Last updated: 2024-02-05

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Knit directory: Cardiotoxicity/

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library(car)
library(tidyverse)
library(tinytex)
library(BiocGenerics)
library(data.table)
library(drc)
library(cowplot)
library(ggsignif)
library(RColorBrewer)
library(broom)
library(ComplexHeatmap)
library(png)

Figure 1

Top2i drugs affect cardiomyocyte viability in a dose-dependent manner.

A. Project overview: Image not given here. Please look at our paper.

knitr::include_graphics("docs/assests/Figure1-A.png")

B. Dose-response curves from 48 hour drug exposure.

daplot <- readRDS("output/daplot.RDS")  
dxplot <- readRDS("output/dxplot.RDS")
epplot<- readRDS("output/epplot.RDS")
mtplot<- readRDS("output/mtplot.RDS")
trplot<- readRDS("output/trplot.RDS")
veplot<- readRDS("output/veplot.RDS")
veplot <- veplot+xlab(NULL)
trplot <- trplot+xlab(NULL)
legend_b <- readRDS("output/legend_b.RDS")
plan2 <-  cowplot::plot_grid(daplot,dxplot,epplot,mtplot, trplot,veplot, legend_b,ncol =7, rel_widths = c(1,1,1,1,1,1,.5))
print(plan2)

Version Author Date
513ca48 reneeisnowhere 2023-07-26
05fe9b1 reneeisnowhere 2023-07-19
e3fe073 reneeisnowhere 2023-07-18
9dd118a reneeisnowhere 2023-07-14
6328422 reneeisnowhere 2023-06-16

Lines are a 4 point log-logistic regression of the mean from two biological replicates for each individual at 8 concentrations for each condition, except(trastuzmab)TRZ, which is 7 concentrations.

You can find the link to initial DRC analysis at thislink

C & D. LD50 and EC50

drug_palc <- c("#8B006D","#DF707E","#F1B72B", "#3386DD","#707031","#41B333")
BC_cell_lines <- read.csv("data/BC_cell_lines.csv",row.names = 1)
LD50_table <- readRDS("data/new_ld50avg.RDS")



graphLD50 <- LD50_table %>%
  mutate(Treatment = factor(Treatment,
                            levels = c('DOX', 'EPI', 'DNR', 'MTX', 'TRZ', 'VEH'))) %>%
  ggplot(., (aes(x = Treatment, y = log10(Estimate)))) +
  geom_boxplot(position = "identity", aes(fill = Treatment)) +
  geom_point(aes(
    color = indv,
    size = 5,
    alpha = 0.5
  )) +
  ggtitle(expression("Experimentally-derived LD"[50] * "s\n from treated cardiomyocytes")) +
  xlab("") +
  geom_signif(
    comparisons = list(
      c("DNR", "MTX"),
      c("DOX", "EPI"),
      c ("DOX", "DNR"),
      c("DOX", "MTX")
    ),
    test = "t.test",
    map_signif_level = TRUE,
    textsize = 4,
    step_increase = 0.15
  ) +
  ylab(bquote('Log'[10] ~ 'LD'[50] ~ 'in ' * mu * M)) +
  scale_color_brewer(palette = "Dark2",
                     name = "Individual") +
  ylim(-2, 3) +
  
  scale_fill_manual(values = drug_palc, name = "Treatment") +
  theme_bw() +
  guides(alpha = "none", size = "none") +
  theme(
    plot.title = element_text(hjust = 0.5, size = 12),
    axis.title = element_text(size = 15, color = "black"),
    axis.ticks = element_line(linewidth = 1.5),
    axis.line = element_line(linewidth = 1.5),
    axis.text.x = element_text(size = 0, color = "white"),
    # axis.text = element_text(size = 12, color = "black", angle = 0),
    strip.text.x = element_text(
      size = 15,
      color = "black",
      face = "bold"
    )
  )

You can find the links to DRC and BCC analysis at thislink.

graphBC <-
  BC_cell_lines %>%
  mutate(Cell_line = factor(Cell_line)) %>%
  pivot_longer(.,
               col = !Cell_line,
               names_to = 'drug',
               values_to = 'LD50') %>%
  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 = log10(LD50)))) +
  geom_boxplot(position = "identity", aes(fill = drug)) +
  geom_point(aes(
    color = Cell_line,
    size = 5,
    alpha = 0.5
  )) +
  ggtitle(expression("Breast cancer cell line reported  ED"[50] * "s")) +
  xlab("") +
  
  geom_signif(
    comparisons = list(c("DOX", "EPI"),
                       c ("DOX", "DNR"),
                       c("DOX", "MTX")),
    test = "t.test",
    map_signif_level = TRUE,
    textsize = 4,
    step_increase = 0.15
  ) +
  ylab(bquote('Log'[10] ~ 'LD'[50] ~ 'in ' * mu * M)) +
  scale_color_brewer(palette = "Spectral",
                     name = "Cell lines") +
  scale_fill_manual(values = drug_palc) +
  ylim(-2, 3) +
  theme_bw() +
  guides(alpha = "none", size = "none", fill = "none") +
  theme(
    plot.title = element_text(hjust = 0.5, size = 12),
    axis.title = element_text(size = 15, color = "black"),
    axis.ticks = element_line(linewidth = 1.5),
    axis.line = element_line(linewidth = 1.5),
    axis.text.x = element_text(size = 0, color = "white"),
    # axis.text = element_text(size = 12, color = "black", angle = 0),
    strip.text.x = element_text(
      size = 15,
      color = "black",
      face = "bold"
    )
  )
# graphBC
plan50ld <-
  cowplot::plot_grid(graphLD50,
                     NULL,
                     graphBC,
                     rel_widths = c(1, .2, 1),
                     nrow = 1)
print(plan50ld)

Version Author Date
9eb4774 reneeisnowhere 2023-09-27
513ca48 reneeisnowhere 2023-07-26
9dd118a reneeisnowhere 2023-07-14
433a442 reneeisnowhere 2023-06-21
6328422 reneeisnowhere 2023-06-16

You can find the code to EC50 breast cancer cell lines at this link.


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] png_0.1-8             ComplexHeatmap_2.16.0 broom_1.0.5          
 [4] RColorBrewer_1.1-3    ggsignif_0.6.4        cowplot_1.1.1        
 [7] drc_3.0-1             MASS_7.3-60           data.table_1.14.8    
[10] BiocGenerics_0.46.0   tinytex_0.48          lubridate_1.9.3      
[13] forcats_1.0.0         stringr_1.5.0         dplyr_1.1.3          
[16] purrr_1.0.2           readr_2.1.4           tidyr_1.3.0          
[19] tibble_3.2.1          ggplot2_3.4.4         tidyverse_2.0.0      
[22] car_3.1-2             carData_3.0-5         workflowr_1.7.1      

loaded via a namespace (and not attached):
 [1] sandwich_3.0-2      rlang_1.1.2         magrittr_2.0.3     
 [4] clue_0.3-65         GetoptLong_1.0.5    git2r_0.32.0       
 [7] multcomp_1.4-25     matrixStats_1.1.0   compiler_4.3.1     
[10] getPass_0.2-2       mgcv_1.9-0          callr_3.7.3        
[13] vctrs_0.6.4         pkgconfig_2.0.3     shape_1.4.6        
[16] crayon_1.5.2        fastmap_1.1.1       backports_1.4.1    
[19] labeling_0.4.3      utf8_1.2.4          promises_1.2.1     
[22] rmarkdown_2.25      tzdb_0.4.0          ps_1.7.5           
[25] xfun_0.41           cachem_1.0.8        jsonlite_1.8.7     
[28] highr_0.10          later_1.3.1         parallel_4.3.1     
[31] cluster_2.1.4       R6_2.5.1            bslib_0.5.1        
[34] stringi_1.7.12      jquerylib_0.1.4     Rcpp_1.0.11        
[37] iterators_1.0.14    knitr_1.45          zoo_1.8-12         
[40] IRanges_2.34.1      httpuv_1.6.12       Matrix_1.6-2       
[43] splines_4.3.1       timechange_0.2.0    tidyselect_1.2.0   
[46] rstudioapi_0.15.0   abind_1.4-5         yaml_2.3.7         
[49] doParallel_1.0.17   codetools_0.2-19    processx_3.8.2     
[52] lattice_0.22-5      withr_2.5.2         evaluate_0.23      
[55] survival_3.5-7      circlize_0.4.15     pillar_1.9.0       
[58] whisker_0.4.1       foreach_1.5.2       stats4_4.3.1       
[61] generics_0.1.3      rprojroot_2.0.4     hms_1.1.3          
[64] S4Vectors_0.38.2    munsell_0.5.0       scales_1.2.1       
[67] gtools_3.9.4        glue_1.6.2          tools_4.3.1        
[70] fs_1.6.3            mvtnorm_1.2-3       plotrix_3.8-3      
[73] colorspace_2.1-0    nlme_3.1-163        cli_3.6.1          
[76] fansi_1.0.5         gtable_0.3.4        sass_0.4.7         
[79] digest_0.6.33       TH.data_1.1-2       farver_2.1.1       
[82] rjson_0.2.21        htmltools_0.5.7     lifecycle_1.0.4    
[85] httr_1.4.7          GlobalOptions_0.1.2