>%
ggplot(., aes (x = Ind, y = Total_Reads_Paired, fill = Species, group_by = Ind))+
geom_col()+
scale_fill_manual(values=reads_by_species)+
ggtitle(expression("Total fragments by species"))+
xlab("")+
ylab(expression("RNA-sequencing fragments"))+
theme_custom()+
theme(
axis.text.x = element_text(size =10, color = "black",
angle = 90, hjust = 1, vjust = 0.2))
####Total Mapped Reads by Species####
total_reads_species_boxplot <- map_align_stats %>%
ggplot(., aes (x = Ind, y= Total_Reads_Paired, fill = Species))+
geom_boxplot()+
scale_fill_manual(values=reads_by_species)+
ggtitle(expression("Total fragments by species"))+
xlab("")+
ylab(expression("RNA-sequencing fragments"))+
theme_custom()+
theme(
axis.text.x = element_text(size =10, color = "black",
angle = 90, hjust = 1, vjust = 0.2))
####Total Mapped Reads by Species####
map_reads_species_plot <- map_align_stats %>%
ggplot(., aes (x = Ind, y= Mapped_Paired, fill = Species))+
geom_boxplot()+
scale_fill_manual(values=reads_by_species)+
ggtitle(expression("Mapped fragments by species"))+
xlab("")+
ylab(expression("RNA-sequencing fragments"))+
theme_custom()+
theme(
axis.text.x = element_text(size =10, color = "black",
angle = 90, hjust = 1, vjust = 0.2))
#make a list so I can save all of these plots
qc_map_plots_list <- list(
total_reads_sample_plot,
map_reads_sample_plot,
total_reads_sample_boxplot,
map_reads_sample_boxplot,
total_reads_ind_plot,
map_reads_ind_plot,
total_reads_species_plot,
total_reads_species_boxplot,
map_reads_species_plot,
total_reads_stimulus_plot,
map_reads_stimulus_plot,
total_reads_time_plot,
total_reads_time_boxplot,
map_reads_time_plot,
map_reads_time_boxplot
)
qc_map_plots_names <- list(
"total_reads_sample",
"map_reads_sample",
"total_reads_sample_boxplot",
"map_reads_sample_boxplot",
"total_reads_ind",
"map_reads_ind",
"total_reads_species",
"total_reads_species_boxplot",
"map_reads_species",
"total_reads_stimulus",
"map_reads_stimulus",
"total_reads_time_plot",
"total_reads_time_boxplot",
"map_reads_time_plot",
"map_reads_time_boxplot"
)
qc_map_plots_names <- paste0(qc_map_plots_names, "_EMP")
stopifnot(length(qc_map_plots_list) == length(qc_map_plots_names))
#print all of these plots here
print(qc_map_plots_list)[[1]]
| Version | Author | Date |
|---|---|---|
| e7f3a80 | Emma M. Pfortmiller | 2026-01-13 |
[[2]]
| Version | Author | Date |
|---|---|---|
| e7f3a80 | Emma M. Pfortmiller | 2026-01-13 |
[[3]]
| Version | Author | Date |
|---|---|---|
| e7f3a80 | Emma M. Pfortmiller | 2026-01-13 |
[[4]]
| Version | Author | Date |
|---|---|---|
| e7f3a80 | Emma M. Pfortmiller | 2026-01-13 |
[[5]]
| Version | Author | Date |
|---|---|---|
| e7f3a80 | Emma M. Pfortmiller | 2026-01-13 |
[[6]]
| Version | Author | Date |
|---|---|---|
| e7f3a80 | Emma M. Pfortmiller | 2026-01-13 |
[[7]]
| Version | Author | Date |
|---|---|---|
| e7f3a80 | Emma M. Pfortmiller | 2026-01-13 |
[[8]]
| Version | Author | Date |
|---|---|---|
| e7f3a80 | Emma M. Pfortmiller | 2026-01-13 |
[[9]]
| Version | Author | Date |
|---|---|---|
| e7f3a80 | Emma M. Pfortmiller | 2026-01-13 |
[[10]]
| Version | Author | Date |
|---|---|---|
| e7f3a80 | Emma M. Pfortmiller | 2026-01-13 |
[[11]]
| Version | Author | Date |
|---|---|---|
| e7f3a80 | Emma M. Pfortmiller | 2026-01-13 |
[[12]]
| Version | Author | Date |
|---|---|---|
| e7f3a80 | Emma M. Pfortmiller | 2026-01-13 |
[[13]]
| Version | Author | Date |
|---|---|---|
| e7f3a80 | Emma M. Pfortmiller | 2026-01-13 |
[[14]]
| Version | Author | Date |
|---|---|---|
| e7f3a80 | Emma M. Pfortmiller | 2026-01-13 |
[[15]]
| Version | Author | Date |
|---|---|---|
| e7f3a80 | Emma M. Pfortmiller | 2026-01-13 |
#save all of these plots to my designated folder
# for (i in seq_along(qc_map_plots_list)) {
# save_plot(
# plot = qc_map_plots_list[[i]],
# filename = qc_map_plots_names[i],
# folder = output_folder
# )
# }Filter Dataframe
Convert my dataframe to log2cpm and filter out lowly-expressed genes via rowMeans.
#first, convert to log2cpm
#ensure that Entrez_ID are the rownames
hc_cpm_unfilt_sub <- cpm(hc_counts_sub, log = TRUE)
dim(hc_cpm_unfilt_sub)[1] 28802 195hc_cpm_unfilt <- hc_cpm_unfilt_sub
#28802 genes of 195 columns as expected
hist(hc_cpm_unfilt_sub,
main = "Unfiltered cpm",
xlab = expression("log"[2]*"cpm"),
col = 4)
| Version | Author | Date |
|---|---|---|
| 92f37b9 | Emma M. Pfortmiller | 2025-12-27 |
#now filter by rowMeans > 0 to exclude lowly expressed genes
hc_cpm_matrix_sub <- subset(hc_cpm_unfilt_sub, (rowMeans(hc_cpm_unfilt_sub) > 0))
dim(hc_cpm_matrix_sub)[1] 13739 195#13739 genes after filtering
#save this as an RDS and csv
# saveRDS(hc_cpm_matrix_sub, "data/counts/hc_cpm_filtered_matrix_subset.RDS")
# write.csv(hc_cpm_matrix_sub, "data/counts/hc_cpm_filtered_matrix_subset.csv")
#now keep the name that is used in the rest of the data to make it easier, assume from now onward that this will be on the 195-sample subset of libraries
hc_cpm_matrix <- readRDS("data/counts/hc_cpm_filtered_matrix_subset.RDS")
#plot this out to show the filtering
hist(hc_cpm_matrix,
main = "Filtered Counts (cpm) rowMeans > 0",
xlab = expression("log"[2]*"cpm"),
col = 2)
| Version | Author | Date |
|---|---|---|
| 92f37b9 | Emma M. Pfortmiller | 2025-12-27 |
QC Boxplots
#make boxplots of all counts vs log2cpm filtered counts
par(mar = c(8,4,2,2))
#boxplot of unfiltered cpm matrix
hc_cpm_unfilt_boxplot <- boxplot(hc_cpm_unfilt,
main = "Unfiltered log2cpm",
names = colnames(hc_cpm_unfilt),
adj=1, las = 2, cex.axis = 0.7)
#set the margins so the x axis isn't cut off
par(mar = c(8,4,2,2))
#boxplot of filtered cpm matrix
hc_cpm_filt_boxplot <- boxplot(hc_cpm_matrix,
main = "Filtered log2cpm (rowMeans > 0)",
names = colnames(hc_cpm_matrix),
adj=1, las = 2, cex.axis = 0.7)
print(hc_cpm_unfilt_boxplot)$stats
[,1] [,2] [,3] [,4] [,5] [,6]
[1,] -1.1799596 -1.1799596 -1.1799596 -1.1799596 -1.179960 -1.17995959
[2,] -1.1799596 -1.1799596 -1.1799596 -1.1799596 -1.179960 -1.17995959
[3,] -0.6375119 -0.4918759 -0.4126215 -0.3605375 -0.326047 0.06885607
[4,] 4.1180051 4.1569900 4.1152449 4.1141077 4.311992 3.74435574
[5,] 12.0608080 12.0925366 11.8956768 11.8362569 12.473131 11.11194073
[,7] [,8] [,9] [,10] [,11] [,12]
[1,] -1.1799596 -1.1799596 -1.1799596 -1.1799596 -1.1799596 -1.1799596
[2,] -1.1799596 -1.1799596 -1.1799596 -1.1799596 -1.1799596 -1.1799596
[3,] -0.3657134 -0.5535614 -0.3183538 -0.3091035 -0.4028863 -0.3483333
[4,] 4.1976251 4.1666622 4.1351114 4.1698265 4.1509861 4.0544439
[5,] 11.9711552 12.0944787 12.0670729 12.1308587 12.1399713 11.9030218
[,13] [,14] [,15] [,16] [,17] [,18]
[1,] -1.1799596 -1.1799596 -1.1799596 -1.17995959 -1.1799596 -1.17995959
[2,] -1.1799596 -1.1799596 -1.1799596 -1.17995959 -1.1799596 -1.17995959
[3,] -0.3436462 -0.2927889 -0.4868986 -0.05569319 -0.1067031 -0.07181269
[4,] 4.1234048 4.1139090 4.3634295 4.47985317 4.3691973 4.45052750
[5,] 12.0406285 11.7667595 12.5934667 12.72608510 12.6208230 12.80387155
[,19] [,20] [,21] [,22] [,23] [,24]
[1,] -1.1799596 -1.17995959 -1.179960 -1.1799596 -1.1799596 -1.1799596
[2,] -1.1799596 -1.17995959 -1.179960 -1.1799596 -1.1799596 -1.1799596
[3,] -0.4948782 0.02058583 -0.462202 -0.5085603 -0.1141915 -0.4974025
[4,] 4.3618171 4.56365634 4.387248 4.3814923 4.3721079 4.4286598
[5,] 12.5570603 12.74505882 12.731929 12.6778898 12.5870219 12.6666181
[,25] [,26] [,27] [,28] [,29] [,30]
[1,] -1.1799596 -1.17995959 -1.1799596 -1.1799596 -1.17995959 -1.1799596
[2,] -1.1799596 -1.17995959 -1.1799596 -1.1799596 -1.17995959 -1.1799596
[3,] -0.5184103 -0.07629974 -0.1231025 -0.4718607 -0.09023682 -0.1046558
[4,] 4.3554361 4.39093318 4.3885883 4.3824797 4.36531133 4.5185263
[5,] 12.6567268 12.74192129 12.7207171 12.6951025 12.25469302 12.4130131
[,31] [,32] [,33] [,34] [,35] [,36]
[1,] -1.17995959 -1.17995959 -1.17995959 -1.1799596 -1.17995959 -1.1799596
[2,] -1.17995959 -1.17995959 -1.17995959 -1.1799596 -1.17995959 -1.1799596
[3,] 0.05788737 -0.04295545 -0.01183807 0.6075777 -0.05197669 -0.2697044
[4,] 4.38038063 4.41607880 4.43524421 4.6000753 4.50331597 4.5083392
[5,] 12.63450479 12.76727297 12.80647548 12.5513115 12.42811091 12.9204051
[,37] [,38] [,39] [,40] [,41] [,42]
[1,] -1.1799596 -1.1799596 -1.1799596 -1.17995959 -1.1799596 -1.1799596
[2,] -1.1799596 -1.1799596 -1.1799596 -1.17995959 -1.1799596 -1.1799596
[3,] -0.4390762 -0.0419923 -0.3785154 -0.09288813 -0.1175793 -0.3849372
[4,] 4.4245602 4.4704891 4.4431148 4.49350780 4.4632282 4.4283108
[5,] 12.3797995 12.8707390 12.6118046 12.34332241 12.3044218 12.5087505
[,43] [,44] [,45] [,46] [,47] [,48]
[1,] -1.1799596 -1.1799596 -1.1799596 -1.1799596 -1.1799596 -1.1799596
[2,] -1.1799596 -1.1799596 -1.1799596 -1.1799596 -1.1799596 -1.1799596
[3,] -0.3067021 -0.2277345 -0.5516216 -0.3776757 -0.2149044 0.2074516
[4,] 4.4898511 4.6193607 4.5137459 4.5192140 4.4649095 4.7649763
[5,] 12.8611983 13.0119416 12.6821198 12.8245322 12.7499040 13.3069489
[,49] [,50] [,51] [,52] [,53] [,54] [,55]
[1,] -1.1799596 -1.179960 -1.1799596 -1.1799596 -1.1799596 -1.1799596 -1.179960
[2,] -1.1799596 -1.179960 -1.1799596 -1.1799596 -1.1799596 -1.1799596 -1.179960
[3,] -0.3046218 -0.241147 -0.2560473 -0.3076116 -0.3806051 -0.3074853 -0.272685
[4,] 4.5060778 4.491723 4.4739297 4.4760525 4.4705589 4.4881597 4.502085
[5,] 12.8255344 12.703873 12.8144153 12.7483384 12.6625611 12.7989240 12.837720
[,56] [,57] [,58] [,59] [,60] [,61]
[1,] -1.1799596 -1.1799596 -1.1799596 -1.179960 -1.1799596 -1.1799596
[2,] -1.1799596 -1.1799596 -1.1799596 -1.179960 -1.1799596 -1.1799596
[3,] -0.2050328 -0.2841614 -0.2471217 -1.179960 -0.2792231 0.2813195
[4,] 4.4983420 4.2229901 4.2553282 4.338378 4.2909333 4.2491398
[5,] 12.7373103 12.2935362 12.3640699 12.579384 12.4761982 12.3730076
[,62] [,63] [,64] [,65] [,66] [,67]
[1,] -1.1799596 -1.1799596 -1.1799596 -1.1799596 -1.1799596 -1.1799596
[2,] -1.1799596 -1.1799596 -1.1799596 -1.1799596 -1.1799596 -1.1799596
[3,] -0.3055336 -0.3763196 -0.2398399 -0.4308467 -0.4464437 -0.2257215
[4,] 4.3565186 4.3113955 4.3149999 4.3414084 4.2819161 4.4153824
[5,] 12.5917703 12.5110658 12.5038421 12.6156067 12.2691671 12.6269941
[,68] [,69] [,70] [,71] [,72] [,73] [,74]
[1,] -1.179960 -1.1799596 -1.1799596 -1.1799596 -1.1799596 -1.1799596 -1.179960
[2,] -1.179960 -1.1799596 -1.1799596 -1.1799596 -1.1799596 -1.1799596 -1.179960
[3,] -1.179960 -0.1825503 -0.5498155 -0.2877254 -0.1392054 -0.1982688 -0.485200
[4,] 4.362544 4.3999251 4.3060328 4.4825534 4.3079791 4.3986581 4.348576
[5,] 12.633722 12.7118965 12.3634464 12.5640362 12.3880740 12.7152192 12.560714
[,75] [,76] [,77] [,78] [,79] [,80]
[1,] -1.1799596 -1.1799596 -1.179960 -1.1799596 -1.1799596 -1.1799596
[2,] -1.1799596 -1.1799596 -1.179960 -1.1799596 -1.1799596 -1.1799596
[3,] 0.3585696 -0.1729232 -0.298689 -0.4263872 -0.1909017 -0.3324444
[4,] 4.2432690 4.3415666 4.409559 4.4750857 4.3221157 4.3612376
[5,] 12.2138167 12.5731730 12.660896 12.9523894 12.4763993 12.6376783
[,81] [,82] [,83] [,84] [,85] [,86]
[1,] -1.1799596 -1.1799596 -1.1799596 -1.17995959 -1.17995959 -1.1799596
[2,] -1.1799596 -1.1799596 -1.1799596 -1.17995959 -1.17995959 -1.1799596
[3,] -0.5434968 -0.1722149 -0.1800353 -0.09224673 -0.01401881 -0.1588217
[4,] 4.4580318 4.4050206 4.4048547 4.52613598 4.58777938 4.5477081
[5,] 12.8883743 12.7369673 12.5911206 12.60628433 13.15014338 12.5348751
[,87] [,88] [,89] [,90] [,91] [,92]
[1,] -1.1799596 -1.1799596 -1.1799596 -1.1799596 -1.1799596 -1.1799596
[2,] -1.1799596 -1.1799596 -1.1799596 -1.1799596 -1.1799596 -1.1799596
[3,] -0.2315928 -0.2325231 0.2312734 -0.2183838 -0.2583343 -0.2089424
[4,] 4.5496602 4.4564696 4.5555295 4.5637355 4.5923190 4.5174744
[5,] 12.7766989 12.7743034 12.6943136 13.0743908 13.2352788 12.6930530
[,93] [,94] [,95] [,96] [,97] [,98]
[1,] -1.1799596 -1.1799596 -1.1799596 -1.1799596 -1.1799596 -1.17995959
[2,] -1.1799596 -1.1799596 -1.1799596 -1.1799596 -1.1799596 -1.17995959
[3,] -0.2546136 -0.2375201 -0.3138191 -0.2464126 -0.3548363 -0.07435652
[4,] 4.5397859 4.5501448 4.5546562 4.5443651 4.5711967 4.42894889
[5,] 12.6230892 12.7495303 12.6813817 12.8090236 13.1648631 12.73375685
[,99] [,100] [,101] [,102] [,103] [,104]
[1,] -1.1799596 -1.1799596 -1.1799596 -1.1799596 -1.17995959 -1.1799596
[2,] -1.1799596 -1.1799596 -1.1799596 -1.1799596 -1.17995959 -1.1799596
[3,] -0.1726456 -0.2681557 -0.2592181 -0.1185534 -0.05625794 -0.1091102
[4,] 4.6417272 4.5480259 4.5178703 4.3470179 4.60758945 4.6140183
[5,] 13.2694871 13.0095871 12.9084430 12.2892008 11.80078909 12.6495558
[,105] [,106] [,107] [,108] [,109] [,110]
[1,] -1.1799596 -1.179960 -1.17995959 -1.1799596 -1.1799596 -1.17995959
[2,] -1.1799596 -1.179960 -1.17995959 -1.1799596 -1.1799596 -1.17995959
[3,] -0.2342794 -0.209496 -0.01761235 -0.3309928 -0.2419441 0.01299707
[4,] 4.5692399 4.555864 4.46128029 4.4250613 4.4900893 4.49704431
[5,] 13.1581066 12.959489 12.50052650 12.6630978 12.7523024 13.01003828
[,111] [,112] [,113] [,114] [,115] [,116]
[1,] -1.179960 -1.1799596 -1.1799596 -1.1799596 -1.1799596 -1.17995959
[2,] -1.179960 -1.1799596 -1.1799596 -1.1799596 -1.1799596 -1.17995959
[3,] -0.253458 -0.1038014 -0.1483831 -0.5051731 -0.2125431 -0.03917952
[4,] 4.554435 4.3574909 4.4203604 4.4083796 4.3993249 4.34947350
[5,] 13.005158 12.6369682 12.3898729 12.4723510 12.6762445 12.62706252
[,117] [,118] [,119] [,120] [,121] [,122]
[1,] -1.1799596 -1.1799596 -1.1799596 -1.17995959 -1.1799596 -1.1799596
[2,] -1.1799596 -1.1799596 -1.1799596 -1.17995959 -1.1799596 -1.1799596
[3,] 0.3266201 -0.3280077 -0.1703835 -0.07896917 -0.1850394 -0.1583148
[4,] 4.5189309 4.4076773 4.4085858 4.38604173 4.3492412 4.3856514
[5,] 12.6221313 12.6528278 12.7718044 12.72862372 12.5010072 12.6864242
[,123] [,124] [,125] [,126] [,127] [,128]
[1,] -1.1799596 -1.1799596 -1.17995959 -1.1799596 -1.1799596 -1.1799596
[2,] -1.1799596 -1.1799596 -1.17995959 -1.1799596 -1.1799596 -1.1799596
[3,] -0.1358415 -0.1972419 -0.09662254 -0.1951503 -0.2025374 -0.1685557
[4,] 4.3808038 4.4153337 4.38794062 4.2659243 4.4048162 4.2684991
[5,] 12.6407709 12.7510978 12.61497509 12.4195393 12.7320388 12.4149241
[,129] [,130] [,131] [,132] [,133] [,134]
[1,] -1.179960 -1.1799596 -1.17995959 -1.1799596 -1.1799596 -1.1799596
[2,] -1.179960 -1.1799596 -1.17995959 -1.1799596 -1.1799596 -1.1799596
[3,] -1.179960 -0.1672917 0.01130032 -0.2270347 -0.2413187 -0.3422471
[4,] 4.300946 4.2876344 4.05194670 4.3113253 4.3265979 4.3271246
[5,] 12.499306 12.2329443 11.82575575 12.5196892 12.5476708 12.5749370
[,135] [,136] [,137] [,138] [,139] [,140]
[1,] -1.1799596 -1.1799596 -1.17995959 -1.179960 -1.1799596 -1.1799596
[2,] -1.1799596 -1.1799596 -1.17995959 -1.179960 -1.1799596 -1.1799596
[3,] -0.4612016 -0.4091996 -0.07824185 -1.179960 -0.2289241 -0.2123866
[4,] 4.2669737 4.3049016 4.35207603 3.699135 4.3075013 4.3557367
[5,] 12.3874544 12.5189273 12.55731059 10.998297 12.5355313 12.6583740
[,141] [,142] [,143] [,144] [,145] [,146]
[1,] -1.1799596 -1.1799596 -1.179960 -1.1799596 -1.1799596 -1.1799596
[2,] -1.1799596 -1.1799596 -1.179960 -1.1799596 -1.1799596 -1.1799596
[3,] -0.2669546 -0.3174002 -0.317271 -0.3301637 0.5476179 -0.1917946
[4,] 4.3855030 4.3047253 4.318128 4.3538186 4.4281221 4.3817343
[5,] 12.6778957 12.2327079 12.554041 12.5392961 12.6955445 12.6319793
[,147] [,148] [,149] [,150] [,151] [,152]
[1,] -1.1799596 -1.1799596 -1.1799596 -1.1799596 -1.1799596 -1.179960
[2,] -1.1799596 -1.1799596 -1.1799596 -1.1799596 -1.1799596 -1.179960
[3,] -0.5240116 -0.4384697 -0.1946984 -0.3159847 -0.2202146 -0.391906
[4,] 4.3584676 4.3650606 4.2829288 4.3722508 4.2947504 4.310866
[5,] 12.6540138 12.6575082 12.2840811 12.6004715 12.2291078 12.184504
[,153] [,154] [,155] [,156] [,157] [,158]
[1,] -1.1799596 -1.17995959 -1.17995959 -1.1799596 -1.17995959 -1.1799596
[2,] -1.1799596 -1.17995959 -1.17995959 -1.1799596 -1.17995959 -1.1799596
[3,] -0.2576226 -0.05278768 -0.07741301 0.1600124 -0.08526516 0.1458237
[4,] 4.3777542 4.45103859 4.37135210 4.3005229 4.42584566 4.3498827
[5,] 12.6492116 12.57278065 12.68913377 12.4954204 12.53715334 12.3757026
[,159] [,160] [,161] [,162] [,163] [,164]
[1,] -1.1799596 -1.17995959 -1.1799596 -1.1799596 -1.1799596 -1.1799596
[2,] -1.1799596 -1.17995959 -1.1799596 -1.1799596 -1.1799596 -1.1799596
[3,] -0.1231278 -0.02095203 -0.2360134 -0.1516428 -0.1304083 -0.2408882
[4,] 4.4683990 4.47314357 4.4493566 4.3984945 4.4546282 4.4792190
[5,] 12.4289557 12.54324522 12.8668049 12.4797509 12.6834501 12.7254382
[,165] [,166] [,167] [,168] [,169] [,170]
[1,] -1.1799596 -1.1799596 -1.17995959 -1.1799596 -1.1799596 -1.17995959
[2,] -1.1799596 -1.1799596 -1.17995959 -1.1799596 -1.1799596 -1.17995959
[3,] -0.2228508 -0.1194806 -0.06715709 -0.1799227 -0.1855317 0.08885748
[4,] 4.4760877 4.4117388 4.42496545 4.3436745 4.6034923 4.39044533
[5,] 12.7808822 12.7209918 12.81485662 12.6131586 13.1324225 12.63795767
[,171] [,172] [,173] [,174] [,175] [,176]
[1,] -1.1799596 -1.179960 -1.1799596 -1.1799596 -1.1799596 -1.1799596
[2,] -1.1799596 -1.179960 -1.1799596 -1.1799596 -1.1799596 -1.1799596
[3,] -0.2519267 -0.100120 0.7813314 -0.2392554 -0.2123573 -0.2866634
[4,] 4.4565118 4.447949 4.6270850 4.6317143 4.4282401 4.4725659
[5,] 12.8956957 12.863171 12.2472717 12.5517375 12.7365179 12.7021859
[,177] [,178] [,179] [,180] [,181] [,182]
[1,] -1.1799596 -1.179960 -1.1799596 -1.1799596 -1.1799596 -1.1799596
[2,] -1.1799596 -1.179960 -1.1799596 -1.1799596 -1.1799596 -1.1799596
[3,] -0.2097145 -0.215788 -0.1091572 -0.3311811 -0.1435065 -0.4372633
[4,] 4.4615241 4.435416 4.4310994 4.4598369 4.4604242 4.3470830
[5,] 12.8672880 12.820487 12.7870549 12.7092664 12.8034355 12.4029527
[,183] [,184] [,185] [,186] [,187] [,188]
[1,] -1.17995959 -1.1799596 -1.1799596 -1.179960 -1.1799596 -1.1799596
[2,] -1.17995959 -1.1799596 -1.1799596 -1.179960 -1.1799596 -1.1799596
[3,] -0.06056847 -0.1631525 -0.1885155 -0.389340 0.4621297 -0.1851374
[4,] 4.56863226 4.3919093 4.3730966 4.418111 4.6037580 4.4392331
[5,] 13.10343681 12.4338348 12.6041324 12.799257 12.2559734 12.0806194
[,189] [,190] [,191] [,192] [,193] [,194]
[1,] -1.1799596 -1.1799596 -1.179959587 -1.1799596 -1.1799596 -1.17995959
[2,] -1.1799596 -1.1799596 -1.179959587 -1.1799596 -1.1799596 -1.17995959
[3,] -0.2699103 -0.2411969 -0.007692304 0.0420052 -0.2511454 -0.09518615
[4,] 4.4955531 4.4387603 4.478806294 4.4727491 4.4840704 4.37348501
[5,] 12.8408371 12.3365178 12.863515330 12.4703811 12.3930524 12.45170867
[,195]
[1,] -1.1799596
[2,] -1.1799596
[3,] -0.1407189
[4,] 4.4504664
[5,] 12.2675499
$n
[1] 28802 28802 28802 28802 28802 28802 28802 28802 28802 28802 28802 28802
[13] 28802 28802 28802 28802 28802 28802 28802 28802 28802 28802 28802 28802
[25] 28802 28802 28802 28802 28802 28802 28802 28802 28802 28802 28802 28802
[37] 28802 28802 28802 28802 28802 28802 28802 28802 28802 28802 28802 28802
[49] 28802 28802 28802 28802 28802 28802 28802 28802 28802 28802 28802 28802
[61] 28802 28802 28802 28802 28802 28802 28802 28802 28802 28802 28802 28802
[73] 28802 28802 28802 28802 28802 28802 28802 28802 28802 28802 28802 28802
[85] 28802 28802 28802 28802 28802 28802 28802 28802 28802 28802 28802 28802
[97] 28802 28802 28802 28802 28802 28802 28802 28802 28802 28802 28802 28802
[109] 28802 28802 28802 28802 28802 28802 28802 28802 28802 28802 28802 28802
[121] 28802 28802 28802 28802 28802 28802 28802 28802 28802 28802 28802 28802
[133] 28802 28802 28802 28802 28802 28802 28802 28802 28802 28802 28802 28802
[145] 28802 28802 28802 28802 28802 28802 28802 28802 28802 28802 28802 28802
[157] 28802 28802 28802 28802 28802 28802 28802 28802 28802 28802 28802 28802
[169] 28802 28802 28802 28802 28802 28802 28802 28802 28802 28802 28802 28802
[181] 28802 28802 28802 28802 28802 28802 28802 28802 28802 28802 28802 28802
[193] 28802 28802 28802
$conf
[,1] [,2] [,3] [,4] [,5] [,6]
[1,] -0.6868355 -0.5415625 -0.4619194 -0.4098249 -0.3771766 0.02301111
[2,] -0.5881883 -0.4421894 -0.3633236 -0.3112502 -0.2749174 0.11470103
[,7] [,8] [,9] [,10] [,11] [,12]
[1,] -0.4157783 -0.6033380 -0.3678367 -0.3589096 -0.4525169 -0.3970652
[2,] -0.3156486 -0.5037848 -0.2688709 -0.2592974 -0.3532556 -0.2996015
[,13] [,14] [,15] [,16] [,17] [,18]
[1,] -0.3930201 -0.3420744 -0.5385071 -0.108385569 -0.15836530 -0.12423206
[2,] -0.2942724 -0.2435034 -0.4352901 -0.003000806 -0.05504093 -0.01939333
[,19] [,20] [,21] [,22] [,23] [,24]
[1,] -0.5464717 -0.03288675 -0.5140323 -0.5603370 -0.16588074 -0.5496183
[2,] -0.4432847 0.07405842 -0.4103718 -0.4567837 -0.06250217 -0.4451867
[,25] [,26] [,27] [,28] [,29] [,30]
[1,] -0.5699444 -0.12816428 -0.17494518 -0.5236465 -0.14186283 -0.15770822
[2,] -0.4668762 -0.02443519 -0.07125975 -0.4200748 -0.03861081 -0.05160337
[,31] [,32] [,33] [,34] [,35] [,36]
[1,] 0.006121072 -0.095054100 -0.06411514 0.5537660 -0.1048875110 -0.3226620
[2,] 0.109653673 0.009143195 0.04043901 0.6613893 0.0009341254 -0.2167468
[,37] [,38] [,39] [,40] [,41] [,42]
[1,] -0.4912538 -0.09459750 -0.4308657 -0.14570763 -0.17011695 -0.4371498
[2,] -0.3868985 0.01061291 -0.3261650 -0.04006862 -0.06504175 -0.3327247
[,43] [,44] [,45] [,46] [,47] [,48]
[1,] -0.3594876 -0.2817256 -0.6046295 -0.4307345 -0.2674577 0.1521047
[2,] -0.2539167 -0.1737433 -0.4986136 -0.3246169 -0.1623512 0.2627984
[,49] [,50] [,51] [,52] [,53] [,54]
[1,] -0.3575584 -0.2939499 -0.3086845 -0.3602686 -0.4332109 -0.3602550
[2,] -0.2516853 -0.1883441 -0.2034101 -0.2549546 -0.3279992 -0.2547156
[,55] [,56] [,57] [,58] [,59] [,60] [,61]
[1,] -0.3255844 -0.2578973 -0.3344624 -0.2977238 -1.231335 -0.3301567 0.2307750
[2,] -0.2197857 -0.1521683 -0.2338604 -0.1965197 -1.128584 -0.2282895 0.3318639
[,62] [,63] [,64] [,65] [,66] [,67]
[1,] -0.3570777 -0.4274437 -0.2909975 -0.4822501 -0.4972933 -0.2778137
[2,] -0.2539894 -0.3251956 -0.1886823 -0.3794432 -0.3955941 -0.1736293
[,68] [,69] [,70] [,71] [,72] [,73]
[1,] -1.231560 -0.2344985 -0.6008896 -0.3404429 -0.19029762 -0.2502053
[2,] -1.128359 -0.1306020 -0.4987414 -0.2350079 -0.08811312 -0.1463324
[,74] [,75] [,76] [,77] [,78] [,79]
[1,] -0.5366702 0.3080798 -0.2243281 -0.3507270 -0.4790352 -0.2421255
[2,] -0.4337298 0.4090594 -0.1215182 -0.2466511 -0.3737392 -0.1396778
[,80] [,81] [,82] [,83] [,84] [,85]
[1,] -0.3840325 -0.5959860 -0.2242105 -0.2320294 -0.14537000 -0.06771597
[2,] -0.2808564 -0.4910076 -0.1202192 -0.1280411 -0.03912346 0.03967836
[,86] [,87] [,88] [,89] [,90] [,91]
[1,] -0.2121458 -0.2849351 -0.2849977 0.1778765 -0.2718571 -0.3120737
[2,] -0.1054976 -0.1782505 -0.1800484 0.2846704 -0.1649105 -0.2045949
[,92] [,93] [,94] [,95] [,96] [,97]
[1,] -0.2619850 -0.3078640 -0.2908669 -0.3672079 -0.2997055 -0.4083791
[2,] -0.1558998 -0.2013633 -0.1841733 -0.2604303 -0.1931196 -0.3012935
[,98] [,99] [,100] [,101] [,102] [,103]
[1,] -0.12657498 -0.2268450 -0.3214828 -0.3122644 -0.17000911 -0.110139536
[2,] -0.02213805 -0.1184462 -0.2148286 -0.2061718 -0.06709772 -0.002376345
[,104] [,105] [,106] [,107] [,108] [,109]
[1,] -0.16305165 -0.2878039 -0.2628961 -0.07013182 -0.3831751 -0.2947318
[2,] -0.05516876 -0.1807548 -0.1560960 0.03490712 -0.2788105 -0.1891564
[,110] [,111] [,112] [,113] [,114] [,115]
[1,] -0.03985536 -0.3068447 -0.15535464 -0.20052157 -0.5572001 -0.2644858
[2,] 0.06584950 -0.2000712 -0.05224825 -0.09624456 -0.4531461 -0.1606005
[,116] [,117] [,118] [,119] [,120] [,121]
[1,] -0.09065807 0.2735639 -0.3800282 -0.2224124 -0.13078818 -0.2365158
[2,] 0.01229904 0.3796763 -0.2759873 -0.1183546 -0.02715017 -0.1335631
[,122] [,123] [,124] [,125] [,126] [,127]
[1,] -0.2101302 -0.18761177 -0.2493336 -0.14845922 -0.2458510 -0.2545312
[2,] -0.1064994 -0.08407129 -0.1451502 -0.04478586 -0.1444496 -0.1505437
[,128] [,129] [,130] [,131] [,132] [,133]
[1,] -0.2192804 -1.230986 -0.2181946 -0.03740829 -0.2781581 -0.2925843
[2,] -0.1178310 -1.128933 -0.1163889 0.06000893 -0.1759113 -0.1900532
[,134] [,135] [,136] [,137] [,138] [,139]
[1,] -0.3935176 -0.5119121 -0.4602632 -0.12974464 -1.225384 -0.2800119
[2,] -0.2909766 -0.4104911 -0.3581360 -0.02673906 -1.134536 -0.1778363
[,140] [,141] [,142] [,143] [,144] [,145]
[1,] -0.2639235 -0.3187686 -0.3684622 -0.3684577 -0.3816827 0.4954072
[2,] -0.1608498 -0.2151406 -0.2663383 -0.2660843 -0.2786446 0.5998287
[,146] [,147] [,148] [,149] [,150] [,151]
[1,] -0.2435735 -0.5755739 -0.4900934 -0.2455575 -0.3676753 -0.2711836
[2,] -0.1400156 -0.4724493 -0.3868460 -0.1438394 -0.2642940 -0.1692455
[,152] [,153] [,154] [,155] [,156] [,157]
[1,] -0.4430252 -0.3093644 -0.1052118027 -0.12909526 0.1089896 -0.13745473
[2,] -0.3407869 -0.2058807 -0.0003635623 -0.02573077 0.2110352 -0.03307558
[,158] [,159] [,160] [,161] [,162] [,163]
[1,] 0.09434131 -0.17571358 -0.07358195 -0.2884219 -0.20357770 -0.18286584
[2,] 0.19730604 -0.07054209 0.03167788 -0.1836050 -0.09970782 -0.07795077
[,164] [,165] [,166] [,167] [,168] [,169]
[1,] -0.2935747 -0.2755082 -0.17153888 -0.11933847 -0.2313473 -0.2393752
[2,] -0.1882017 -0.1701935 -0.06742239 -0.01497571 -0.1284981 -0.1316883
[,170] [,171] [,172] [,173] [,174] [,175]
[1,] 0.03699748 -0.3044018 -0.15251540 0.7272683 -0.2933616 -0.2645692
[2,] 0.14071748 -0.1994516 -0.04772468 0.8353945 -0.1851492 -0.1601455
[,176] [,177] [,178] [,179] [,180] [,181]
[1,] -0.3392880 -0.2622362 -0.2680667 -0.16139569 -0.3836871 -0.19601803
[2,] -0.2340389 -0.1571928 -0.1635094 -0.05691872 -0.2786751 -0.09099503
[,182] [,183] [,184] [,185] [,186] [,187]
[1,] -0.4887196 -0.114087381 -0.2150261 -0.240214 -0.4414575 0.4082837
[2,] -0.3858070 -0.007049566 -0.1112788 -0.136817 -0.3372224 0.5159756
[,188] [,189] [,190] [,191] [,192] [,193]
[1,] -0.2374516 -0.3227488 -0.2935067 -0.06037494 -0.01062104 -0.3038771
[2,] -0.1328232 -0.2170717 -0.1888871 0.04499033 0.09463145 -0.1984138
[,194] [,195]
[1,] -0.14688825 -0.19313768
[2,] -0.04348404 -0.08830009
$out
[1] 12.20868 14.51922 12.46102 14.50585 13.53455 13.02746 12.61123 12.66063
[9] 12.92284 15.59563 12.58899 14.22168 12.18672 14.64884 12.49714 14.50392
[17] 13.56375 13.10048 12.77171 12.81256 12.97575 12.36669 15.80694 12.22122
[25] 13.97455 12.08251 14.52691 12.47447 14.44967 13.59624 13.06311 12.65746
[33] 12.89565 12.80291 12.27291 15.81248 12.51967 12.10658 14.04791 12.27165
[41] 12.10756 14.58482 12.51630 14.41901 13.73574 13.22077 12.77295 13.14660
[49] 12.40538 12.47943 15.90213 12.61984 12.17966 13.98108 12.06439 12.21045
[57] 12.55529 13.55874 13.60982 12.81469 12.70185 15.63622 13.00488 13.66329
[65] 12.66111 12.99587 14.92055 12.85746 12.24716 15.09424 14.16820 13.59455
[73] 12.64282 12.21661 12.29184 11.14134 12.15920 15.85878 11.61315 12.12944
[81] 11.56257 12.14694 12.33324 11.17484 11.30316 13.21172 11.47547 11.29609
[89] 11.75405 11.38879 12.17962 11.76198 11.69993 11.92224 14.54368 11.70790
[97] 11.81151 11.23340 12.11812 11.23073 11.49949 11.63371 12.03138 11.57210
[105] 14.62444 12.27700 14.21884 13.82685 13.34233 12.80533 12.92779 12.34420
[113] 12.29335 15.66089 12.55026 13.84754 14.65209 12.50900 14.37952 13.71459
[121] 13.15931 12.72985 13.09179 12.31468 12.43040 15.82912 12.51895 12.19647
[129] 13.91965 14.76074 12.63771 14.52795 13.74953 13.31611 12.92095 12.82531
[137] 12.19525 12.30254 15.98183 12.38427 12.22125 14.05185 12.17918 14.35858
[145] 12.33186 14.20542 13.56157 13.00907 12.49921 12.69778 16.03356 12.49702
[153] 13.90235 14.66367 12.57746 14.43825 13.72239 13.23396 12.77225 13.09418
[161] 12.38479 12.37224 15.85135 12.49874 13.99575 12.18891 14.57092 12.47650
[169] 14.35200 13.75699 13.20749 12.76514 13.13723 12.51462 12.52687 15.99091
[177] 12.73741 12.15268 14.14822 12.14047 12.10324 14.57259 12.54389 14.38011
[185] 13.65169 13.11801 12.67184 13.06168 12.41450 12.35398 15.86812 12.62985
[193] 14.04954 12.14713 12.16765 12.09231 14.49534 12.48366 14.34088 13.66790
[201] 13.08786 12.66659 13.27683 12.35610 12.52137 15.95148 12.59526 12.07555
[209] 13.89356 12.10372 12.18752 13.96404 12.87250 13.66585 13.91204 13.59847
[217] 14.09275 12.77197 13.75183 13.87376 13.23052 14.10358 13.10370 12.69996
[225] 12.76817 12.73733 14.27446 13.14433 14.23711 12.79450 13.95708 14.34712
[233] 12.70202 12.77778 13.87052 14.33720 12.89907 13.86083 14.30767 12.75083
[241] 12.67304 12.77951 13.85249 14.30436 12.75838 12.80493 13.73958 14.24565
[249] 13.96558 14.08240 12.73133 12.73465 13.94174 13.17638 13.41119 13.50866
[257] 12.93513 13.37294 13.37002 13.43097 13.07256 13.43012 13.54327 13.41019
[265] 12.93047 13.28372 13.05393 13.26347 13.22121 13.42907 13.53561 13.20093
[273] 13.20949 13.52287 13.20387 13.26088 12.97821 13.33787 13.25106 13.23607
[281] 13.27574 13.29687 13.23241 13.33359 13.27940 13.17318 13.21090 14.53387
[289] 14.38016 14.23534 14.11293 13.33759 14.15581 13.52039 13.22651 12.99038
[297] 14.43095 14.45174 14.57844 14.81908 12.97081 14.53392 14.40983 14.48179
[305] 14.50137 13.12669 13.09873 12.97701 12.46103 12.44697 12.75958 15.05218
[313] 12.82107 13.74815 12.81531 12.34669 13.04291 13.03335 12.93022 12.43540
[321] 12.81012 14.92715 12.74310 13.75915 12.97843 12.75252 12.72661 15.07122
[329] 13.27012 13.38321 12.69727 12.84636 12.79414 12.69983 15.09578 13.35852
[337] 13.52218 12.89977 12.82558 12.61110 12.62351 13.35317 13.36715 13.10056
[345] 12.54379 14.62010 13.16892 14.19504 13.00074 12.74490 12.97471 14.85880
[353] 13.64071 13.06192 13.01024 12.96566 12.60496 15.01442 12.70127 13.81118
[361] 12.67668 13.16150 12.98053 12.97480 12.63194 15.09341 12.67502 13.71537
[369] 12.78080 12.67236 12.88527 15.12061 13.70195 12.82783 13.17017 13.03914
[377] 12.97067 12.50725 12.54608 12.63228 15.12822 12.71104 13.78416 12.57617
[385] 14.69806 13.49748 12.85635 13.01942 12.84273 12.81002 14.96887 13.61448
[393] 12.75333 12.99274 14.78002 13.56103 12.80731 12.70387 14.05297 12.77630
[401] 12.54476 12.63508 13.58090 14.03574 13.31614 12.72100 14.09849 12.58166
[409] 12.82301 13.46814 13.81749 13.18347 13.75687 13.20879 12.74638 12.66817
[417] 12.83985 12.74979 12.52876 13.28985 12.66474 12.95075 12.65505 13.03587
[425] 12.81880 13.09409 13.29195 12.59463 12.43132 12.45145 12.63745 13.56000
[433] 12.90993 14.08523 12.89168 13.41894 13.64302 12.80479 14.27065 13.05163
[441] 13.31931 14.18448 12.82091 13.32533 13.92691 13.77491 13.41634 14.04764
[449] 12.90785 13.22013 13.39261 13.33653 13.16372 13.41348 13.39543 13.34264
[457] 13.15638 13.14864 13.60215 13.16208 13.20863 13.39093 13.28416 13.25148
[465] 13.32893 13.75418 13.41225 13.16330 14.03694 13.41789 13.27727 13.57513
[473] 13.26379 13.24750 13.57542 13.28028 13.21760 13.54215 13.21562 13.45739
[481] 13.86031 13.99475 13.34375 13.18537 13.57818 13.24341 13.64451 13.90896
[489] 13.88119 14.05812 13.68167 13.25100 13.82204 13.96463 12.77326 12.75877
[497] 13.09736 14.15922 13.19713 13.73971 12.96938 12.96419 13.80508 13.48263
[505] 12.81110 13.05579 13.93012 13.39541 12.82557 12.84640 12.78657 13.21891
[513] 13.91440 14.17967 12.77617 14.23837 13.13990 14.58014 12.66181 14.26229
[521] 12.80863 14.23853 14.13422 12.87339 14.13093 12.87640 12.46375 12.84024
[529] 13.56573 12.91152 12.64203 13.13123 13.90238 13.52740 12.73302 13.45269
[537] 12.56546 12.73088 12.83423 12.78898 12.90725 13.58786 13.55040 12.84461
[545] 13.03011 12.68491 12.95970 13.73151 13.57154 12.55138 12.75009 12.80540
[553] 12.98442 12.85010 13.44626 12.04487 13.15857 12.74707 13.25849 12.32756
[561] 12.17402 12.17870 12.54753 12.01931 12.63133 12.37233 13.51810 12.59064
[569] 12.12920 12.40788 11.98917 11.91766 13.02909 12.70105 13.77908 12.95868
[577] 12.96585 13.86736 13.07783 12.78356 12.93480 13.96422 13.01332 12.86018
[585] 12.51158 13.15844 12.68104 14.18402 13.05592 13.05292 12.91845 13.91173
[593] 12.93332 12.76103 12.87872 13.79037 12.82924 12.86392 11.47920 11.15733
[601] 11.84665 12.01136 11.73425 12.67033 12.21925 11.67459 12.16510 11.31839
[609] 11.10487 11.57724 11.83431 11.70744 11.53212 14.44004 12.00256 12.29684
[617] 12.13925 11.22695 14.30616 11.66068 11.94863 12.23435 11.14938 11.54125
[625] 11.47603 12.36018 11.76838 12.18467 13.28050 15.07423 12.09235 11.72359
[633] 11.13738 11.19642 12.96995 12.58410 14.00549 13.02735 12.84189 12.95607
[641] 15.10510 12.71372 12.98907 15.42804 12.83008 12.73086 12.75605 12.59800
[649] 12.89701 15.42621 12.65433 12.91835 12.73379 12.77775 15.58668 13.02239
[657] 12.70461 15.44671 13.29615 12.75125 12.68325 12.69370 13.14947 13.13577
[665] 14.68326 13.64058 13.06149 12.89521 15.32671 12.93190 12.76608 15.36194
[673] 12.72592 12.68480 15.43243 12.83505 12.86744 12.69829 12.95013 12.65111
[681] 12.68172 15.64413 13.07571 12.71460 12.78361 15.36485 12.70712 12.87529
[689] 12.65976 12.79354 12.59660 12.58607 15.61926 12.86068 12.98603 12.76096
[697] 12.88742 12.70152 12.58129 15.61388 12.88418 12.72959 15.30953 14.50324
[705] 13.39722 14.63156 13.50320 12.80491 14.59941 13.92907 12.60004 13.33696
[713] 13.31723 14.57841 13.13440 14.84258 14.14304 13.55828 13.30879 13.01013
[721] 14.54274 13.00069 14.90479 13.03781 12.80552 12.78873 14.94586 13.37004
[729] 14.74243 13.46149 12.97206 14.72213 13.29643 14.74185 13.24003 14.71440
[737] 13.62834 14.72497 13.65294 13.47975 13.63932 13.44648 13.79156 12.93153
[745] 13.40747 13.25361 13.06621 13.61251 13.78478 13.64966 13.61085 13.50645
[753] 13.66009 13.64869 13.79146 13.66843 13.64436 13.43762 13.68146 13.51086
[761] 13.78077 13.72042 13.05951 13.24135 13.67457 13.25070 13.11442 13.28153
[769] 12.79630 12.87965 13.36828 12.73500 13.32086 13.10154 12.85846 13.01603
[777] 13.16874 13.29886 13.27623 12.99475 13.00458 13.28883 13.01768 13.25958
[785] 13.24645 13.03788 13.16235 13.05799 13.05596 13.14119 13.32549 13.90132
[793] 13.47320 12.91376 12.99217
$group
[1] 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2
[19] 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3
[37] 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[55] 4 4 5 5 5 5 5 5 5 5 5 6 6 6 6 6 6 6
[73] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
[91] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 7 7 7 7
[109] 7 7 7 7 7 7 7 7 8 8 8 8 8 8 8 8 8 8
[127] 8 8 8 9 9 9 9 9 9 9 9 9 9 9 9 9 9 10
[145] 10 10 10 10 10 10 10 10 10 11 11 11 11 11 11 11 11 11
[163] 11 11 11 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12
[181] 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 14 14 14
[199] 14 14 14 14 14 14 14 14 14 14 14 14 14 15 15 15 16 16
[217] 17 17 17 18 18 19 19 19 19 19 21 21 22 22 22 23 23 23
[235] 23 24 24 24 25 25 25 25 25 26 26 26 26 27 27 28 28 28
[253] 28 29 29 29 29 30 30 30 31 31 31 32 32 33 33 35 36 36
[271] 37 37 37 38 38 39 39 39 40 40 40 41 41 41 42 42 42 43
[289] 44 45 46 47 47 47 47 47 49 50 51 52 53 53 54 55 56 57
[307] 57 57 57 57 57 57 57 57 57 57 58 58 58 58 58 58 58 58
[325] 58 59 59 59 59 59 59 59 60 60 60 60 60 60 60 60 61 61
[343] 61 61 61 61 61 61 62 62 62 62 62 63 63 63 63 63 63 63
[361] 63 64 64 64 64 64 64 64 64 65 65 65 65 65 66 66 66 66
[379] 66 66 66 66 66 66 67 67 67 68 68 68 68 68 68 69 69 69
[397] 69 70 70 70 70 70 70 71 71 72 72 72 72 72 73 73 74 74
[415] 74 74 75 75 75 75 75 75 75 75 75 75 75 75 75 75 76 76
[433] 76 77 77 77 78 79 79 79 79 80 80 80 81 82 82 83 83 83
[451] 84 84 84 85 86 86 86 87 88 88 89 89 91 91 92 92 92 93
[469] 93 93 94 94 94 95 95 95 96 96 96 97 98 98 102 102 102 106
[487] 107 107 108 108 109 109 110 112 112 112 113 113 113 114 114 115 115 115
[505] 115 116 116 116 116 116 116 117 118 119 120 120 121 121 121 122 123 123
[523] 124 124 125 125 126 126 126 126 126 126 127 127 128 128 128 128 128 128
[541] 129 129 129 129 129 129 130 130 130 130 130 130 130 130 131 131 131 131
[559] 131 131 131 131 131 131 131 131 131 131 131 131 131 131 132 132 132 132
[577] 133 133 133 133 134 134 134 134 135 135 135 135 135 135 136 136 136 136
[595] 137 137 137 137 138 138 138 138 138 138 138 138 138 138 138 138 138 138
[613] 138 138 138 138 138 138 138 138 138 138 138 138 138 138 138 138 138 138
[631] 138 138 138 138 139 139 139 139 139 140 140 140 141 141 142 142 142 142
[649] 142 142 142 143 143 143 143 143 144 144 144 144 144 144 145 145 145 145
[667] 146 146 146 146 147 147 148 148 148 148 149 149 149 149 149 149 149 150
[685] 150 150 150 151 151 151 151 151 151 151 152 152 152 152 152 152 152 153
[703] 153 154 154 155 155 156 156 156 156 156 156 157 157 158 158 158 159 160
[721] 160 161 161 161 162 162 162 162 163 163 164 164 164 165 165 166 166 167
[739] 167 168 168 170 170 171 171 172 172 175 175 176 176 177 177 178 178 179
[757] 179 180 180 181 181 182 182 182 183 184 184 184 185 185 185 185 186 186
[775] 186 188 189 189 190 190 190 191 191 192 192 192 193 193 193 194 194 194
[793] 195 195 195
$names
[1] "TUN_2_C1" "THA_2_C1" "DMSO_2_C1" "TUN_24_C1" "THA_24_C1"
[6] "DOX_24_C1" "NUTL_24_C1" "DMSO_24_C1" "LPS_24_C1" "TNFa_24_C1"
[11] "H2O_24_C1" "BPA_24_C1" "PFOA_24_C1" "EtOH_24_C1" "TUN_2_C2"
[16] "THA_2_C2" "DMSO_2_C2" "TUN_24_C2" "THA_24_C2" "DOX_24_C2"
[21] "NUTL_24_C2" "DMSO_24_C2" "LPS_24_C2" "TNFa_24_C2" "H2O_24_C2"
[26] "BPA_24_C2" "PFOA_24_C2" "EtOH_24_C2" "TUN_2_C3" "THA_2_C3"
[31] "DMSO_2_C3" "TUN_24_C3" "THA_24_C3" "DOX_24_C3" "NUTL_24_C3"
[36] "DMSO_24_C3" "LPS_24_C3" "TNFa_24_C3" "H2O_24_C3" "BPA_24_C3"
[41] "PFOA_24_C3" "EtOH_24_C3" "TUN_2_C4" "THA_2_C4" "DMSO_2_C4"
[46] "TUN_24_C4" "THA_24_C4" "DOX_24_C4" "NUTL_24_C4" "DMSO_24_C4"
[51] "LPS_24_C4" "TNFa_24_C4" "H2O_24_C4" "BPA_24_C4" "PFOA_24_C4"
[56] "EtOH_24_C4" "TUN_2_C5" "DMSO_2_C5" "TUN_24_C5" "THA_24_C5"
[61] "DOX_24_C5" "NUTL_24_C5" "DMSO_24_C5" "LPS_24_C5" "TNFa_24_C5"
[66] "H2O_24_C5" "BPA_24_C5" "PFOA_24_C5" "EtOH_24_C5" "TUN_2_C6"
[71] "THA_2_C6" "DMSO_2_C6" "TUN_24_C6" "THA_24_C6" "DOX_24_C6"
[76] "NUTL_24_C6" "DMSO_24_C6" "LPS_24_C6" "TNFa_24_C6" "H2O_24_C6"
[81] "BPA_24_C6" "PFOA_24_C6" "EtOH_24_C6" "TUN_2_C7" "THA_2_C7"
[86] "DMSO_2_C7" "TUN_24_C7" "THA_24_C7" "DOX_24_C7" "NUTL_24_C7"
[91] "DMSO_24_C7" "LPS_24_C7" "TNFa_24_C7" "H2O_24_C7" "BPA_24_C7"
[96] "PFOA_24_C7" "EtOH_24_C7" "TUN_2_H1" "THA_2_H1" "DMSO_2_H1"
[101] "TUN_24_H1" "THA_24_H1" "DOX_24_H1" "NUTL_24_H1" "DMSO_24_H1"
[106] "LPS_24_H1" "TNFa_24_H1" "H2O_24_H1" "BPA_24_H1" "PFOA_24_H1"
[111] "EtOH_24_H1" "TUN_2_H2" "THA_2_H2" "DMSO_2_H2" "TUN_24_H2"
[116] "THA_24_H2" "DOX_24_H2" "NUTL_24_H2" "DMSO_24_H2" "LPS_24_H2"
[121] "TNFa_24_H2" "H2O_24_H2" "BPA_24_H2" "PFOA_24_H2" "EtOH_24_H2"
[126] "TUN_2_H3" "THA_2_H3" "DMSO_2_H3" "TUN_24_H3" "THA_24_H3"
[131] "DOX_24_H3" "NUTL_24_H3" "DMSO_24_H3" "LPS_24_H3" "TNFa_24_H3"
[136] "H2O_24_H3" "BPA_24_H3" "PFOA_24_H3" "EtOH_24_H3" "TUN_2_H4"
[141] "THA_2_H4" "DMSO_2_H4" "TUN_24_H4" "THA_24_H4" "DOX_24_H4"
[146] "NUTL_24_H4" "DMSO_24_H4" "LPS_24_H4" "TNFa_24_H4" "H2O_24_H4"
[151] "BPA_24_H4" "PFOA_24_H4" "EtOH_24_H4" "TUN_2_H5" "THA_2_H5"
[156] "DMSO_2_H5" "TUN_24_H5" "THA_24_H5" "DOX_24_H5" "NUTL_24_H5"
[161] "DMSO_24_H5" "LPS_24_H5" "TNFa_24_H5" "H2O_24_H5" "BPA_24_H5"
[166] "PFOA_24_H5" "EtOH_24_H5" "TUN_2_H6" "THA_2_H6" "DMSO_2_H6"
[171] "TUN_24_H6" "THA_24_H6" "DOX_24_H6" "NUTL_24_H6" "DMSO_24_H6"
[176] "LPS_24_H6" "TNFa_24_H6" "H2O_24_H6" "BPA_24_H6" "PFOA_24_H6"
[181] "EtOH_24_H6" "TUN_2_H7" "THA_2_H7" "DMSO_2_H7" "TUN_24_H7"
[186] "THA_24_H7" "DOX_24_H7" "NUTL_24_H7" "DMSO_24_H7" "LPS_24_H7"
[191] "TNFa_24_H7" "H2O_24_H7" "BPA_24_H7" "PFOA_24_H7" "EtOH_24_H7"print(hc_cpm_filt_boxplot)$stats
[,1] [,2] [,3] [,4] [,5] [,6] [,7]
[1,] -1.179960 -1.179960 -1.179960 -1.179960 -1.179960 -1.179960 -1.179960
[2,] 2.284932 2.329662 2.256971 2.271724 2.441042 2.167198 2.260850
[3,] 4.276370 4.311965 4.262275 4.248265 4.459488 3.869561 4.347472
[4,] 5.803278 5.817919 5.772748 5.766676 5.927034 5.397727 5.862470
[5,] 11.079946 10.961200 11.045489 10.936161 11.143075 10.239360 11.207775
[,8] [,9] [,10] [,11] [,12] [,13] [,14]
[1,] -1.179960 -1.179960 -1.179960 -1.179960 -1.179960 -1.179960 -1.179960
[2,] 2.322169 2.307172 2.325848 2.324399 2.192475 2.255472 2.248956
[3,] 4.313182 4.276046 4.309443 4.296834 4.196605 4.273188 4.276257
[4,] 5.810313 5.764318 5.836357 5.791953 5.735113 5.796079 5.791106
[5,] 11.011033 10.935084 11.071187 10.977390 11.046747 11.082662 11.010721
[,15] [,16] [,17] [,18] [,19] [,20] [,21]
[1,] -1.179960 -1.179960 -1.179960 -1.179960 -1.179960 -1.179960 -1.179960
[2,] 2.488783 2.625227 2.536587 2.597465 2.469433 2.753700 2.475762
[3,] 4.525657 4.623930 4.545137 4.609697 4.538741 4.728639 4.571160
[4,] 6.015226 6.095958 6.011834 6.060728 6.012456 6.270984 6.022944
[5,] 11.216885 11.272834 11.199746 11.244433 11.317929 11.466270 11.322207
[,22] [,23] [,24] [,25] [,26] [,27] [,28]
[1,] -1.179960 -1.179960 -1.179960 -1.179960 -1.179960 -1.179960 -1.179960
[2,] 2.503999 2.523377 2.596631 2.544406 2.589697 2.507587 2.593298
[3,] 4.535870 4.515971 4.565101 4.509756 4.539898 4.529296 4.546853
[4,] 6.005892 5.981192 6.029597 5.985311 5.996203 5.996886 6.014032
[5,] 11.241336 11.096205 11.158004 11.131132 11.095677 11.228637 11.058997
[,29] [,30] [,31] [,32] [,33] [,34] [,35]
[1,] -1.179960 -1.179960 -1.179960 -1.179960 -1.179960 -1.179960 -1.179960
[2,] 2.503454 2.599553 2.545364 2.451467 2.503546 2.867136 2.569367
[3,] 4.529857 4.664663 4.556126 4.584676 4.608014 4.723417 4.645823
[4,] 6.018108 6.115675 6.033322 6.068955 6.084771 6.196107 6.108255
[5,] 11.237818 11.348428 11.258926 11.463928 11.357072 11.185659 11.383223
[,36] [,37] [,38] [,39] [,40] [,41] [,42]
[1,] -1.179960 -1.179960 -1.179960 -1.179960 -1.179960 -1.179960 -1.179960
[2,] 2.601503 2.529505 2.648650 2.507719 2.560823 2.576669 2.574583
[3,] 4.660005 4.575588 4.617888 4.587823 4.629886 4.612342 4.592523
[4,] 6.091050 6.042448 6.065011 6.051855 6.075515 6.049890 6.051033
[5,] 11.241495 11.229002 11.142029 11.367620 11.327235 11.243545 11.224755
[,43] [,44] [,45] [,46] [,47] [,48] [,49]
[1,] -1.179960 -1.179960 -1.179960 -1.179960 -1.179960 -1.179960 -1.179960
[2,] 2.527236 2.587752 2.521282 2.629202 2.612257 3.019552 2.531463
[3,] 4.646086 4.768771 4.672076 4.675663 4.633155 4.900349 4.661121
[4,] 6.102793 6.195096 6.121776 6.123405 6.071665 6.285603 6.107341
[5,] 11.434152 11.552393 11.496385 11.355649 11.168072 11.166340 11.431822
[,50] [,51] [,52] [,53] [,54] [,55] [,56]
[1,] -1.179960 -1.179960 -1.179960 -1.179960 -1.179960 -1.179960 -1.179960
[2,] 2.511613 2.532957 2.525386 2.503193 2.525643 2.548721 2.528147
[3,] 4.639593 4.632053 4.622808 4.621816 4.644391 4.642622 4.665133
[4,] 6.105114 6.086655 6.089196 6.085670 6.112592 6.096982 6.105160
[5,] 11.368947 11.325320 11.369060 11.441014 11.454367 11.337871 11.437900
[,57] [,58] [,59] [,60] [,61] [,62] [,63]
[1,] -1.179960 -1.179960 -1.179960 -1.179960 -1.179960 -1.179960 -1.179960
[2,] 2.321819 2.393940 2.536205 2.437250 2.550644 2.434663 2.470258
[3,] 4.362728 4.399429 4.504801 4.450943 4.397790 4.515756 4.469741
[4,] 5.904211 5.928938 5.970188 5.946466 5.910823 6.030212 5.953526
[5,] 11.275650 11.104133 11.099633 11.201403 10.897027 11.286893 11.169426
[,64] [,65] [,66] [,67] [,68] [,69] [,70]
[1,] -1.179960 -1.179960 -1.179960 -1.179960 -1.179960 -1.179960 -1.179960
[2,] 2.462002 2.472286 2.436594 2.541219 2.476456 2.515694 2.399509
[3,] 4.468212 4.503068 4.426287 4.574178 4.508874 4.542873 4.456579
[4,] 5.965302 5.996365 5.937307 6.045032 6.019176 6.034058 5.950567
[5,] 11.215255 11.145886 11.141853 11.276337 11.317913 11.267869 11.224008
[,71] [,72] [,73] [,74] [,75] [,76] [,77]
[1,] -1.179960 -1.179960 -1.179960 -1.179960 -1.179960 -1.179960 -1.179960
[2,] 2.565568 2.417176 2.540837 2.492882 2.596132 2.417877 2.496754
[3,] 4.654730 4.453353 4.552056 4.512869 4.385472 4.520308 4.553498
[4,] 6.080975 5.960539 6.017054 6.007450 5.910192 6.043438 5.999765
[5,] 11.250366 11.227686 11.231180 11.240833 10.878150 11.468775 11.241518
[,78] [,79] [,80] [,81] [,82] [,83] [,84]
[1,] -1.179960 -1.179960 -1.179960 -1.179960 -1.179960 -1.179960 -1.179960
[2,] 2.558519 2.500214 2.519479 2.542429 2.589237 2.574788 2.678461
[3,] 4.624223 4.485003 4.502674 4.610351 4.549441 4.574779 4.687783
[4,] 6.080100 5.963134 5.986884 6.066330 6.020258 6.029343 6.118572
[5,] 11.355470 11.141957 11.180626 11.324796 11.129388 11.142381 11.267257
[,85] [,86] [,87] [,88] [,89] [,90] [,91]
[1,] -1.179960 -1.179960 -1.179960 -1.179960 -1.179960 -1.179960 -1.179960
[2,] 2.695992 2.666408 2.676076 2.528201 2.814330 2.654357 2.756251
[3,] 4.728859 4.694175 4.705259 4.609611 4.699748 4.709205 4.759308
[4,] 6.182502 6.126183 6.154278 6.101607 6.230078 6.181394 6.171475
[5,] 11.395063 11.311152 11.354667 11.352510 11.266759 11.414247 11.289423
[,92] [,93] [,94] [,95] [,96] [,97] [,98]
[1,] -1.179960 -1.179960 -1.179960 -1.179960 -1.179960 -1.179960 -1.179960
[2,] 2.672350 2.676737 2.710159 2.705628 2.692819 2.753254 2.530965
[3,] 4.669367 4.691513 4.715651 4.702830 4.708487 4.727749 4.574577
[4,] 6.136880 6.135177 6.142024 6.140133 6.144168 6.152076 6.033793
[5,] 11.222843 11.298542 11.267530 11.249902 11.300841 11.241922 11.273264
[,99] [,100] [,101] [,102] [,103] [,104] [,105]
[1,] -1.179960 -1.179960 -1.179960 -1.179960 -1.179960 -1.179960 -1.179960
[2,] 2.691611 2.604570 2.667669 2.389245 2.796236 2.703502 2.716453
[3,] 4.800183 4.685236 4.670817 4.492476 4.767736 4.776264 4.722286
[4,] 6.205231 6.160559 6.145717 6.063639 6.289833 6.210222 6.165258
[5,] 11.218333 11.438325 11.316937 11.566378 11.481140 11.401212 11.316855
[,106] [,107] [,108] [,109] [,110] [,111] [,112]
[1,] -1.179960 -1.179960 -1.179960 -1.179960 -1.179960 -1.179960 -1.179960
[2,] 2.718473 2.627523 2.566220 2.608984 2.613112 2.679008 2.541692
[3,] 4.703855 4.597783 4.593011 4.626196 4.635281 4.693911 4.520121
[4,] 6.165851 6.114797 6.102050 6.107693 6.087377 6.153802 6.006383
[5,] 11.322722 11.332812 11.383653 11.282844 11.214617 11.341216 11.201528
[,113] [,114] [,115] [,116] [,117] [,118] [,119]
[1,] -1.179960 -1.179960 -1.179960 -1.179960 -1.179960 -1.179960 -1.179960
[2,] 2.521474 2.512379 2.566285 2.526021 2.850786 2.483573 2.592612
[3,] 4.567830 4.560960 4.562679 4.509622 4.664734 4.556677 4.553014
[4,] 6.062004 6.032425 6.011949 5.957342 6.185319 6.067270 6.021391
[5,] 11.350367 11.290539 11.067584 11.097270 11.113490 11.378755 11.152576
[,120] [,121] [,122] [,123] [,124] [,125] [,126]
[1,] -1.179960 -1.179960 -1.179960 -1.179960 -1.179960 -1.179960 -1.179960
[2,] 2.522997 2.511088 2.560189 2.544059 2.595133 2.554289 2.377030
[3,] 4.519185 4.496562 4.534605 4.534619 4.554096 4.533485 4.419477
[4,] 6.008332 5.989842 6.014995 5.999106 6.023904 6.008893 5.939462
[5,] 11.209052 11.204729 11.192378 11.170073 11.103392 11.167469 11.262321
[,127] [,128] [,129] [,130] [,131] [,132] [,133]
[1,] -1.179960 -1.179960 -1.179960 -1.179960 -1.179960 -1.179960 -1.179960
[2,] 2.559007 2.425842 2.394742 2.428143 2.469850 2.432336 2.511282
[3,] 4.569775 4.412140 4.467277 4.459465 4.214852 4.454089 4.478337
[4,] 6.050948 5.940626 5.893735 5.916542 5.731342 5.977616 5.952840
[5,] 11.280717 11.192588 11.114836 11.068810 10.600254 11.287515 11.105734
[,134] [,135] [,136] [,137] [,138] [,139] [,140]
[1,] -1.179960 -1.179960 -1.179960 -1.179960 -1.179960 -1.179960 -1.179960
[2,] 2.499038 2.402355 2.494667 2.524255 -1.179960 2.482901 2.566582
[3,] 4.469681 4.418873 4.455958 4.488369 3.699135 4.450257 4.510571
[4,] 5.952791 5.920769 5.945654 5.975298 5.830416 5.935405 6.002810
[5,] 11.121938 11.189129 11.118434 11.129172 15.074229 11.103710 11.114889
[,141] [,142] [,143] [,144] [,145] [,146] [,147]
[1,] -1.179960 -1.179960 -1.179960 -1.179960 -1.179960 -1.179960 -1.179960
[2,] 2.560049 2.458769 2.505661 2.610389 2.822395 2.498555 2.530287
[3,] 4.550540 4.466654 4.455105 4.507703 4.569148 4.524645 4.526928
[4,] 6.038205 5.979592 5.950684 5.969733 6.049873 6.051456 5.993339
[5,] 11.204885 11.133772 11.066168 10.978333 10.736879 11.353621 11.162336
[,148] [,149] [,150] [,151] [,152] [,153] [,154]
[1,] -1.179960 -1.179960 -1.179960 -1.179960 -1.179960 -1.179960 -1.179960
[2,] 2.530899 2.493151 2.553470 2.499473 2.519510 2.578403 2.630207
[3,] 4.522269 4.434865 4.516114 4.440430 4.465488 4.521185 4.582750
[4,] 5.977602 5.945439 5.980646 5.950828 5.954823 5.992260 6.084716
[5,] 11.125847 11.123152 11.114010 11.116279 11.075279 11.083573 11.258786
[,155] [,156] [,157] [,158] [,159] [,160] [,161]
[1,] -1.179960 -1.179960 -1.179960 -1.179960 -1.179960 -1.179960 -1.179960
[2,] 2.587767 2.548147 2.633596 2.683999 2.678466 2.621912 2.713087
[3,] 4.537855 4.441832 4.569817 4.526777 4.629924 4.624047 4.590326
[4,] 5.991264 5.968717 6.057581 6.006325 6.183511 6.117298 6.030588
[5,] 11.089913 11.074814 11.121848 10.985384 11.412826 11.290243 11.005900
[,162] [,163] [,164] [,165] [,166] [,167] [,168]
[1,] -1.179960 -1.179960 -1.179960 -1.179960 -1.179960 -1.179960 -1.179960
[2,] 2.679549 2.665387 2.703603 2.692921 2.630225 2.605506 2.520548
[3,] 4.547475 4.602167 4.604553 4.617108 4.565621 4.556629 4.492538
[4,] 5.994674 6.056380 6.051640 6.032722 6.035766 6.027394 6.007464
[5,] 10.902639 11.020600 10.974698 11.034172 11.053095 11.148878 11.231493
[,169] [,170] [,171] [,172] [,173] [,174] [,175]
[1,] -1.179960 -1.179960 -1.179960 -1.179960 -1.179960 -1.179960 -1.179960
[2,] 2.716512 2.518474 2.636523 2.664588 3.018897 2.706783 2.566637
[3,] 4.763252 4.552219 4.593759 4.601908 4.769019 4.766881 4.575866
[4,] 6.168691 6.025613 6.039591 6.067209 6.234467 6.187451 6.054134
[5,] 11.324207 11.273814 11.131530 11.148709 10.987011 11.370246 11.237194
[,176] [,177] [,178] [,179] [,180] [,181] [,182]
[1,] -1.179960 -1.179960 -1.179960 -1.179960 -1.179960 -1.179960 -1.179960
[2,] 2.656968 2.622114 2.610576 2.591600 2.608267 2.641723 2.533671
[3,] 4.613048 4.593871 4.581666 4.585022 4.592595 4.618643 4.506471
[4,] 6.062989 6.050315 6.056280 6.060661 6.071611 6.071473 5.953164
[5,] 11.118151 11.159193 11.182894 11.258802 11.246227 11.179169 11.071233
[,183] [,184] [,185] [,186] [,187] [,188] [,189]
[1,] -1.179960 -1.179960 -1.179960 -1.179960 -1.179960 -1.179960 -1.179960
[2,] 2.733708 2.605899 2.559043 2.564943 2.899744 2.565325 2.733242
[3,] 4.717950 4.539320 4.531124 4.562770 4.748960 4.591215 4.637252
[4,] 6.097938 6.005511 6.019737 6.026501 6.261953 6.085858 6.053168
[5,] 11.078412 11.092307 11.163591 11.121656 11.304701 11.343119 11.007874
[,190] [,191] [,192] [,193] [,194] [,195]
[1,] -1.179960 -1.179960 -1.179960 -1.179960 -1.179960 -1.179960
[2,] 2.703002 2.706612 2.726232 2.727654 2.556804 2.699442
[3,] 4.603920 4.615341 4.618047 4.618985 4.520686 4.596472
[4,] 6.031051 6.062631 6.028747 6.049938 5.994900 6.046699
[5,] 11.017509 11.057873 10.970414 10.998184 11.118310 11.051968
$n
[1] 13739 13739 13739 13739 13739 13739 13739 13739 13739 13739 13739 13739
[13] 13739 13739 13739 13739 13739 13739 13739 13739 13739 13739 13739 13739
[25] 13739 13739 13739 13739 13739 13739 13739 13739 13739 13739 13739 13739
[37] 13739 13739 13739 13739 13739 13739 13739 13739 13739 13739 13739 13739
[49] 13739 13739 13739 13739 13739 13739 13739 13739 13739 13739 13739 13739
[61] 13739 13739 13739 13739 13739 13739 13739 13739 13739 13739 13739 13739
[73] 13739 13739 13739 13739 13739 13739 13739 13739 13739 13739 13739 13739
[85] 13739 13739 13739 13739 13739 13739 13739 13739 13739 13739 13739 13739
[97] 13739 13739 13739 13739 13739 13739 13739 13739 13739 13739 13739 13739
[109] 13739 13739 13739 13739 13739 13739 13739 13739 13739 13739 13739 13739
[121] 13739 13739 13739 13739 13739 13739 13739 13739 13739 13739 13739 13739
[133] 13739 13739 13739 13739 13739 13739 13739 13739 13739 13739 13739 13739
[145] 13739 13739 13739 13739 13739 13739 13739 13739 13739 13739 13739 13739
[157] 13739 13739 13739 13739 13739 13739 13739 13739 13739 13739 13739 13739
[169] 13739 13739 13739 13739 13739 13739 13739 13739 13739 13739 13739 13739
[181] 13739 13739 13739 13739 13739 13739 13739 13739 13739 13739 13739 13739
[193] 13739 13739 13739
$conf
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
[1,] 4.228944 4.264944 4.214884 4.201154 4.412498 3.826014 4.298924 4.266163
[2,] 4.323796 4.358985 4.309667 4.295375 4.506478 3.913107 4.396021 4.360201
[,9] [,10] [,11] [,12] [,13] [,14] [,15] [,16]
[1,] 4.229445 4.262123 4.250092 4.148852 4.225461 4.228510 4.478122 4.577146
[2,] 4.322647 4.356764 4.343575 4.244359 4.320914 4.324004 4.573193 4.670715
[,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24]
[1,] 4.498291 4.563014 4.490982 4.681227 4.523345 4.488665 4.469360 4.518826
[2,] 4.591982 4.656381 4.586500 4.776051 4.618975 4.583074 4.562581 4.611376
[,25] [,26] [,27] [,28] [,29] [,30] [,31] [,32]
[1,] 4.463374 4.493979 4.482262 4.500742 4.482481 4.617267 4.509110 4.535914
[2,] 4.556138 4.585817 4.576331 4.592963 4.577233 4.712060 4.603143 4.633439
[,33] [,34] [,35] [,36] [,37] [,38] [,39] [,40]
[1,] 4.559740 4.678543 4.598120 4.612967 4.528235 4.571837 4.540049 4.582509
[2,] 4.656288 4.768290 4.693526 4.707043 4.622941 4.663940 4.635597 4.677263
[,41] [,42] [,43] [,44] [,45] [,46] [,47] [,48]
[1,] 4.565524 4.545661 4.597888 4.720145 4.623542 4.628562 4.586523 4.856324
[2,] 4.659160 4.639384 4.694283 4.817397 4.720609 4.722764 4.679787 4.944375
[,49] [,50] [,51] [,52] [,53] [,54] [,55] [,56]
[1,] 4.612920 4.591153 4.584150 4.574769 4.573525 4.596040 4.594793 4.616916
[2,] 4.709323 4.688032 4.679956 4.670847 4.670107 4.692742 4.690452 4.713350
[,57] [,58] [,59] [,60] [,61] [,62] [,63] [,64]
[1,] 4.314438 4.351779 4.458512 4.403639 4.352496 4.467289 4.422788 4.420988
[2,] 4.411017 4.447080 4.551090 4.498246 4.443085 4.564223 4.516694 4.515435
[,65] [,66] [,67] [,68] [,69] [,70] [,71] [,72]
[1,] 4.455564 4.379099 4.526947 4.461119 4.495446 4.408712 4.607344 4.405590
[2,] 4.550571 4.473476 4.621408 4.556628 4.590299 4.504446 4.702117 4.501117
[,73] [,74] [,75] [,76] [,77] [,78] [,79] [,80]
[1,] 4.505197 4.465494 4.340799 4.471437 4.506278 4.576753 4.438324 4.455934
[2,] 4.598914 4.560245 4.430144 4.569180 4.600717 4.671693 4.531682 4.549413
[,81] [,82] [,83] [,84] [,85] [,86] [,87] [,88]
[1,] 4.562850 4.503192 4.528213 4.641412 4.681862 4.647538 4.658374 4.561443
[2,] 4.657852 4.595690 4.621346 4.734155 4.775856 4.740812 4.752144 4.657779
[,89] [,90] [,91] [,92] [,93] [,94] [,95] [,96]
[1,] 4.653705 4.661662 4.713272 4.622666 4.644894 4.669391 4.656534 4.661964
[2,] 4.745791 4.756748 4.805344 4.716068 4.738132 4.761912 4.749126 4.755010
[,97] [,98] [,99] [,100] [,101] [,102] [,103] [,104]
[1,] 4.681934 4.527360 4.752821 4.637302 4.623934 4.442947 4.720643 4.728994
[2,] 4.773564 4.621794 4.847546 4.733170 4.717700 4.542006 4.814829 4.823533
[,105] [,106] [,107] [,108] [,109] [,110] [,111] [,112]
[1,] 4.675798 4.657386 4.550775 4.545349 4.579034 4.588449 4.647072 4.473418
[2,] 4.768775 4.750325 4.644790 4.640673 4.673357 4.682113 4.740750 4.566823
[,113] [,114] [,115] [,116] [,117] [,118] [,119] [,120]
[1,] 4.520105 4.513511 4.516233 4.463369 4.619785 4.508370 4.506795 4.472204
[2,] 4.615555 4.608410 4.609126 4.555875 4.709682 4.604984 4.599233 4.566166
[,121] [,122] [,123] [,124] [,125] [,126] [,127] [,128]
[1,] 4.449669 4.488035 4.488046 4.507877 4.486918 4.371457 4.522704 4.364762
[2,] 4.543454 4.581175 4.581192 4.600315 4.580052 4.467498 4.616845 4.459518
[,129] [,130] [,131] [,132] [,133] [,134] [,135] [,136]
[1,] 4.420112 4.412443 4.170888 4.406300 4.431946 4.423126 4.371446 4.409440
[2,] 4.514443 4.506488 4.258816 4.501878 4.524728 4.516237 4.466300 4.502476
[,137] [,138] [,139] [,140] [,141] [,142] [,143] [,144]
[1,] 4.441850 3.604637 4.403718 4.464251 4.503656 4.419195 4.408667 4.462420
[2,] 4.534888 3.793632 4.496795 4.556890 4.597425 4.514114 4.501543 4.552986
[,145] [,146] [,147] [,148] [,149] [,150] [,151] [,152]
[1,] 4.525643 4.476753 4.480247 4.475809 4.388330 4.469916 4.393906 4.419181
[2,] 4.612654 4.572537 4.573609 4.568729 4.481401 4.562311 4.486953 4.511794
[,153] [,154] [,155] [,156] [,157] [,158] [,159] [,160]
[1,] 4.475168 4.536184 4.491977 4.395724 4.523663 4.481993 4.582677 4.576930
[2,] 4.567203 4.629315 4.583733 4.487940 4.615971 4.571560 4.677171 4.671163
[,161] [,162] [,163] [,164] [,165] [,166] [,167] [,168]
[1,] 4.545607 4.502788 4.556457 4.559423 4.572088 4.519715 4.510503 4.445536
[2,] 4.635044 4.592162 4.647876 4.649684 4.662127 4.611526 4.602755 4.539541
[,169] [,170] [,171] [,172] [,173] [,174] [,175] [,176]
[1,] 4.716717 4.504943 4.547886 4.556041 4.725674 4.719963 4.528856 4.567136
[2,] 4.809786 4.599494 4.639631 4.647774 4.812364 4.813799 4.622876 4.658960
[,177] [,178] [,179] [,180] [,181] [,182] [,183] [,184]
[1,] 4.547660 4.535219 4.538261 4.54591 4.572411 4.460378 4.672601 4.493494
[2,] 4.640082 4.628113 4.631784 4.63928 4.664875 4.552565 4.763298 4.585146
[,185] [,186] [,187] [,188] [,189] [,190] [,191] [,192]
[1,] 4.484475 4.516109 4.703639 4.543759 4.592501 4.559058 4.570103 4.573530
[2,] 4.577773 4.609431 4.794282 4.638671 4.682004 4.648781 4.660579 4.662564
[,193] [,194] [,195]
[1,] 4.574202 4.474342 4.551352
[2,] 4.663768 4.567031 4.641592
$out
[1] 12.20868 14.51922 12.46102 14.50585 13.53455 13.02746 12.61123 11.64457
[9] 12.66063 12.92284 11.27786 11.95892 15.59563 11.08704 11.11286 11.67369
[17] 11.09730 12.58899 11.19388 11.89137 11.14299 11.59545 11.57483 14.22168
[25] 11.21604 11.86028 12.18672 12.06081 11.83210 11.34677 11.95463 14.64884
[33] 12.49714 14.50392 13.56375 13.10048 12.77171 11.67444 12.81256 12.97575
[41] 11.18846 12.36669 15.80694 11.21514 11.49597 12.22122 11.29095 12.07054
[49] 11.36364 11.33274 11.27062 13.97455 11.16017 11.61483 11.18579 12.09254
[57] 11.63986 11.78001 11.11588 12.08251 14.52691 12.47447 14.44967 13.59624
[65] 13.06311 12.65746 11.66642 12.89565 12.80291 11.25308 11.13290 12.27291
[73] 15.81248 11.66835 11.14133 11.05739 12.51967 11.35874 12.10658 11.41795
[81] 11.51776 14.04791 11.15669 11.76837 12.27165 11.89538 11.89568 11.18395
[89] 11.14624 12.10756 14.58482 12.51630 14.41901 13.73574 13.22077 12.77295
[97] 11.75307 13.14660 12.40538 11.28920 12.47943 15.90213 11.07558 11.01298
[105] 11.63487 11.21279 11.24273 12.61984 11.47882 12.17966 11.38862 11.27708
[113] 13.98108 11.11574 11.36158 12.06439 11.83626 11.13643 12.21045 11.27040
[121] 11.24848 12.55529 11.79132 13.55874 12.20811 13.60982 12.81469 12.70185
[129] 12.24162 11.78033 12.18340 12.27229 11.21783 12.11272 15.63622 13.00488
[137] 11.42582 12.12676 11.44825 11.48351 11.25554 11.36572 13.66329 11.26987
[145] 12.66111 11.19641 11.24371 12.47313 11.54218 12.09667 10.90446 10.87955
[153] 10.80998 10.63529 12.99587 14.92055 12.85746 12.24716 15.09424 14.16820
[161] 13.59455 12.64282 12.21661 10.60184 12.29184 11.14134 10.91100 11.05897
[169] 10.26926 10.56026 10.64754 12.15920 10.61065 15.85878 10.69290 11.08045
[177] 10.83082 11.61315 10.52044 10.86866 12.12944 11.56257 12.14694 12.33324
[185] 10.64700 11.17484 11.30316 13.21172 10.83722 11.09828 11.47547 10.33723
[193] 10.42634 10.67657 10.81744 11.29609 11.75405 10.87893 10.43649 10.53094
[201] 11.38879 10.52640 10.45326 10.24443 11.01969 10.93074 10.69783 11.11194
[209] 12.17962 11.76198 10.24779 11.69993 11.92224 11.10479 10.42187 10.46323
[217] 14.54368 11.70790 11.81151 10.42485 10.48561 11.23340 12.11812 10.93403
[225] 10.27068 11.23073 10.25379 11.49949 11.63371 10.91600 12.03138 11.57210
[233] 10.90186 10.39977 11.97116 11.89851 14.62444 12.27700 14.21884 13.82685
[241] 13.34233 12.80533 11.66611 12.92779 12.34420 12.29335 15.66089 11.85303
[249] 12.55026 11.93338 11.77402 13.84754 11.87280 11.33828 11.26678 11.69713
[257] 12.09448 14.65209 12.50900 14.37952 13.71459 13.15931 12.72985 11.73056
[265] 13.09179 12.31468 11.32435 12.43040 15.82912 11.51445 11.16326 11.20301
[273] 12.51895 11.41336 12.19647 11.35150 11.32151 13.91965 11.11833 11.56709
[281] 12.05361 11.76330 11.14120 12.07752 11.14717 12.06707 14.76074 12.63771
[289] 14.52795 13.74953 13.31611 12.92095 11.68333 12.82531 12.19525 11.44531
[297] 10.96298 11.09609 12.30254 15.98183 11.59535 10.96857 11.65416 11.03157
[305] 12.38427 11.41448 12.22125 11.51612 11.43464 14.05185 11.26669 11.45434
[313] 12.17918 11.70585 11.20335 11.90871 11.09549 12.07606 14.35858 12.33186
[321] 14.20542 13.56157 13.00907 12.49921 11.46916 12.69778 11.87361 11.63339
[329] 11.14970 12.13086 16.03356 11.66705 11.50736 11.34726 11.35986 12.49702
[337] 11.12664 12.03879 11.28009 13.90235 11.74729 12.12284 11.64109 11.17947
[345] 11.93372 11.31458 12.10605 14.66367 12.57746 14.43825 13.72239 13.23396
[353] 12.77225 11.70931 13.09418 12.38479 11.32837 11.04756 11.04261 12.37224
[361] 15.85135 11.16847 11.07161 11.60259 11.07193 11.02036 12.49874 11.34515
[369] 11.99320 11.58087 11.41638 13.99575 11.17713 11.74622 12.13997 11.84025
[377] 11.08778 11.99476 11.20233 11.18569 12.18891 14.57092 12.47650 14.35200
[385] 13.75699 13.20749 12.76514 11.79721 13.13723 12.51462 11.20748 12.52687
[393] 15.99091 11.05971 11.71017 11.34914 11.34189 12.73741 11.53520 12.15268
[401] 11.20654 11.28653 14.14822 11.15619 11.34724 11.90302 11.68845 11.19832
[409] 12.14047 11.34905 12.10324 14.57259 12.54389 14.38011 13.65169 13.11801
[417] 12.67184 11.73588 13.06168 12.41450 11.37976 12.35398 15.86812 11.12639
[425] 11.54671 11.20709 11.21784 12.62985 11.36115 12.04063 11.47095 11.40109
[433] 14.04954 11.66757 12.14713 11.77943 12.16765 11.32720 11.13583 12.09231
[441] 14.49534 12.48366 14.34088 13.66790 13.08786 12.66659 11.72582 13.27683
[449] 12.35610 11.27669 12.52137 15.95148 11.56640 11.25218 11.26322 12.59526
[457] 11.45501 12.07555 11.41019 11.27462 13.89356 11.54121 12.10372 11.76676
[465] 11.11086 12.18752 11.28127 12.05922 12.17227 12.53207 12.28774 11.90341
[473] 11.67785 12.22169 11.41526 11.42700 13.96404 11.63466 11.53178 11.80434
[481] 11.35147 12.36392 12.50834 11.61889 11.56080 12.87250 12.59347 12.52649
[489] 12.47803 13.66585 12.02843 12.29370 12.04989 11.67947 12.19087 12.06089
[497] 11.94724 11.63351 11.58383 12.72609 11.30660 13.91204 11.32999 11.62140
[505] 11.39378 11.34998 12.39799 12.37129 12.62386 12.02339 12.39534 12.04204
[513] 13.59847 11.97214 12.16550 12.07175 12.27240 12.36867 12.58643 12.32419
[521] 11.98874 11.69283 12.11179 11.40152 11.55533 14.09275 11.53924 11.55755
[529] 11.76884 11.48041 12.46912 12.62082 11.71276 11.77105 12.77197 12.55744
[537] 12.60036 12.54655 13.75183 12.25920 12.44650 12.11116 12.30860 11.54593
[545] 11.43695 11.69919 11.64898 11.61454 11.68328 11.87200 13.87376 13.23052
[553] 11.35124 11.53109 11.45161 11.30444 12.57211 11.91703 11.98868 12.21651
[561] 12.44214 11.96006 11.91475 12.49287 11.81819 12.80387 12.48628 11.44924
[569] 11.45258 12.20060 11.79078 11.95575 11.60535 11.88404 12.55706 12.26098
[577] 14.10358 13.10370 11.51157 11.90296 11.69399 12.25189 11.99183 11.51130
[585] 12.36479 12.14879 11.62202 11.60162 12.69996 11.77319 12.76817 11.75349
[593] 12.73733 11.74720 12.22112 11.70312 12.74506 11.65996 11.82468 12.57679
[601] 11.75055 12.29449 12.17690 12.24198 12.63317 12.20020 12.41618 11.61015
[609] 14.27446 12.00041 11.99609 11.84071 12.57671 11.56114 12.49708 12.63730
[617] 12.57651 12.73193 13.14433 12.27937 12.03388 11.93493 12.04318 12.25068
[625] 12.38662 12.51331 12.08043 12.05245 11.52383 11.42066 14.23711 11.62180
[633] 11.63574 11.85756 11.55678 11.34071 12.79450 12.60961 11.69794 11.61496
[641] 12.64032 12.66470 12.61383 12.67789 13.95708 12.28526 12.46807 12.30053
[649] 11.27447 11.19720 12.06170 11.21737 12.29777 12.35670 12.55655 12.11549
[657] 12.09741 11.63377 11.53588 11.32630 14.34712 11.50914 11.57839 12.00135
[665] 11.24961 12.53325 12.45246 11.68847 11.43716 12.58702 12.70202 12.58668
[673] 12.77778 13.87052 12.15189 12.47110 12.06374 11.21122 11.95838 12.18476
[681] 12.20391 12.66662 12.26357 11.69975 11.60115 11.26033 11.32797 11.56625
[689] 14.33720 11.68520 11.49970 11.87120 11.39302 12.29104 12.10454 11.71220
[697] 11.53089 12.17338 12.22555 12.65776 12.89907 13.86083 12.28055 12.53520
[705] 11.89283 11.32163 12.10516 12.36415 12.50783 12.58902 12.15613 12.23785
[713] 11.66597 11.36217 14.30767 11.52677 11.62973 11.97409 11.43996 11.23898
[721] 12.55402 12.55038 11.77051 11.60139 12.65673 12.75083 12.67304 12.77951
[729] 13.85249 12.27301 12.47626 12.15426 11.21472 11.24785 11.18794 11.12136
[737] 12.27172 11.15720 12.45319 12.41358 12.52676 12.14733 12.19024 11.67859
[745] 11.35465 14.30436 11.42005 11.69634 11.94167 11.23484 11.21158 12.55943
[753] 12.41505 11.84852 11.53028 12.67133 12.74192 12.75838 12.80493 13.73958
[761] 12.13005 12.41210 11.96336 11.14465 11.17613 11.20958 12.02509 12.16745
[769] 12.32102 12.38582 12.01805 12.15144 11.63743 11.57465 14.24565 11.65204
[777] 11.61216 11.92169 11.29763 12.53898 12.44914 11.52637 11.51313 12.55106
[785] 12.64460 12.72072 12.70215 13.96558 12.27394 12.59731 12.09500 11.25491
[793] 11.27498 11.89169 12.04038 12.34974 12.41488 11.85382 12.05932 11.64367
[801] 11.31797 11.57233 14.08240 11.64984 11.57762 11.95932 11.69264 11.21243
[809] 12.37358 12.41974 11.87482 11.77841 12.48567 12.73133 12.69510 12.73465
[817] 13.94174 12.43162 12.56591 12.10161 11.25366 11.20629 11.26223 11.57482
[825] 11.99940 11.78486 11.56643 11.56299 11.86785 11.64414 13.17638 11.37777
[833] 11.76374 11.77023 11.80709 13.41119 11.65598 11.82066 12.25469 12.08383
[841] 12.00483 11.73272 13.50866 12.13264 12.19274 12.93513 11.39481 11.66796
[849] 11.40425 11.54030 11.95389 11.74723 11.44730 11.62164 11.50326 11.42067
[857] 13.37294 11.81150 13.37002 11.46474 11.80914 11.38987 12.15572 11.52052
[865] 13.43097 12.08275 12.09069 12.41301 11.72470 11.51150 11.89994 11.80329
[873] 11.49586 11.44309 11.81126 11.61095 13.07256 11.49436 11.76913 11.61729
[881] 11.88023 13.43012 11.65023 11.92128 12.27066 12.08344 12.09910 11.66788
[889] 13.54327 12.32334 12.23063 12.63450 11.43686 11.54343 11.44044 12.76727
[897] 12.71138 12.75091 13.41019 11.66743 11.54283 12.23829 11.60077 11.71852
[905] 11.56303 12.66992 11.87370 12.63841 12.93047 11.83962 11.67042 12.62245
[913] 11.68050 12.55977 12.80648 13.28372 11.79923 11.76061 11.94865 11.62475
[921] 11.52722 11.58291 12.66556 11.53367 12.73541 11.85410 13.05393 11.57043
[929] 11.80432 11.82656 11.83471 12.55131 11.24391 11.98800 11.25119 12.30482
[937] 11.28683 11.84380 12.42213 11.63152 11.43931 11.54829 12.22780 12.32636
[945] 11.82799 11.47467 11.87794 11.75999 11.55199 12.31688 12.10052 11.69056
[953] 12.00393 13.26347 11.52073 11.91846 11.72754 11.45072 11.55186 11.95248
[961] 12.25653 11.96688 12.04943 12.14728 12.42811 12.20462 11.57593 11.66626
[969] 12.09086 11.39087 12.00489 11.82722 11.36520 11.70608 12.92041 11.78999
[977] 11.77209 11.88684 13.22121 11.73339 11.78951 12.17195 12.10781 12.32483
[985] 11.87928 13.42907 12.49913 12.22713 12.26175 11.44901 11.67019 11.31790
[993] 11.54341 12.31208 12.07016 11.76344 12.07026 11.31371 13.53561 11.95700
[1001] 11.32258 11.69548 13.20093 11.42250 11.61111 12.30322 12.12448 12.08430
[1009] 12.00656 13.20949 12.28055 12.11083 12.37980 11.57079 11.35100 11.58296
[1017] 11.31719 11.53985 12.43491 12.14822 11.48782 12.02439 11.21830 13.52287
[1025] 11.84793 11.50015 11.64577 12.87074 11.27878 11.70754 11.93300 11.96720
[1033] 12.22289 12.07382 13.20387 12.10027 12.10843 12.24511 11.53509 11.42108
[1041] 11.71456 12.08959 11.75904 11.82428 11.90962 13.26088 11.89901 11.61191
[1049] 11.60797 12.97821 11.46135 11.62829 12.22360 12.27079 12.00523 12.01738
[1057] 13.33787 11.84166 11.91073 12.61180 11.58614 11.44465 11.76082 11.55644
[1065] 12.10002 11.88506 11.65665 11.83939 13.25106 11.84115 11.49290 11.70098
[1073] 13.23607 11.58888 11.69969 12.17950 12.16039 12.27056 11.99816 13.27574
[1081] 12.34332 12.15718 12.27721 11.56922 11.63069 11.47394 12.17739 11.98924
[1089] 11.60698 11.87515 11.40073 13.29687 11.89631 11.34720 11.83256 13.23241
[1097] 11.60509 11.87275 12.28365 12.19664 12.27487 12.05619 13.33359 12.30442
[1105] 12.18657 12.16862 11.51655 11.29988 11.59415 11.26601 11.56872 12.06654
[1113] 11.81879 11.77899 11.90150 13.27940 11.90529 11.46060 11.66757 13.17318
[1121] 11.71234 11.70927 12.32026 12.18406 12.10200 12.00056 13.21090 12.38880
[1129] 12.12198 12.50875 11.60012 11.43107 11.64962 11.60303 12.32508 12.67492
[1137] 12.24103 11.90420 12.86120 11.75734 14.53387 12.10715 11.48604 11.61490
[1145] 12.42900 12.44951 12.10306 11.80185 11.91670 13.01194 14.38016 11.62684
[1153] 11.71066 12.16535 12.28457 11.98709 11.56590 12.29688 12.58164 12.15249
[1161] 11.72496 12.68212 11.61531 14.23534 11.97994 11.78632 12.33050 12.59588
[1169] 12.19580 12.46777 11.64445 12.75264 12.20204 11.87625 12.06472 14.11293
[1177] 12.82453 11.87164 12.39656 11.43537 12.09465 11.69197 12.75000 12.69080
[1185] 12.50439 11.50710 11.64723 13.33759 11.46128 12.27597 11.70067 11.51593
[1193] 12.22192 11.37637 11.91632 14.15581 13.52039 11.87277 11.61532 11.29523
[1201] 11.47817 11.57644 12.18059 13.22651 11.61445 12.99038 11.63537 12.74990
[1209] 12.04736 11.30691 12.46103 11.87054 11.34943 11.29375 13.30695 11.61037
[1217] 12.04091 11.31804 12.52476 11.74305 12.42829 12.74597 12.24577 11.82873
[1225] 12.82553 11.79249 14.43095 12.09446 11.80680 12.62905 12.17043 12.18704
[1233] 11.66741 12.27575 12.66673 12.17437 11.89057 12.70387 11.92896 14.45174
[1241] 11.99496 11.55935 11.82875 12.47176 12.53304 12.21761 11.72147 12.25715
[1249] 12.64382 12.14096 11.86032 12.81442 11.83388 14.57844 11.96643 11.57054
[1257] 11.84371 12.50393 12.54480 12.30541 11.70291 12.13515 12.74834 12.17298
[1265] 11.93654 12.72755 11.79651 14.81908 11.90888 11.53608 12.67619 12.35492
[1273] 12.21174 11.76522 12.37777 12.64662 12.14333 11.85640 12.97081 11.82815
[1281] 14.53392 12.00584 12.03522 12.66256 12.47736 12.26780 11.49876 11.68370
[1289] 12.24699 12.56526 12.01374 11.89999 12.79892 11.87970 14.40983 11.90641
[1297] 12.01725 12.44821 12.57716 12.28962 11.68225 12.29379 12.57646 12.05830
[1305] 11.85292 12.83772 11.85924 14.48179 11.94126 11.47030 12.09035 12.48777
[1313] 12.48461 12.32932 11.52003 11.56884 12.33170 12.60787 12.18480 11.86048
[1321] 12.73731 11.92309 14.50137 11.87143 11.59462 11.99587 12.46300 12.46321
[1329] 12.31376 11.94416 13.12669 12.29354 13.09873 12.97701 12.46103 11.69234
[1337] 12.44697 12.75958 11.68769 15.05218 11.65654 12.82107 11.52985 11.67333
[1345] 11.55784 11.58414 11.71729 11.44816 13.74815 12.81531 11.30919 12.34669
[1353] 11.79476 11.94026 11.55675 11.95807 13.04291 12.08153 13.03335 12.93022
[1361] 12.43540 11.65651 12.31567 12.81012 11.59104 14.92715 11.65301 12.74310
[1369] 11.41229 11.57053 11.27748 11.26801 11.49323 11.60057 11.92138 11.48780
[1377] 13.75915 12.97843 11.24673 12.36407 11.23837 11.75952 11.88611 11.57945
[1385] 11.23681 11.26898 12.75252 11.69440 12.72661 11.78432 12.57938 12.47147
[1393] 12.36139 11.61225 11.12356 11.56905 12.11754 11.49082 11.50317 15.07122
[1401] 13.27012 11.47064 12.19150 11.88226 11.33148 11.96879 11.32963 11.18334
[1409] 13.38321 11.18360 11.55943 12.69727 11.19962 12.84636 11.25323 12.41301
[1417] 11.41470 11.30563 12.79414 11.72550 12.08727 11.86612 12.38041 12.37340
[1425] 12.07138 11.42558 12.05649 12.69983 11.70952 11.60555 15.09578 13.35852
[1433] 11.52438 12.47620 11.21238 11.23455 11.63977 11.50057 11.41298 11.32631
[1441] 13.52218 11.27755 12.89977 12.82558 11.82045 12.61110 12.62351 13.35317
[1449] 12.33663 11.38542 13.36715 13.10056 12.54379 11.56100 11.20500 11.45030
[1457] 11.00402 14.62010 10.95349 11.56788 11.94466 11.69074 11.99285 11.35491
[1465] 13.16892 11.43062 12.02259 11.22084 11.03787 12.37301 11.20279 11.70407
[1473] 11.96231 14.19504 11.31778 12.11702 11.91529 12.04795 11.06817 11.01287
[1481] 11.48110 12.00513 11.73082 12.04163 11.68478 13.00074 11.92066 12.74490
[1489] 12.97471 12.56242 11.71484 12.50182 12.36479 11.82651 14.85880 11.72177
[1497] 12.59177 11.72389 11.60134 11.55765 11.78644 13.64071 12.50160 11.46628
[1505] 12.06937 11.73210 12.00109 13.06192 12.11895 13.01024 12.96566 12.51107
[1513] 11.65530 12.35549 12.60496 11.76399 15.01442 11.56591 12.70127 11.31878
[1521] 11.82092 11.72595 11.29186 11.54549 11.67533 11.34885 13.81118 12.67668
[1529] 11.50995 12.33671 11.61992 12.04382 11.39047 11.92268 13.16150 12.25097
[1537] 12.98053 12.97480 12.50384 11.70517 12.39738 12.63194 11.79959 15.09341
[1545] 11.46040 12.67502 11.70718 11.56942 11.40851 11.77301 13.71537 12.78080
[1553] 11.40442 12.29394 11.52713 11.91445 11.41174 11.78384 12.67236 11.79548
[1561] 12.61561 12.88527 12.45110 11.52786 11.99386 12.36875 11.44664 15.12061
[1569] 11.34215 11.28580 12.59403 11.74648 11.74016 11.81209 11.65817 13.70195
[1577] 12.82783 11.56539 12.35284 11.50089 11.73114 11.39058 11.90553 13.17017
[1585] 12.26917 13.03914 12.97067 12.50725 11.79457 12.54608 12.63228 11.85323
[1593] 15.12822 11.44478 11.22070 12.71104 11.24889 11.71057 11.72572 11.30200
[1601] 11.48802 11.50554 11.24941 13.78416 12.57617 11.27143 12.24824 11.64013
[1609] 12.11190 11.46701 11.70162 12.62699 11.73607 12.56574 12.60756 12.13270
[1617] 11.35069 12.14837 12.56337 11.35525 14.69806 12.59058 11.84248 11.31575
[1625] 11.48827 11.93816 13.49748 12.85635 11.48848 12.31802 11.54121 11.87598
[1633] 11.34507 11.79399 13.01942 12.04115 12.84273 12.81002 12.31851 11.58799
[1641] 12.38201 12.56533 11.62875 14.96887 11.33437 12.63372 11.42837 11.48731
[1649] 11.36031 11.66351 13.61448 12.75333 12.33332 11.73030 12.03197 11.40363
[1657] 11.67011 12.99274 12.04847 12.71190 12.70512 12.21153 11.57724 12.29722
[1665] 12.52722 11.49478 14.78002 12.63588 11.62342 11.35426 11.38850 11.77757
[1673] 13.56103 12.80731 12.26697 11.70759 11.87901 11.34745 11.37438 12.04272
[1681] 12.23708 12.00826 12.70387 12.34634 11.90582 11.84947 11.61975 14.05297
[1689] 11.38889 12.18979 11.50923 12.12763 12.77630 11.90864 12.36345 12.54476
[1697] 12.26243 12.31211 12.63508 13.58090 11.87533 12.21517 12.04479 11.35999
[1705] 11.38472 11.46019 11.85101 11.94206 11.72753 12.37636 12.02667 11.74089
[1713] 12.17272 11.41152 14.03574 11.47005 11.63287 11.51768 12.05334 12.56404
[1721] 11.63424 12.10250 11.53679 12.16592 12.25916 13.31614 11.64543 11.94179
[1729] 11.63484 11.36828 12.23428 12.14121 12.02688 12.72100 12.38807 11.91810
[1737] 11.97874 11.68730 14.09849 11.38171 12.15205 11.45008 11.98032 12.58166
[1745] 11.91104 12.37533 12.37647 12.14307 12.28230 12.82301 13.46814 11.84796
[1753] 12.17378 12.03973 11.35523 11.38851 11.40697 11.27091 12.42947 11.54318
[1761] 11.45904 11.94605 12.03067 11.26045 11.46300 12.15033 13.81749 13.18347
[1769] 11.30871 11.78688 11.28030 11.26234 11.32622 12.61697 11.75204 11.77241
[1777] 11.29322 12.27836 11.97309 11.91376 11.60038 12.55885 11.94653 12.71522
[1785] 12.53374 11.41038 11.34520 11.37399 12.55143 11.45001 11.98898 11.90540
[1793] 11.74274 12.34886 12.54650 13.75687 11.46053 13.20879 11.32385 11.98811
[1801] 11.36431 12.08431 11.42530 11.48465 11.34005 12.51400 11.54561 11.50377
[1809] 11.52371 12.56071 11.71019 12.74638 11.73108 12.66817 11.07354 11.90227
[1817] 11.92647 12.83985 11.93424 12.74979 12.52876 11.37881 10.98095 13.28985
[1825] 11.79006 11.89220 11.61627 12.21382 11.73062 12.66474 11.71891 11.87884
[1833] 11.08569 11.24093 11.73335 12.95075 12.65505 11.85426 11.35878 13.03587
[1841] 11.01105 11.86741 12.81880 13.09409 13.29195 12.59463 12.43132 10.97192
[1849] 11.99037 11.99563 12.45145 12.18391 11.92237 12.00908 11.55250 12.63745
[1857] 12.43335 12.12942 13.56000 11.77641 12.05234 12.45235 12.08430 12.09558
[1865] 12.24194 12.57317 12.90993 12.26296 11.72263 11.51224 11.31407 12.20891
[1873] 12.04058 11.89670 12.66090 12.35607 11.76948 11.60916 11.35864 14.08523
[1881] 11.31027 11.28019 11.95512 11.59033 12.47517 12.43429 11.86614 12.10248
[1889] 12.12053 12.12534 12.51418 12.89168 13.41894 12.33121 12.28383 11.95741
[1897] 11.37056 11.57021 11.46521 11.45715 12.38268 12.03451 11.68569 12.04489
[1905] 13.64302 12.16550 11.72313 11.61058 11.36775 11.84982 11.89835 12.13291
[1913] 12.78858 12.95239 11.74072 12.08254 11.38075 11.38435 11.60684 11.91710
[1921] 12.10989 11.90087 12.80479 12.47640 11.84928 11.88844 11.16194 14.27065
[1929] 11.19267 11.27508 11.29693 11.52419 12.26721 11.93627 12.13528 11.60704
[1937] 11.93419 12.26523 12.09743 12.07819 13.05163 13.31931 11.70928 12.01618
[1945] 11.64846 11.44114 11.47425 11.44913 11.33903 11.20907 11.22170 12.22088
[1953] 11.99964 11.83162 12.63768 12.32380 12.18656 11.73158 11.30343 14.18448
[1961] 11.32343 12.13758 11.36879 11.43720 12.25783 12.37931 11.81235 11.76590
[1969] 12.26384 12.23240 12.33808 12.82091 13.32533 12.08002 12.05679 12.38241
[1977] 11.26661 11.30393 11.49446 11.81618 11.38742 11.68517 12.52703 12.12998
[1985] 11.94301 12.06059 13.92691 12.24268 11.79973 11.85339 11.81431 12.03801
[1993] 12.87448 12.88837 11.69766 12.03086 11.36538 11.37790 11.29068 11.27207
[2001] 11.89212 12.05216 11.74847 12.43801 12.14141 11.85577 11.65676 11.28863
[2009] 13.77491 11.25347 11.99433 11.64736 12.24375 12.36425 11.98333 12.03654
[2017] 12.23463 12.26535 12.58599 12.73697 13.41634 12.32257 12.23603 11.89787
[2025] 11.19001 11.27571 11.36882 11.28813 12.13306 11.70749 11.83991 12.59112
[2033] 12.24773 12.08821 11.87246 11.24655 14.04764 11.22023 12.15584 11.23352
[2041] 12.02736 11.81901 11.67690 11.51109 12.08492 12.09798 12.24149 12.90785
[2049] 13.22013 12.01465 12.06574 11.59035 11.21368 11.27020 11.26102 12.14966
[2057] 12.46242 11.46234 13.39261 12.23002 11.96725 11.30796 12.48578 13.33653
[2065] 11.70541 12.06498 11.50438 11.34107 11.64337 11.30824 11.53163 13.16372
[2073] 12.60628 12.01867 11.91241 12.21416 13.15014 11.86118 11.55430 13.07646
[2081] 13.41348 11.41666 11.77680 12.91623 12.20219 11.80463 12.20088 12.35022
[2089] 11.31656 13.39543 12.17836 11.85459 11.35110 12.45248 13.34264 11.69044
[2097] 12.15713 11.39643 11.62895 11.35586 13.15638 12.53488 12.01096 12.73193
[2105] 12.00013 12.09364 12.77670 13.14864 12.72701 11.71223 11.60152 12.63833
[2113] 12.18209 12.52197 12.47022 12.68654 12.23827 11.82554 12.55391 11.72347
[2121] 11.48608 11.53082 12.77430 11.50654 13.60215 12.62358 11.55814 12.18740
[2129] 13.16208 12.30918 12.58311 11.78264 12.47926 12.69431 12.26295 13.20863
[2137] 11.94299 11.49163 12.62132 11.56244 12.18075 11.45441 12.24581 13.39093
[2145] 11.84920 11.77858 12.48293 13.07439 12.34890 11.97539 11.71531 11.95571
[2153] 13.04753 11.79480 12.01609 11.64264 11.51838 13.06509 12.20363 11.75224
[2161] 12.32619 12.27386 13.23528 12.15235 11.82873 11.76945 13.28416 11.64598
[2169] 12.16201 11.67935 11.44386 11.54860 13.25148 12.65792 11.36380 11.94890
[2177] 12.37844 12.53900 13.32893 12.32736 11.92482 11.39630 11.40004 12.03061
[2185] 13.75418 11.68231 12.29810 11.48845 11.40568 11.37850 13.41225 12.69305
[2193] 12.03841 12.34340 12.18858 13.16330 12.40297 12.03641 11.72982 14.03694
[2201] 11.51853 12.25948 11.40667 13.41789 12.62309 11.95179 12.33899 12.34594
[2209] 13.27727 12.21676 11.82002 11.29454 11.46907 11.94333 13.57513 11.60727
[2217] 12.23556 11.53504 11.43185 11.29594 11.51070 13.26379 12.74953 12.03758
[2225] 12.30770 12.44054 13.24750 12.24265 11.90777 11.32023 11.33815 11.93826
[2233] 13.57542 11.59597 12.12431 11.65427 11.48949 11.55355 13.28028 12.68138
[2241] 11.29678 12.05065 12.27931 12.36770 13.21760 12.12156 11.70995 11.90303
[2249] 13.54215 11.41707 11.55064 12.17573 11.75280 11.53969 11.39001 13.21562
[2257] 12.80902 12.06636 12.08962 12.41131 11.25314 13.16486 12.01750 11.72578
[2265] 11.63476 13.45739 11.42900 11.47279 12.04787 11.75905 11.46837 11.54141
[2273] 11.28903 11.57916 13.04780 12.95307 11.43814 12.12024 11.30463 11.35618
[2281] 12.37019 11.74073 12.32130 13.86031 11.40811 11.79653 12.04881 11.49505
[2289] 12.60646 11.35981 13.99475 12.63971 12.73376 12.44418 12.06969 11.52735
[2297] 11.63846 11.50661 11.83296 12.07527 12.00126 13.26949 11.77081 11.75182
[2305] 12.62584 11.72223 12.14446 12.11119 12.11692 11.90763 12.49322 11.81861
[2313] 11.50617 11.60893 11.80832 11.90508 11.85255 13.00959 11.77363 11.86125
[2321] 12.88780 12.19718 11.99155 12.14497 11.94953 11.71014 12.30016 12.41744
[2329] 12.90844 11.95342 11.89138 12.23601 11.87905 11.43296 11.57803 11.89638
[2337] 11.48448 12.23607 11.89257 12.55387 12.74869 11.98071 11.98976 13.34375
[2345] 11.59686 12.13471 12.23861 11.65554 13.18537 12.28920 13.57818 12.15291
[2353] 11.93698 12.10571 11.79685 12.21216 11.60344 11.73569 11.80079 11.54308
[2361] 11.50457 12.35789 11.92076 12.13661 12.18745 11.58946 11.89044 11.85729
[2369] 11.80732 11.81415 12.64956 11.83617 12.01165 11.43370 11.62610 11.36996
[2377] 11.34490 11.92148 12.16519 11.92846 13.15811 11.77563 11.87160 11.57473
[2385] 12.15430 12.77318 12.57984 12.04349 12.16846 11.61250 11.40072 11.49380
[2393] 11.56479 11.44105 11.35503 11.90454 12.17735 13.24341 11.64691 11.37392
[2401] 11.75215 11.79833 12.11997 12.95949 12.61357 12.08010 12.22215 11.37587
[2409] 12.15193 12.21465 11.67056 11.93823 11.60753 12.08627 11.87771 13.64451
[2417] 11.77942 11.41437 11.48001 12.05382 11.34802 13.90896 12.33039 12.01777
[2425] 12.50053 12.22217 12.43198 12.17125 12.36121 11.81115 11.95931 11.98735
[2433] 13.88119 11.86672 11.59894 11.69770 12.35684 14.05812 12.50879 12.18112
[2441] 12.66310 11.48831 11.44934 11.53978 11.64441 11.79290 12.17207 11.48772
[2449] 13.68167 11.35698 11.94123 11.93089 11.51219 12.55889 13.25100 12.75230
[2457] 12.41269 12.49273 11.59577 11.59990 11.71753 11.69424 11.88626 12.13696
[2465] 13.82204 11.91751 12.03428 11.51933 12.30846 11.31238 11.47139 13.01004
[2473] 12.75234 12.60072 12.77892 11.70496 11.59226 11.42432 11.86287 11.77854
[2481] 11.86840 11.89040 13.00516 12.01285 11.39842 11.42390 12.19169 11.40573
[2489] 12.51028 12.55542 12.24801 12.34782 11.44607 11.56365 11.47172 12.21400
[2497] 12.51343 11.99712 12.63697 11.58380 11.21792 13.96463 11.65107 11.65917
[2505] 11.72976 11.37578 12.05200 11.37578 11.82436 11.67868 11.98315 11.81577
[2513] 12.77326 12.75877 11.49562 12.16520 11.41702 11.23515 11.39766 11.98629
[2521] 12.38987 11.93745 13.09736 11.69649 14.15922 11.80351 11.88013 11.47928
[2529] 11.41985 11.86541 11.37636 11.57665 11.39944 11.42699 12.18679 13.19713
[2537] 11.75960 11.70458 11.46657 12.08112 12.44326 11.92201 12.38034 11.48616
[2545] 13.73971 11.56021 11.56861 11.64043 11.39336 12.15908 11.48762 11.83061
[2553] 11.67893 11.95729 11.71507 12.96938 12.47235 11.46375 12.20420 11.44167
[2561] 11.39787 11.60060 12.96419 11.90863 11.83628 11.81432 11.42124 11.23442
[2569] 12.17974 13.80508 13.48263 11.27794 11.42766 11.53795 11.74724 12.01210
[2577] 11.35242 11.44849 11.95671 11.24874 11.44958 11.48226 12.67624 11.54060
[2585] 12.81110 11.27843 12.57161 11.83209 13.05579 11.79089 12.14441 11.88539
[2593] 11.15729 12.15033 11.36965 11.84622 13.93012 13.39541 11.64949 11.63954
[2601] 11.45535 11.88994 11.20244 11.43942 11.47165 12.15640 11.29360 12.62706
[2609] 11.30046 12.82557 11.17100 12.84640 11.70329 12.78657 12.62213 12.36352
[2617] 11.84532 11.82801 13.21891 11.32540 11.88534 11.21234 11.33753 12.02814
[2625] 11.58773 11.81928 11.33572 11.76427 11.48048 11.97535 11.75424 11.54101
[2633] 11.24909 11.54547 11.55493 11.68826 12.65283 12.21352 11.72681 11.94179
[2641] 11.86259 13.91440 11.79546 11.70433 11.92211 11.77455 11.46096 11.56866
[2649] 11.46831 11.48932 12.34750 11.72044 11.56579 11.45194 11.61484 11.87907
[2657] 12.77180 12.31828 11.57690 11.99797 11.35889 11.97860 11.37501 14.17967
[2665] 11.40375 11.38891 11.72167 11.65671 11.46848 11.41301 12.29683 11.52289
[2673] 11.76820 11.52234 11.24200 11.75557 11.59756 12.54724 12.50141 11.43846
[2681] 11.94389 11.82025 11.18772 11.56481 11.92725 12.77617 12.24181 11.58808
[2689] 12.08425 11.40568 11.89018 14.23837 11.54200 11.86860 11.65438 11.39001
[2697] 11.35842 12.14097 11.37485 11.84966 11.58322 11.90546 11.68947 12.72862
[2705] 12.64601 11.45144 12.10972 11.68471 11.26715 11.58372 11.93786 13.13990
[2713] 12.50101 11.63562 12.33232 11.57370 11.92434 14.58014 11.53448 11.46785
[2721] 12.43014 11.72425 11.29657 11.76206 11.58156 11.52304 11.38930 11.58845
[2729] 11.83223 12.41523 12.66181 12.06112 11.25669 11.24957 11.55943 11.88364
[2737] 12.68642 12.14254 11.64272 11.94997 11.36409 11.87038 14.26229 11.47737
[2745] 11.28507 11.79559 11.73163 11.36447 11.37278 12.11234 11.34946 11.76340
[2753] 11.53731 11.71553 11.63398 12.60710 12.66981 11.39390 12.07801 11.71992
[2761] 11.29018 11.54596 11.18613 11.96993 12.80863 12.30483 11.59072 11.94047
[2769] 11.29079 11.95774 14.23853 11.50268 11.33793 11.81074 11.74764 11.41994
[2777] 12.23191 11.38395 11.89928 11.60707 11.84855 11.65850 12.64077 12.62870
[2785] 11.41326 12.09315 11.72314 11.34925 11.50348 11.23983 11.82547 11.31775
[2793] 12.75110 12.28306 11.46090 11.79317 11.36423 11.86892 11.30331 14.13422
[2801] 11.57031 11.33012 11.65139 11.62811 11.23313 11.25631 12.37889 11.38971
[2809] 11.84664 11.61371 12.01634 11.53587 12.87339 12.36494 11.55490 12.09652
[2817] 11.68529 11.21586 11.51059 11.24921 11.84671 11.20960 12.61498 12.13094
[2825] 11.48385 11.86708 11.32532 11.88273 11.28220 14.13093 11.58321 11.32226
[2833] 11.69617 11.67791 11.33652 11.34572 12.40992 11.41049 11.81473 11.59811
[2841] 11.96023 11.62035 12.87640 12.49526 11.50974 12.21681 11.69874 11.34469
[2849] 12.46375 11.45262 11.82636 12.84024 12.41954 11.34181 12.26846 11.64495
[2857] 13.56573 11.42749 11.94274 11.73128 11.34421 11.37517 12.26979 11.34625
[2865] 11.50353 11.87406 11.39822 11.57403 12.91152 12.64203 13.13123 11.30224
[2873] 12.09876 11.44570 11.31942 11.94836 11.45489 11.76800 12.73204 12.40109
[2881] 11.31466 12.61132 11.57432 13.90238 11.71457 11.30713 11.63707 11.36991
[2889] 12.10880 11.43174 11.61894 11.45390 12.41353 12.47313 13.52740 11.99627
[2897] 11.30372 11.30014 12.41492 11.50208 11.74532 12.73302 12.39176 11.22983
[2905] 12.13637 11.23949 11.47473 13.45269 11.52980 11.21399 11.82886 11.68206
[2913] 11.44220 11.38143 12.56546 11.23171 11.37011 11.57259 11.92349 11.47847
[2921] 11.91170 12.73088 11.22565 12.83423 12.78898 11.53820 12.31392 11.27451
[2929] 11.24949 11.21752 11.59154 12.90725 12.49931 12.40378 12.29077 11.39328
[2937] 11.25509 11.26147 12.08900 13.58786 13.55040 11.29988 11.63862 11.53469
[2945] 11.24582 11.62877 11.51997 11.26994 11.96236 11.27275 11.18964 11.70101
[2953] 11.69263 11.98996 12.41432 12.39057 11.58589 12.84461 13.03011 11.22201
[2961] 12.68491 11.60629 12.95970 12.23294 11.43093 12.22924 12.18351 11.49699
[2969] 11.67894 11.16944 12.06186 13.73151 13.57154 11.72580 11.55089 11.62118
[2977] 11.15175 11.75628 11.42870 11.57775 11.76329 11.61221 12.09970 12.55138
[2985] 12.75009 11.36133 12.80540 12.98442 11.69633 12.85010 11.11689 11.01354
[2993] 10.93703 10.64535 13.44626 11.63696 12.04487 13.15857 12.74707 11.48272
[3001] 11.23535 11.17354 11.49834 11.50412 13.25849 10.68138 11.73684 10.72186
[3009] 12.32756 11.67232 10.63947 11.04618 10.81758 11.00561 12.17402 11.82576
[3017] 11.48857 12.17870 12.54753 11.20261 10.87789 10.70139 11.72482 12.01931
[3025] 11.06366 11.35905 11.67744 11.42653 10.85446 11.63362 11.63657 11.27662
[3033] 11.11191 11.28729 12.63133 11.35921 12.37233 11.76177 10.85689 13.51810
[3041] 10.74419 10.67089 12.59064 12.12920 12.40788 10.65222 10.65273 10.78205
[3049] 11.41744 11.49283 11.98917 11.91766 11.22498 10.63021 11.89737 12.27102
[3057] 11.93438 11.70222 13.02909 12.70105 11.60533 11.47722 11.92283 11.86226
[3065] 13.77908 11.31092 11.46673 11.39895 11.78450 11.76126 11.98023 11.66784
[3073] 11.44702 11.32667 11.62311 11.86450 11.66270 12.95868 11.33880 12.51969
[3081] 11.74477 11.43329 11.50428 12.50939 11.90680 11.98686 12.96585 12.54767
[3089] 11.43239 11.28801 11.79618 11.37465 11.67983 13.86736 11.16502 11.17402
[3097] 11.14930 11.13406 12.03994 11.74480 11.25471 11.33780 11.40854 11.16044
[3105] 12.26861 11.38549 11.31918 11.98607 11.41218 11.47802 13.07783 11.20241
[3113] 12.44317 12.78356 11.20835 12.12871 11.14119 11.18447 11.41771 12.42677
[3121] 12.00854 11.99981 12.93480 12.57494 11.47641 11.13403 11.82218 11.29470
[3129] 11.72654 13.96422 11.28750 11.98195 11.72172 11.39182 11.35374 11.42182
[3137] 12.14223 11.41496 11.40510 11.88078 11.40103 11.49867 13.01332 11.18185
[3145] 12.39077 12.86018 11.16121 12.07870 11.20731 11.22950 11.32828 12.51158
[3153] 11.65724 11.73837 13.15844 12.68104 11.51612 12.12443 11.28321 11.86624
[3161] 14.18402 11.25539 11.65150 12.38745 11.71067 11.25278 11.47330 12.05935
[3169] 11.62473 11.27061 11.75768 11.71526 11.28329 11.31495 13.05592 11.20245
[3177] 12.25214 13.05292 12.04531 11.49433 11.37449 12.44548 11.84960 11.96237
[3185] 12.91845 12.51060 11.35540 11.25183 11.73300 11.30505 11.72447 13.91173
[3193] 11.21800 11.29375 12.02308 11.67385 11.34990 11.37557 11.46900 11.29333
[3201] 12.41224 11.35906 11.47037 11.92670 11.37286 11.56319 12.93332 11.20358
[3209] 12.51893 12.76103 11.42639 12.14342 11.66811 11.19785 11.27707 12.34725
[3217] 11.79441 11.83703 12.87872 12.55731 11.47476 11.70718 11.18258 11.82471
[3225] 13.79037 11.30459 11.38470 11.82923 11.63948 11.27810 11.34555 11.30301
[3233] 11.22146 12.27863 11.61944 11.38224 11.82772 11.30920 11.53096 12.82924
[3241] 11.20918 12.25635 12.86392 11.53650 12.22232 11.37278 11.23727 11.38730
[3249] 12.53553 11.80795 11.96788 12.96995 12.58410 11.48414 11.12449 11.86943
[3257] 11.30126 11.80347 14.00549 11.15530 11.18163 11.31753 11.92612 11.74433
[3265] 11.16478 11.34589 11.49447 11.18534 12.23437 11.56736 11.32693 11.84590
[3273] 11.40866 11.57722 13.02735 11.12715 12.44811 12.84189 11.35158 12.15314
[3281] 11.14707 11.20787 11.33965 11.55434 12.65837 11.64699 12.58625 12.51792
[3289] 12.41238 11.37616 12.95607 11.65418 15.10510 11.50107 11.24493 11.60591
[3297] 11.22905 11.28559 11.90650 11.38426 11.33883 11.35738 11.66106 11.72976
[3305] 12.71372 11.30544 12.39949 12.09673 11.76986 12.04007 11.22955 11.28537
[3313] 12.54059 11.46237 12.52150 12.67790 12.55145 11.68625 12.98907 11.78753
[3321] 15.42804 11.63068 11.28255 11.29379 11.56947 11.48931 11.41126 12.39808
[3329] 12.20278 12.55004 11.52323 11.85837 11.44594 11.53142 12.83008 11.80180
[3337] 12.73086 12.75605 12.59800 11.26331 11.74161 12.89701 11.87320 15.42621
[3345] 11.34960 11.57271 11.68284 11.77329 11.36529 11.51762 11.29580 11.59495
[3353] 11.31146 12.65433 12.06257 12.23271 11.32645 11.86623 11.37091 11.41594
[3361] 11.58458 12.91835 12.03065 12.73379 12.77775 12.55404 11.24138 11.93978
[3369] 12.52529 11.91948 15.58668 11.29474 11.71168 11.72159 11.16865 11.22319
[3377] 11.60740 11.62450 11.31400 11.29867 11.67844 11.37394 13.02239 12.21540
[3385] 12.08624 11.51016 11.88614 11.16548 11.47306 11.69549 12.70461 11.27339
[3393] 11.54864 11.67579 11.82447 12.03125 11.95095 11.34564 12.32923 11.20849
[3401] 11.30240 15.44671 13.29615 11.05044 11.39180 11.56955 11.11485 11.97767
[3409] 11.56812 11.36861 11.48849 11.09163 11.93701 12.53930 11.78846 12.75125
[3417] 12.68325 11.57156 12.69370 12.45385 13.14947 12.04932 13.13577 12.69554
[3425] 12.58609 11.13399 11.20628 11.60262 14.68326 11.54631 11.95305 11.30130
[3433] 11.27719 11.23526 12.18026 11.05444 11.92714 11.63260 11.31331 11.00265
[3441] 10.89919 12.19922 11.28202 11.76763 11.76107 13.64058 11.37566 11.59344
[3449] 11.88255 11.43792 10.94794 11.54770 11.37308 12.10767 13.06149 11.74765
[3457] 12.36042 12.63198 12.60252 11.43109 11.68350 12.89521 11.89562 15.32671
[3465] 11.57641 11.38663 11.62362 11.49737 11.82858 11.39235 12.93190 11.69686
[3473] 11.77194 11.40400 11.74095 12.58105 11.41196 12.32414 12.76608 12.58887
[3481] 11.66160 12.65401 12.00110 15.36194 11.47229 11.47603 11.69028 12.13140
[3489] 11.33983 11.67029 11.63937 11.30375 12.62981 12.12567 12.20421 11.26152
[3497] 11.89210 11.26897 11.33870 11.66467 12.72592 11.74814 12.50791 12.68480
[3505] 12.57535 11.20246 11.64397 12.65751 11.83513 15.43243 11.41855 11.53342
[3513] 11.51029 12.00359 11.47766 11.52590 11.52474 11.54163 12.83505 12.28575
[3521] 12.21948 11.34924 11.92414 11.37653 11.52110 11.64616 12.86744 11.93451
[3529] 12.69829 12.95013 12.65111 11.26640 11.85330 12.68172 11.87165 15.64413
[3537] 11.33827 12.06661 11.73568 11.88721 11.25993 11.45052 11.47692 11.22152
[3545] 13.07571 12.10301 12.28408 11.24459 11.83967 11.42409 11.41631 11.80670
[3553] 12.60047 11.60095 12.45014 12.71460 12.57280 11.17468 11.59860 12.78361
[3561] 11.90522 15.36485 11.35713 11.38912 11.69049 11.16970 12.35800 11.23148
[3569] 11.61267 11.17445 11.60624 11.39659 12.70712 11.24682 12.11163 12.21530
[3577] 11.34041 11.85062 11.27775 11.42642 11.61720 12.87529 11.92967 12.65976
[3585] 12.79354 12.59660 11.27281 11.91557 12.58607 11.94723 15.61926 11.30432
[3593] 11.67355 11.69295 11.17432 11.91767 11.46173 11.44143 11.21703 11.65492
[3601] 11.41567 12.86068 12.12741 12.22911 11.21438 11.75754 11.44326 11.39768
[3609] 11.53220 12.98603 12.04517 12.76096 12.88742 12.70152 11.32865 11.12191
[3617] 11.85052 12.58129 11.97143 15.61388 11.33413 11.61147 11.67405 11.21654
[3625] 11.19933 11.84103 11.56724 11.47815 11.30159 11.58655 11.42386 12.88418
[3633] 11.11129 12.18450 12.12078 11.49693 11.86213 11.44493 11.44587 11.73320
[3641] 12.38567 11.35294 12.39072 12.64921 12.45777 11.14917 11.66664 12.72959
[3649] 11.98871 15.30953 11.52479 11.51080 11.74935 11.17430 12.36345 11.16452
[3657] 11.63751 11.53530 11.27181 12.47744 12.05357 12.26550 11.39651 11.94954
[3665] 11.33275 11.83192 12.19816 11.77216 12.22408 12.57278 12.05557 11.36639
[3673] 12.19293 14.50324 11.28199 11.43513 11.83337 11.38258 12.15854 11.93451
[3681] 12.11356 12.10614 11.79685 12.15772 11.93460 13.39722 11.50879 12.27076
[3689] 12.00363 11.41152 11.36453 11.83598 11.21027 12.68913 12.13254 12.55401
[3697] 12.65793 12.14331 11.38707 11.50236 11.77444 14.63156 11.48730 11.67952
[3705] 11.59643 12.12658 12.15233 12.27304 12.59006 11.66317 12.29130 12.19315
[3713] 13.50320 12.33727 12.16575 11.62817 11.34823 11.19698 12.09163 12.80491
[3721] 12.04613 12.47235 12.49542 12.12345 11.11889 11.58046 11.34321 11.62080
[3729] 11.16846 14.59941 13.92907 11.57998 12.60004 12.26995 12.47672 12.19164
[3737] 11.33701 11.64962 11.19386 11.19470 11.77804 13.33696 11.64917 11.42120
[3745] 13.31723 11.13806 11.22332 11.39252 12.53715 12.16368 12.02672 11.81384
[3753] 12.10440 11.90170 12.02620 14.57841 13.13440 11.25903 11.82365 12.38147
[3761] 12.21219 11.26888 11.91852 11.78688 11.30091 11.86272 11.26183 12.29834
[3769] 11.44534 12.32607 12.33904 11.41798 12.22494 11.52668 11.30270 11.53317
[3777] 11.71425 12.37570 11.90594 11.70796 11.30687 11.36513 11.34683 11.69441
[3785] 14.84258 11.34167 12.07691 11.13792 14.14304 11.04925 11.24655 12.21488
[3793] 11.95655 11.22266 11.58687 11.58998 11.05828 11.60330 11.39857 11.79558
[3801] 13.55828 11.13877 12.01009 11.20489 12.22032 12.00317 11.63663 12.42896
[3809] 11.99704 11.51884 11.67015 11.80594 11.49842 13.30879 11.53875 11.51849
[3817] 11.91747 11.92146 12.20811 11.79320 13.01013 12.21944 12.22658 12.54325
[3825] 12.18292 11.46497 11.49711 11.39469 14.54274 11.48374 12.24101 11.76427
[3833] 12.02299 12.25842 11.61782 11.93138 12.16679 12.11507 11.45983 11.33082
[3841] 11.70718 11.05580 13.00069 12.41933 12.45821 12.86680 12.40823 11.57315
[3849] 11.15870 11.43562 11.44441 14.90479 11.51455 11.55655 11.62836 12.47500
[3857] 12.10455 12.13815 12.49455 11.27459 12.10061 12.21161 13.03781 12.48524
[3865] 12.03435 11.51916 11.11773 11.24569 11.34398 11.84182 11.03473 12.80552
[3873] 12.28346 12.47975 12.78873 12.23065 11.61990 11.35754 11.76077 11.34995
[3881] 11.06872 14.94586 11.08953 11.54799 11.62892 10.97612 11.49703 12.32489
[3889] 12.19784 12.10832 12.47824 11.60170 12.18851 12.03851 13.37004 11.21611
[3897] 12.43813 12.12368 11.79926 11.14685 11.24830 11.29916 11.75067 12.47262
[3905] 11.99697 12.21082 12.68345 12.11908 11.15842 11.29075 11.92179 11.14314
[3913] 14.74243 11.16095 11.25635 11.57915 11.23079 12.26109 11.83243 12.01716
[3921] 12.14322 11.78727 12.12038 12.10554 13.46149 12.31743 12.07878 11.32952
[3929] 11.32544 11.69084 12.97206 12.35873 12.51548 12.72544 12.30768 11.50488
[3937] 11.23879 11.64998 11.43624 14.72213 11.26549 11.91468 11.63353 12.48540
[3945] 11.78198 12.15795 12.33502 11.37390 12.25237 12.12857 13.29643 12.61292
[3953] 12.07624 11.44946 11.17479 11.29520 11.84017 12.78088 12.18571 12.35792
[3961] 12.70508 12.25486 11.46957 11.16576 11.60380 11.33809 11.08556 14.74185
[3969] 11.34444 11.70827 11.66092 12.50526 12.24093 12.15166 12.55328 11.54764
[3977] 12.46384 11.90327 13.24003 12.66073 12.22399 11.54142 11.15896 11.28958
[3985] 11.24118 11.80612 12.71566 12.12521 12.37488 12.68968 12.25823 11.40908
[3993] 11.15028 11.75002 11.37148 14.71440 11.29141 11.40360 11.56881 12.72099
[4001] 12.07169 12.29549 12.41511 11.68632 12.31067 11.91949 13.62834 12.55843
[4009] 12.09313 11.54588 11.32400 11.24931 11.84981 12.64096 12.04254 12.36878
[4017] 12.63713 12.18377 11.42786 11.80085 11.33577 14.72497 11.28461 11.66997
[4025] 11.53191 12.81486 11.96410 12.33865 12.36387 11.70077 12.20101 11.91107
[4033] 13.65294 12.44451 12.09060 11.72459 11.27657 11.48189 11.27570 11.86451
[4041] 11.76034 11.57061 11.92561 12.61316 11.70199 11.40179 11.33409 12.00460
[4049] 13.47975 11.83997 12.22213 11.77903 12.16822 11.42929 13.63932 12.35545
[4057] 12.01787 12.49629 12.20577 11.88654 11.73378 11.85719 12.29181 12.01604
[4065] 11.49110 11.82751 12.07302 12.99185 11.67287 11.92992 13.13242 12.36598
[4073] 12.02925 12.28708 11.76933 11.30820 11.40024 11.30146 11.43324 11.37140
[4081] 12.15027 11.97819 11.86448 12.06045 12.36355 13.44648 11.54034 12.18056
[4089] 11.81614 11.95170 11.30484 13.79156 11.89520 12.23473 12.63253 12.63796
[4097] 12.04450 11.66245 12.58499 11.38380 11.37105 11.18336 12.93153 12.63970
[4105] 13.40747 11.43047 11.71700 11.90521 12.13682 11.97705 11.29039 11.90431
[4113] 11.19836 11.42463 11.88317 12.54751 12.06164 12.77029 11.27133 12.89570
[4121] 11.68793 12.47667 12.86317 12.09648 13.25361 11.46134 12.21519 12.14089
[4129] 12.00316 11.40334 11.84086 11.42697 12.48059 11.85615 12.64097 11.70013
[4137] 13.06621 11.44204 11.07558 11.21606 11.51476 11.23224 11.26301 11.80166
[4145] 11.38172 11.92384 11.53067 11.56938 12.19037 11.40052 11.23805 11.82998
[4153] 11.75955 11.61780 11.83210 11.85755 12.24727 12.16611 11.11749 11.48137
[4161] 11.43490 11.49090 12.09699 11.77072 11.73131 12.55174 12.08915 11.76080
[4169] 11.88217 11.98779 12.02352 12.02472 11.91862 11.43236 11.41172 11.45820
[4177] 11.43388 11.60606 12.01638 11.39396 11.30261 11.71514 12.18502 13.61251
[4185] 11.34463 12.03745 11.74133 11.40545 12.29772 11.30982 13.78478 12.45587
[4193] 12.73652 12.45825 11.93869 11.22909 11.41182 11.37457 11.52453 11.19678
[4201] 11.32734 11.50334 12.07892 11.27421 12.25968 12.28605 13.64966 11.23522
[4209] 11.93826 11.76131 11.23465 12.32354 11.22636 11.21676 13.61085 12.50651
[4217] 12.65069 12.70219 11.79573 11.28531 11.30536 11.87536 11.86189 11.29422
[4225] 11.56707 12.86729 11.23019 11.31078 11.31762 11.73719 11.96776 13.50645
[4233] 11.47266 11.78401 11.63854 11.32882 12.35470 11.30633 13.66009 11.36655
[4241] 12.28059 12.73956 12.35272 11.69240 11.23432 11.40792 11.46302 11.55926
[4249] 11.31496 11.39562 11.49965 12.24757 11.35897 12.13179 12.19946 13.64869
[4257] 11.45290 11.93784 11.77625 11.36610 12.40613 13.79146 12.55724 12.82049
[4265] 12.58279 11.81121 11.42693 11.34713 11.49771 11.41094 11.43619 11.98881
[4273] 12.20995 12.15223 13.66843 11.98458 11.82027 11.36079 12.23131 11.33917
[4281] 11.27416 13.64436 12.43272 12.67375 12.78705 11.77889 11.39587 11.38027
[4289] 11.35771 11.41748 11.53402 12.19567 11.34815 11.92944 12.01314 13.43762
[4297] 11.93492 11.71696 11.40906 12.21189 13.68146 12.39481 12.70927 12.60948
[4305] 11.76435 11.31673 11.49160 11.52253 11.27371 11.37106 12.39288 11.22169
[4313] 11.33355 12.13007 12.12514 13.51086 11.72325 11.81917 11.71680 11.24532
[4321] 12.46397 13.78077 12.52714 12.80344 12.56822 11.74301 11.33093 11.47885
[4329] 11.25682 11.84514 12.10223 12.14886 12.04859 11.87707 11.25314 11.71218
[4337] 13.72042 11.70488 11.12923 11.35584 11.48933 11.52090 11.90428 13.05951
[4345] 11.82248 11.88309 12.15902 11.85455 11.11982 13.24135 12.20033 12.01542
[4353] 12.40295 12.12660 11.60547 11.53360 11.58991 11.88778 12.05608 11.81547
[4361] 12.05827 11.26745 13.67457 12.07334 11.23390 11.76755 11.72562 12.82924
[4369] 11.14488 11.20431 12.24597 11.33520 13.10344 12.35858 11.98455 12.38541
[4377] 11.77364 11.28952 11.48122 11.78862 12.02315 12.03438 12.03430 11.61178
[4385] 11.74534 13.25070 11.30608 11.15666 11.29575 11.33513 12.05006 11.10996
[4393] 13.11442 12.02854 11.89589 11.13813 12.16059 11.26827 11.77511 13.28153
[4401] 11.71764 12.11467 12.43383 11.96018 11.75376 11.32901 12.60413 11.30472
[4409] 11.46677 11.62336 12.79630 12.87965 13.36828 11.27684 11.58414 11.36214
[4417] 11.56225 12.47326 11.95144 12.22402 11.24660 11.54298 11.55202 12.49020
[4425] 12.12861 12.73500 12.59425 11.47538 12.50619 11.56476 11.64604 11.52951
[4433] 12.79926 13.32086 13.10154 11.24089 11.23647 11.60396 11.99834 11.22215
[4441] 11.84913 11.26356 11.83190 11.31627 11.76671 11.38739 12.45554 11.64357
[4449] 12.85846 11.52368 12.75839 11.66093 11.78763 12.13574 11.68091 11.70923
[4457] 12.25597 11.69768 11.52557 11.30897 11.87356 11.73778 11.46725 11.51476
[4465] 12.13093 11.57864 11.46594 11.42539 12.04284 11.67434 11.76059 11.72237
[4473] 12.07257 12.08062 11.66444 11.41434 13.01603 11.42081 12.00496 11.52466
[4481] 11.51130 11.80791 11.78345 11.54321 11.75767 11.41132 11.94586 11.57246
[4489] 11.56058 11.17480 11.59038 11.13404 11.81019 12.15209 11.94251 11.96887
[4497] 11.08798 11.61700 13.16874 11.23515 11.66588 11.14705 12.18558 11.47319
[4505] 13.29886 11.81222 11.92239 11.04676 12.24069 11.22384 11.66709 11.06164
[4513] 12.84084 12.25788 12.31128 12.36453 11.67541 11.08800 11.57646 11.08222
[4521] 11.58570 11.99964 12.10631 12.11607 11.10824 11.27831 11.44012 13.27623
[4529] 11.16858 11.03903 11.13950 11.67143 11.07763 12.00704 11.41853 12.99475
[4537] 11.03306 11.84929 11.82737 11.10228 12.16457 11.20872 11.64597 13.00458
[4545] 11.20006 12.29524 12.33652 12.31600 11.50353 11.16170 11.58894 11.44633
[4553] 11.83581 12.18249 12.07077 11.16083 11.16013 11.18811 13.28883 11.46594
[4561] 11.21523 11.72179 11.74849 11.18435 12.86352 11.69146 11.68747 11.22660
[4569] 12.04701 11.15629 11.62654 13.01768 11.25937 12.22163 12.28050 12.05632
[4577] 11.38930 11.03189 11.63648 11.19279 11.18662 11.72399 11.95238 12.00060
[4585] 12.00515 11.10190 11.26388 11.45448 13.25958 11.32898 11.28511 11.59099
[4593] 11.02172 12.02269 11.26956 13.24645 11.92198 11.85838 11.05401 12.37703
[4601] 11.18589 11.64233 11.01620 13.03788 11.07933 12.24909 12.47038 12.30147
[4609] 11.69190 11.10954 11.70302 11.07741 11.12817 11.58875 11.87044 11.90761
[4617] 11.89407 11.21012 11.10315 11.35480 13.16235 11.15933 11.23027 11.51684
[4625] 11.95353 11.31729 13.05799 11.93982 11.86698 11.22967 12.07786 11.32852
[4633] 11.66445 13.05596 11.03964 12.26206 12.38113 12.39305 11.57316 11.44377
[4641] 11.43219 12.03292 11.97053 11.93788 11.39424 11.50191 11.49722 13.14119
[4649] 11.35240 11.18513 11.35744 11.16071 11.28463 11.67212 11.26083 13.32549
[4657] 11.88091 11.81518 11.51964 12.24865 11.49698 11.62261 13.90132 12.18254
[4665] 12.45171 12.34269 11.67344 11.19682 11.49004 11.59455 11.68190 11.94877
[4673] 12.14992 12.06591 11.06833 11.08991 11.24993 11.25075 13.47320 11.24827
[4681] 11.38015 11.21219 11.64231 12.91376 11.77973 11.82479 12.14688 11.08683
[4689] 11.85185 12.99217 11.44280 12.06458 12.26755 12.05506 11.43408
$group
[1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[19] 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2
[37] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[55] 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3
[73] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 4 4
[91] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[109] 4 4 4 4 4 4 4 4 4 4 4 4 5 5 5 5 5 5
[127] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
[145] 5 5 5 5 5 5 6 6 6 6 6 6 6 6 6 6 6 6
[163] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
[181] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
[199] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
[217] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
[235] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
[253] 7 7 7 7 8 8 8 8 8 8 8 8 8 8 8 8 8 8
[271] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 9 9 9
[289] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
[307] 9 9 9 9 9 9 9 9 9 9 9 10 10 10 10 10 10 10
[325] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
[343] 10 10 10 10 11 11 11 11 11 11 11 11 11 11 11 11 11 11
[361] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
[379] 11 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12
[397] 12 12 12 12 12 12 12 12 12 12 12 12 12 12 13 13 13 13
[415] 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13
[433] 13 13 13 13 13 13 14 14 14 14 14 14 14 14 14 14 14 14
[451] 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 15
[469] 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15
[487] 15 15 15 15 15 15 15 16 16 16 16 16 16 16 16 16 16 16
[505] 16 16 16 16 16 16 16 16 16 16 16 16 17 17 17 17 17 17
[523] 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17
[541] 17 17 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18
[559] 18 18 18 18 18 18 18 18 18 18 19 19 19 19 19 19 19 19
[577] 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 20
[595] 20 20 20 20 20 20 21 21 21 21 21 21 21 21 21 21 21 21
[613] 21 21 21 21 21 21 21 21 21 21 22 22 22 22 22 22 22 22
[631] 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22
[649] 22 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23
[667] 23 23 23 23 23 23 23 23 23 23 23 23 24 24 24 24 24 24
[685] 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24
[703] 24 24 24 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25
[721] 25 25 25 25 25 25 25 25 25 25 25 25 25 25 26 26 26 26
[739] 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26 26
[757] 26 26 26 26 26 26 26 26 26 26 27 27 27 27 27 27 27 27
[775] 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27
[793] 28 28 28 28 28 28 28 28 28 28 28 28 28 28 28 28 28 28
[811] 28 28 28 28 28 28 28 28 28 28 28 28 28 29 29 29 29 29
[829] 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29
[847] 29 29 29 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30
[865] 30 30 30 30 30 31 31 31 31 31 31 31 31 31 31 31 31 31
[883] 31 31 31 31 31 31 31 31 31 31 31 31 31 32 32 32 32 32
[901] 32 32 32 32 32 32 32 32 32 32 33 33 33 33 33 33 33 33
[919] 33 33 33 33 33 33 33 33 33 33 34 34 34 34 34 34 34 34
[937] 34 34 34 34 34 34 34 34 34 34 34 34 35 35 35 35 35 35
[955] 35 35 35 35 35 35 35 35 35 35 35 35 35 35 35 36 36 36
[973] 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36
[991] 36 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37
[1009] 37 37 37 37 37 37 37 37 38 38 38 38 38 38 38 38 38 38
[1027] 38 38 38 38 38 38 38 38 38 38 38 38 38 38 39 39 39 39
[1045] 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39 39
[1063] 39 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40
[1081] 40 40 40 40 40 41 41 41 41 41 41 41 41 41 41 41 41 41
[1099] 41 41 41 41 41 41 41 41 41 41 41 42 42 42 42 42 42 42
[1117] 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 43
[1135] 43 43 43 43 43 43 43 43 43 43 43 43 43 44 44 44 44 44
[1153] 44 44 44 44 45 45 45 45 45 45 45 45 45 45 45 45 45 46
[1171] 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 47 47 47
[1189] 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47
[1207] 47 47 48 48 48 48 48 48 48 48 48 48 48 49 49 49 49 49
[1225] 49 49 49 49 49 49 49 49 50 50 50 50 50 50 50 50 50 50
[1243] 50 50 50 50 51 51 51 51 51 51 51 51 51 51 51 51 51 51
[1261] 52 52 52 52 52 52 52 52 52 52 52 52 52 53 53 53 53 53
[1279] 53 53 53 53 53 53 53 53 53 54 54 54 54 54 54 54 54 54
[1297] 54 54 54 54 55 55 55 55 55 55 55 55 55 55 55 55 55 55
[1315] 55 56 56 56 56 56 56 56 56 56 56 56 56 56 56 57 57 57
[1333] 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57
[1351] 57 57 57 57 57 58 58 58 58 58 58 58 58 58 58 58 58 58
[1369] 58 58 58 58 58 58 58 58 58 58 58 58 58 58 58 58 58 59
[1387] 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59
[1405] 59 59 59 59 59 59 59 59 59 59 59 59 60 60 60 60 60 60
[1423] 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60
[1441] 60 60 60 60 60 60 61 61 61 61 61 61 61 61 61 61 61 61
[1459] 61 61 61 61 61 61 61 61 61 61 61 61 61 61 61 61 61 61
[1477] 61 61 61 61 61 61 61 62 62 62 62 62 62 62 62 62 62 62
[1495] 62 62 62 62 62 62 62 62 62 62 62 62 63 63 63 63 63 63
[1513] 63 63 63 63 63 63 63 63 63 63 63 63 63 63 63 63 63 63
[1531] 63 63 63 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64
[1549] 64 64 64 64 64 64 64 64 64 65 65 65 65 65 65 65 65 65
[1567] 65 65 65 65 65 65 65 65 65 65 65 65 65 65 65 65 66 66
[1585] 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66
[1603] 66 66 66 66 66 66 66 66 67 67 67 67 67 67 67 67 67 67
[1621] 67 67 67 67 67 67 67 67 67 67 67 67 67 68 68 68 68 68
[1639] 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68
[1657] 69 69 69 69 69 69 69 69 69 69 69 69 69 69 69 69 69 69
[1675] 69 69 69 69 70 70 70 70 70 70 70 70 70 70 70 70 70 70
[1693] 70 70 70 70 70 70 70 70 70 70 70 70 70 70 71 71 71 71
[1711] 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71
[1729] 71 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72
[1747] 72 72 72 72 72 72 72 72 72 72 72 73 73 73 73 73 73 73
[1765] 73 73 73 73 73 73 73 73 73 73 73 73 73 73 73 73 73 73
[1783] 73 73 73 73 74 74 74 74 74 74 74 74 74 74 74 74 74 74
[1801] 74 74 74 74 74 74 74 74 74 74 74 74 74 74 75 75 75 75
[1819] 75 75 75 75 75 75 75 75 75 75 75 75 75 75 75 75 75 75
[1837] 75 75 75 75 75 75 75 75 75 75 75 75 75 75 75 76 76 76
[1855] 76 76 76 76 76 76 76 76 76 76 76 76 76 76 76 76 77 77
[1873] 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77
[1891] 77 77 77 77 77 77 78 78 78 78 78 78 78 78 78 78 78 78
[1909] 78 78 78 78 78 78 78 78 78 78 79 79 79 79 79 79 79 79
[1927] 79 79 79 79 79 79 79 79 79 79 79 79 79 79 79 79 79 79
[1945] 79 79 79 79 80 80 80 80 80 80 80 80 80 80 80 80 80 80
[1963] 80 80 80 80 80 80 80 80 80 80 80 80 80 80 80 80 81 81
[1981] 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81
[1999] 82 82 82 82 82 82 82 82 82 82 82 82 82 82 82 82 82 82
[2017] 82 82 82 82 82 82 82 82 82 82 82 83 83 83 83 83 83 83
[2035] 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83
[2053] 83 83 83 84 84 84 84 84 84 84 84 84 84 84 84 84 84 84
[2071] 84 84 84 84 85 85 85 85 85 85 85 85 85 85 85 85 86 86
[2089] 86 86 86 86 86 86 86 86 86 86 86 86 86 86 86 87 87 87
[2107] 87 87 87 87 87 87 87 87 87 88 88 88 88 88 88 88 88 88
[2125] 88 88 88 88 88 88 88 88 88 89 89 89 89 89 89 89 89 89
[2143] 89 89 90 90 90 90 90 90 90 90 90 90 90 90 90 90 90 90
[2161] 91 91 91 91 91 91 91 91 91 91 91 91 91 91 91 91 92 92
[2179] 92 92 92 92 92 92 92 92 92 92 92 92 92 92 92 93 93 93
[2197] 93 93 93 93 93 93 93 93 93 93 94 94 94 94 94 94 94 94
[2215] 94 94 94 94 94 94 94 94 94 94 95 95 95 95 95 95 95 95
[2233] 95 95 95 95 95 95 95 95 95 95 96 96 96 96 96 96 96 96
[2251] 96 96 96 96 96 96 96 96 97 97 97 97 97 97 97 97 97 97
[2269] 97 97 97 97 97 97 97 97 97 97 98 98 98 98 98 98 98 98
[2287] 98 98 98 98 98 98 98 98 98 99 99 99 99 99 99 99 99 99
[2305] 99 99 99 99 99 100 100 100 100 100 100 100 100 100 100 100 100 100
[2323] 100 100 100 100 101 101 101 101 101 101 101 101 101 101 101 101 101 101
[2341] 101 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 103 103
[2359] 103 103 104 104 104 104 104 104 104 104 104 104 104 104 104 105 105 105
[2377] 105 105 105 105 105 105 105 105 105 105 105 105 105 106 106 106 106 106
[2395] 106 106 106 106 106 106 106 106 106 106 106 106 106 107 107 107 107 107
[2413] 107 107 107 107 107 107 107 107 107 107 107 107 107 108 108 108 108 108
[2431] 108 108 108 108 108 108 108 108 108 108 108 108 109 109 109 109 109 109
[2449] 109 109 109 109 109 109 109 109 109 109 109 110 110 110 110 110 110 110
[2467] 110 110 110 110 110 110 110 110 110 110 111 111 111 111 111 111 111 111
[2485] 111 111 111 111 111 111 111 111 111 111 112 112 112 112 112 112 112 112
[2503] 112 112 112 112 112 112 112 112 112 112 112 112 112 112 112 112 112 113
[2521] 113 113 113 113 113 113 113 113 113 113 113 113 113 113 113 113 113 113
[2539] 114 114 114 114 114 114 114 114 114 114 114 114 114 114 114 114 114 114
[2557] 114 114 114 114 114 115 115 115 115 115 115 115 115 115 115 115 115 115
[2575] 115 115 115 115 115 115 115 115 115 115 115 115 115 116 116 116 116 116
[2593] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
[2611] 116 116 116 116 117 117 117 117 117 117 117 117 117 117 117 117 117 117
[2629] 117 117 117 117 117 117 118 118 118 118 118 118 118 118 118 118 118 118
[2647] 118 118 118 118 118 118 118 118 119 119 119 119 119 119 119 119 119 119
[2665] 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119
[2683] 119 120 120 120 120 120 120 120 120 120 120 120 120 120 120 120 120 120
[2701] 120 120 120 120 120 120 120 120 120 121 121 121 121 121 121 121 121 121
[2719] 121 121 121 121 121 121 121 121 121 121 121 121 121 121 121 121 122 122
[2737] 122 122 122 122 122 122 122 122 122 122 122 122 122 122 122 122 122 122
[2755] 122 122 122 122 122 122 122 123 123 123 123 123 123 123 123 123 123 123
[2773] 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 124 124
[2791] 124 124 124 124 124 124 124 124 124 124 124 124 124 124 124 124 124 124
[2809] 124 124 124 124 124 124 124 124 124 124 125 125 125 125 125 125 125 125
[2827] 125 125 125 125 125 125 125 125 125 125 125 125 125 125 125 125 125 125
[2845] 125 125 125 126 126 126 126 126 126 126 126 126 126 126 126 126 126 126
[2863] 126 126 126 126 126 126 126 126 126 126 126 126 126 127 127 127 127 127
[2881] 127 127 127 127 127 127 127 127 127 127 127 127 127 127 127 127 127 128
[2899] 128 128 128 128 128 128 128 128 128 128 128 128 128 128 128 128 128 128
[2917] 128 128 128 128 128 128 128 128 128 128 128 128 128 128 129 129 129 129
[2935] 129 129 129 129 129 129 129 129 129 129 129 129 129 129 129 129 129 129
[2953] 129 129 129 129 129 129 129 129 129 130 130 130 130 130 130 130 130 130
[2971] 130 130 130 130 130 130 130 130 130 130 130 130 130 130 130 130 130 130
[2989] 130 130 131 131 131 131 131 131 131 131 131 131 131 131 131 131 131 131
[3007] 131 131 131 131 131 131 131 131 131 131 131 131 131 131 131 131 131 131
[3025] 131 131 131 131 131 131 131 131 131 131 131 131 131 131 131 131 131 131
[3043] 131 131 131 131 131 131 131 131 131 131 131 131 132 132 132 132 132 132
[3061] 132 132 132 132 132 132 132 132 132 132 132 132 132 132 132 132 132 132
[3079] 132 132 132 132 133 133 133 133 133 133 133 133 133 133 133 133 133 133
[3097] 133 133 133 133 133 133 133 133 133 133 133 133 133 133 133 133 133 133
[3115] 133 133 133 133 134 134 134 134 134 134 134 134 134 134 134 134 134 134
[3133] 134 134 134 134 134 134 134 134 134 134 134 134 134 134 134 134 134 134
[3151] 135 135 135 135 135 135 135 135 135 135 135 135 135 135 135 135 135 135
[3169] 135 135 135 135 135 135 135 135 135 135 135 135 136 136 136 136 136 136
[3187] 136 136 136 136 136 136 136 136 136 136 136 136 136 136 136 136 136 136
[3205] 136 136 136 136 136 136 136 136 136 136 137 137 137 137 137 137 137 137
[3223] 137 137 137 137 137 137 137 137 137 137 137 137 137 137 137 137 137 137
[3241] 137 137 137 137 137 137 137 139 139 139 139 139 139 139 139 139 139 139
[3259] 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139
[3277] 139 139 139 139 139 139 140 140 140 140 140 140 140 140 140 140 140 140
[3295] 140 140 140 140 140 140 140 140 140 140 140 140 140 140 140 140 140 141
[3313] 141 141 141 141 141 141 141 141 141 141 141 141 141 141 141 141 141 141
[3331] 141 141 142 142 142 142 142 142 142 142 142 142 142 142 142 142 142 142
[3349] 142 142 142 142 142 142 142 142 142 142 142 143 143 143 143 143 143 143
[3367] 143 143 143 143 143 143 143 143 143 143 143 143 143 143 143 143 143 143
[3385] 143 143 143 143 143 144 144 144 144 144 144 144 144 144 144 144 144 144
[3403] 144 144 144 144 144 144 144 144 144 144 144 144 144 144 144 144 144 145
[3421] 145 145 145 145 145 145 145 145 145 145 145 145 145 145 145 145 145 145
[3439] 145 145 145 145 145 145 145 145 145 145 145 145 145 145 145 146 146 146
[3457] 146 146 146 146 146 146 146 146 146 146 146 146 146 146 146 146 146 147
[3475] 147 147 147 147 147 147 147 147 147 147 147 147 147 147 147 147 147 147
[3493] 147 147 147 147 147 147 148 148 148 148 148 148 148 148 148 148 148 148
[3511] 148 148 148 148 148 148 148 148 148 148 148 148 148 148 149 149 149 149
[3529] 149 149 149 149 149 149 149 149 149 149 149 149 149 149 149 149 149 149
[3547] 149 149 149 149 150 150 150 150 150 150 150 150 150 150 150 150 150 150
[3565] 150 150 150 150 150 150 150 150 150 150 150 150 150 150 150 151 151 151
[3583] 151 151 151 151 151 151 151 151 151 151 151 151 151 151 151 151 151 151
[3601] 151 151 151 151 151 151 151 152 152 152 152 152 152 152 152 152 152 152
[3619] 152 152 152 152 152 152 152 152 152 152 152 152 152 152 152 152 152 152
[3637] 152 152 153 153 153 153 153 153 153 153 153 153 153 153 153 153 153 153
[3655] 153 153 153 153 153 153 153 153 153 153 153 154 154 154 154 154 154 154
[3673] 154 154 154 154 154 154 154 154 154 154 154 154 154 154 154 154 154 154
[3691] 155 155 155 155 155 155 155 155 155 155 155 155 155 155 155 155 155 155
[3709] 155 155 155 155 155 155 155 155 155 156 156 156 156 156 156 156 156 156
[3727] 156 156 156 156 156 156 156 156 156 156 156 156 156 156 156 156 156 156
[3745] 156 156 156 157 157 157 157 157 157 157 157 157 157 157 157 157 157 157
[3763] 157 157 157 157 157 157 157 157 157 158 158 158 158 158 158 158 158 158
[3781] 158 158 158 158 158 158 158 158 158 158 158 158 158 158 158 158 158 158
[3799] 158 158 158 158 158 158 158 159 159 159 159 159 159 159 159 159 159 159
[3817] 159 159 160 160 160 160 160 160 160 160 160 160 160 160 160 160 160 160
[3835] 160 160 160 160 160 161 161 161 161 161 161 161 161 161 161 161 161 161
[3853] 161 161 161 161 161 161 161 161 161 161 161 161 161 161 161 161 162 162
[3871] 162 162 162 162 162 162 162 162 162 162 162 162 162 162 162 162 162 162
[3889] 162 162 162 162 162 162 162 162 162 162 162 162 162 163 163 163 163 163
[3907] 163 163 163 163 163 163 163 163 163 163 163 163 163 163 163 163 163 163
[3925] 163 163 163 163 164 164 164 164 164 164 164 164 164 164 164 164 164 164
[3943] 164 164 164 164 164 164 164 164 164 164 164 164 164 165 165 165 165 165
[3961] 165 165 165 165 165 165 165 165 165 165 165 165 165 165 165 165 165 165
[3979] 165 165 165 165 165 165 166 166 166 166 166 166 166 166 166 166 166 166
[3997] 166 166 166 166 166 166 166 166 166 166 166 166 166 166 166 167 167 167
[4015] 167 167 167 167 167 167 167 167 167 167 167 167 167 167 167 167 167 167
[4033] 167 167 167 167 167 168 168 168 168 168 168 168 168 168 168 168 168 168
[4051] 168 168 168 168 168 168 168 168 168 168 169 169 169 169 169 169 169 169
[4069] 169 169 169 169 169 169 169 170 170 170 170 170 170 170 170 170 170 170
[4087] 170 170 170 170 170 170 170 170 170 170 170 171 171 171 171 171 171 171
[4105] 171 171 171 171 171 171 171 171 171 171 171 171 171 171 171 171 172 172
[4123] 172 172 172 172 172 172 172 172 172 172 172 172 172 172 172 173 173 173
[4141] 173 173 173 173 173 173 173 173 173 173 173 173 173 173 173 173 173 173
[4159] 173 173 174 174 174 174 174 174 174 174 174 174 174 174 174 174 175 175
[4177] 175 175 175 175 175 175 175 175 175 175 175 175 175 175 175 175 175 175
[4195] 175 176 176 176 176 176 176 176 176 176 176 176 176 176 176 176 176 176
[4213] 176 176 176 176 176 176 176 177 177 177 177 177 177 177 177 177 177 177
[4231] 177 177 177 177 177 177 177 177 177 177 177 177 177 177 178 178 178 178
[4249] 178 178 178 178 178 178 178 178 178 178 178 178 178 178 178 178 178 178
[4267] 179 179 179 179 179 179 179 179 179 179 179 179 179 179 179 179 179 179
[4285] 179 179 180 180 180 180 180 180 180 180 180 180 180 180 180 180 180 180
[4303] 180 180 180 181 181 181 181 181 181 181 181 181 181 181 181 181 181 181
[4321] 181 181 181 181 181 181 182 182 182 182 182 182 182 182 182 182 182 182
[4339] 182 182 182 182 182 182 182 182 182 182 182 182 182 182 182 182 182 183
[4357] 183 183 183 183 183 183 183 183 183 183 183 183 183 183 183 183 183 183
[4375] 183 183 183 184 184 184 184 184 184 184 184 184 184 184 184 184 184 184
[4393] 184 184 184 184 184 184 184 184 184 184 184 184 184 185 185 185 185 185
[4411] 185 185 185 185 185 185 185 185 185 185 185 185 185 185 185 185 185 186
[4429] 186 186 186 186 186 186 186 186 186 186 186 186 186 186 186 186 186 186
[4447] 186 186 186 186 186 187 187 187 187 187 187 187 187 187 187 187 187 187
[4465] 187 187 187 187 188 188 188 188 188 188 188 188 188 188 188 188 188 188
[4483] 188 188 188 188 188 188 188 189 189 189 189 189 189 189 189 189 189 189
[4501] 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 190
[4519] 190 190 190 190 190 190 190 190 190 190 190 190 190 190 190 190 190 190
[4537] 190 190 190 190 190 190 190 190 190 190 190 190 190 191 191 191 191 191
[4555] 191 191 191 191 191 191 191 191 191 191 191 191 191 191 191 191 191 191
[4573] 191 191 191 191 191 192 192 192 192 192 192 192 192 192 192 192 192 192
[4591] 192 192 192 192 192 192 192 192 192 192 192 192 192 192 192 192 192 192
[4609] 192 193 193 193 193 193 193 193 193 193 193 193 193 193 193 193 193 193
[4627] 193 193 193 193 193 193 193 193 193 193 193 193 193 194 194 194 194 194
[4645] 194 194 194 194 194 194 194 194 194 194 194 194 194 194 194 194 194 194
[4663] 194 194 194 194 194 195 195 195 195 195 195 195 195 195 195 195 195 195
[4681] 195 195 195 195 195 195 195 195 195 195 195 195 195 195 195
$names
[1] "TUN_2_C1" "THA_2_C1" "DMSO_2_C1" "TUN_24_C1" "THA_24_C1"
[6] "DOX_24_C1" "NUTL_24_C1" "DMSO_24_C1" "LPS_24_C1" "TNFa_24_C1"
[11] "H2O_24_C1" "BPA_24_C1" "PFOA_24_C1" "EtOH_24_C1" "TUN_2_C2"
[16] "THA_2_C2" "DMSO_2_C2" "TUN_24_C2" "THA_24_C2" "DOX_24_C2"
[21] "NUTL_24_C2" "DMSO_24_C2" "LPS_24_C2" "TNFa_24_C2" "H2O_24_C2"
[26] "BPA_24_C2" "PFOA_24_C2" "EtOH_24_C2" "TUN_2_C3" "THA_2_C3"
[31] "DMSO_2_C3" "TUN_24_C3" "THA_24_C3" "DOX_24_C3" "NUTL_24_C3"
[36] "DMSO_24_C3" "LPS_24_C3" "TNFa_24_C3" "H2O_24_C3" "BPA_24_C3"
[41] "PFOA_24_C3" "EtOH_24_C3" "TUN_2_C4" "THA_2_C4" "DMSO_2_C4"
[46] "TUN_24_C4" "THA_24_C4" "DOX_24_C4" "NUTL_24_C4" "DMSO_24_C4"
[51] "LPS_24_C4" "TNFa_24_C4" "H2O_24_C4" "BPA_24_C4" "PFOA_24_C4"
[56] "EtOH_24_C4" "TUN_2_C5" "DMSO_2_C5" "TUN_24_C5" "THA_24_C5"
[61] "DOX_24_C5" "NUTL_24_C5" "DMSO_24_C5" "LPS_24_C5" "TNFa_24_C5"
[66] "H2O_24_C5" "BPA_24_C5" "PFOA_24_C5" "EtOH_24_C5" "TUN_2_C6"
[71] "THA_2_C6" "DMSO_2_C6" "TUN_24_C6" "THA_24_C6" "DOX_24_C6"
[76] "NUTL_24_C6" "DMSO_24_C6" "LPS_24_C6" "TNFa_24_C6" "H2O_24_C6"
[81] "BPA_24_C6" "PFOA_24_C6" "EtOH_24_C6" "TUN_2_C7" "THA_2_C7"
[86] "DMSO_2_C7" "TUN_24_C7" "THA_24_C7" "DOX_24_C7" "NUTL_24_C7"
[91] "DMSO_24_C7" "LPS_24_C7" "TNFa_24_C7" "H2O_24_C7" "BPA_24_C7"
[96] "PFOA_24_C7" "EtOH_24_C7" "TUN_2_H1" "THA_2_H1" "DMSO_2_H1"
[101] "TUN_24_H1" "THA_24_H1" "DOX_24_H1" "NUTL_24_H1" "DMSO_24_H1"
[106] "LPS_24_H1" "TNFa_24_H1" "H2O_24_H1" "BPA_24_H1" "PFOA_24_H1"
[111] "EtOH_24_H1" "TUN_2_H2" "THA_2_H2" "DMSO_2_H2" "TUN_24_H2"
[116] "THA_24_H2" "DOX_24_H2" "NUTL_24_H2" "DMSO_24_H2" "LPS_24_H2"
[121] "TNFa_24_H2" "H2O_24_H2" "BPA_24_H2" "PFOA_24_H2" "EtOH_24_H2"
[126] "TUN_2_H3" "THA_2_H3" "DMSO_2_H3" "TUN_24_H3" "THA_24_H3"
[131] "DOX_24_H3" "NUTL_24_H3" "DMSO_24_H3" "LPS_24_H3" "TNFa_24_H3"
[136] "H2O_24_H3" "BPA_24_H3" "PFOA_24_H3" "EtOH_24_H3" "TUN_2_H4"
[141] "THA_2_H4" "DMSO_2_H4" "TUN_24_H4" "THA_24_H4" "DOX_24_H4"
[146] "NUTL_24_H4" "DMSO_24_H4" "LPS_24_H4" "TNFa_24_H4" "H2O_24_H4"
[151] "BPA_24_H4" "PFOA_24_H4" "EtOH_24_H4" "TUN_2_H5" "THA_2_H5"
[156] "DMSO_2_H5" "TUN_24_H5" "THA_24_H5" "DOX_24_H5" "NUTL_24_H5"
[161] "DMSO_24_H5" "LPS_24_H5" "TNFa_24_H5" "H2O_24_H5" "BPA_24_H5"
[166] "PFOA_24_H5" "EtOH_24_H5" "TUN_2_H6" "THA_2_H6" "DMSO_2_H6"
[171] "TUN_24_H6" "THA_24_H6" "DOX_24_H6" "NUTL_24_H6" "DMSO_24_H6"
[176] "LPS_24_H6" "TNFa_24_H6" "H2O_24_H6" "BPA_24_H6" "PFOA_24_H6"
[181] "EtOH_24_H6" "TUN_2_H7" "THA_2_H7" "DMSO_2_H7" "TUN_24_H7"
[186] "THA_24_H7" "DOX_24_H7" "NUTL_24_H7" "DMSO_24_H7" "LPS_24_H7"
[191] "TNFa_24_H7" "H2O_24_H7" "BPA_24_H7" "PFOA_24_H7" "EtOH_24_H7"Principal Component Analysis
Perform PCA analysis on subset of data.
prcomp_hc_unfilt <- prcomp(t(hc_cpm_unfilt_sub), center = TRUE)
prcomp_hc_filt <- prcomp(t(hc_cpm_matrix), center = TRUE)
#add in labels for individual numbers
ind_num <- metadata_sub$Ind
metadata_sub$Time <- factor(metadata_sub$Time)
metadata_sub$Stimulus <- factor(
metadata_sub$Stimulus,
levels = c("TUN", "THA", "DOX", "NUTL", "DMSO",
"LPS", "TNFa", "H2O", "BPA", "PFOA", "EtOH")
)
#now plot my PCA for unfiltered log2cpm
####PC1/PC2####
unfilt_PC1_PC2 <- ggplot2::autoplot(prcomp_hc_unfilt,
data = metadata_sub,
colour = "Stimulus",
shape = "Time",
size =4, x=1, y=2) +
ggrepel::geom_text_repel(label=ind_num,
vjust = -.5,
max.overlaps = 100,
segment.color = "grey",
segment.size = 0.1) +
scale_color_manual(values = stim_col) +
ggtitle(expression("Unfiltered log"[2]*"cpm PC1/PC2")) +
theme_custom()
####PC2/PC3####
unfilt_PC2_PC3 <- ggplot2::autoplot(prcomp_hc_unfilt,
data = metadata_sub,
colour = "Stimulus",
shape = "Time",
size =4, x=2, y=3) +
ggrepel::geom_text_repel(label = ind_num,
vjust = -.5,
max.overlaps = 100,
segment.color = "grey",
segment.size = 0.1) +
scale_color_manual(values = stim_col) +
ggtitle(expression("Unfiltered log"[2]*"cpm PC2/PC3")) +
theme_custom()
####PC3/PC4####
unfilt_PC3_PC4 <- ggplot2::autoplot(prcomp_hc_unfilt,
data = metadata_sub,
colour = "Stimulus",
shape = "Time",
size =4, x=3, y=4) +
ggrepel::geom_text_repel(label = ind_num,
vjust = -.5,
max.overlaps = 100,
segment.color = "grey",
segment.size = 0.1) +
scale_color_manual(values = stim_col) +
ggtitle(expression("Unfiltered log"[2]*"cpm PC3/PC4")) +
theme_custom()
#Now plot my PCA for filtered log2cpm
####PC1/PC2####
filt_PC1_PC2 <- ggplot2::autoplot(prcomp_hc_filt,
data = metadata_sub,
colour = "Stimulus",
shape = "Time",
size =4, x=1, y=2) +
ggrepel::geom_text_repel(label = ind_num,
vjust = -.5,
max.overlaps = 100,
segment.color = "grey",
segment.size = 0.1) +
scale_color_manual(values = stim_col) +
ggtitle(expression("Filtered log"[2]*"cpm PC1/PC2")) +
theme_custom()
####PC2/PC3####
filt_PC2_PC3 <- ggplot2::autoplot(prcomp_hc_filt,
data = metadata_sub,
colour = "Stimulus",
shape = "Time",
size =4, x=2, y=3) +
ggrepel::geom_text_repel(label=ind_num,
vjust = -.5,
max.overlaps = 100,
segment.color = "grey",
segment.size = 0.1) +
scale_color_manual(values = stim_col) +
ggtitle(expression("Filtered log"[2]*"cpm PC2/PC3")) +
theme_custom()
####PC3/PC4####
filt_PC3_PC4 <- ggplot2::autoplot(prcomp_hc_filt,
data = metadata_sub,
colour = "Stimulus",
shape = "Time",
size =4, x=3, y=4) +
ggrepel::geom_text_repel(label=ind_num,
vjust = -.5,
max.overlaps = 100,
segment.color = "grey",
segment.size = 0.1) +
scale_color_manual(values = stim_col) +
ggtitle(expression("Filtered log"[2]*"cpm PC2/PC3")) +
theme_custom()
#now save all of these together in pdf and png
hc_pca_plots <- list(
unfilt_PC1_PC2 = unfilt_PC1_PC2,
unfilt_PC2_PC3 = unfilt_PC2_PC3,
unfilt_PC3_PC4 = unfilt_PC3_PC4,
filt_PC1_PC2 = filt_PC1_PC2,
filt_PC2_PC3 = filt_PC2_PC3,
filt_PC3_PC4 = filt_PC3_PC4
)
print(hc_pca_plots)$unfilt_PC1_PC2
$unfilt_PC2_PC3
$unfilt_PC3_PC4
$filt_PC1_PC2
$filt_PC2_PC3
$filt_PC3_PC4
# for (plot_name in names(hc_pca_plots)) {
# save_plot(
# plot = hc_pca_plots[[plot_name]],
# filename = paste0("PCA_HC_", plot_name, "_EMP"),
# folder = output_folder
# )
# }PCA from 24hr Samples
Only plot 24hr samples on these PCA plots.
#now filter out all of the 2hr points and plot your filtered PCA plots
metadata_24 <- metadata_sub %>%
dplyr::filter(Time == "24")
#now make my matrices into only 24hr samples
hc_cpm_unfilt_24 <- hc_cpm_unfilt_sub[, metadata_24$Final_sample_name, drop = FALSE]
hc_cpm_mat_24 <- hc_cpm_matrix[, metadata_24$Final_sample_name, drop = FALSE]
# saveRDS(hc_cpm_mat_24, "data/counts/hc_filtered_cpm_matrix_24hr_subset.RDS")
#prcomp transformation
prcomp_hc_unfilt_24 <- prcomp(t(hc_cpm_unfilt_24),
center = TRUE)
prcomp_hc_filt_24 <- prcomp(t(hc_cpm_mat_24),
center = TRUE)
#add in labels for individual numbers
ind_num <- metadata_24$Ind
metadata_24$Stimulus <- factor(
metadata_24$Stimulus,
levels = c("TUN", "THA", "DOX", "NUTL", "DMSO",
"LPS", "TNFa", "H2O", "BPA", "PFOA", "EtOH")
)
metadata_24$Species <- factor(
metadata_24$Species,
levels = c("H", "C")
)
#now plot my PCA for unfiltered/filtered log2cpm
####PC1/PC2####
unfilt24_PC1_PC2 <- ggplot2::autoplot(prcomp_hc_unfilt_24,
data = metadata_24,
colour = "Stimulus",
shape = "Species",
size =4, x=1, y=2) +
ggrepel::geom_text_repel(label = ind_num,
vjust = -.5,
max.overlaps = 50,
segment.color = "grey",
segment.size = 0.1) +
scale_color_manual(values = stim_col) +
ggtitle(expression("log"[2]*"cpm PC1/PC2 24hr (unfilt)")) +
theme_custom()
filt24_PC1_PC2 <- ggplot2::autoplot(prcomp_hc_filt_24,
data = metadata_24,
colour = "Stimulus",
shape = "Species",
size =3, x=1, y=2) +
ggrepel::geom_text_repel(label=ind_num,
vjust = -.5,
max.overlaps = 50,
segment.color = "grey",
segment.size = 0.1) +
scale_color_manual(values = stim_col) +
ggtitle(expression("log"[2]*"cpm PC1/PC2 24hr")) +
theme_custom()
####PC2/3####
unfilt24_PC2_PC3 <- ggplot2::autoplot(prcomp_hc_unfilt_24,
data = metadata_24,
colour = "Stimulus",
shape = "Species",
size =4, x=2, y=3) +
ggrepel::geom_text_repel(label=ind_num,
vjust = -.5,
max.overlaps = 50,
segment.color = "grey",
segment.size = 0.1) +
scale_color_manual(values = stim_col) +
ggtitle(expression("log"[2]*"cpm PC2/PC3 24hr (unfilt)")) +
theme_custom()
filt24_PC2_PC3 <- ggplot2::autoplot(prcomp_hc_filt_24,
data = metadata_24,
colour = "Stimulus",
shape = "Species",
size =4, x=2, y=3) +
ggrepel::geom_text_repel(label=ind_num,
vjust = -.5,
max.overlaps = 50,
segment.color = "grey",
segment.size = 0.1) +
scale_color_manual(values = stim_col) +
ggtitle(expression("log"[2]*"cpm PC2/PC3 24hr")) +
theme_custom()
####PC3/4####
unfilt24_PC3_PC4 <- ggplot2::autoplot(prcomp_hc_unfilt_24,
data = metadata_24,
colour = "Stimulus",
shape = "Species",
size =4, x=3, y=4) +
ggrepel::geom_text_repel(label=ind_num,
vjust = -.5,
max.overlaps = 50,
segment.color = "grey",
segment.size = 0.1) +
scale_color_manual(values = stim_col) +
ggtitle(expression("log"[2]*"cpm PC3/PC4 24hr (unfilt)")) +
theme_custom()
filt24_PC3_PC4 <- ggplot2::autoplot(prcomp_hc_filt_24,
data = metadata_24,
colour = "Stimulus",
shape = "Species",
size =4, x=3, y=4) +
ggrepel::geom_text_repel(label=ind_num,
vjust = -.5,
max.overlaps = 50,
segment.color = "grey",
segment.size = 0.1) +
scale_color_manual(values = stim_col) +
ggtitle(expression("log"[2]*"cpm PC3/PC4 24hr")) +
theme_custom()
#make a list of your 24hr PCA plots and print them all
pca_24_list <- list(
unfilt24_PC1_PC2 = unfilt24_PC1_PC2,
unfilt24_PC2_PC3 = unfilt24_PC2_PC3,
unfilt24_PC3_PC4 = unfilt24_PC3_PC4,
filt24_PC1_PC2 = filt24_PC1_PC2,
filt24_PC2_PC3 = filt24_PC2_PC3,
filt24_PC3_PC4 = filt24_PC3_PC4
)
print(pca_24_list)$unfilt24_PC1_PC2
$unfilt24_PC2_PC3
$unfilt24_PC3_PC4
$filt24_PC1_PC2
$filt24_PC2_PC3
$filt24_PC3_PC4
# for (plot_name in names(pca_24_list)) {
# save_plot(
# plot = pca_24_list[[plot_name]],
# filename = paste0("PCA_24hr_HC_", plot_name, "_EMP"),
# folder = output_folder
# )
# }
# save_plot(
# plot = filt24_PC1_PC2,
# filename = "PCA_24hr_HC_filt24_PC1_PC2_EMP",
# folder = output_folder
# )PCA from 2hr Samples
metadata_2 <- metadata_sub %>%
filter(Time == "2")
#now make my matrices into only 24hr samples
hc_cpm_unfilt_2 <- hc_cpm_unfilt_sub[, metadata_2$Final_sample_name, drop = FALSE]
hc_cpm_mat_2 <- hc_cpm_matrix[, metadata_2$Final_sample_name, drop = FALSE]
#prcomp transformation
prcomp_hc_unfilt_2 <- prcomp(t(hc_cpm_unfilt_2),
center = TRUE)
prcomp_hc_filt_2 <- prcomp(t(hc_cpm_mat_2),
center = TRUE)
#add in labels for individual numbers
ind_num_2 <- metadata_2$Ind
metadata_2$Stimulus <- factor(
metadata_2$Stimulus,
levels = c("TUN", "THA", "DMSO")
)
metadata_2$Species <- factor(
metadata_2$Species,
levels = c("H", "C")
)
#now plot my PCA for unfiltered/filtered log2cpm
####PC1/PC2####
unfilt2_PC1_PC2 <- ggplot2::autoplot(prcomp_hc_unfilt_2,
data = metadata_2,
colour = "Stimulus",
shape = "Species",
size =4, x=1, y=2) +
ggrepel::geom_text_repel(label = ind_num_2,
vjust = -.5,
max.overlaps = 50,
segment.color = "grey",
segment.size = 0.1) +
scale_color_manual(values = stim_col) +
ggtitle(expression("log"[2]*"cpm PC1/PC2 2hr (unfilt)")) +
theme_custom()
filt2_PC1_PC2 <- ggplot2::autoplot(prcomp_hc_filt_2,
data = metadata_2,
colour = "Stimulus",
shape = "Species",
size =3, x=1, y=2) +
ggrepel::geom_text_repel(label=ind_num_2,
vjust = -.5,
max.overlaps = 50,
segment.color = "grey",
segment.size = 0.1) +
scale_color_manual(values = stim_col) +
ggtitle(expression("log"[2]*"cpm PC1/PC2 2hr")) +
theme_custom()
####PC2/3####
unfilt2_PC2_PC3 <- ggplot2::autoplot(prcomp_hc_unfilt_2,
data = metadata_2,
colour = "Stimulus",
shape = "Species",
size =4, x=2, y=3) +
ggrepel::geom_text_repel(label=ind_num_2,
vjust = -.5,
max.overlaps = 50,
segment.color = "grey",
segment.size = 0.1) +
scale_color_manual(values = stim_col) +
ggtitle(expression("log"[2]*"cpm PC2/PC3 2hr (unfilt)")) +
theme_custom()
filt2_PC2_PC3 <- ggplot2::autoplot(prcomp_hc_filt_2,
data = metadata_2,
colour = "Stimulus",
shape = "Species",
size =4, x=2, y=3) +
ggrepel::geom_text_repel(label=ind_num_2,
vjust = -.5,
max.overlaps = 50,
segment.color = "grey",
segment.size = 0.1) +
scale_color_manual(values = stim_col) +
ggtitle(expression("log"[2]*"cpm PC2/PC3 2hr")) +
theme_custom()
####PC3/4####
unfilt2_PC3_PC4 <- ggplot2::autoplot(prcomp_hc_unfilt_2,
data = metadata_2,
colour = "Stimulus",
shape = "Species",
size =4, x=3, y=4) +
ggrepel::geom_text_repel(label=ind_num_2,
vjust = -.5,
max.overlaps = 50,
segment.color = "grey",
segment.size = 0.1) +
scale_color_manual(values = stim_col) +
ggtitle(expression("log"[2]*"cpm PC3/PC4 2hr (unfilt)")) +
theme_custom()
filt2_PC3_PC4 <- ggplot2::autoplot(prcomp_hc_filt_2,
data = metadata_2,
colour = "Stimulus",
shape = "Species",
size =4, x=3, y=4) +
ggrepel::geom_text_repel(label=ind_num_2,
vjust = -.5,
max.overlaps = 50,
segment.color = "grey",
segment.size = 0.1) +
scale_color_manual(values = stim_col) +
ggtitle(expression("log"[2]*"cpm PC3/PC4 2hr")) +
theme_custom()
#make a list of your 24hr PCA plots and print them all
pca_2_list <- list(
unfilt2_PC1_PC2 = unfilt2_PC1_PC2,
unfilt2_PC2_PC3 = unfilt2_PC2_PC3,
unfilt2_PC3_PC4 = unfilt2_PC3_PC4,
filt2_PC1_PC2 = filt2_PC1_PC2,
filt2_PC2_PC3 = filt2_PC2_PC3,
filt2_PC3_PC4 = filt2_PC3_PC4
)
print(pca_2_list)$unfilt2_PC1_PC2
$unfilt2_PC2_PC3
$unfilt2_PC3_PC4
$filt2_PC1_PC2
$filt2_PC2_PC3
$filt2_PC3_PC4
# for (plot_name in names(pca_2_list)) {
# save_plot(
# plot = pca_2_list[[plot_name]],
# filename = paste0("PCA_2hr_HC_", plot_name, "_EMP"),
# folder = output_folder
# )
# }
# save_plot(
# plot = filt2_PC1_PC2,
# filename = "PCA_2hr_HC_filt2_PC1_PC2_EMP",
# folder = output_folder
# )PCA Subset by Response Category
Now take the samples from my subset and divide them out by response category to get an idea of how the different stimuli are working together within a category.
#Subset PCA
#Copy down the original full dataset that I'll divide into categories
ind_num <- metadata_sub$Ind
metadata_sub$Time <- factor(metadata_sub$Time)
metadata_sub$Stimulus <- factor(
metadata_sub$Stimulus,
levels = c("TUN", "THA", "DOX", "NUTL", "DMSO",
"LPS", "TNFa", "H2O", "BPA", "PFOA", "EtOH")
)
#UPR Subset
upr_tx <- c("TUN", "THA", "DMSO")
upr_meta <- metadata_sub %>%
dplyr::filter(Stimulus %in% upr_tx)
upr_cpm <- hc_cpm_matrix[, colnames(hc_cpm_matrix) %in% upr_meta$Final_sample_name]
upr_cols <- list(
TUN = "#5A874A",
THA = "#C4E1AE",
DMSO = "#999999"
)
#DDR Subset
ddr_tx <- c("DOX", "NUTL", "DMSO")
ddr_meta <- metadata_sub %>%
dplyr::filter(Stimulus %in% ddr_tx)
ddr_cpm <- hc_cpm_matrix[, colnames(hc_cpm_matrix) %in% ddr_meta$Final_sample_name]
ddr_cols <- c(
"DOX" = "#3B4DA3",
"NUTL" = "#A0CFF8",
"DMSO" = "#999999")
#IMR Subset
imr_tx <- c("LPS", "TNFa", "H2O")
imr_meta <- metadata_sub %>%
dplyr::filter(Stimulus %in% imr_tx)
imr_cpm <- hc_cpm_matrix[, colnames(hc_cpm_matrix) %in% imr_meta$Final_sample_name]
imr_cols <- c(
"LPS" = "#C0329A",
"TNFa" = "#F09DD6",
"H2O" = "#777777")
#MMR Subset
mmr_tx <- c("BPA", "PFOA", "EtOH")
mmr_meta <- metadata_sub %>%
dplyr::filter(Stimulus %in% mmr_tx)
mmr_cpm <- hc_cpm_matrix[, colnames(hc_cpm_matrix) %in% mmr_meta$Final_sample_name]
mmr_cols <- c(
"BPA" = "#7359A6",
"PFOA" = "#D7BDF3",
"EtOH" = "#555555")
#now make a function to plot these PCA plots in subsets
make_pca_plots <- function(subset_name, subset_drugs, hc_matrix, metadata, ind_num, stim_col) {
# filter metadata
keep_idx <- metadata$Stimulus %in% subset_drugs
meta_sub <- metadata[keep_idx, ]
meta_sub$Ind_label <- ind_num[keep_idx]
# subset expression matrix to same samples
hc_matrix_sub <- hc_matrix[, keep_idx]
# run PCA on subset
prcomp_sub <- prcomp(t(hc_matrix_sub), scale. = TRUE)
## PC1/PC2
p1 <- autoplot(prcomp_sub, data = meta_sub,
colour = "Stimulus", shape = "Time", size = 4,
x = 1, y = 2) +
geom_text_repel(aes(label = Ind_label),
max.overlaps = 100,
segment.color = "grey",
segment.size = 0.1) +
scale_color_manual(values = stim_col) +
ggtitle(bquote(.(subset_name) ~ "subset: Filtered log"[2] * "cpm PC1/PC2")) +
coord_cartesian(clip = "off") +
theme_custom()
## PC2/PC3
p2 <- autoplot(prcomp_sub, data = meta_sub,
colour = "Stimulus", shape = "Time",
size = 4, x = 2, y = 3) +
geom_text_repel(aes(label = Ind_label),
max.overlaps = 100,
segment.color = "grey",
segment.size = 0.1) +
scale_color_manual(values = stim_col) +
ggtitle(bquote(.(subset_name) ~ "subset: Filtered log"[2] * "cpm PC2/PC3")) +
coord_cartesian(clip = "off") +
theme_custom()
## PC3/PC4
p3 <- autoplot(prcomp_sub, data = meta_sub,
colour = "Stimulus", shape = "Time",
size = 4, x = 3, y = 4) +
geom_text_repel(aes(label = Ind_label),
max.overlaps = 100,
segment.color = "grey",
segment.size = 0.1) +
scale_color_manual(values = stim_col) +
ggtitle(bquote(.(subset_name) ~ "subset: Filtered log"[2] * "cpm PC3/PC4")) +
coord_cartesian(clip = "off") +
theme_custom()
list(p1 = p1, p2 = p2, p3 = p3)
}
# ---- Run for each subset ----
upr_plots <- make_pca_plots("UPR", c("TUN", "THA", "DMSO"), hc_cpm_matrix, metadata_sub, ind_num, stim_col)
ddr_plots <- make_pca_plots("DDR", c("DOX", "NUTL", "DMSO"), hc_cpm_matrix, metadata_sub, ind_num, stim_col)
imr_plots <- make_pca_plots("IMR", c("LPS", "TNFa", "H2O"), hc_cpm_matrix, metadata_sub, ind_num, stim_col)
mmr_plots <- make_pca_plots("MMR", c("BPA", "PFOA", "EtOH"), hc_cpm_matrix, metadata_sub, ind_num, stim_col)
all_plots_subset_pca <- list(
upr_PC1_PC2 = upr_plots$p1,
upr_PC2_PC3 = upr_plots$p2,
upr_PC3_PC4 = upr_plots$p3,
ddr_PC1_PC2 = ddr_plots$p1,
ddr_PC2_PC3 = ddr_plots$p2,
ddr_PC3_PC4 = ddr_plots$p3,
imr_PC1_PC2 = imr_plots$p1,
imr_PC2_PC3 = imr_plots$p2,
imr_PC3_PC4 = imr_plots$p3,
mmr_PC1_PC2 = mmr_plots$p1,
mmr_PC2_PC3 = mmr_plots$p2,
mmr_PC3_PC4 = mmr_plots$p3
)
print(all_plots_subset_pca)$upr_PC1_PC2
$upr_PC2_PC3
$upr_PC3_PC4
$ddr_PC1_PC2
$ddr_PC2_PC3
$ddr_PC3_PC4
$imr_PC1_PC2
$imr_PC2_PC3
$imr_PC3_PC4
$mmr_PC1_PC2
$mmr_PC2_PC3
$mmr_PC3_PC4
# # Loop through each plot and save
# for (plot_name in names(all_plots_subset_pca)) {
# save_plot(
# plot = all_plots_subset_pca[[plot_name]],
# filename = paste0("Subset_PCA_", plot_name, "_EMP"),
# folder = output_folder
# )
# }Correlation Heatmaps
Create Spearman correlation heatmaps of all samples in the subset.
#first make correlation heatmaps of all samples
#using ComplexHeatmap
#compute Spearman correlation
cor_matrix_spearman <- cor(hc_cpm_matrix,
method = "spearman",
use = "everything")
#define factors for legend order
ind_levels <- c(paste0("H", 1:7), paste0("C", 1:7))
inf_status <- c("infected", "uninfected")
stim_levels <- c(
"TUN", "THA",
"DOX", "NUTL", "DMSO",
"LPS", "TNFa", "H2O",
"BPA", "PFOA", "EtOH"
)
spec_levels <- c("H", "C")
time_levels <- c("2", "24")
#extract metadata for annotations
Individual <- factor(metadata_sub$Ind,
levels = ind_levels)
Stimulus <- factor(metadata_sub$Stimulus,
levels = stim_levels)
Species <- factor(metadata_sub$Species,
levels = spec_levels)
Time <- factor(metadata_sub$Time,
levels = time_levels)
#colors defined as above, reinforce ordering for legends
indiv_col <- unlist(ind_col)[ind_levels]
stim_col <- stim_col[stim_levels]
spec_col <- spec_col[spec_levels]
time_col <- time_col[time_levels]
#create my annotations
top_annotation <- HeatmapAnnotation(
Individual = Individual,
Time = Time,
Stimulus = Stimulus,
Species = Species,
col = list(
Individual = indiv_col,
Time = time_col,
Stimulus = stim_col,
Species = spec_col
)
)
#because this is so large, I want to save the legends separately from the plot
#Spearman heatmap for all samples in subset
heatmap_spearman_all <- Heatmap(
cor_matrix_spearman,
name = "Spearman",
top_annotation = top_annotation,
show_row_names = TRUE,
show_column_names = TRUE,
cluster_rows = TRUE,
cluster_columns = TRUE,
show_row_dend = FALSE,
show_column_dend = TRUE,
row_names_gp = gpar(fontsize = 7),
column_names_gp = gpar(fontsize = 7),
column_names_centered = FALSE,
row_names_centered = FALSE,
rect_gp = gpar(col = "black", lwd = 0.5),
border = gpar(col = "black", lwd = 1),
column_title = "Spearman Correlation log2cpm 195 Samples",
column_title_gp = gpar(fontsize = 10, fontface = "plain"),
heatmap_legend_param = list(
title_gp = gpar(fontsize = 9, fontface = "plain"),
labels_gp = gpar(fontsize = 8)
)
)
draw(heatmap_spearman_all)
#save all samples heatmap with my custom function
# save_complex_heatmap(
# ht = heatmap_spearman_all,
# filename = "CorHM_Spearman_AllSamples_EMP",
# folder = output_folder,
# height = 20, width = 20)Response Category Subset Correlation Heatmaps
Now create a Spearman correlation heatmap for each response category.
#define subsets for each response category
subsets <- list(
UPR = c("TUN", "THA", "DMSO"),
DDR = c("DOX", "NUTL", "DMSO"),
IMR = c("LPS", "TNFa", "H2O"),
MMR = c("BPA", "PFOA", "EtOH")
)
#loop through subsets by response category
subset_heatmaps <- list()
for (subset_name in names(subsets)) {
#filter columns for each subset
cols_subset <- metadata_sub$Final_sample_name[metadata_sub$Stimulus %in% subsets[[subset_name]]]
mat_subset <- hc_cpm_matrix[, cols_subset, drop = FALSE]
#perform Spearman correlation on each subset
cor_subset <- cor(mat_subset, method = "spearman",
use = "everything")
#subset annotation factors
subset_inds <- factor(metadata_sub$Ind[metadata_sub$Final_sample_name %in% cols_subset],
levels = ind_levels)
subset_stim <- factor(metadata_sub$Stimulus[metadata_sub$Final_sample_name %in% cols_subset],
levels = stim_levels)
subset_spec <- factor(metadata_sub$Species[metadata_sub$Final_sample_name %in% cols_subset],
levels = spec_levels)
subset_time <- factor(metadata_sub$Time[metadata_sub$Final_sample_name %in% cols_subset],
levels = time_levels)
#subset color vectors to match what's present in subset
indiv_col_sub <- indiv_col[as.character(levels(subset_inds))]
stim_col_sub <- stim_col[as.character(levels(subset_stim))]
spec_col_sub <- spec_col[as.character(levels(subset_spec))]
time_col_sub <- time_col[as.character(levels(subset_time))]
#heatmap annotations
top_ann_subset <- HeatmapAnnotation(
Individual = subset_inds,
Time = subset_time,
Stimulus = subset_stim,
Species = subset_spec,
col = list(
Individual = indiv_col_sub,
Time = time_col_sub,
Stimulus = stim_col_sub,
Species = spec_col_sub
)
)
#draw heatmap for each subset
heatmap_subset <- Heatmap(
cor_subset,
name = paste0("Spearman_", subset_name),
top_annotation = top_ann_subset,
show_row_names = TRUE,
show_column_names = TRUE,
cluster_rows = TRUE,
cluster_columns = TRUE,
show_row_dend = FALSE,
show_column_dend = TRUE,
row_names_gp = gpar(fontsize = 7),
column_names_gp = gpar(fontsize = 7),
column_names_centered = FALSE,
row_names_centered = FALSE,
rect_gp = gpar(col = "black", lwd = 0.5),
border = gpar(col = "black", lwd = 1),
column_title = "Spearman Correlation log2cpm 195 Samples",
column_title_gp = gpar(fontsize = 10, fontface = "plain"),
heatmap_legend_param = list(
title_gp = gpar(fontsize = 9, fontface = "plain"),
labels_gp = gpar(fontsize = 8)
)
)
subset_heatmaps[[subset_name]] <- heatmap_subset
}
#save the subset heatmaps with my custom function
# for (nm in names(subset_heatmaps)) {
# ht <- subset_heatmaps[[nm]]
# save_complex_heatmap(
# ht = ht,
# filename = paste0("CorHM_Spearman_", nm, "_EMP"),
# folder = output_folder,
# height = 18,
# width = 18
# )
# message("Saved subset heatmap: ", nm)
# }
#ignore time in each set besides UPR, all other 2hr samples have been filtered out
sessionInfo()R version 4.4.2 (2024-10-31 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 22000)
Matrix products: default
locale:
[1] LC_COLLATE=English_United States.utf8
[2] LC_CTYPE=English_United States.utf8
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.utf8
time zone: America/Chicago
tzcode source: internal
attached base packages:
[1] grid stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] magick_2.9.0 broom_1.0.8
[3] gprofiler2_0.2.3 car_3.1-3
[5] carData_3.0-5 patchwork_1.3.0
[7] eulerr_7.0.2 ggrastr_1.0.2
[9] rstatix_0.7.2 ggsignif_0.6.4
[11] RUVSeq_1.40.0 EDASeq_2.40.0
[13] ShortRead_1.64.0 GenomicAlignments_1.42.0
[15] SummarizedExperiment_1.36.0 MatrixGenerics_1.18.1
[17] matrixStats_1.5.0 Rsamtools_2.22.0
[19] GenomicRanges_1.58.0 Biostrings_2.74.0
[21] GenomeInfoDb_1.42.3 XVector_0.46.0
[23] BiocParallel_1.40.0 VennDetail_1.22.0
[25] VennDiagram_1.7.3 futile.logger_1.4.3
[27] ggpubr_0.6.0 UpSetR_1.4.0
[29] ggVennDiagram_1.5.2 reshape2_1.4.4
[31] circlize_0.4.16 ComplexHeatmap_2.22.0
[33] org.Hs.eg.db_3.20.0 AnnotationDbi_1.68.0
[35] IRanges_2.40.0 S4Vectors_0.44.0
[37] corrplot_0.95 ggfortify_0.4.17
[39] ggrepel_0.9.6 biomaRt_2.62.1
[41] readxl_1.4.5 scales_1.4.0
[43] edgebundleR_0.1.4 edgeR_4.4.0
[45] limma_3.62.1 Biobase_2.66.0
[47] BiocGenerics_0.52.0 lubridate_1.9.4
[49] forcats_1.0.0 stringr_1.5.1
[51] dplyr_1.1.4 purrr_1.0.4
[53] readr_2.1.5 tidyr_1.3.1
[55] tibble_3.2.1 ggplot2_3.5.2
[57] tidyverse_2.0.0 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] later_1.4.2 BiocIO_1.16.0 bitops_1.0-9
[4] filelock_1.0.3 R.oo_1.27.1 cellranger_1.1.0
[7] XML_3.99-0.18 lifecycle_1.0.4 httr2_1.1.2
[10] pwalign_1.2.0 doParallel_1.0.17 rprojroot_2.0.4
[13] vroom_1.6.5 MASS_7.3-61 processx_3.8.6
[16] lattice_0.22-6 backports_1.5.0 magrittr_2.0.3
[19] plotly_4.10.4 sass_0.4.10 rmarkdown_2.29
[22] jquerylib_0.1.4 yaml_2.3.10 httpuv_1.6.16
[25] DBI_1.2.3 RColorBrewer_1.1-3 abind_1.4-8
[28] zlibbioc_1.52.0 R.utils_2.13.0 RCurl_1.98-1.17
[31] rappdirs_0.3.3 git2r_0.36.2 GenomeInfoDbData_1.2.13
[34] codetools_0.2-20 DelayedArray_0.32.0 xml2_1.3.8
[37] tidyselect_1.2.1 shape_1.4.6.1 UCSC.utils_1.2.0
[40] farver_2.1.2 BiocFileCache_2.14.0 jsonlite_2.0.0
[43] GetoptLong_1.0.5 Formula_1.2-5 iterators_1.0.14
[46] foreach_1.5.2 tools_4.4.2 progress_1.2.3
[49] Rcpp_1.0.14 glue_1.8.0 gridExtra_2.3
[52] SparseArray_1.6.0 xfun_0.52 withr_3.0.2
[55] formatR_1.14 fastmap_1.2.0 latticeExtra_0.6-30
[58] callr_3.7.6 digest_0.6.37 timechange_0.3.0
[61] R6_2.6.1 mime_0.13 colorspace_2.1-1
[64] Cairo_1.6-5 jpeg_0.1-11 RSQLite_2.3.9
[67] R.methodsS3_1.8.2 generics_0.1.4 data.table_1.17.0
[70] rtracklayer_1.66.0 prettyunits_1.2.0 httr_1.4.7
[73] htmlwidgets_1.6.4 S4Arrays_1.6.0 whisker_0.4.1
[76] pkgconfig_2.0.3 gtable_0.3.6 blob_1.2.4
[79] hwriter_1.3.2.1 htmltools_0.5.8.1 clue_0.3-66
[82] png_0.1-8 knitr_1.50 lambda.r_1.2.4
[85] rstudioapi_0.17.1 tzdb_0.5.0 rjson_0.2.23
[88] curl_6.0.1 cachem_1.1.0 GlobalOptions_0.1.2
[91] vipor_0.4.7 parallel_4.4.2 restfulr_0.0.15
[94] pillar_1.10.2 vctrs_0.6.5 promises_1.3.2
[97] dbplyr_2.5.0 xtable_1.8-4 cluster_2.1.6
[100] beeswarm_0.4.0 evaluate_1.0.3 GenomicFeatures_1.58.0
[103] cli_3.6.3 locfit_1.5-9.12 compiler_4.4.2
[106] futile.options_1.0.1 rlang_1.1.6 crayon_1.5.3
[109] labeling_0.4.3 aroma.light_3.36.0 interp_1.1-6
[112] ps_1.9.1 ggbeeswarm_0.7.2 getPass_0.2-4
[115] plyr_1.8.9 fs_1.6.6 stringi_1.8.7
[118] viridisLite_0.4.2 deldir_2.0-4 lazyeval_0.2.2
[121] Matrix_1.7-1 hms_1.1.3 bit64_4.5.2
[124] KEGGREST_1.46.0 statmod_1.5.0 shiny_1.10.0
[127] igraph_2.1.4 memoise_2.0.1 bslib_0.9.0
[130] bit_4.5.0