Supplementary figures
ERM
2023-08-10
Last updated: 2023-08-10
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Knit directory: Cardiotoxicity/
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|---|---|---|---|---|
| Rmd | b30e0e4 | reneeisnowhere | 2023-08-10 | adding fig3 |
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| Rmd | d7b9ff1 | reneeisnowhere | 2023-06-26 | Adding supplementary figures |
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| Rmd | 2b109c3 | reneeisnowhere | 2023-06-23 | adding some supp graphs |
| Rmd | c1d667f | reneeisnowhere | 2023-06-23 | updating the codes at Friday start. |
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)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)
}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(-.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)
} 
Fig. S3
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")) 
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'))) %>%
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"))
Fig. S5
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"))
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)
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"),
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"))
# drug_palNoVeh <- c("#8B006D" ,"#DF707E", "#F1B72B" ,"#3386DD", "#707031")
ggsave("output/Figures/Percent_DEG-1.eps",width = 6, height =4, units = "in")
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"))
ggsave("output/Figures/Percent_DEG-2.eps",width = 6, height =4, units = "in")Fig. S10
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
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")+
# 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)
}
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] ggVennDiagram_1.2.2 broom_1.0.5 drc_3.0-1
[7] MASS_7.3-60 cowplot_1.1.1 gridExtra_2.3
[10] ComplexHeatmap_2.16.0 scales_1.2.1 RColorBrewer_1.1-3
[13] ggsignif_0.6.4 zoo_1.8-12 rstatix_0.7.2
[16] ggpubr_0.6.0 lubridate_1.9.2 forcats_1.0.0
[19] stringr_1.5.0 dplyr_1.1.2 purrr_1.0.1
[22] readr_2.1.4 tidyr_1.3.0 tibble_3.2.1
[25] 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 GlobalOptions_0.1.2
[10] fs_1.6.3 ragg_1.2.5 vctrs_0.6.3
[13] webshot_0.5.5 htmltools_0.5.5 plotrix_3.8-2
[16] sass_0.4.7 KernSmooth_2.23-22 bslib_0.5.0
[19] sandwich_3.0-2 cachem_1.0.8 whisker_0.4.1
[22] lifecycle_1.0.3 iterators_1.0.14 pkgconfig_2.0.3
[25] Matrix_1.6-0 R6_2.5.1 fastmap_1.1.1
[28] clue_0.3-64 digest_0.6.33 colorspace_2.1-0
[31] S4Vectors_0.38.1 ps_1.7.5 rprojroot_2.0.3
[34] textshaping_0.3.6 labeling_0.4.2 fansi_1.0.4
[37] timechange_0.2.0 httr_1.4.6 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 highr_0.10
[49] rjson_0.2.21 classInt_0.4-9 gtools_3.9.4
[52] units_0.8-2 tools_4.3.1 httpuv_1.6.11
[55] glue_1.6.2 callr_3.7.3 nlme_3.1-162
[58] promises_1.2.0.1 sf_1.0-14 getPass_0.2-2
[61] cluster_2.1.4 generics_0.1.3 gtable_0.3.3
[64] tzdb_0.4.0 class_7.3-22 hms_1.1.3
[67] xml2_1.3.5 car_3.1-2 utf8_1.2.3
[70] BiocGenerics_0.46.0 foreach_1.5.2 pillar_1.9.0
[73] later_1.3.1 circlize_0.4.15 splines_4.3.1
[76] lattice_0.21-8 survival_3.5-5 tidyselect_1.2.0
[79] locfit_1.5-9.8 knitr_1.43 git2r_0.32.0
[82] IRanges_2.34.1 svglite_2.1.1 stats4_4.3.1
[85] xfun_0.39 matrixStats_1.0.0 stringi_1.7.12
[88] yaml_2.3.7 evaluate_0.21 codetools_0.2-19
[91] RVenn_1.1.0 cli_3.6.1 systemfonts_1.0.4
[94] munsell_0.5.0 processx_3.8.2 jquerylib_0.1.4
[97] Rcpp_1.0.11 png_0.1-8 parallel_4.3.1
[100] viridisLite_0.4.2 mvtnorm_1.2-2 e1071_1.7-13
[103] crayon_1.5.2 GetoptLong_1.0.5 rlang_1.1.1
[106] rvest_1.0.3 multcomp_1.4-25