GOI_plots

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Last updated: 2023-06-23

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Knit directory: Cardiotoxicity/

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cpm_boxplot <-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=factor(time, levels =c("3h", "24h"))) %>%
      mutate(indv=factor(indv, levels = c(1,2,3,4,5,6))) %>%
      mutate(drug =case_match(drug, "Da"~"Daunorubicin",
                            "Do"~"Doxorubicin",
                            "Ep"~"Epirubicin",
                            "Mi"~"Mitoxantrone",
                            "Tr"~"Trastuzumab",
                            "Ve"~"Vehicle", .default = drug))
    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, guide = "none")+
      scale_fill_manual(values=fill_colors)+
      facet_wrap("time", nrow=1, ncol=2)+
      theme_bw()+
      ylab(ylab)+
      xlab("")+
      theme(strip.background = element_rect(fill = "white"),
          plot.title = element_text(size=18,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.x = element_text(size = 12, color = "white", angle = 0),
          strip.text.x = element_text(size = 15, color = "black", face = "bold"))
    print(plot)
}

library(ComplexHeatmap)
library(tidyverse)
library(ggsignif)
library(RColorBrewer)
library(scales)
library(sjmisc)
library(kableExtra)
library(broom)
library(ggstats)
library(Hmisc)
library(ggpubr)

<environment: R_GlobalEnv>

<environment: R_GlobalEnv>

Genes of Interest log2 cpm

TOP2B

### CDKN1a

Top2a

### ATM ### ATR

Rictor

### mTOR ### RARG

KAT6b

cpm_boxplot(cpmcounts,GOI='23522',"Dark2",drug_pal_vehend,
  ylab=(expression(atop(" ",italic("KAT6B")~log[2]~"cpm "))))

KDM5b

cpm_boxplot(cpmcounts,GOI='10765',"Dark2",drug_pal_vehend,
            ylab=(expression(atop(" ",italic("KDM5B")~log[2]~"cpm "))))

KDM4b

cpm_boxplot(cpmcounts,GOI='23030',"Dark2",drug_pal_vehend,
  ylab=(expression(atop(" ",italic("KDM4B")~log[2]~"cpm "))))

## expression correlation:

Significant (adj. P value of <0.05) Genes of interest by treatment

	time	id	ENTREZID	SYMBOL	adj.P.Val
23254…1	24_hours	Daunorubicin	23254	KAZN	0.0000175
23030…2	24_hours	Daunorubicin	23030	KDM4B	0.0001975
283337…3	24_hours	Daunorubicin	283337	ZNF740	0.0002275
10818…4	24_hours	Daunorubicin	10818	FRS2	0.0005879
51020…5	24_hours	Daunorubicin	51020	HDDC2	0.0020530
5916…6	24_hours	Daunorubicin	5916	RARG	0.0052878
10818…7	24_hours	Doxorubicin	10818	FRS2	0.0000482
23254…8	24_hours	Doxorubicin	23254	KAZN	0.0001905
23030…9	24_hours	Doxorubicin	23030	KDM4B	0.0013753
51020…10	24_hours	Doxorubicin	51020	HDDC2	0.0065896
283337…11	24_hours	Doxorubicin	283337	ZNF740	0.0141077
64078…12	24_hours	Doxorubicin	64078	SLC28A3	0.0292968
5916…13	24_hours	Doxorubicin	5916	RARG	0.0439585
10818…14	24_hours	Epirubicin	10818	FRS2	0.0001665
23254…15	24_hours	Epirubicin	23254	KAZN	0.0007793
51020…16	24_hours	Epirubicin	51020	HDDC2	0.0010982
5916…17	24_hours	Epirubicin	5916	RARG	0.0124123
64078…18	24_hours	Epirubicin	64078	SLC28A3	0.0149064
283337…19	24_hours	Epirubicin	283337	ZNF740	0.0177562
23030…20	24_hours	Epirubicin	23030	KDM4B	0.0236417

   entrezgene_id ensembl_gene_id hgnc_symbol
1          10818 ENSG00000166225        FRS2
2          51020 ENSG00000111906       HDDC2
3          23522 ENSG00000281813       KAT6B
4          23254 ENSG00000189337        KAZN
5          23030 ENSG00000127663       KDM4B
6           5916 ENSG00000172819        RARG
7          64078 ENSG00000197506     SLC28A3
8           6579 ENSG00000084453     SLCO1A2
9          28234 ENSG00000111700     SLCO1B3
10         54575 ENSG00000242515     UGT1A10
11        283337 ENSG00000139651      ZNF740

   entrezgene_id ensembl_gene_id hgnc_symbol
1          10818 ENSG00000166225        FRS2
2          51020 ENSG00000111906       HDDC2
3          23522 ENSG00000281813       KAT6B
4          23254 ENSG00000189337        KAZN
5          23030 ENSG00000127663       KDM4B
6           5916 ENSG00000172819        RARG
7          64078 ENSG00000197506     SLC28A3
8           6579 ENSG00000084453     SLCO1A2
9          28234 ENSG00000111700     SLCO1B3
10         54575 ENSG00000242515     UGT1A10
11        283337 ENSG00000139651      ZNF740

   entrezgene_id ensembl_gene_id hgnc_symbol
1          10818 ENSG00000166225        FRS2
2          51020 ENSG00000111906       HDDC2
3          23522 ENSG00000281813       KAT6B
4          23254 ENSG00000189337        KAZN
5          23030 ENSG00000127663       KDM4B
6           5916 ENSG00000172819        RARG
7          64078 ENSG00000197506     SLC28A3
8           6579 ENSG00000084453     SLCO1A2
9          28234 ENSG00000111700     SLCO1B3
10         54575 ENSG00000242515     UGT1A10
11        283337 ENSG00000139651      ZNF740

RARG correlation

ld50_via_RARG <- read.csv("data/LD50_05via.csv",row.names=1)
ld50_via_RARG <- ld50_via_RARG %>% 
  mutate(indv=factor(indv))
test <- RNAnormlist %>% 
  mutate(indv=factor(indv,levels = level_order2)) %>% 
  mutate(indv=as.numeric(indv)) %>%
  mutate(indv=factor(indv)) %>% 
  mutate(Drug = factor(Drug, levels = c("Daunorubicin", 
                                       "Doxorubicin",
                                       "Epirubicin",
                                       "Mitoxantrone",
                                       "Trastuzumab",
                                       "Vehicle"))) %>% 
  dplyr::select(indv, Drug,rldh,rtnni) #%>% 
  



RARG_corr_frame <- gene_corr_df %>% 
    filter(entrezgene_id ==5916) %>%  
    left_join(., ld50_via_RARG, by=c("Drug","indv")) %>% 
    dplyr::select(indv, Drug,sDrug,entrezgene_id,counts, Viability,LD50) %>% 
    mutate(Drug=factor(Drug)) %>% 
    full_join(.,GOI_genelist, by="entrezgene_id") %>% 
  full_join(., test, by=c("Drug","indv" )) %>% as.data.frame()

SL25_corr_frame <- gene_corr_df %>% 
    filter( entrezgene_id ==64078) %>%  
    left_join(., ld50_via_RARG, by=c("Drug","indv")) %>% 
    dplyr::select(indv, Drug,sDrug,entrezgene_id,counts, Viability,LD50) %>% 
    mutate(Drug=factor(Drug)) %>% 
    full_join(.,GOI_genelist, by="entrezgene_id") %>% 
  full_join(., test, by=c("Drug","indv" )) %>% as.data.frame()


 
 RARG_plotld50 <- RARG_corr_frame %>%
      dplyr::filter(entrezgene_id == 5916) %>%
      ggplot(., aes(x=LD50, y=counts))+
      geom_point(aes(col=indv))+
      geom_smooth(method="lm")+
      facet_wrap(hgnc_symbol~Drug, scales="free")+
      theme_classic()+
      xlab(bquote('LD'[50]~'in '*mu*M)) +
      ylab(bquote("Gene counts in log"[2]~" cpm")) +
      ggtitle(bquote("Correlation of LD"[50]~" and Log"[2]~"cpm"))+
      scale_color_brewer(palette = "Dark2",name = "Individual", label = c("1","2","3","4","5","6"))+
      stat_cor(method="pearson",
               aes(label = paste(..r.label.., ..p.label.., sep = "~`,`~")),
               color = "red",
               label.x.npc = 0,
               label.y.npc=1,
               size = 3)+
      theme(plot.title = element_text(size = rel(1.5), hjust = 0.5,face = "bold"),
            axis.title = element_text(size = 15, color = "black"),
            axis.ticks = element_line(size = 1.5),
            axis.text = element_text(size = 8, color = "black", angle = 20),
            strip.text.x = element_text(size = 12, color = "black", face = "italic"))
 print(RARG_plotld50)

RARG_plotrtnni <- RARG_corr_frame %>%
      dplyr::filter(entrezgene_id == 5916) %>%
      ggplot(., aes(x=rtnni, y=counts))+
      geom_point(aes(col=indv))+
      geom_smooth(method="lm")+
      facet_wrap(hgnc_symbol~Drug, scales="free")+
      theme_classic()+
      xlab(bquote("relative Troponin I release")) +
      ylab(bquote("Gene counts in log "[2]~" cpm")) +
      ggtitle(bquote("Correlation of cTNNT at 0.5"*mu*"M and Log"[2]~"cpm"))+
      scale_color_brewer(palette = "Dark2",name = "Individual", label = c("1","2","3","4","5","6"))+
      stat_cor(method="pearson",
               aes(label = paste(..r.label.., ..p.label.., sep = "~`,`~")),
               color = "red",
               label.x.npc = 0,
               label.y.npc=1,
               size = 3)+
      theme(plot.title = element_text(size = rel(1.5), hjust = 0.5,face = "bold"),
            axis.title = element_text(size = 15, color = "black"),
            axis.ticks = element_line(size = 1.5),
            axis.text = element_text(size = 8, color = "black", angle = 20),
            strip.text.x = element_text(size = 12, color = "black", face = "italic"))
rarg_plot_data <- ggplot_build(RARG_plotrtnni)
 rarg_dataT <- data.frame('rho_tnni'= rarg_plot_data$data[[3]]$r, 'sig'=c(rarg_plot_data$data[[3]]$p.value))
 row.names(rarg_dataT) <- list("DNR","DOX","EPI","MTX", "TRX", "VEH")

 slc_plotld50 <- SL25_corr_frame %>%
      dplyr::filter(entrezgene_id == 64078) %>%
      ggplot(., aes(x=LD50, y=counts))+
      geom_point(aes(col=indv))+
      geom_smooth(method="lm")+
      facet_wrap(hgnc_symbol~Drug, scales="free")+
      theme_classic()+
      xlab(bquote('LD'[50]~'in '*mu*M)) +
      ylab(bquote("Gene counts in log"[2]~" cpm")) +
      ggtitle(bquote("Correlation of LD"[50]~" and Log"[2]~"cpm"))+
      scale_color_brewer(palette = "Dark2",name = "Individual", label = c("1","2","3","4","5","6"))+
      stat_cor(method="pearson",
               aes(label = paste(..r.label.., ..p.label.., sep = "~`,`~")),
               color = "red",
               label.x.npc = 0,
               label.y.npc=1,
               size = 3)+
      theme(plot.title = element_text(size = rel(1.5), hjust = 0.5,face = "bold"),
            axis.title = element_text(size = 15, color = "black"),
            axis.ticks = element_line(size = 1.5),
            axis.text = element_text(size = 8, color = "black", angle = 20),
            strip.text.x = element_text(size = 12, color = "black", face = "italic"))
print (slc_plotld50)

slc_plotvia <- SL25_corr_frame %>%
      dplyr::filter(entrezgene_id == 64078) %>%
      ggplot(., aes(x=Viability, y=counts))+
      geom_point(aes(col=indv))+
      geom_smooth(method="lm")+
      facet_wrap(hgnc_symbol~Drug, scales="free")+
      theme_classic()+
      xlab(bquote("viability/100")) +
      ylab(bquote("Gene counts in log "[2]~" cpm")) +
      ggtitle(bquote("Correlation of viability at 0.5"*mu*"M and Log"[2]~"cpm"))+
      scale_color_brewer(palette = "Dark2",name = "Individual", label = c("1","2","3","4","5","6"))+
      stat_cor(method="pearson",
               aes(label = paste(..r.label.., ..p.label.., sep = "~`,`~")),
               color = "red",
               label.x.npc = 0,
               label.y.npc=1,
               size = 3)+
      theme(plot.title = element_text(size = rel(1.5), hjust = 0.5,face = "bold"),
            axis.title = element_text(size = 15, color = "black"),
            axis.ticks = element_line(size = 1.5),
            axis.text = element_text(size = 8, color = "black", angle = 20),
            strip.text.x = element_text(size = 12, color = "black", face = "italic"))
print(slc_plotvia)

slc_plottnni <- SL25_corr_frame %>%
      dplyr::filter(entrezgene_id == 64078) %>%
      ggplot(., aes(x=rtnni, y=counts))+
      geom_point(aes(col=indv))+
      geom_smooth(method="lm")+
      facet_wrap(hgnc_symbol~Drug, scales="free")+
      theme_classic()+
      xlab(bquote("relative Troponin I")) +
      ylab(bquote("Gene counts in log "[2]~" cpm")) +
      ggtitle(bquote("Correlation Troponin I release and Log"[2]~"cpm"))+
      scale_color_brewer(palette = "Dark2",name = "Individual", label = c("1","2","3","4","5","6"))+
      stat_cor(method="pearson",
               aes(label = paste(..r.label.., ..p.label.., sep = "~`,`~")),
               color = "red",
               label.x.npc = 0,
               label.y.npc=1,
               size = 3)+
      theme(plot.title = element_text(size = rel(1.5), hjust = 0.5,face = "bold"),
            axis.title = element_text(size = 15, color = "black"),
            axis.ticks = element_line(size = 1.5),
            axis.text = element_text(size = 8, color = "black", angle = 20),
            strip.text.x = element_text(size = 12, color = "black", face = "italic"))
print(slc_plottnni)

##RARG info:
rarg_plot_dataL <- ggplot_build(RARG_plotld50)
 rarg_data <- data.frame('rho_LD50'= c(rarg_plot_dataL$data[[3]]$r,NA,NA), 'sig_LD50'=c(rarg_plot_dataL$data[[3]]$p.value,NA,NA),'rowname'=c("DNR","DOX","EPI","MTX", "TRX", "VEH"))

 
rarg_plot_data <- ggplot_build(RARG_plotrtnni)
 rarg_dataT <- data.frame('rho_tnni'= rarg_plot_data$data[[3]]$r, 'sig_tnni'=c(rarg_plot_data$data[[3]]$p.value),'rowname'=c("DNR","DOX","EPI","MTX", "TRX", "VEH"))
 row.names(rarg_dataT) <- list("DNR","DOX","EPI","MTX", "TRX", "VEH")
 
rarg_mat <- rarg_data %>% 
  left_join(.,rarg_dataT,join_by(rowname)) %>% 
  column_to_rownames('rowname') %>% 
  select(rho_LD50,rho_tnni) %>% 
  as.matrix()

rarg_mat_sig <- rarg_data %>% 
  left_join(.,rarg_dataT,join_by(rowname)) %>% 
  column_to_rownames('rowname') %>% 
  select(sig_LD50,sig_tnni) %>% 
  mutate_all(~replace(., is.na(.), 1)) %>% 
  as.matrix()
  

# col_fun5 = circlize::colorRamp2(c(0, 5), c("white", "purple"))

Heatmap( rarg_mat, name = "correlation value", 
         column_title = "Correlations of LD50 and troponin release to log2cpm",
         cluster_rows = FALSE, cluster_columns = FALSE,
         # col=col_fun1,
         column_names_rot = 0,na_col = "grey",
         cell_fun = function(j, i, x, y, width, height, fill) {
           if(rarg_mat_sig[i, j]<0.05)
            grid.text("*", x, y, gp = gpar(fontsize = 20))
          })

##Slc info:
slc_plot_dataL <- ggplot_build(slc_plotld50)
 slc_data <- data.frame('rho_LD50'= c(slc_plot_dataL$data[[3]]$r,NA,NA), 'sig_LD50'=c(slc_plot_dataL$data[[3]]$p.value,NA,NA),'rowname'=c("DNR","DOX","EPI","MTX", "TRX", "VEH"))

 
slc_plot_data <- ggplot_build(slc_plottnni)
 slc_dataT <- data.frame('rho_tnni'= slc_plot_data$data[[3]]$r, 'sig_tnni'=c(slc_plot_data$data[[3]]$p.value),'rowname'=c("DNR","DOX","EPI","MTX", "TRX", "VEH"))
 # row.names(rarg_dataT) <- list("DNR","DOX","EPI","MTX", "TRX", "VEH")
 
slc_mat <- slc_data %>% 
  left_join(.,slc_dataT,join_by(rowname)) %>% 
  column_to_rownames('rowname') %>% 
  select(rho_LD50,rho_tnni) %>% 
  as.matrix()

slc_mat_sig <- slc_data %>% 
  left_join(.,slc_dataT,join_by(rowname)) %>% 
  column_to_rownames('rowname') %>% 
  select(sig_LD50,sig_tnni) %>% 
  mutate_all(~replace(., is.na(.), 1)) %>% 
  as.matrix()
  

# col_fun5 = circlize::colorRamp2(c(0, 5), c("white", "purple"))

Heatmap( rarg_mat, name = "correlation value", 
         column_title = "Correlations of LD50 and troponin release to log2cpm of SCL28A3",
         cluster_rows = FALSE, cluster_columns = FALSE,
         # col=col_fun1,
         column_names_rot = 0,na_col = "grey",
         cell_fun = function(j, i, x, y, width, height, fill) {
           if(slc_mat_sig[i, j]<0.05)
            grid.text("*", x, y, gp = gpar(fontsize = 20))
          })

Session information

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] grid      stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] ggpubr_0.6.0          Hmisc_5.1-0           ggstats_0.3.0        
 [4] broom_1.0.5           kableExtra_1.3.4      sjmisc_2.8.9         
 [7] scales_1.2.1          RColorBrewer_1.1-3    ggsignif_0.6.4       
[10] lubridate_1.9.2       forcats_1.0.0         stringr_1.5.0        
[13] dplyr_1.1.2           purrr_1.0.1           readr_2.1.4          
[16] tidyr_1.3.0           tibble_3.2.1          ggplot2_3.4.2        
[19] tidyverse_2.0.0       ComplexHeatmap_2.12.1 workflowr_1.7.0      

loaded via a namespace (and not attached):
  [1] colorspace_2.1-0    rjson_0.2.21        sjlabelled_1.2.0   
  [4] rprojroot_2.0.3     circlize_0.4.15     htmlTable_2.4.1    
  [7] GlobalOptions_0.1.2 base64enc_0.1-3     fs_1.6.2           
 [10] clue_0.3-64         rstudioapi_0.14     farver_2.1.1       
 [13] fansi_1.0.4         xml2_1.3.4          codetools_0.2-19   
 [16] splines_4.2.2       doParallel_1.0.17   cachem_1.0.8       
 [19] knitr_1.43          Formula_1.2-5       jsonlite_1.8.5     
 [22] cluster_2.1.4       png_0.1-8           compiler_4.2.2     
 [25] httr_1.4.6          backports_1.4.1     Matrix_1.5-4.1     
 [28] fastmap_1.1.1       cli_3.6.1           later_1.3.1        
 [31] htmltools_0.5.5     tools_4.2.2         gtable_0.3.3       
 [34] glue_1.6.2          Rcpp_1.0.10         carData_3.0-5      
 [37] jquerylib_0.1.4     vctrs_0.6.3         nlme_3.1-162       
 [40] svglite_2.1.1       iterators_1.0.14    insight_0.19.2     
 [43] xfun_0.39           ps_1.7.5            rvest_1.0.3        
 [46] timechange_0.2.0    lifecycle_1.0.3     rstatix_0.7.2      
 [49] getPass_0.2-2       hms_1.1.3           promises_1.2.0.1   
 [52] parallel_4.2.2      yaml_2.3.7          gridExtra_2.3      
 [55] sass_0.4.6          rpart_4.1.19        stringi_1.7.12     
 [58] highr_0.10          S4Vectors_0.34.0    foreach_1.5.2      
 [61] checkmate_2.2.0     BiocGenerics_0.42.0 shape_1.4.6        
 [64] rlang_1.1.1         pkgconfig_2.0.3     systemfonts_1.0.4  
 [67] matrixStats_1.0.0   lattice_0.21-8      evaluate_0.21      
 [70] htmlwidgets_1.6.2   labeling_0.4.2      processx_3.8.1     
 [73] tidyselect_1.2.0    magrittr_2.0.3      R6_2.5.1           
 [76] IRanges_2.30.1      generics_0.1.3      pillar_1.9.0       
 [79] whisker_0.4.1       foreign_0.8-84      withr_2.5.0        
 [82] mgcv_1.8-42         abind_1.4-5         nnet_7.3-19        
 [85] crayon_1.5.2        car_3.1-2           utf8_1.2.3         
 [88] tzdb_0.4.0          rmarkdown_2.22      GetoptLong_1.0.5   
 [91] data.table_1.14.8   callr_3.7.3         git2r_0.32.0       
 [94] digest_0.6.31       webshot_0.5.4       httpuv_1.6.11      
 [97] stats4_4.2.2        munsell_0.5.0       viridisLite_0.4.2  
[100] bslib_0.5.0