Appendix 1: Supplementary figures
ERM
2023-09-28
Last updated: 2023-09-28
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Knit directory: Cardiotoxicity/
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library(tidyverse)
library(ggpubr)
library(rstatix)
library(zoo)
library(ggsignif)
library(RColorBrewer)
library(grid)
library(scales)
library(ComplexHeatmap)
library(gridExtra)
library(cowplot)
library(drc)
library(kableExtra)Error: package or namespace load failed for 'kableExtra':
.onLoad failed in loadNamespace() for 'kableExtra', details:
call: !is.null(rmarkdown::metadata$output) && rmarkdown::metadata$output %in%
error: 'length = 3' in coercion to 'logical(1)'library(broom)
library(ggVennDiagram)Function to graph gene expression across treatments
cpm_boxplot_time <-function(cpmcounts,timex, GOI,brewer_palette, fill_colors, ylab) {
##GOI needs to be ENTREZID
df <- cpmcounts
df_plot <- df %>%
dplyr::filter(rownames(.)==GOI) %>%
pivot_longer(everything(),
names_to = "treatment",
values_to = "counts") %>%
separate(treatment, c("drug","indv","time")) %>%
dplyr::filter(time == paste(timex)) %>%
mutate(time=factor(time, levels =c("3h", "24h"), labels=c("3 hours", "24 hours"))) %>%
mutate(indv=factor(indv, levels = c(1,2,3,4,5,6))) %>%
mutate(drug =case_match(drug, "Da"~"DNR",
"Do"~"DOX",
"Ep"~"EPI",
"Mi"~"MTX",
"Tr"~"TRZ",
"Ve"~"VEH", .default = drug)) %>%
mutate(drug=factor(drug, levels = c('DOX','EPI','DNR','MTX','TRZ','VEH')))
plot <- ggplot2::ggplot(df_plot, aes(x=drug, y=counts))+
geom_boxplot(position="identity",aes(fill=drug))+
geom_point(aes(col=indv, size=1.5, alpha=0.5))+
guides(alpha= "none", size= "none")+
scale_color_brewer(palette = brewer_palette, guide = "none")+
scale_fill_manual(values=fill_colors)+
# try(facet_wrap("time", nrow=1, ncol=2))+
theme_bw()+
ylab(ylab)+
xlab("")+
theme(strip.background = element_rect(fill = "white",linetype=1, linewidth = 0.5),
plot.title = element_text(size=10,hjust = 0.5,face="bold"),
axis.title = element_text(size = 10, color = "black"),
axis.ticks = element_line(linewidth = 1.0),
axis.line = element_line(linewidth = 1.0),
axis.text.x = element_blank(),
strip.text.x = element_text(margin = margin(2,0,2,0, "pt"),face = "bold"))
print(plot)
}S1 Fig: Cardiomyocytes can be generated at high purity across six individuals.
S1 FigA Flow cytometry results
Please see the paper for this figure. It was generated using FloJo.
S1 FigB
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("% TNNT2+ ")+
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")) 
| Version | Author | Date |
|---|---|---|
| b31f596 | reneeisnowhere | 2023-07-28 |
# 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")S2 Fig: Dose-response curves are reproducible across replicate cardiomyocyte differentiations from the same individual.
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")
color_order <- c('1','2','5','4','3','6')
intervals <- rbindlist(conf_int,idcol="trt")
drug_list <- c("DNR","DOX","EPI","MTX", "TRZ", "VEH")
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=color_order)) %>%
dplyr::filter(SampleID %in% doubl_plot)
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(-.2,1.5)+
coord_cartesian(ylim = c(-0.1, 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", color="black"),
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)
} 
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| 021ae6d | reneeisnowhere | 2023-08-08 |
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| 021ae6d | reneeisnowhere | 2023-08-08 |
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| 021ae6d | reneeisnowhere | 2023-08-08 |
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| 021ae6d | reneeisnowhere | 2023-08-08 |
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| 021ae6d | reneeisnowhere | 2023-08-08 |
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| 021ae6d | reneeisnowhere | 2023-08-08 |
S3 Fig: Cancer drugs that decrease cardiomyocyte viability induce cellular stress.
viability <- readRDS("data/viability.RDS")
norm_LDH48 <- readRDS("data/supp_normLDH48.RDS")
viability %>%
left_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"))+
ggpubr::stat_cor(method="pearson",
aes(label = paste(..r.label.., ..p.label.., sep = "*`,`~")),
color = "red")+
theme(plot.title = element_text(size = rel(1), hjust = 0.5,face = "bold"),
axis.title = element_text(size = 12, color = "black"),
axis.ticks = element_line(size = 1.5),
axis.text = element_text(size = 8, color = "black", angle = 0),
strip.text.x = element_text(size = 12, color = "black", face = "bold"),
strip.background = element_rect(fill = "transparent")) 
S4 Fig: Treatment with ACs at a dose of 0.5 \(\mu\)M for 48 hours induces effects on cardiomyocyte viability.
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'))) %>%
dplyr::filter(Conc <5) %>%
mutate(Conc= factor(as.numeric(Conc))) %>%
group_by(indv,sDrug,Conc) %>%
dplyr::summarize(Viability=mean(Mean)) %>%
ggplot(., aes(x=sDrug, y= Viability*100 )) +
geom_boxplot(position="dodge",
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("")+
ylab("% Viability") +
facet_wrap(~Conc)+
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"))
S5 Fig: RNA-seq sample quality is equivalent across individuals, treatments, and time points.
S5 FigA RIN vs treatment time by drug
library(limma)
library(edgeR)
library(cowplot)
filcpm_matrix <- readRDS("data/filcpm_counts.RDS")
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")
)
S5 FigB Total number of reads by treatment
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 = count, 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")
)
S5 FigC Total number of reads by sample
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 = count,
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")
)
To see more RNA-seq related analysis click here
S6 Fig: RNA-seq samples cluster by treatment type, timepoint, and individual. Pearson correlation of log2 cpm values across all pairs of samples.
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 == "DNR", "AC", if_else(
drug == "DOX", "AC", if_else(drug == "EPI", "AC", "nAC")
))) %>%
mutate(TOP2i = if_else(drug == "DNR", "yes", if_else(
drug == "DOX", "yes", if_else(drug == "EPI", "yes", if_else(drug == "MTX", "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","darkorange1"),
TOP2i =c("darkgreen","lightgreen"))
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$TOP2i)
ComplexHeatmap::pheatmap(mcor,
# column_title=(paste0("RNA-seq log"[2]~"cpm correlation")),
annotation_col = counts_corr_mat,
annotation_colors = mat_colors,
heatmap_legend_param = mat_colors,
fontsize=10,
fontsize_row = 8,
angle_col="90",
treeheight_row=25,
fontsize_col = 8,
treeheight_col = 20)
To see more RNA-seq related analysis click here
S7 Fig: PC1 associates with drug treatment and treatment time, while PC2 associates with individual.
pca_all_anno <- readRDS("data/supp_pca_all_anno.RDS")
pca_all_anno <- pca_all_anno %>%
mutate(
drug = case_match(
drug,
"Daunorubicin" ~ "DNR",
"Doxorubicin" ~ "DOX",
"Epirubicin" ~ "EPI",
"Mitoxantrone" ~ "MTX",
"Trastuzumab" ~ "TRX",
"Vehicle" ~ "VEH",
.default = drug
)
)
facs <- c("indv", "drug", "time")
names(facs) <- c("Individual", "Treatment", "Time")
get_regr_pval <- function(mod) {
# Returns the p-value for the Fstatistic of a linear model
# mod: class lm
stopifnot(class(mod) == "lm")
fstat <- summary(mod)$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)])
}
To see more RNA-seq related analysis click herehttps://mward-lab.github.io/Matthews_TOP2i_cardiotox_2023/run_all_analysis.html)
S8 Fig: Thousands of gene expression changes are induced in response to TOP2i treatment over 24 hours.
Volcanoplots <- readRDS("output/Volcanoplot_10.RDS")
Volcanoplots
(To see more RNA-seq related analysis click here
S9 Fig: ACs affect expression of nearly half of all expressed genes after 24 hours of treatment.
S9 FigA Proportion of genes DE in response to drug treatment
toplistall <- readRDS("data/toplistall.RDS")
drug_pal_fact <- c("#8B006D","#DF707E","#F1B72B", "#3386DD","#707031","#41B333")
toplistall %>%
mutate(id=factor(id, levels = c('DOX', 'EPI', 'DNR', 'MTX', 'TRZ','VEH'))) %>%
mutate(time= factor(time,
levels=c("3_hours","24_hours"),
labels=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(~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"))
S9 FigB Magnitude of response to drug treatment
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"),
labels=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"))
S10 Fig: A small number of genes respond to a single drug only.
3 hours
supp10_3hlist <- readRDS("data/supp10_3hlist.RDS")
list2env(supp10_3hlist, envir = .GlobalEnv)<environment: R_GlobalEnv>mito3pg <- plot_grid(densityMTX3sp, MTX3Bplot, MTX3genesp_example,
nrow = 1,
rel_heights = c(.8,2,1),
rel_widths=c(1.2,1,1.5),
scale=c(1,0.8,0.8))
Daun3pg <- plot_grid(densityDNR3sp, DNR3Bplot, DNR3genesp_example, nrow = 1, rel_heights = c(.8,2,1), rel_widths=c(1.2,1,1.5),scale=c(1,0.8,0.8))
Doxo3pg <- plot_grid(NA, NA, NA, nrow = 1, rel_heights = c(.8,2,1), rel_widths=c(1.2,1,1.5),scale=c(1,0.8,0.8))
Epi3pg <- plot_grid(densityEPI3sp, NA, NA, nrow = 1, rel_heights = c(.8,2,1), rel_widths=c(1.2,1,1.5),scale=c(1,0.8,0.8))
allfinal3hour <- plot_grid(Doxo3pg,Epi3pg,Daun3pg,mito3pg,nrow=4, rel_heights = c(1,1,1,1))
# allfinal3hour <- readRDS("data/allfinal3hour.RDS")
allfinal3hour
24 hours
supp10_24hlist <- readRDS("data/supp10_24hlist.RDS")
list2env(supp10_24hlist, envir = .GlobalEnv)<environment: R_GlobalEnv>mitopg <- plot_grid(densityMTXsp, MtxBplot, Mtxgenesp_example,
nrow = 1,
rel_heights = c(.8,2,1),
rel_widths=c(1.2,1,1.5),
scale=c(1,0.8,0.8))
Daunpg <- plot_grid(densityDNRsp, DnrBplot, Dnrgenesp_example, nrow = 1, rel_heights = c(.8,2,1), rel_widths=c(1.2,1,1.5),scale=c(1,0.8,0.8))
Doxopg <- plot_grid(densityDOXsp, DoxBplot, Doxgenesp_example, nrow = 1, rel_heights = c(.8,2,1), rel_widths=c(1.2,1,1.5),scale=c(1,0.8,0.8))
Epipg <- plot_grid(densityEPIsp, EpiBplot, Epigenesp_example, nrow = 1, rel_heights = c(.8,2,1), rel_widths=c(1.2,1,1.5),scale=c(1,0.8,0.8))
allfinal <- plot_grid(Doxopg,Epipg,Daunpg,mitopg,nrow=4, labels = "AUTO")
# allfinal <- readRDS("output/allfinal_sup10.RDS")
plot(allfinal)
S11 Fig: Stringently-identified drug-specific response genes are enriched in biological processes.
3 hours
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"))
###24 hours
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"))
## S12 Fig: Most genes that are differentially expressed in response to
treatment at three hours are also differentially expressed at 24
hours.
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)
More on DE gene analysis click here
S13 Fig: Four gene expression signatures capture the response to TOP2i over time.
S13 FigA
cormotif_initial <- readRDS("data/cormotif_initialall.RDS")
Cormotif::plotIC(cormotif_initial)
S13 FigB
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)
S13 FigC
motif_list_GO <- readRDS("output/supplementary_motif_list_GO.RDS")
list2env(motif_list_GO, envir = .GlobalEnv)<environment: R_GlobalEnv>motifcol <- c("#F8766D", "#00BFC4", "#7CAE00", "#C77CFF")
GOmotiflong <- list(
"EAR" = ER3f,
"ESR" = TI3f,
"LR" = LR3f,
"NR" = NR1f
)
GOBPlong <- data.table::rbindlist(GOmotiflong, idcol = "motif")
p <-
GOBPlong %>% mutate(motif = factor(motif, levels = c("EAR", "ESR", "LR", "NR"))) %>%
ggplot(., aes(
x = log_val,
y = reorder(term_name, order_val, desc = FALSE),
col = motif
)) +
geom_point(aes(size = intersection_size, col = motif)) +
scale_y_discrete(labels = scales::label_wrap(40)) +
facet_grid(motif ~ ., scales = "free_y") +
scale_color_discrete(type = motifcol) +
guides(col = guide_legend(title = "Motif"),
size = guide_legend(title = "# of intersected \n terms")) +
ggtitle('Top 10 enriched GO:BP terms for each motif') +
xlab(expression("-log"[10] ~ "(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 = 8,
color = "black",
angle = 0
),
strip.text.x = element_text(
size = 15,
color = "black",
face = "bold"
)
)
g <- ggplot_gtable(ggplot_build(p))
stripr <- which(grepl('strip-r', g$layout$name))
fills <- c("#F8766D", "#00BFC4", "#7CAE00", "#C77CFF")
k <- 1
for (i in stripr) {
j <- which(grepl('rect', g$grobs[[i]]$grobs[[1]]$childrenOrder))
g$grobs[[i]]$grobs[[1]]$children[[j]]$gp$fill <- fills[k]
k <- k + 1
}
grid.draw(g)
More on Baysian gene analysis click here.
S14 Fig: AC treatments induce a small number of gene expression variation changes across individuals.
library(qvalue)
organized_drugframe <- read.csv("data/organized_drugframe.csv",
row.names = 1)
Var_test_list24 <- readRDS("data/Var_test_list24.RDS")
set24 <- names(Var_test_list24)
framefun24 <- data.frame(ENTREZID=rownames(organized_drugframe))
for (i in 1:length(set24)){
a <- set24[i]
s_list<- Var_test_list24[[i]][[13]]
names(s_list) <- rownames(organized_drugframe)
hold<- map_df(s_list, ~as.data.frame(.x$p.value), .id="ENTREZID")
framefun24[paste0(a)] <- hold$`.x$p.value`
}
est24pDNR <- qvalue(p=framefun24$DNR.VEH.24)
est24pDOX <- qvalue(p=framefun24$DOX.VEH.24)
est24pEPI <- qvalue(p=framefun24$EPI.VEH.24)
est24pMTX <- qvalue(p=framefun24$MTX.VEH.24)
est24pTRZ <- qvalue(p=framefun24$TRZ.VEH.24)
hist(est24pDNR)+labs(caption= "DNR 24")
hist(est24pDOX)+labs(caption= "DOX 24")
hist(est24pEPI)+labs(caption= "EPI 24")
hist(est24pMTX)+labs(caption= "MTX 24")
hist(est24pTRZ)+labs(caption= "TRZ 24")
S15 Fig: TOP2i treatments induce expression changes in some genes that correlate with cardiotoxicity.
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")+
# scale_y_continuous(labels = scales::number_format(accuracy = 0.01))+
facet_wrap(hgnc_symbol~Drug, scales="free", nrow = 1)+
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"))+
guides(color="none")+
ggpubr::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(hjust = 0.5,face = "bold"),
axis.title = element_text(size = 12, color = "black"),
axis.ticks = element_line(size = 1.5),
axis.text = element_text(size = 8, color = "black", angle =0),
strip.text.x = element_text(size = 12, color = "black", face = "italic"))
plot(gene_plot)
}
| Version | Author | Date |
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| 021ae6d | reneeisnowhere | 2023-08-08 |
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| 021ae6d | reneeisnowhere | 2023-08-08 |
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| 021ae6d | reneeisnowhere | 2023-08-08 |
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| 021ae6d | reneeisnowhere | 2023-08-08 |
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| 021ae6d | reneeisnowhere | 2023-08-08 |
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| 021ae6d | reneeisnowhere | 2023-08-08 |
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| 021ae6d | reneeisnowhere | 2023-08-08 |
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| 021ae6d | reneeisnowhere | 2023-08-08 |
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| 021ae6d | reneeisnowhere | 2023-08-08 |
| Version | Author | Date |
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| 021ae6d | reneeisnowhere | 2023-08-08 |
| Version | Author | Date |
|---|---|---|
| 021ae6d | reneeisnowhere | 2023-08-08 |
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] qvalue_2.32.0 edgeR_3.42.4 limma_3.56.2
[4] data.table_1.14.8 ggVennDiagram_1.2.3 broom_1.0.5
[7] drc_3.0-1 MASS_7.3-60 cowplot_1.1.1
[10] gridExtra_2.3 ComplexHeatmap_2.16.0 scales_1.2.1
[13] RColorBrewer_1.1-3 ggsignif_0.6.4 zoo_1.8-12
[16] rstatix_0.7.2 ggpubr_0.6.0 lubridate_1.9.2
[19] forcats_1.0.0 stringr_1.5.0 dplyr_1.1.3
[22] purrr_1.0.2 readr_2.1.4 tidyr_1.3.0
[25] tibble_3.2.1 ggplot2_3.4.3 tidyverse_2.0.0
[28] workflowr_1.7.1
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_2.7.5
[7] farver_2.1.1 rmarkdown_2.24 zlibbioc_1.46.0
[10] GlobalOptions_0.1.2 fs_1.6.3 vctrs_0.6.3
[13] webshot_0.5.5 htmltools_0.5.6 plotrix_3.8-2
[16] sass_0.4.7 KernSmooth_2.23-22 bslib_0.5.1
[19] plyr_1.8.8 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_1.6-1 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 labeling_0.4.3 fansi_1.0.4
[37] timechange_0.2.0 httr_1.4.7 abind_1.4-5
[40] mgcv_1.9-0 compiler_4.3.1 proxy_0.4-27
[43] withr_2.5.0 doParallel_1.0.17 backports_1.4.1
[46] carData_3.0-5 DBI_1.1.3 rjson_0.2.21
[49] classInt_0.4-10 gtools_3.9.4 units_0.8-4
[52] tools_4.3.1 httpuv_1.6.11 Cormotif_1.46.0
[55] glue_1.6.2 callr_3.7.3 nlme_3.1-163
[58] promises_1.2.1 sf_1.0-14 getPass_0.2-2
[61] reshape2_1.4.4 cluster_2.1.4 generics_0.1.3
[64] gtable_0.3.4 tzdb_0.4.0 preprocessCore_1.62.1
[67] class_7.3-22 hms_1.1.3 xml2_1.3.5
[70] car_3.1-2 utf8_1.2.3 BiocGenerics_0.46.0
[73] foreach_1.5.2 pillar_1.9.0 later_1.3.1
[76] circlize_0.4.15 splines_4.3.1 lattice_0.21-8
[79] survival_3.5-7 tidyselect_1.2.0 locfit_1.5-9.8
[82] knitr_1.44 git2r_0.32.0 IRanges_2.34.1
[85] svglite_2.1.1 stats4_4.3.1 xfun_0.40
[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_1.30.22 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_1.2-3 affy_1.78.2 e1071_1.7-13
[109] crayon_1.5.2 GetoptLong_1.0.5 rlang_1.1.1
[112] rvest_1.0.3 multcomp_1.4-25