QC

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Last updated: 2025-12-27

Checks: 6 1

Knit directory: Dissrt/

This reproducible R Markdown analysis was created with workflowr (version 1.7.1). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

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R Markdown file: uncommitted changes

The R Markdown is untracked by Git. To know which version of the R Markdown file created these results, you’ll want to first commit it to the Git repo. If you’re still working on the analysis, you can ignore this warning. When you’re finished, you can run wflow_publish to commit the R Markdown file and build the HTML.

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Repository version: 988100a

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The results in this page were generated with repository version 988100a. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

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Ignored files:
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/
    Ignored:    analysis/figure/
    Ignored:    data/counts/
    Ignored:    data/de/
    Ignored:    data/genesets/
    Ignored:    data/theme/

Untracked files:
    Untracked:  DDR_DEGs_direction.RDS
    Untracked:  DOX_vs_NUTL_logFC_correlation.pdf_251003.pdf
    Untracked:  DOX_vs_NUTL_logFC_correlation.pdf_251003.png
    Untracked:  PCA_2hr_HC_filt2_PC1_PC2_EMP_251010.png, folder = output_folder, height = 5, width = 5_251010.pdf
    Untracked:  PCA_2hr_HC_filt2_PC1_PC2_EMP_251010.png, folder = output_folder, height = 5, width = 5_251010.png
    Untracked:  PCA_2hr_HC_filt2_PC1_PC2_EMP_251010.png, folder = output_folder, height =5, width = 5_251010.pdf
    Untracked:  PCA_2hr_HC_filt2_PC1_PC2_EMP_251010.png, folder = output_folder, height =5, width = 5_251010.png
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    Untracked:  analysis/QC_Subset.Rmd

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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"))
  ggsave(filename = pdf_file, plot = plot, device = cairo_pdf, width = width, height = height, units = units, bg = "transparent")
  ggsave(filename = png_file, plot = plot, device = "png", width = width, height = height, units = units, dpi = dpi, bg = "transparent")
  message("Saved plot as Cairo 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, height

Complex 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")

Rows: 195 Columns: 17
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (13): Sample_name, Sample_ID, ID, Line, Species, Sex, Stimulus, Response...
dbl  (4): Number, Ex_Num, Time, Conc

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

# 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")

# 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 <- read_csv("data/counts/hc_counts_subset.csv")

Rows: 28802 Columns: 195
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
dbl (195): TUN_2_C1, THA_2_C1, DMSO_2_C1, TUN_24_C1, THA_24_C1, DOX_24_C1, N...

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

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))

#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
  )
}

Saved plot as Cairo PDF: C:/Users/emmap/OneDrive/Desktop/Ward Lab/Experiments/Stressor Project/Full Set RNAseq/plots/total_reads_sample_EMP_251227.pdf

Saved plot as PNG: C:/Users/emmap/OneDrive/Desktop/Ward Lab/Experiments/Stressor Project/Full Set RNAseq/plots/total_reads_sample_EMP_251227.png

Saved plot as Cairo PDF: C:/Users/emmap/OneDrive/Desktop/Ward Lab/Experiments/Stressor Project/Full Set RNAseq/plots/map_reads_sample_EMP_251227.pdf

Saved plot as PNG: C:/Users/emmap/OneDrive/Desktop/Ward Lab/Experiments/Stressor Project/Full Set RNAseq/plots/map_reads_sample_EMP_251227.png

Saved plot as Cairo PDF: C:/Users/emmap/OneDrive/Desktop/Ward Lab/Experiments/Stressor Project/Full Set RNAseq/plots/total_reads_sample_boxplot_EMP_251227.pdf

Saved plot as PNG: C:/Users/emmap/OneDrive/Desktop/Ward Lab/Experiments/Stressor Project/Full Set RNAseq/plots/total_reads_sample_boxplot_EMP_251227.png

Saved plot as Cairo PDF: C:/Users/emmap/OneDrive/Desktop/Ward Lab/Experiments/Stressor Project/Full Set RNAseq/plots/map_reads_sample_boxplot_EMP_251227.pdf

Saved plot as PNG: C:/Users/emmap/OneDrive/Desktop/Ward Lab/Experiments/Stressor Project/Full Set RNAseq/plots/map_reads_sample_boxplot_EMP_251227.png

Saved plot as Cairo PDF: C:/Users/emmap/OneDrive/Desktop/Ward Lab/Experiments/Stressor Project/Full Set RNAseq/plots/total_reads_ind_EMP_251227.pdf

Saved plot as PNG: C:/Users/emmap/OneDrive/Desktop/Ward Lab/Experiments/Stressor Project/Full Set RNAseq/plots/total_reads_ind_EMP_251227.png

Saved plot as Cairo PDF: C:/Users/emmap/OneDrive/Desktop/Ward Lab/Experiments/Stressor Project/Full Set RNAseq/plots/map_reads_ind_EMP_251227.pdf

Saved plot as PNG: C:/Users/emmap/OneDrive/Desktop/Ward Lab/Experiments/Stressor Project/Full Set RNAseq/plots/map_reads_ind_EMP_251227.png

Saved plot as Cairo PDF: C:/Users/emmap/OneDrive/Desktop/Ward Lab/Experiments/Stressor Project/Full Set RNAseq/plots/total_reads_species_EMP_251227.pdf

Saved plot as PNG: C:/Users/emmap/OneDrive/Desktop/Ward Lab/Experiments/Stressor Project/Full Set RNAseq/plots/total_reads_species_EMP_251227.png

Saved plot as Cairo PDF: C:/Users/emmap/OneDrive/Desktop/Ward Lab/Experiments/Stressor Project/Full Set RNAseq/plots/total_reads_species_boxplot_EMP_251227.pdf

Saved plot as PNG: C:/Users/emmap/OneDrive/Desktop/Ward Lab/Experiments/Stressor Project/Full Set RNAseq/plots/total_reads_species_boxplot_EMP_251227.png

Saved plot as Cairo PDF: C:/Users/emmap/OneDrive/Desktop/Ward Lab/Experiments/Stressor Project/Full Set RNAseq/plots/map_reads_species_EMP_251227.pdf

Saved plot as PNG: C:/Users/emmap/OneDrive/Desktop/Ward Lab/Experiments/Stressor Project/Full Set RNAseq/plots/map_reads_species_EMP_251227.png

Saved plot as Cairo PDF: C:/Users/emmap/OneDrive/Desktop/Ward Lab/Experiments/Stressor Project/Full Set RNAseq/plots/total_reads_stimulus_EMP_251227.pdf

Saved plot as PNG: C:/Users/emmap/OneDrive/Desktop/Ward Lab/Experiments/Stressor Project/Full Set RNAseq/plots/total_reads_stimulus_EMP_251227.png

Saved plot as Cairo PDF: C:/Users/emmap/OneDrive/Desktop/Ward Lab/Experiments/Stressor Project/Full Set RNAseq/plots/map_reads_stimulus_EMP_251227.pdf

Saved plot as PNG: C:/Users/emmap/OneDrive/Desktop/Ward Lab/Experiments/Stressor Project/Full Set RNAseq/plots/map_reads_stimulus_EMP_251227.png

Saved plot as Cairo PDF: C:/Users/emmap/OneDrive/Desktop/Ward Lab/Experiments/Stressor Project/Full Set RNAseq/plots/total_reads_time_plot_EMP_251227.pdf

Saved plot as PNG: C:/Users/emmap/OneDrive/Desktop/Ward Lab/Experiments/Stressor Project/Full Set RNAseq/plots/total_reads_time_plot_EMP_251227.png

Saved plot as Cairo PDF: C:/Users/emmap/OneDrive/Desktop/Ward Lab/Experiments/Stressor Project/Full Set RNAseq/plots/total_reads_time_boxplot_EMP_251227.pdf

Saved plot as PNG: C:/Users/emmap/OneDrive/Desktop/Ward Lab/Experiments/Stressor Project/Full Set RNAseq/plots/total_reads_time_boxplot_EMP_251227.png

Saved plot as Cairo PDF: C:/Users/emmap/OneDrive/Desktop/Ward Lab/Experiments/Stressor Project/Full Set RNAseq/plots/map_reads_time_plot_EMP_251227.pdf

Saved plot as PNG: C:/Users/emmap/OneDrive/Desktop/Ward Lab/Experiments/Stressor Project/Full Set RNAseq/plots/map_reads_time_plot_EMP_251227.png

Saved plot as Cairo PDF: C:/Users/emmap/OneDrive/Desktop/Ward Lab/Experiments/Stressor Project/Full Set RNAseq/plots/map_reads_time_boxplot_EMP_251227.pdf

Saved plot as PNG: C:/Users/emmap/OneDrive/Desktop/Ward Lab/Experiments/Stressor Project/Full Set RNAseq/plots/map_reads_time_boxplot_EMP_251227.png

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   195

hc_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)

#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)

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)

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_sub, 
        main = "Unfiltered log2cpm", 
        names = colnames(hc_cpm_unfilt_sub), 
        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)

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

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$unfilt_PC2_PC3

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$unfilt_PC3_PC4

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font family not found in Windows font database
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font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database

$filt_PC1_PC2

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database

Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database

$filt_PC2_PC3

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database

Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database

$filt_PC3_PC4

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database

Warning: ggrepel: 2 unlabeled data points (too many overlaps). Consider
increasing max.overlaps

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font family not found in Windows font database
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font family not found in Windows font database
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font family not found in Windows font database
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font family not found in Windows font database
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font family not found in Windows font database
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font family not found in Windows font database
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font family not found in Windows font database
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font family not found in Windows font database
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font family not found in Windows font database
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font family not found in Windows font database
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font family not found in Windows font database
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font family not found in Windows font database

# 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

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
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family not found in Windows font database
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family not found in Windows font database
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family not found in Windows font database
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family not found in Windows font database
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family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database

Warning: ggrepel: 4 unlabeled data points (too many overlaps). Consider
increasing max.overlaps

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font family not found in Windows font database
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font family not found in Windows font database
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font family not found in Windows font database
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font family not found in Windows font database
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font family not found in Windows font database
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font family not found in Windows font database
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font family not found in Windows font database
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font family not found in Windows font database
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font family not found in Windows font database
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font family not found in Windows font database
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font family not found in Windows font database
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$unfilt24_PC2_PC3

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database

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family not found in Windows font database
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family not found in Windows font database
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family not found in Windows font database
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family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database

Warning: ggrepel: 2 unlabeled data points (too many overlaps). Consider
increasing max.overlaps

Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
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font family not found in Windows font database
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font family not found in Windows font database
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font family not found in Windows font database
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font family not found in Windows font database
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font family not found in Windows font database
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font family not found in Windows font database
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font family not found in Windows font database
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font family not found in Windows font database
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font family not found in Windows font database
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$unfilt24_PC3_PC4

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family not found in Windows font database

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family not found in Windows font database
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family not found in Windows font database
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family not found in Windows font database
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family not found in Windows font database
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family not found in Windows font database
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family not found in Windows font database
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family not found in Windows font database
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family not found in Windows font database
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family not found in Windows font database

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font family not found in Windows font database
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font family not found in Windows font database
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font family not found in Windows font database
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font family not found in Windows font database
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font family not found in Windows font database
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font family not found in Windows font database
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font family not found in Windows font database
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font family not found in Windows font database
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font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database

$filt24_PC1_PC2

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family not found in Windows font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
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$filt24_PC2_PC3

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family not found in Windows font database
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family not found in Windows font database
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family not found in Windows font database
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$filt24_PC3_PC4

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family not found in Windows font database
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family not found in Windows font database
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family not found in Windows font database
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family not found in Windows font database
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family not found in Windows font database
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family not found in Windows font database

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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
  )
}

Saved plot as Cairo PDF: C:/Users/emmap/OneDrive/Desktop/Ward Lab/Experiments/Stressor Project/Full Set RNAseq/plots/PCA_24hr_HC_unfilt24_PC1_PC2_EMP_251227.pdf

Saved plot as PNG: C:/Users/emmap/OneDrive/Desktop/Ward Lab/Experiments/Stressor Project/Full Set RNAseq/plots/PCA_24hr_HC_unfilt24_PC1_PC2_EMP_251227.png

Saved plot as Cairo PDF: C:/Users/emmap/OneDrive/Desktop/Ward Lab/Experiments/Stressor Project/Full Set RNAseq/plots/PCA_24hr_HC_unfilt24_PC2_PC3_EMP_251227.pdf

Saved plot as PNG: C:/Users/emmap/OneDrive/Desktop/Ward Lab/Experiments/Stressor Project/Full Set RNAseq/plots/PCA_24hr_HC_unfilt24_PC2_PC3_EMP_251227.png

Saved plot as Cairo PDF: C:/Users/emmap/OneDrive/Desktop/Ward Lab/Experiments/Stressor Project/Full Set RNAseq/plots/PCA_24hr_HC_unfilt24_PC3_PC4_EMP_251227.pdf

Saved plot as PNG: C:/Users/emmap/OneDrive/Desktop/Ward Lab/Experiments/Stressor Project/Full Set RNAseq/plots/PCA_24hr_HC_unfilt24_PC3_PC4_EMP_251227.png

Saved plot as Cairo PDF: C:/Users/emmap/OneDrive/Desktop/Ward Lab/Experiments/Stressor Project/Full Set RNAseq/plots/PCA_24hr_HC_filt24_PC1_PC2_EMP_251227.pdf

Saved plot as PNG: C:/Users/emmap/OneDrive/Desktop/Ward Lab/Experiments/Stressor Project/Full Set RNAseq/plots/PCA_24hr_HC_filt24_PC1_PC2_EMP_251227.png

Saved plot as Cairo PDF: C:/Users/emmap/OneDrive/Desktop/Ward Lab/Experiments/Stressor Project/Full Set RNAseq/plots/PCA_24hr_HC_filt24_PC2_PC3_EMP_251227.pdf

Saved plot as PNG: C:/Users/emmap/OneDrive/Desktop/Ward Lab/Experiments/Stressor Project/Full Set RNAseq/plots/PCA_24hr_HC_filt24_PC2_PC3_EMP_251227.png

Saved plot as Cairo PDF: C:/Users/emmap/OneDrive/Desktop/Ward Lab/Experiments/Stressor Project/Full Set RNAseq/plots/PCA_24hr_HC_filt24_PC3_PC4_EMP_251227.pdf

Saved plot as PNG: C:/Users/emmap/OneDrive/Desktop/Ward Lab/Experiments/Stressor Project/Full Set RNAseq/plots/PCA_24hr_HC_filt24_PC3_PC4_EMP_251227.png

# 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

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$unfilt2_PC2_PC3

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$unfilt2_PC3_PC4

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$filt2_PC1_PC2

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$filt2_PC2_PC3

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$filt2_PC3_PC4

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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
  )
}

Saved plot as Cairo PDF: C:/Users/emmap/OneDrive/Desktop/Ward Lab/Experiments/Stressor Project/Full Set RNAseq/plots/PCA_2hr_HC_unfilt2_PC1_PC2_EMP_251227.pdf

Saved plot as PNG: C:/Users/emmap/OneDrive/Desktop/Ward Lab/Experiments/Stressor Project/Full Set RNAseq/plots/PCA_2hr_HC_unfilt2_PC1_PC2_EMP_251227.png

Saved plot as Cairo PDF: C:/Users/emmap/OneDrive/Desktop/Ward Lab/Experiments/Stressor Project/Full Set RNAseq/plots/PCA_2hr_HC_unfilt2_PC2_PC3_EMP_251227.pdf

Saved plot as PNG: C:/Users/emmap/OneDrive/Desktop/Ward Lab/Experiments/Stressor Project/Full Set RNAseq/plots/PCA_2hr_HC_unfilt2_PC2_PC3_EMP_251227.png

Saved plot as Cairo PDF: C:/Users/emmap/OneDrive/Desktop/Ward Lab/Experiments/Stressor Project/Full Set RNAseq/plots/PCA_2hr_HC_unfilt2_PC3_PC4_EMP_251227.pdf

Saved plot as PNG: C:/Users/emmap/OneDrive/Desktop/Ward Lab/Experiments/Stressor Project/Full Set RNAseq/plots/PCA_2hr_HC_unfilt2_PC3_PC4_EMP_251227.png

Saved plot as Cairo PDF: C:/Users/emmap/OneDrive/Desktop/Ward Lab/Experiments/Stressor Project/Full Set RNAseq/plots/PCA_2hr_HC_filt2_PC1_PC2_EMP_251227.pdf

Saved plot as PNG: C:/Users/emmap/OneDrive/Desktop/Ward Lab/Experiments/Stressor Project/Full Set RNAseq/plots/PCA_2hr_HC_filt2_PC1_PC2_EMP_251227.png

Saved plot as Cairo PDF: C:/Users/emmap/OneDrive/Desktop/Ward Lab/Experiments/Stressor Project/Full Set RNAseq/plots/PCA_2hr_HC_filt2_PC2_PC3_EMP_251227.pdf

Saved plot as PNG: C:/Users/emmap/OneDrive/Desktop/Ward Lab/Experiments/Stressor Project/Full Set RNAseq/plots/PCA_2hr_HC_filt2_PC2_PC3_EMP_251227.png

Saved plot as Cairo PDF: C:/Users/emmap/OneDrive/Desktop/Ward Lab/Experiments/Stressor Project/Full Set RNAseq/plots/PCA_2hr_HC_filt2_PC3_PC4_EMP_251227.pdf

Saved plot as PNG: C:/Users/emmap/OneDrive/Desktop/Ward Lab/Experiments/Stressor Project/Full Set RNAseq/plots/PCA_2hr_HC_filt2_PC3_PC4_EMP_251227.png

# 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

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$upr_PC2_PC3

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font family not found in Windows font database

$upr_PC3_PC4

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database

Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database

$ddr_PC1_PC2

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database

Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database

$ddr_PC2_PC3

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database

Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database

$ddr_PC3_PC4

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database

Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database

$imr_PC1_PC2

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database

Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database

$imr_PC2_PC3

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database

Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database
Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database

$imr_PC3_PC4

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
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$mmr_PC1_PC2

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$mmr_PC2_PC3

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$mmr_PC3_PC4

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# # 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))

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

Session information

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] systemfonts_1.2.2       foreach_1.5.2           tools_4.4.2            
 [49] progress_1.2.3          ragg_1.4.0              Rcpp_1.0.14            
 [52] glue_1.8.0              gridExtra_2.3           SparseArray_1.6.0      
 [55] xfun_0.52               withr_3.0.2             formatR_1.14           
 [58] fastmap_1.2.0           latticeExtra_0.6-30     callr_3.7.6            
 [61] digest_0.6.37           timechange_0.3.0        R6_2.6.1               
 [64] mime_0.13               textshaping_1.0.1       colorspace_2.1-1       
 [67] Cairo_1.6-5             jpeg_0.1-11             RSQLite_2.3.9          
 [70] R.methodsS3_1.8.2       generics_0.1.4          data.table_1.17.0      
 [73] rtracklayer_1.66.0      prettyunits_1.2.0       httr_1.4.7             
 [76] htmlwidgets_1.6.4       S4Arrays_1.6.0          whisker_0.4.1          
 [79] pkgconfig_2.0.3         gtable_0.3.6            blob_1.2.4             
 [82] hwriter_1.3.2.1         htmltools_0.5.8.1       clue_0.3-66            
 [85] png_0.1-8               knitr_1.50              lambda.r_1.2.4         
 [88] rstudioapi_0.17.1       tzdb_0.5.0              rjson_0.2.23           
 [91] curl_6.0.1              cachem_1.1.0            GlobalOptions_0.1.2    
 [94] vipor_0.4.7             parallel_4.4.2          restfulr_0.0.15        
 [97] pillar_1.10.2           vctrs_0.6.5             promises_1.3.2         
[100] dbplyr_2.5.0            xtable_1.8-4            cluster_2.1.6          
[103] beeswarm_0.4.0          evaluate_1.0.3          GenomicFeatures_1.58.0 
[106] cli_3.6.3               locfit_1.5-9.12         compiler_4.4.2         
[109] futile.options_1.0.1    rlang_1.1.6             crayon_1.5.3           
[112] labeling_0.4.3          aroma.light_3.36.0      interp_1.1-6           
[115] ps_1.9.1                ggbeeswarm_0.7.2        getPass_0.2-4          
[118] plyr_1.8.9              fs_1.6.6                stringi_1.8.7          
[121] viridisLite_0.4.2       deldir_2.0-4            lazyeval_0.2.2         
[124] Matrix_1.7-1            hms_1.1.3               bit64_4.5.2            
[127] KEGGREST_1.46.0         statmod_1.5.0           shiny_1.10.0           
[130] igraph_2.1.4            memoise_2.0.1           bslib_0.9.0            
[133] bit_4.5.0