Figure 8
ERM
2023-07-06
Last updated: 2023-07-06
Checks: 7 0
Knit directory: Cardiotoxicity/
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library(tidyverse)
library(BiocGenerics)
library(data.table)
library(cowplot)
library(ggsignif)
library(RColorBrewer)
library(broom)Figure 8
Dox respose eQTLS are enriched in ACi response genes
A. 24 hour DEG enrichment in GTEx genes
## create GTEx data set from my data
GTEx <- intersect(GTEx_genes$entrezgene_id,my_exp_genes$ENTREZID)
## exclude GTEX and create nQTL set with other expressed genes
nQTLmy <- my_exp_genes %>%
dplyr:: filter(!ENTREZID %in%GTEx)
drug_palspc <- c("#8B006D","#DF707E","#8B006D","#DF707E")
drug_pal_fact <- c("#8B006D" ,"#DF707E", "#F1B72B" ,"#3386DD", "#707031","#41B333")
#GET nQTL umbers
nQTLsum <- toplistall %>%
mutate(id =dplyr::case_match(id, "Daunorubicin"~"DNR",
"Doxorubicin"~"DOX",
"Epirubicin"~"EPI",
"Mitoxantrone"~"MTX",
"Trastuzumab"~"TRZ",
"Vehicle"~"VEH",
.default = id)) %>%
dplyr::filter(time=="24_hours") %>%
dplyr::filter(adj.P.Val <0.05) %>%
mutate(nQTL=if_else(ENTREZID %in% nQTLmy$ENTREZID,'nQTL_y','nQTL_no')) %>%
group_by(id,nQTL) %>%
tally() %>%
separate(nQTL, into=c('set', 'group')) %>%
mutate(total=length(nQTLmy$ENTREZID) - n) %>%
dplyr::filter(group=="y")
#GETx GTEX numbers
GTExsum <- toplistall %>%
mutate(id =dplyr::case_match(id, "Daunorubicin"~"DNR",
"Doxorubicin"~"DOX",
"Epirubicin"~"EPI",
"Mitoxantrone"~"MTX",
"Trastuzumab"~"TRZ",
"Vehicle"~"VEH",
.default = id)) %>%
dplyr::filter(time=="24_hours") %>%
dplyr::filter(adj.P.Val <0.05) %>%
mutate(GTEx=if_else(ENTREZID %in%GTEx,"GTEx_y","GTEx_no")) %>%
group_by(id,GTEx) %>%
tally() %>%
separate(GTEx, into=c('set', 'group')) %>%
mutate(total=length(GTEx) - n) %>%
dplyr::filter(group=="y")
##combine and create long data frame for plot
GTEXcr8z <- GTExsum %>%
rbind(., nQTLsum) %>%
dplyr::select(id,set, n,total) %>%
mutate(id=factor(id, levels = c('DOX','EPI','DNR','MTX','TRZ','VEH'))) %>%
pivot_longer(cols=n:total,
names_to="group",
values_to="total") %>%
mutate(group=case_match(group,
"n"~"In set",
"total"~"Not in set",
.default = group)) %>%
mutate(group=factor(group,levels = c("Not in set" ,"In set"))) #%>%
GTEXcr8z %>%
ggplot(., aes(x=set,y=total, fill=group))+
geom_col(position='fill')+
facet_wrap(~id,nrow=2,ncol=4)+
theme_classic()+
scale_fill_manual(values=drug_palspc)+
ylab(" ")+
scale_y_continuous( expand = expansion(c(0, 0.01))) +
theme(strip.background = element_rect(fill = "white",
linetype=1,
linewidth = 0.5),
plot.title = element_text(size=12,
hjust = 0.5,
face="bold"),
axis.title = element_text(size = 10,
color = "black"),
axis.ticks = element_line(linewidth = 1.0),
axis.text = element_text(color = "black"),
panel.background = element_rect(colour = "black",
size=1),
strip.text.x = element_text(size=12,
face = "bold"))#+
# geom_text(aes(label = Label, vjust = 1.5, position = position_fill))B. 24 hour DEG enrichment in reQTLs
knowles4 <-readRDS("output/knowles4.RDS")
knowles5 <-readRDS("output/knowles5.RDS")
Knowles_count <-
toplistall %>%
mutate(id = dplyr::case_match(id, "Daunorubicin"~"DNR",
"Doxorubicin"~"DOX",
"Epirubicin"~"EPI",
"Mitoxantrone"~"MTX",
"Trastuzumab"~"TRZ",
"Vehicle"~"VEH",
.default = id)) %>%
filter(id!='TRZ') %>%
mutate(id=factor(id, levels = c('DOX','EPI','DNR','MTX','TRZ','VEH'))) %>%
mutate(time=factor(time, levels=c("3_hours","24_hours"))) %>%
group_by(time, id) %>%
mutate(K4 = if_else(ENTREZID %in% knowles4$entrezgene_id,1,0))%>%
mutate(K5 = if_else(ENTREZID %in% knowles5$entrezgene_id,1,0))%>%
filter(adj.P.Val<0.05) %>%
dplyr::summarize(n=n(), K4=sum(K4), K5=sum(K5)) %>%
as.tibble() %>%
dplyr::select(time,id,K4,K5) %>%
rename("K4_y"='K4',"K5_y"='K5') %>%
mutate(time = case_match(time, '3_hours'~'3 hrs',
'24_hours'~'24 hrs',
.default = time)) %>%
mutate(K4_n= 417-K4_y, K5_n=273-K5_y) %>%
pivot_longer(!c(time,id),
names_to='QTL',
values_to="gene_count") %>%
separate(QTL,into=c("QTL_type",'group'),sep = '_') %>%
mutate(group=case_match(group,
"y"~"In set",
"n"~"Not in set",
.default = group)) %>%
mutate(QTL_type =case_match(QTL_type,
'K4'~'base QTLs',
'K5'~'reQTLs',.default = QTL_type)) %>%
mutate(time=factor(time, levels=c("3 hrs","24 hrs"))) %>%
mutate(group=factor(group,levels = c("Not in set" ,"In set"))) %>%
group_by(id,time,QTL_type) %>%
mutate(percent=gene_count/sum(gene_count)*100) %>%
ungroup() %>%
filter(time=="24 hrs") %>%
mutate(id=factor(id, levels = c('DOX','EPI','DNR','MTX','TRZ','VEH')))
ggplot(Knowles_count, aes(x=QTL_type,y=gene_count, group=group, fill=group))+
geom_col(position='fill')+
facet_wrap(~id,nrow=1,ncol=4)+
theme_classic()+
ylab(" ")+
xlab(" ")+
scale_color_manual(values=drug_pal_fact)+
scale_fill_manual(values=drug_pal_fact)+
scale_y_continuous( expand = expansion(c(0, 0.01))) +
theme(strip.background = element_rect(fill = "white",
linetype=1,
linewidth = 0.5),
plot.title = element_text(size=12,
hjust = 0.5,
face="bold"),
axis.title = element_text(size = 10,
color = "black"),
axis.ticks = element_line(linewidth = 1.0),
axis.text = element_text(color = "black"),
panel.background = element_rect(colour = "black",
size=1),
strip.text.x = element_text(size=12,
face = "bold"))
C. DOXreQTLS strongly overlap other AC drugs
DOXreQTLs <- readRDS("output/DOXreQTLs.RDS")
DOXeQTL_table <- toplistall %>%
mutate(id = dplyr::case_match(id, "Daunorubicin"~"DNR",
"Doxorubicin"~"DOX",
"Epirubicin"~"EPI",
"Mitoxantrone"~"MTX",
"Trastuzumab"~"TRZ",
"Vehicle"~"VEH",
.default = id)) %>%
mutate(sigcount = if_else(adj.P.Val < 0.05,'sig','notsig'))%>%
mutate(DOXreQTLs=if_else(ENTREZID %in%DOXreQTLs$ENTREZID,"y","no")) %>%
dplyr::filter(time =="24_hours") %>%
dplyr::select(ENTREZID,id,DOXreQTLs,sigcount) %>%
group_by(id,DOXreQTLs,sigcount) %>%
tally() %>%
as.data.frame() %>%
pivot_wider(.,id_cols = c(id,DOXreQTLs),
names_from = sigcount,
values_from = n) %>%
dplyr::select(id,DOXreQTLs,sig)
DOXeQTL_table %>%
add_row(id=c("Dox-specific DEGs","Dox-specific\n DEGs"),
DOXreQTLs=c("no","y"),
sig = c(62,1)) %>%
dplyr::filter(DOXreQTLs=="y") %>%
mutate(opp= 142-sig) %>%
filter(id !=c('TRZ','DOX')) %>%
rename("yes"=sig, "no"=opp) %>%
mutate(y_percent= paste0(sprintf("%2.1f", yes/(yes+no)*100), "%"),
n_percent = paste0(sprintf("%2.1f", no/(yes+no)*100),"%")) %>%
pivot_longer(!c(id,DOXreQTLs,y_percent,n_percent),
names_to="group",
values_to = "count") %>%
mutate(id=factor(id, levels=c("EPI","DNR", "MTX","Dox-specific\n DEGs"))) %>%
ggplot(., aes(y=id,x=count,fill=group))+
geom_col(position='fill')+
theme_classic()+
ylab("")+
xlab(" ")+
scale_fill_manual(values=c("#2297E6","red2"),
labels=c("not DOXreQTL","DOXreQTL"))+
ggtitle("DOXreQTLs found in other reQTLS")+
scale_y_discrete(limits=rev)+
scale_x_continuous(expand = expansion(c(0, 0.05)))+
geom_text(aes(y=id,x=1, label = n_percent,hjust =.8))+
theme(plot.title = element_text( hjust = 0.5),
axis.title = element_text(size = 16, color = "black"),
axis.ticks = element_line(linewidth = 1.0),
axis.line = element_line(linewidth = 1.0),
axis.text = element_text(size = 12, color = "black", angle = 0),
strip.text.x = element_text(size = 12, color = "black", face = "bold"))
D. Only one DOXreQTL is associated with Dox-specific DEGs
cpm_boxplot24h <-function(cpmcounts, 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")) %>%
mutate(time = case_match(time,"24h"~"24 hours", "3h"~"3 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'))) %>%
dplyr::filter(time=="24 hours")
plot <- ggplot2::ggplot(df_plot, aes(x=drug, y=counts))+
geom_boxplot(position="identity",aes(fill=drug))+
geom_point(aes(col=indv, size=2, alpha=0.5))+
guides(alpha= "none", size= "none")+
scale_color_brewer(palette = brewer_palette, name = "Individual")+
scale_fill_manual(values=fill_colors)+
# facet_wrap("time", nrow=1, ncol=2)+
theme_bw()+
ylab(ylab)+
xlab("")+
ggtitle("24 hours")+
theme(strip.background = element_rect(fill = "white",linetype=1, linewidth = 0.5),
plot.title = element_text(size=12,hjust = 0.5,face="bold"),
axis.title = element_text(size = 10, color = "black"),
axis.ticks = element_line(linewidth = 1.0),
panel.background = element_rect(colour = "black", size=1),
axis.text.x = element_blank(),
strip.text.x = element_text(margin = margin(2,0,2,0, "pt"),face = "bold"))
print(plot)
}
cpm_boxplot24h(cpmcounts,GOI='57338',"Dark2",drug_pal_fact,
ylab=(expression(atop(" ",italic("JPH3")~log[2]~"cpm "))))
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] stats graphics grDevices utils datasets methods base
other attached packages:
[1] broom_1.0.5 RColorBrewer_1.1-3 ggsignif_0.6.4
[4] cowplot_1.1.1 data.table_1.14.8 BiocGenerics_0.42.0
[7] lubridate_1.9.2 forcats_1.0.0 stringr_1.5.0
[10] dplyr_1.1.2 purrr_1.0.1 readr_2.1.4
[13] tidyr_1.3.0 tibble_3.2.1 ggplot2_3.4.2
[16] tidyverse_2.0.0 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] tidyselect_1.2.0 xfun_0.39 bslib_0.5.0 colorspace_2.1-0
[5] vctrs_0.6.3 generics_0.1.3 htmltools_0.5.5 yaml_2.3.7
[9] utf8_1.2.3 rlang_1.1.1 jquerylib_0.1.4 later_1.3.1
[13] pillar_1.9.0 glue_1.6.2 withr_2.5.0 lifecycle_1.0.3
[17] munsell_0.5.0 gtable_0.3.3 evaluate_0.21 labeling_0.4.2
[21] knitr_1.43 callr_3.7.3 tzdb_0.4.0 fastmap_1.1.1
[25] httpuv_1.6.11 ps_1.7.5 fansi_1.0.4 highr_0.10
[29] Rcpp_1.0.10 backports_1.4.1 promises_1.2.0.1 scales_1.2.1
[33] cachem_1.0.8 jsonlite_1.8.5 farver_2.1.1 fs_1.6.2
[37] hms_1.1.3 digest_0.6.31 stringi_1.7.12 processx_3.8.1
[41] getPass_0.2-2 rprojroot_2.0.3 grid_4.2.2 cli_3.6.1
[45] tools_4.2.2 magrittr_2.0.3 sass_0.4.6 whisker_0.4.1
[49] pkgconfig_2.0.3 timechange_0.2.0 rmarkdown_2.22 httr_1.4.6
[53] rstudioapi_0.14 R6_2.5.1 git2r_0.32.0 compiler_4.2.2