Figure 1: TOP2i drugs affect cardiomyocyte viability in a dose-dependent manner
ERM
2023-09-27
Last updated: 2023-09-27
Checks: 7 0
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)Figure 1
Top2i drugs affect cardiomyocyte viability in a dose-dependent manner.
A. Project overview: Image not given here. Please look at our paper.
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)
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
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.
C & D. LD50 and EC50
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)
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] ComplexHeatmap_2.16.0 broom_1.0.5 RColorBrewer_1.1-3
[4] ggsignif_0.6.4 cowplot_1.1.1 drc_3.0-1
[7] MASS_7.3-60 data.table_1.14.8 BiocGenerics_0.46.0
[10] tinytex_0.46 lubridate_1.9.2 forcats_1.0.0
[13] stringr_1.5.0 dplyr_1.1.3 purrr_1.0.2
[16] readr_2.1.4 tidyr_1.3.0 tibble_3.2.1
[19] ggplot2_3.4.3 tidyverse_2.0.0 car_3.1-2
[22] carData_3.0-5 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] sandwich_3.0-2 rlang_1.1.1 magrittr_2.0.3
[4] clue_0.3-64 GetoptLong_1.0.5 git2r_0.32.0
[7] multcomp_1.4-25 matrixStats_1.0.0 compiler_4.3.1
[10] getPass_0.2-2 mgcv_1.9-0 png_0.1-8
[13] callr_3.7.3 vctrs_0.6.3 pkgconfig_2.0.3
[16] shape_1.4.6 crayon_1.5.2 fastmap_1.1.1
[19] backports_1.4.1 labeling_0.4.3 utf8_1.2.3
[22] promises_1.2.1 rmarkdown_2.24 tzdb_0.4.0
[25] ps_1.7.5 xfun_0.40 cachem_1.0.8
[28] jsonlite_1.8.7 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_1.44 zoo_1.8-12
[40] IRanges_2.34.1 httpuv_1.6.11 Matrix_1.6-1
[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_0.21-8 withr_2.5.0 evaluate_0.21
[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_2.0.3 hms_1.1.3
[64] S4Vectors_0.38.1 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_3.8-2
[73] colorspace_2.1-0 nlme_3.1-163 cli_3.6.1
[76] fansi_1.0.4 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_0.5.6 lifecycle_1.0.3
[85] httr_1.4.7 GlobalOptions_0.1.2