Last updated: 2023-07-06

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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