Supplementary figures

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Last updated: 2023-06-26

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Knit directory: Cardiotoxicity/

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Unstaged changes:
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These are the previous versions of the repository in which changes were made to the R Markdown (analysis/Supplementary_figures.Rmd) and HTML (docs/Supplementary_figures.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File	Version	Author	Date	Message
Rmd	d7b9ff1	reneeisnowhere	2023-06-26	Adding supplementary figures
html	537bc2e	reneeisnowhere	2023-06-26	Build site.
Rmd	6e50959	reneeisnowhere	2023-06-26	Adding supplementary figures
html	dace8ba	reneeisnowhere	2023-06-23	Build site.
Rmd	2b109c3	reneeisnowhere	2023-06-23	adding some supp graphs
Rmd	c1d667f	reneeisnowhere	2023-06-23	updating the codes at Friday start.

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library(tidyverse)
library(ggpubr)
library(rstatix)
library(zoo)
library(ggsignif)
library(RColorBrewer)
library(kableExtra)
library(ComplexHeatmap)
library(gridExtra)
library(cowplot)

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")) 

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

Stats summary of cTNNT+ FACs readings

	Percent	Individual
	Min. :63.10	Length:17
	1st Qu.:91.80	Class :character
	Median :96.65	Mode :character
	Mean :92.80	NA
	3rd Qu.:98.50	NA
	Max. :99.90	NA

  # scroll_box(width = "60%", height = "400px")

Fig. S2

ld50_table <- read.csv("data/ld50_table.csv",row.names = 1)
ld_corr <- cor(ld50_table)
col_fun1 = circlize::colorRamp2(c(-1, 1), c("white", "purple"))

Heatmap(ld_corr, cluster_rows = FALSE,cluster_columns = FALSE, col = col_fun1,column_title = expression("LD "[50]*" correlation"),row_title=" ",row_split = factor(rep(c("1","2","3","4","5","6"),each=2)), column_split = 
    factor(rep(c("1","2","3","4","5","6"),each=2)))

Heatmap(ld_corr, cluster_rows = FALSE,cluster_columns = FALSE, column_title = expression("LD "[50]*" correlation"),row_title=" ",row_split = factor(rep(c("1","2","3","4","5","6"),each=2)), column_split = 
    factor(rep(c("1","2","3","4","5","6"),each=2)))

### Fig. S3

drug_pal_fact <- c("#8B006D","#DF707E","#F1B72B", "#3386DD","#707031","#41B333")
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("Relative viability and relative LDH release at 48 hours")+
  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(as.numeric(indv))) %>%
    filter(Conc <5) %>% 
    mutate(Conc= factor(as.numeric(Conc))) %>% 
    group_by(indv,Drug,Conc,sDrug) %>% 
    dplyr::summarize(Viability=mean(Mean)) %>%
    ungroup() %>% 
    ggplot(.,  aes(x=sDrug, y= Viability*100 )) +
    geom_boxplot(position="dodge", outlier.colour = "transparent",
                 aes(fill=Drug))+
    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)+
    theme_classic() +
    # geom_hline(yintercept = 1,lty = 4)+
    ylab("Viability") +
  xlab("Treatment")+
    facet_wrap(~Conc)+ 
    ggtitle("Viablity across concentrations at 48 hours")+
    theme(axis.title=element_text(size=10),
          axis.ticks=element_line(size =2),
          axis.text=element_text(size=9, face = "bold"),
          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)
x <- readRDS("data/filtermatrix_x.RDS")
ggplot(x$samples, 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") %>% 
  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 %>% 
  separate(samplenames, into=c(NA,NA,NA,"samplenames")) %>% 
  mutate(shortnames = paste("Sample",str_trim(samplenames))) %>% 
  filter(type=="Total_reads") %>% 
  ggplot(., aes (x =shortnames, 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) 
# pheatmap::pheatmap(mcor)
Heatmap(mcor)

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(time=factor(time, levels=c("3_hours","24_hours"))) %>% 
  # mutate(sigcount = if_else(adj.P.Val < 0.05,'sig','notsig'))%>%
  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("Log 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_text(size = 8, color = "white", angle = 0),
        strip.text.x = element_text(size = 12, color = "black", face = "bold"))

### Fig. S10

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

### Fig. S11

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+theme(axis.title.y = element_text(size=1))

plot_grid(motif_ERrep,motif_LRrep,motif_TIrep,motif_NRrep,nrow = 4,ncol = 1)

motif_NRrep 

motif_ERrep 

motif_TIrep

motif_LRrep

<environment: R_GlobalEnv>

Session information

sessionInfo()

R version 4.2.2 (2022-10-31 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    

attached base packages:
[1] grid      stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] edgeR_3.38.4          limma_3.52.4          cowplot_1.1.1        
 [4] gridExtra_2.3         ComplexHeatmap_2.12.1 kableExtra_1.3.4     
 [7] RColorBrewer_1.1-3    ggsignif_0.6.4        zoo_1.8-12           
[10] rstatix_0.7.2         ggpubr_0.6.0          lubridate_1.9.2      
[13] forcats_1.0.0         stringr_1.5.0         dplyr_1.1.2          
[16] purrr_1.0.1           readr_2.1.4           tidyr_1.3.0          
[19] tibble_3.2.1          ggplot2_3.4.2         tidyverse_2.0.0      
[22] workflowr_1.7.0      

loaded via a namespace (and not attached):
  [1] ggVennDiagram_1.2.2   colorspace_2.1-0      rjson_0.2.21         
  [4] class_7.3-22          rprojroot_2.0.3       circlize_0.4.15      
  [7] GlobalOptions_0.1.2   fs_1.6.2              proxy_0.4-27         
 [10] clue_0.3-64           rstudioapi_0.14       farver_2.1.1         
 [13] affyio_1.66.0         fansi_1.0.4           xml2_1.3.4           
 [16] codetools_0.2-19      splines_4.2.2         doParallel_1.0.17    
 [19] cachem_1.0.8          knitr_1.43            jsonlite_1.8.5       
 [22] broom_1.0.5           cluster_2.1.4         png_0.1-8            
 [25] BiocManager_1.30.21   compiler_4.2.2        httr_1.4.6           
 [28] backports_1.4.1       Matrix_1.5-4.1        fastmap_1.1.1        
 [31] cli_3.6.1             later_1.3.1           htmltools_0.5.5      
 [34] tools_4.2.2           affy_1.74.0           gtable_0.3.3         
 [37] glue_1.6.2            Rcpp_1.0.10           Biobase_2.56.0       
 [40] carData_3.0-5         jquerylib_0.1.4       vctrs_0.6.3          
 [43] preprocessCore_1.58.0 svglite_2.1.1         nlme_3.1-162         
 [46] iterators_1.0.14      Cormotif_1.42.0       xfun_0.39            
 [49] ps_1.7.5              rvest_1.0.3           timechange_0.2.0     
 [52] lifecycle_1.0.3       zlibbioc_1.42.0       getPass_0.2-2        
 [55] scales_1.2.1          hms_1.1.3             promises_1.2.0.1     
 [58] parallel_4.2.2        yaml_2.3.7            sass_0.4.6           
 [61] stringi_1.7.12        highr_0.10            S4Vectors_0.34.0     
 [64] foreach_1.5.2         e1071_1.7-13          BiocGenerics_0.42.0  
 [67] shape_1.4.6           rlang_1.1.1           pkgconfig_2.0.3      
 [70] systemfonts_1.0.4     matrixStats_1.0.0     evaluate_0.21        
 [73] lattice_0.21-8        sf_1.0-13             labeling_0.4.2       
 [76] processx_3.8.1        tidyselect_1.2.0      magrittr_2.0.3       
 [79] R6_2.5.1              IRanges_2.30.1        generics_0.1.3       
 [82] DBI_1.1.3             pillar_1.9.0          whisker_0.4.1        
 [85] withr_2.5.0           mgcv_1.8-42           units_0.8-2          
 [88] abind_1.4-5           crayon_1.5.2          car_3.1-2            
 [91] KernSmooth_2.23-21    utf8_1.2.3            RVenn_1.1.0          
 [94] tzdb_0.4.0            rmarkdown_2.22        GetoptLong_1.0.5     
 [97] locfit_1.5-9.8        data.table_1.14.8     callr_3.7.3          
[100] git2r_0.32.0          classInt_0.4-9        digest_0.6.31        
[103] webshot_0.5.4         httpuv_1.6.11         stats4_4.2.2         
[106] munsell_0.5.0         viridisLite_0.4.2     bslib_0.5.0