Last updated: 2023-07-28

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Knit directory: Cardiotoxicity/

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File Version Author Date Message
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html b31f596 reneeisnowhere 2023-07-28 Build site.
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library(tidyverse)
library(ggpubr)
library(rstatix)
library(zoo)
library(ggsignif)
library(RColorBrewer)

library(ComplexHeatmap)
library(gridExtra)
library(cowplot)
library(drc)

Fig. S1

ctnnt <- read.csv("data/ctnnt_results.txt", row.names = 1)
ctnnt %>% 
  mutate(Individual=fct_inorder(Individual)) %>% 
  ggplot(., aes(Individual,Percent , fill=Individual))+
  geom_boxplot()+
  geom_point()+
  geom_hline(yintercept =70,linetype="dashed", alpha=0.75)+###adds a line indicating high positivity +
coord_cartesian(ylim = c(0,105))+ ##set those limits
  theme_bw()+  ##white background
  labs(title="Cardiomyocyte Purity")+ #subtitle = "from  n>3 differentiations")+
  geom_boxplot(color="black",alpha =0.2, fill=NA, fatten=0, show.legend = FALSE)+
  scale_fill_brewer(palette = "Dark2",name="" )+
  xlab(NULL)+ 
  ylab("% cTNNT+ ")+
  guides(fill = NULL)+
  theme(plot.title = element_text(hjust = 0.5, size =20, face= "bold"), 
        axis.title.x=element_blank(),
        axis.text.x=element_blank(),###removes all axis names and tick names etc.####
        axis.ticks.x=element_blank(),
        # legend.text=element_text(size=15), 
        axis.title.y=element_text(size=15),
        axis.ticks.y=element_line(size =2),
        axis.text.y=element_text(size=10, face = "bold"),
       panel.grid.major = element_line(colour = 'grey'),
       panel.border=element_rect(fill = NA, size = 3),
       plot.subtitle=element_text(size=18, hjust=0.5, face="italic", color="black")) 

# ctnnt %>%
#   summary() %>% 
#   kable(., caption= "Stats summary of cTNNT+ FACs readings") %>% 
#   kable_paper("striped", full_width = FALSE) %>%  
#   kable_styling(full_width = FALSE,font_size = 18) #%>% 
#   # scroll_box(width = "60%", height = "400px")

Fig. S2

drug_pal_fact <- c("#8B006D","#DF707E","#F1B72B", "#3386DD","#707031","#41B333")
library(data.table)
conf_int <- readRDS("data/plot_intv_list.RDS")
DRC_list <- readRDS("data/plot_list_DRC.RDS")
pull_drc2 <- data.frame("ind1", "ind2","ind3","ind4","ind5","ind6")
doubl_plot <- data.frame("ind3a", "ind3b", "ind5a", "ind5b")
lvl_order <- c('1','2','3','4','5','6')
intervals <- rbindlist(conf_int,idcol="trt")
drug_list <- c("DNR","DOX","EPI","MTX", "TRZ", "VEH")
GeomRibbon$handle_na <- function(data, params) {  data }

# brewer.pal(n=6,"Dark2")
# [1] "#1B9E77" "#D95F02" "#7570B3" "#E7298A" "#66A61E" "#E6AB02"
# > display.brewer.pal(n=6,"Dark2")
for (each in 1:6){
  newdata <- intervals %>% 
    separate("trt", into=c("sDrug",NA)) %>% 
    dplyr::filter(sDrug ==drug_list[each]) %>% 
    mutate(SampleID=indv) %>% 
    mutate(indv=substr(indv,4,4)) %>% 
    mutate(indv=factor(indv, levels=lvl_order)) %>% 
    dplyr::filter(SampleID %in% doubl_plot)
    
  
  # newdata <- sub_intv %>% 
  #  filter(indv %in% doubl_plot) %>% 
  #   mutate(sub_ind=substr(indv,4,4)) %>% 
  #   mutate(sub_ind=factor(sub_ind,levels=lvl_order))
  drug_plot <- DRC_list[[each]]
     f <-
       drug_plot %>% 
         filter(SampleID %in% doubl_plot) %>% 
         ggplot(., aes(x=Conc, y= Percent, group=SampleID,linetype=SampleID, color= indv,alpha =0.6 )) +
         guides(color="none", alpha = "none")+
         stat_smooth(method = "drm",
                     method.args = list(fct = L.4(c(NA,NA,1,NA))),
                     se = FALSE)+
         geom_ribbon(data = newdata, 
                     aes(x = Conc, y = Prediction, 
                         ymin = Lower, 
                         ymax = Upper, 
                         fill=indv),
                         alpha = 0.1, 
                         color = "transparent")+
         ylim(-.4,1.5)+
         scale_linetype_manual(values = c("dotted","solid","dotted","solid"),
                               name="replicate",
                               labels=c("Rep 1", "Rep 2","Rep 1", "Rep 2"))+
         scale_color_brewer(palette = "Dark2")+
         scale_fill_brewer(palette = "Dark2")+
         scale_x_log10() +  # Change the x-axis scale to log 10 scale
         theme_classic() +
         xlab(NULL)+
         ylab(NULL)+
        # scale_y_continuous(oob=scales::rescale_max, limits = -.4, 1.5)+
        ggtitle(drug_list[each])+
      theme(plot.title = element_text(hjust = 0.5, size =15, face ="bold"),
            axis.title=element_text(size=10),
            axis.ticks=element_line(linewidth = 2),
            axis.text=element_text(size=10, face = "bold"),
            panel.grid.major = element_line(colour = 'lightgrey'),
            panel.border=element_rect(fill = NA, linewidth = 2),
            plot.background = element_rect(fill = "white", colour = NA))
       
  print(f)
} 

Fig. S3

viability <- readRDS("data/viability.RDS")
norm_LDH48 <- readRDS("data/supp_normLDH48.RDS")
viability %>% 
  full_join(., norm_LDH48,by = c("indv","Drug","Conc")) %>% 
  ggplot(., aes(x=per.live, y=ldh))+
  geom_point(aes(col=indv))+
  geom_smooth(method="lm")+
  facet_wrap("Drug")+
  theme_bw()+
  xlab("Average viability of cardiomyocytes/100") +
  ylab("Average LDH") +
  ggtitle("Cell stress and viability correlation")+
  scale_color_brewer(palette = "Dark2",name = "Individual", label = c("1","2","3","4","5","6"))+
  stat_cor(method="pearson",aes(label = paste(..r.label.., ..p.label.., sep = "~`,`~")),
           color = "red")+
  theme(plot.title = element_text(size = rel(1.5), hjust = 0.5,face = "bold"),
        axis.title = element_text(size = 15, color = "black"),
        axis.ticks = element_line(size = 1.5),
        axis.text = element_text(size = 8, color = "black", angle = 20),
        strip.text.x = element_text(size = 15, color = "black", face = "bold"),
        strip.background = element_rect(fill = "white")) 

Fig. S4

viabilitytable <- readRDS("data/averageviabilitytable.RDS")
viabilitytable %>% 
  ungroup() %>% 
    mutate(indv=substr(SampleID,4,4)) %>% 
     mutate(indv=factor(indv, levels= c('1','2','3','4','5','6'))) %>% 
    filter(Conc ==0.5) %>%
    # mutate(Conc= factor(as.numeric(Conc))) %>% 
    group_by(indv,sDrug,Conc) %>% 
    dplyr::summarize(Viability=mean(Mean)) %>%
    ungroup() %>% 
    ggplot(.,  aes(x=sDrug, y= Viability*100 )) +
    geom_boxplot(position="dodge", outlier.colour = "transparent",
                 aes(fill=sDrug))+
    geom_point(aes(color=indv))+
    guides(alpha = "none")+
    ylim(0,150.5)+
    scale_color_brewer(palette = "Dark2",
                       guide="legend",
                       name ="Individual", 
                       labels(c(1,2,3,4,5,6)))+
    scale_fill_manual(values=drug_pal_fact, name ="Treatment")+
    theme_classic() +
  xlab("")+
    # geom_hline(yintercept = 1,lty = 4)+
    ylab("% Viability") +
    facet_wrap(~Conc)+ 
    ggtitle(expression(paste("Viablity across treatment at 0.5", mu, "M at 48 hours")))+
    theme(axis.title=element_text(size=10),
          axis.ticks=element_line(size =2),
          axis.text.y=element_text(size=9, face = "bold"),
          axis.text.x=element_blank(),
          panel.grid.major = element_line(colour = 'darkgrey'),
          panel.border=element_rect(fill = NA, size = 2),
          plot.title = element_text(hjust = 0.5, size =15, face = "bold"))

viabilitytable %>% 
  ungroup() %>% 
    mutate(indv=substr(SampleID,4,4)) %>% 
     mutate(indv=factor(indv, levels= c('1','2','3','4','5','6'))) %>% 
    filter(Conc <5) %>%
    mutate(Conc= factor(as.numeric(Conc))) %>%
    group_by(indv,sDrug,Conc) %>% 
    dplyr::summarize(Viability=mean(Mean)) %>%
    ungroup() %>% 
    ggplot(.,  aes(x=sDrug, y= Viability*100 )) +
    geom_boxplot(position="dodge", outlier.colour = "transparent",
                 aes(fill=sDrug))+
    geom_point(aes(color=indv))+
    guides(alpha = "none")+
    ylim(0,150.5)+
    scale_color_brewer(palette = "Dark2",
                       guide="legend",
                       name ="individual", 
                       labels(c(1,2,3,4,5,6)))+
    scale_fill_manual(values=drug_pal_fact, name ="treatment")+
    theme_classic() +
  xlab("")+
    # geom_hline(yintercept = 1,lty = 4)+
    ylab("% Viability") +
    facet_wrap(~Conc)+ 
    # ggtitle(expression(paste("Viablity across treatment at 0.5", mu, "M at 48 hours")))+
    theme(axis.title=element_text(size=10),
          axis.ticks=element_line(size =2),
          axis.text.y=element_text(size=9, face = "bold"),
          axis.text.x=element_blank(),
          panel.grid.major = element_line(colour = 'darkgrey'),
          panel.border=element_rect(fill = NA, size = 2),
          plot.title = element_text(hjust = 0.5, size =15, face = "bold"))

Fig. S5

library(limma)
library(edgeR)
library(cowplot)
filcpm_matrix <- readRDS("data/filcpm_counts.RDS")
colnames(filcpm_matrix)
 [1] "Da.1.3h"  "Do.1.3h"  "Ep.1.3h"  "Mi.1.3h"  "Tr.1.3h"  "Ve.1.3h" 
 [7] "Da.1.24h" "Do.1.24h" "Ep.1.24h" "Mi.1.24h" "Tr.1.24h" "Ve.1.24h"
[13] "Da.2.3h"  "Do.2.3h"  "Ep.2.3h"  "Mi.2.3h"  "Tr.2.3h"  "Ve.2.3h" 
[19] "Da.2.24h" "Do.2.24h" "Ep.2.24h" "Mi.2.24h" "Tr.2.24h" "Ve.2.24h"
[25] "Da.3.3h"  "Do.3.3h"  "Ep.3.3h"  "Mi.3.3h"  "Tr.3.3h"  "Ve.3.3h" 
[31] "Da.3.24h" "Do.3.24h" "Ep.3.24h" "Mi.3.24h" "Tr.3.24h" "Ve.3.24h"
[37] "Da.4.3h"  "Do.4.3h"  "Ep.4.3h"  "Mi.4.3h"  "Tr.4.3h"  "Ve.4.3h" 
[43] "Da.4.24h" "Do.4.24h" "Ep.4.24h" "Mi.4.24h" "Tr.4.24h" "Ve.4.24h"
[49] "Da.5.3h"  "Do.5.3h"  "Ep.5.3h"  "Mi.5.3h"  "Tr.5.3h"  "Ve.5.3h" 
[55] "Da.5.24h" "Do.5.24h" "Ep.5.24h" "Mi.5.24h" "Tr.5.24h" "Ve.5.24h"
[61] "Da.6.3h"  "Do.6.3h"  "Ep.6.3h"  "Mi.6.3h"  "Tr.6.3h"  "Ve.6.3h" 
[67] "Da.6.24h" "Do.6.24h" "Ep.6.24h" "Mi.6.24h" "Tr.6.24h" "Ve.6.24h"
x <- readRDS("data/filtermatrix_x.RDS")
x$samples %>% 
  mutate(drug=case_match(drug, "Daunorubicin"~"DNR",
                        "Doxorubicin"~"DOX",
                        "Epirubicin"~"EPI",
                        "Mitoxantrone"~"MTX",
                        "Trastuzumab"~"TRZ",
                        "Vehicle"~"VEH", .default = drug)) %>%
   mutate(drug=factor(drug, levels = c('DOX','EPI','DNR','MTX','TRZ','VEH'))) %>% 
  mutate(time=factor(time, labels= c("3 hours","24 hours"))) %>% 
 ggplot(., aes(x = as.factor(time), y = RIN)) +
  geom_boxplot(aes(fill=as.factor(time)))+ 
  theme_bw()+
  ylim(c(0,10))+
  labs(x= "", fill ="Time in hours",y ="RNA Integrity Number")+ 
  ggtitle("Boxplot of RIN by time and drug")+
  facet_wrap(~drug)+
  theme(plot.title = element_text(size = rel(1.5), hjust = 0.5),
        axis.title = element_text(size = 15, color = "black"),
        axis.text.y = element_text(size =10, color = "black", angle = 0, hjust = 0.8, vjust = 0.5),
        axis.text.x = element_text(size =10, color = "black", angle = 0, hjust = 1, vjust = 0.2),
        strip.text.x = element_text(size = 15, color = "black", face = "bold"),
        strip.background = element_rect(fill = "white"))

seq_info <-read.csv("output/sequencing_info.txt", row.names = 1)
seq_info %>% 
   
  filter(type=="Total_reads") %>% 
  mutate(drug=case_match(drug, "Daunorubicin"~"DNR",
                        "Doxorubicin"~"DOX",
                        "Epirubicin"~"EPI",
                        "Mitoxantrone"~"MTX",
                        "Trastuzumab"~"TRZ",
                        "Vehicle"~"VEH", .default = drug)) %>%
   mutate(drug=factor(drug, levels = c('DOX','EPI','DNR','MTX','TRZ','VEH'))) %>% 
    ggplot(., aes (x =drug, y=Total.Sequences, fill = drug))+
  geom_boxplot()+
  scale_fill_manual(values=drug_pal_fact)+
  ggtitle(expression("Total number of reads by treatment"))+
  xlab(" ")+
  ylab(expression("RNA -sequencing reads"))+
  theme_bw()+
  theme(plot.title = element_text(size = rel(1.5), hjust = 0.5),
        axis.title = element_text(size = 12, color = "black"),
        axis.ticks = element_line(linewidth = 1.5),
        axis.line = element_line(linewidth = 1.5),
        axis.text.y = element_text(size =10, color = "black", angle = 0, hjust = 0.8, vjust = 0.5),
        axis.text.x = element_text(size =10, color = "white"),
        #strip.text.x = element_text(size = 15, color = "black", face = "bold"),
        strip.text.y = element_text(color = "white"))

seq_info %>% 
  mutate(drug=case_match(drug, "Daunorubicin"~"DNR",
                        "Doxorubicin"~"DOX",
                        "Epirubicin"~"EPI",
                        "Mitoxantrone"~"MTX",
                        "Trastuzumab"~"TRZ",
                        "Vehicle"~"VEH", .default = drug)) %>%
   mutate(drug=factor(drug, levels = c('DOX','EPI','DNR','MTX','TRZ','VEH'))) %>% 
  # separate(samplenames, into=c(NA,NA,NA,"samplenames")) %>% 
  # mutate(shortnames = paste("Sample",str_trim(samplenames))) %>% 
  filter(type=="Total_reads") %>% 
  mutate(sampleID=colnames(filcpm_matrix)) %>% 
  ggplot(., aes (x =sampleID, y=Total.Sequences, fill = drug, group_by=indv))+
  geom_col()+
 geom_hline(aes(yintercept=20000000))+
 scale_fill_manual(values=drug_pal_fact)+
  ggtitle(expression("Total number of reads by sample"))+
  xlab("")+
  ylab(expression("RNA -sequencing reads"))+
  theme_bw()+
  theme(plot.title = element_text(size = rel(2), hjust = 0.5),
        axis.title = element_text(size = 15, color = "black"),
        axis.ticks = element_line(linewidth = 1.5),
        axis.line = element_line(linewidth = 1.5),
        axis.text.y = element_text(size =10, color = "black", angle = 0, hjust = 0.8, vjust = 0.5),
        axis.text.x = element_text(size =6, color = "black", angle = 90, hjust = 1, vjust = 0.2),
        #strip.text.x = element_text(size = 15, color = "black", face = "bold"),
        strip.text.y = element_text(color = "white"))

### Fig. S6

filcpm_matrix <- readRDS("data/filcpm_counts.RDS")

mcor <- cor(filcpm_matrix) 

filmat_groupmat_col <- data.frame(timeset = colnames(filcpm_matrix))
counts_corr_mat<- filmat_groupmat_col %>% 
  separate(timeset, into= c("drug","indv","time")) %>% 
  mutate(class = if_else(drug=="Da","AC", if_else(drug=="Do","AC", if_else(drug=="Ep","AC","nAC")))) %>% 
  mutate(TOP2i = if_else(drug=="Da","yes", if_else(drug=="Do","yes", if_else(drug=="Ep","yes",if_else(drug=="Mi","yes","no"))))) 
                         
 mat_colors <- list( 
   drug= c("#F1B72B","#8B006D","#DF707E","#3386DD","#707031","#41B333"),
   indv=c("#1B9E77", "#D95F02" ,"#7570B3", "#E7298A" ,"#66A61E", "#E6AB02"),
   time=c("pink", "chocolate4"),
   class=c("yellow1","lightgreen"), 
   TOP2i =c("darkgreen","goldenrod"))                        
                         
names(mat_colors$drug)   <- unique(counts_corr_mat$drug)                      
names(mat_colors$indv) <- unique(counts_corr_mat$indv)
names(mat_colors$time) <- unique(counts_corr_mat$time)
names(mat_colors$class) <- unique(counts_corr_mat$class)
names(mat_colors$TOP2i) <- unique(counts_corr_mat$TOP2i)


ComplexHeatmap::pheatmap(mcor,
                         # column_title=(paste0("RNA-seq log"[2]~"cpm correlation")),
        annotation_col = counts_corr_mat,
        annotation_colors = mat_colors,
                         fontsize=10,
                         fontsize_row = 8,
                         angle_col="90",
                         treeheight_row=25,
                         fontsize_col = 8,
                         treeheight_col = 20)

heatmap is pending a few changes! just not my focus today.

Fig. S7

pca_all_anno <- readRDS("data/supp_pca_all_anno.RDS")
pca_all_anno <- pca_all_anno %>% 
  mutate(drug = case_match(drug, "Daunorubicin"~"DNR","Doxorubicin"~"DOX", "Epirubicin"~"EPI","Mitoxantrone"~"MTX","Trastuzumab"~"TRX","Vehicle"~"VEH", .default = drug))


facs <- c("indv", "drug", "time")
names(facs) <- c("Individual", "Treatment", "Time")

get_regr_pval <- function(mod) {
  # Returns the p-value for the Fstatistic of a linear model
  # mod: class lm
  stopifnot(class(mod) == "lm")
  fstat <- summary(mod)$fstatistic
  pval <- 1 - pf(fstat[1], fstat[2], fstat[3])
  return(pval)
}

plot_versus_pc <- function(df, pc_num, fac) {
  # df: data.frame
  # pc_num: numeric, specific PC for plotting
  # fac: column name of df for plotting against PC
  pc_char <- paste0("PC", pc_num)
  # Calculate F-statistic p-value for linear model
  pval <- get_regr_pval(lm(df[, pc_char] ~ df[, fac]))
  if (is.numeric(df[, f])) {
    ggplot(df, aes_string(x = f, y = pc_char)) + geom_point() +
      geom_smooth(method = "lm") + labs(title = sprintf("p-val: %.2f", pval))
  } else {
    ggplot(df, aes_string(x = f, y = pc_char)) + geom_boxplot() +
      labs(title = sprintf("p-val: %.2f", pval))
  }
}

for (f in facs) {
    # Plot f versus PC1 and PC2
  f_v_pc1 <- arrangeGrob(plot_versus_pc(pca_all_anno, 1, f)+theme_bw())
  f_v_pc2 <- arrangeGrob(plot_versus_pc(pca_all_anno, 2, f)+theme_bw())
  grid.arrange(f_v_pc1, f_v_pc2, ncol = 2, top = names(facs)[which(facs == f)])
}

Fig. S8

Volcanoplots <- readRDS("output/Volcanoplot_10.RDS")
Volcanoplots

Fig. S9

toplistall <- readRDS("data/toplistall.RDS")
toplistall %>% 
  group_by(time, id) %>% 
  mutate(id=factor(id, levels = c('DOX', 'EPI', 'DNR', 'MTX', 'TRZ','VEH'))) %>% 
  mutate(time= factor(time,
     levels=c("3_hours","24_hours"),
              label=c("3 hours","24 hours"))) %>%
  ggplot(., aes(x=id, y=logFC))+
  geom_boxplot(aes(fill=id))+
  ggpubr::fill_palette(palette =drug_pal_fact)+
  guides(fill=guide_legend(title = "Treatment"))+
  # facet_wrap(sigcount~time)+
  theme_bw()+
  xlab("")+
  ylab(expression("Log"[2]*" fold change"))+
  theme_bw()+
  facet_wrap(~time)+
  theme(plot.title = element_text(size = rel(1.5), hjust = 0.5),
        axis.title = element_text(size = 15, color = "black"),
        # axis.ticks = element_line(linewidth = 1.5),
        axis.line = element_line(linewidth = 1.5),
        strip.background = element_rect(fill = "transparent"),
        axis.text.x = element_blank(),
        strip.text.x = element_text(size = 12, color = "black", face = "bold"))

# drug_palNoVeh <- c("#8B006D" ,"#DF707E", "#F1B72B" ,"#3386DD", "#707031")


toplistall %>%
    mutate(id=factor(id, levels = c('DOX', 'EPI', 'DNR', 'MTX', 'TRZ','VEH'))) %>% 
  mutate(time= factor(time,
     levels=c("3_hours","24_hours"),
              label=c("3 hours","24 hours"))) %>%
  group_by(time, id) %>% 
  mutate(sigcount = if_else(adj.P.Val < 0.05,'sig','notsig'))%>%
  count(sigcount) %>% 
  pivot_wider(id_cols = c(time,id), names_from=sigcount, values_from=n) %>% 
  mutate(prop = sig/(sig+notsig)*100) %>% 
  mutate(prop=if_else(is.na(prop),0,prop)) %>% 
  ggplot(., aes(x=id, y= prop))+
  geom_col(aes(fill=id))+
  geom_text(aes(label = sprintf("%.2f",prop)),
            position=position_dodge(0.9),vjust=-.2 )+
  scale_fill_manual(values =drug_pal_fact)+
  guides(fill=guide_legend(title = "Treatment"))+
  facet_wrap(id~time)+#labeller = (time = facettimelabel) )+
  theme_bw()+
  xlab("")+
  ylab("Percentage of expressed genes")+
  theme_bw()+
  ggtitle("Percent DEGs (adj. P value <0.05)")+
  scale_y_continuous(expand=expansion(c(0.02,.2)))+
  theme(plot.title = element_text(size = rel(1.5), hjust = 0.5),
        axis.title = element_text(size = 15, color = "black"),
        # axis.ticks = element_line(linewidth = 1.5),
        axis.line = element_line(linewidth = 1.5),
        strip.background = element_rect(fill = "transparent"),
        axis.text.x = element_text(size = 8, color = "white", angle = 0),
        axis.text.y = element_text(size = 8, color = "black", angle = 0),
        strip.text.x = element_text(size = 12, color = "black", face = "bold"))

ggsave("output/Figures/Percent_DEG.eps",width = 6, height =4, units = "in")

Fig. S10

allfinal <- readRDS("output/allfinal_sup10.RDS") 
allfinal

allfinal3hour <- readRDS("data/allfinal3hour.RDS")
allfinal3hour

### Fig S10 Drug Specific Pathways

gostres3Dnrdeg_sp <- readRDS("data/DEG-GO/gostres3Dnrdeg_sp.RDS")

Dnr3_sp_DEGtable <- gostres3Dnrdeg_sp$result %>%
  dplyr::select(c(source, term_id,term_name,intersection_size, 
                   term_size, p_value))


Dnr3_sp_DEGtable  %>% 
    dplyr::filter(source=="GO:BP") %>% 
    dplyr::select(p_value,term_name,intersection_size) %>%
    slice_min(., n=10 ,order_by=p_value) %>%
    mutate(log_val = -log10(p_value)) %>%
   # slice_max(., n=10,order_by = p_value) %>%
   ggplot(., aes(x = log_val, y =reorder(term_name,p_value), col= intersection_size)) +
    geom_point(aes(size = intersection_size)) +
    scale_y_discrete(labels =scales::label_wrap(30))+
    guides(col="none", size=guide_legend(title = "# of intersected \n terms"))+
    ggtitle('DNR 3 hour specific(stringent)\n gene set GO:BP terms') +
    xlab(expression(" -"~log[10]~("adj. p-value")))+
    ylab("GO: BP term")+
    theme_bw()+
    theme(plot.title = element_text(size = rel(1.5), hjust = 0.5),
        axis.title = element_text(size = 15, color = "black"),
        axis.ticks = element_line(linewidth = 1.5),
        axis.line = element_line(linewidth = 1.5),
        axis.text = element_text(size = 10, color = "black", angle = 0),
        strip.text.x = element_text(size = 12, color = "black", face = "bold"))

gostres3Mtxdeg_sp <- readRDS("data/DEG-GO/gostres3Mtxdeg_sp.RDS")
Mtx3_sp_DEGtable <- gostres3Mtxdeg_sp$result %>%
  dplyr::select(c(source, term_id,term_name,intersection_size, 
                   term_size, p_value))
Mtx3_sp_DEGtable  %>% 
    dplyr::filter(source=="GO:BP") %>% 
    dplyr::select(p_value,term_name,intersection_size) %>%
    slice_min(., n=5,order_by=p_value) %>%
    mutate(log_val = -log10(p_value)) %>%
   # slice_max(., n=10,order_by = p_value) %>%
   ggplot(., aes(x = log_val, y =reorder(term_name,p_value), col= intersection_size)) +
    geom_point(aes(size = intersection_size)) +
    scale_y_discrete(labels = scales::label_wrap(30))+
  geom_vline(xintercept = (-log10(0.05)))+
    guides(col="none", size=guide_legend(title = "# of intersected \n terms"))+
    ggtitle('MTX 3 hour specific(stringent)\n gene set GO:BP terms') +
    xlab(expression(" -"~log[10]~("adj. p-value")))+
    ylab("GO: BP term")+
    theme_bw()+
    theme(plot.title = element_text(size = rel(1.5), hjust = 0.5),
        axis.title = element_text(size = 15, color = "black"),
        axis.ticks = element_line(linewidth = 1.5),
        axis.line = element_line(linewidth = 1.5),
        axis.text = element_text(size = 10, color = "black", angle = 0),
        strip.text.x = element_text(size = 12, color = "black", face = "bold"))

DX_sp_DEGgostres <- readRDS("data/DEG-GO/gostresDOXdeg_sp.RDS")
MT_sp_DEGgostres <- readRDS("data/DEG-GO/gostresMTXdeg_sp.RDS")

DX_sp_DEGtable <- DX_sp_DEGgostres$result %>%
  dplyr::select(c(source, term_id,term_name,intersection_size, 
                   term_size, p_value))
MT_sp_DEGtable <- MT_sp_DEGgostres$result %>%
  dplyr::select(c(source, term_id,term_name,intersection_size, 
                   term_size, p_value))
  
  
DX_sp_DEGtable  %>% 
    dplyr::filter(source=="GO:BP") %>% 
    dplyr::select(p_value,term_name,intersection_size) %>%
    slice_min(., n=10 ,order_by=p_value) %>%
    mutate(log_val = -log10(p_value)) %>%
   # slice_max(., n=10,order_by = p_value) %>%
   ggplot(., aes(x = log_val, y =reorder(term_name,p_value), col= intersection_size)) +
    geom_point(aes(size = intersection_size)) +
    scale_y_discrete(labels = scales::wrap_format(30))+
    guides(col="none", size=guide_legend(title = "# of intersected \n terms"))+
    ggtitle('DOX specific 24 hour gene set GO:BP terms') +
    xlab(expression(" -"~log[10]~("adj. p-value")))+
    ylab("GO: BP term")+
    theme_bw()+
    theme(plot.title = element_text(size = rel(1.5), hjust = 0.5),
        axis.title = element_text(size = 15, color = "black"),
        axis.ticks = element_line(linewidth = 1.5),
        axis.line = element_line(linewidth = 1.5),
        axis.text = element_text(size = 10, color = "black", angle = 0),
        strip.text.x = element_text(size = 12, color = "black", face = "bold"))

MT_sp_DEGtable  %>% 
    dplyr::filter(source=="GO:BP") %>% 
    dplyr::select(p_value,term_name,intersection_size) %>%
    slice_min(., n=10 ,order_by=p_value) %>%
    mutate(log_val = -log10(p_value)) %>%
   # slice_max(., n=10,order_by = p_value) %>%
   ggplot(., aes(x = log_val, y =reorder(term_name,p_value), col= intersection_size)) +
    geom_point(aes(size = intersection_size)) +
    scale_y_discrete(labels = scales::wrap_format(30))+
    guides(col="none", size=guide_legend(title = "# of intersected \n terms"))+
    ggtitle('MTX specific 24 hour gene set GO:BP terms') +
    xlab(expression(" -"~log[10]~("adj. p-value")))+
    ylab("GO: BP term")+
    theme_bw()+
    theme(plot.title = element_text(size = rel(1.5), hjust = 0.5),
        axis.title = element_text(size = 15, color = "black"),
        axis.ticks = element_line(linewidth = 1.5),
        axis.line = element_line(linewidth = 1.5),
        axis.text = element_text(size = 10, color = "black", angle = 0),
        strip.text.x = element_text(size = 12, color = "black", face = "bold"))

Fig. S12

DNRvenn<- readRDS ("output/DNRvenn.RDS")
DOXvenn<- readRDS ("output/DOXvenn.RDS")
EPIvenn<- readRDS ("output/EPIvenn.RDS")
MTXvenn<- readRDS ("output/MTXvenn.RDS")

plot_grid(DNRvenn,DOXvenn,EPIvenn,MTXvenn,nrow=2, ncol = 2)

Fig. S12

cormotif_initial <- readRDS("data/cormotif_initialall.RDS")
Cormotif::plotIC(cormotif_initial)

motif_NRrep <-  readRDS("output/motif_NRrep.RDS")
motif_ERrep <-  readRDS("output/motif_ERrep.RDS")
motif_TIrep <-  readRDS("output/motif_TI_rep.RDS")
motif_LRrep <-  readRDS("output/motif_LRrep.RDS")
motif_NRrep <- motif_NRrep +
  scale_y_continuous(labels = scales::number_format(accuracy = 0.1))+
  theme(legend.position = "NULL", axis.title.y =element_text(size =12))

motif_ERrep <- motif_ERrep+
  scale_y_continuous(labels = scales::number_format(accuracy = 0.1))+
  theme(legend.position = "NULL", axis.title.y =element_text(size =12))

motif_TIrep <- motif_TIrep+
  scale_y_continuous(labels = scales::number_format(accuracy = 0.1))+ 
  theme(legend.position = "NULL", axis.title.y =element_text(size =12))

motif_LRrep <- motif_LRrep+
  scale_y_continuous(labels = scales::number_format(accuracy = 0.1))+
  theme(legend.position = "NULL", axis.title.y =element_text(size =12))
plot_grid(motif_ERrep,motif_TIrep,motif_LRrep,motif_NRrep,nrow = 4,ncol = 1)

<environment: R_GlobalEnv>

Fig. S15

gene_corr_frame <- readRDS("data/gene_corr_frame.RDS")
GOI_genelist <- read.csv("output/GOI_genelist.txt",row.names = 1)

for (gene in GOI_genelist$entrezgene_id){
    gene_plot <- gene_corr_frame %>% 
      dplyr::filter(entrezgene_id == gene) %>%
      ggplot(., aes(x=tox, y=counts))+
      geom_point(aes(col=indv))+
      geom_smooth(method="lm")+
      facet_wrap(hgnc_symbol~Drug, scales="free")+
      theme_classic()+
      xlab("Toxicity score") +
      ylab(expression("counts in log"[2]*" cpm")) +
      ggtitle(expression(paste("Correlation between counts and toxicity by drug")))+
      scale_color_brewer(palette = "Dark2",name = "Individual", label = c("1","2","3","4","5","6"))+
      scale_y_continuous(labels = scales::number_format(accuracy = 0.01))+
      stat_cor(method="pearson",
               cor.coef.name="rho",
               aes(label = paste(..r.label.., ..p.label.., sep = "~`,`~")),
               color = "red",
               label.x.npc = 0.01,
               label.y.npc=0.01, 
               size = 3)+
      theme(plot.title = element_text(size = rel(1.5), hjust = 0.5,face = "bold"),
            axis.title = element_text(size = 15, color = "black"),
            axis.ticks = element_line(size = 1.5),
            axis.text = element_text(size = 8, color = "black", angle = 20),
            strip.text.x = element_text(size = 10, color = "black", face = "italic"))
   print(gene_plot)
   
 }


sessionInfo()
R version 4.3.1 (2023-06-16 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19045)

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      stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] edgeR_3.42.4          limma_3.56.2          data.table_1.14.8    
 [4] drc_3.0-1             MASS_7.3-60           cowplot_1.1.1        
 [7] gridExtra_2.3         ComplexHeatmap_2.16.0 RColorBrewer_1.1-3   
[10] ggsignif_0.6.4        zoo_1.8-12            rstatix_0.7.2        
[13] ggpubr_0.6.0          lubridate_1.9.2       forcats_1.0.0        
[16] stringr_1.5.0         dplyr_1.1.2           purrr_1.0.1          
[19] readr_2.1.4           tidyr_1.3.0           tibble_3.2.1         
[22] ggplot2_3.4.2         tidyverse_2.0.0       workflowr_1.7.0      

loaded via a namespace (and not attached):
  [1] rstudioapi_0.15.0     jsonlite_1.8.7        shape_1.4.6          
  [4] magrittr_2.0.3        TH.data_1.1-2         magick_2.7.4         
  [7] farver_2.1.1          rmarkdown_2.23        zlibbioc_1.46.0      
 [10] GlobalOptions_0.1.2   fs_1.6.3              ragg_1.2.5           
 [13] vctrs_0.6.3           htmltools_0.5.5       plotrix_3.8-2        
 [16] broom_1.0.5           sass_0.4.7            KernSmooth_2.23-22   
 [19] bslib_0.5.0           sandwich_3.0-2        cachem_1.0.8         
 [22] whisker_0.4.1         lifecycle_1.0.3       iterators_1.0.14     
 [25] pkgconfig_2.0.3       Matrix_1.6-0          R6_2.5.1             
 [28] fastmap_1.1.1         clue_0.3-64           digest_0.6.33        
 [31] colorspace_2.1-0      S4Vectors_0.38.1      ps_1.7.5             
 [34] rprojroot_2.0.3       textshaping_0.3.6     labeling_0.4.2       
 [37] fansi_1.0.4           timechange_0.2.0      httr_1.4.6           
 [40] abind_1.4-5           mgcv_1.9-0            compiler_4.3.1       
 [43] proxy_0.4-27          withr_2.5.0           doParallel_1.0.17    
 [46] backports_1.4.1       carData_3.0-5         DBI_1.1.3            
 [49] highr_0.10            rjson_0.2.21          classInt_0.4-9       
 [52] gtools_3.9.4          tools_4.3.1           units_0.8-2          
 [55] httpuv_1.6.11         Cormotif_1.46.0       glue_1.6.2           
 [58] callr_3.7.3           nlme_3.1-162          promises_1.2.0.1     
 [61] sf_1.0-14             getPass_0.2-2         cluster_2.1.4        
 [64] generics_0.1.3        gtable_0.3.3          tzdb_0.4.0           
 [67] preprocessCore_1.62.1 class_7.3-22          hms_1.1.3            
 [70] ggVennDiagram_1.2.2   car_3.1-2             utf8_1.2.3           
 [73] BiocGenerics_0.46.0   foreach_1.5.2         pillar_1.9.0         
 [76] later_1.3.1           circlize_0.4.15       splines_4.3.1        
 [79] lattice_0.21-8        survival_3.5-5        tidyselect_1.2.0     
 [82] locfit_1.5-9.8        knitr_1.43            git2r_0.32.0         
 [85] IRanges_2.34.1        stats4_4.3.1          xfun_0.39            
 [88] Biobase_2.60.0        matrixStats_1.0.0     stringi_1.7.12       
 [91] yaml_2.3.7            evaluate_0.21         codetools_0.2-19     
 [94] BiocManager_1.30.21.1 RVenn_1.1.0           affyio_1.70.0        
 [97] cli_3.6.1             systemfonts_1.0.4     munsell_0.5.0        
[100] processx_3.8.2        jquerylib_0.1.4       Rcpp_1.0.11          
[103] png_0.1-8             parallel_4.3.1        viridisLite_0.4.2    
[106] mvtnorm_1.2-2         affy_1.78.2           scales_1.2.1         
[109] e1071_1.7-13          crayon_1.5.2          GetoptLong_1.0.5     
[112] rlang_1.1.1           multcomp_1.4-25