Last updated: 2023-07-20

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Rmd	d68fa43	reneeisnowhere	2023-07-20	adding variance top 24h gene plots
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Rmd	4cc1c40	reneeisnowhere	2023-07-18	updating pearson correlation plots for each GOI
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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"), 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=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("")+
      # 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)
}

pearson_extract <- function(corr_df,ENTREZID) {
  ld50_plot <- corr_df %>%
    dplyr::filter(entrezgene_id == ENTREZID) %>%
    ggplot(., aes(x=LD50, y=counts))+
    geom_point(aes(col=indv))+
    geom_smooth(method="lm")+
    facet_wrap(hgnc_symbol~Drug, scales="free")+
    theme_classic()+
    stat_cor(method="pearson",
             aes(label = paste(..r.label.., ..p.label.., sep = "~`,`~")),
             color = "red",
             label.x.npc = 0,
             label.y.npc=1,
             size = 3)

  tnni_plot <-  corr_df %>%
    dplyr::filter(entrezgene_id == ENTREZID) %>%
    ggplot(., aes(x=rtnni, y=counts))+
    geom_point(aes(col=indv))+
    geom_smooth(method="lm")+
    facet_wrap(hgnc_symbol~Drug, scales="free")+
    theme_classic()+
    stat_cor(method="pearson",
             aes(label = paste(..r.label.., ..p.label.., sep = "~`,`~")),
             color = "red",
             label.x.npc = 0,
             label.y.npc=1,
             size = 3)

  ldh_plot <-   corr_df %>%
    dplyr::filter(entrezgene_id == ENTREZID) %>%
    ggplot(., aes(x=rldh, y=counts))+
    geom_point(aes(col=indv))+
    geom_smooth(method="lm")+
    facet_wrap(hgnc_symbol~Drug, scales="free")+
    theme_classic()+
    stat_cor(method="pearson",
             aes(label = paste(..r.label.., ..p.label.., sep = "~`,`~")),
             color = "red",
             label.x.npc = 0,
             label.y.npc=1,
             size = 3)

  ##ggbuild to get model:
  ld50_build <- ggplot_build(ld50_plot)
  ld50_data <- data.frame('rho_LD50'= c(ld50_build$data[[3]]$r,NA,NA),
                          'sig_LD50'=c(ld50_build$data[[3]]$p.value,NA,NA),
                          'rowname'=c("DOX","EPI","DNR","MTX", "TRZ", "VEH"))

  tnni_build <- ggplot_build(tnni_plot)
  tnni_data <- data.frame('rho_tnni'= c(tnni_build$data[[3]]$r),
                          'sig_tnni'=c(tnni_build$data[[3]]$p.value),
                          'rowname'=c("DOX","EPI","DNR","MTX", "TRZ", "VEH"))
  ldh_build <- ggplot_build(ldh_plot)
  ldh_data <- data.frame('rho_ldh'= c(ldh_build$data[[3]]$r),
                         'sig_ldh'=c(ldh_build$data[[3]]$p.value),
                         'rowname'=c("DOX","EPI","DNR","MTX", "TRZ", "VEH"))

  results <- cbind(ldh_data,tnni_data[,1:2],ld50_data[,1:2])

  return(results)

}

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

Top2a

CDKN1a

ATM

### ATR

RARG

KAT6b

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

KDM4b

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

## expression correlation:

first, will do expression the following GOIs:

Significant (adj. P value of <0.05) Genes of interest by treatment
	time	id	ENTREZID	SYMBOL	adj.P.Val
23371…1	24_hours	DNR	23371	TNS2	0.0000011
23254…2	24_hours	DNR	23254	KAZN	0.0000165
283337…3	24_hours	DNR	283337	ZNF740	0.0001526
23030…4	24_hours	DNR	23030	KDM4B	0.0003429
51020…5	24_hours	DNR	51020	HDDC2	0.0013039
10818…6	24_hours	DNR	10818	FRS2	0.0019914
5916…7	24_hours	DNR	5916	RARG	0.0070726
80010…8	24_hours	DNR	80010	RMI1	0.0403160
23371…9	24_hours	DOX	23371	TNS2	0.0000007
10818…10	24_hours	DOX	10818	FRS2	0.0001291
23254…11	24_hours	DOX	23254	KAZN	0.0001461
23030…12	24_hours	DOX	23030	KDM4B	0.0017905
51020…13	24_hours	DOX	51020	HDDC2	0.0057312
283337…14	24_hours	DOX	283337	ZNF740	0.0111769
64078…15	24_hours	DOX	64078	SLC28A3	0.0298732
80010…16	24_hours	DOX	80010	RMI1	0.0357673
23371…17	24_hours	EPI	23371	TNS2	0.0000025
10818…18	24_hours	EPI	10818	FRS2	0.0002169
23254…19	24_hours	EPI	23254	KAZN	0.0003003
51020…20	24_hours	EPI	51020	HDDC2	0.0014213
283337…21	24_hours	EPI	283337	ZNF740	0.0071182
5916…22	24_hours	EPI	5916	RARG	0.0147965
64078…23	24_hours	EPI	64078	SLC28A3	0.0170798
23030…24	24_hours	EPI	23030	KDM4B	0.0209094
80010…25	24_hours	MTX	80010	RMI1	0.0066041

24 hour TNNI to cpm

# # now I have the long dataframe, time to combine with other data snf limit to just the GOIs
# gene_corr_frame <- gene_corr_df %>%
#     filter(entrezgene_id %in% GOI_genelist$entrezgene_id) %>%
#     left_join(., GOI_join, by=c("Drug","indv")) %>%
#     dplyr::select(indv, Drug,entrezgene_id,counts, rldh,rtnni,LD50,tox) %>%
#     mutate(Drug=factor(Drug)) %>%
#     full_join(.,GOI_genelist, by="entrezgene_id")
#
#   saveRDS(gene_corr_df,"data/gene_corr_df.RDS")
# saveRDS(gene_corr_frame,"data/gene_corr_frame.RDS")
gene_corr_frame <- readRDS("data/gene_corr_frame.RDS")
   gene_corr_df <- readRDS("data/gene_corr_df.RDS")
   
## remove nas!

   
for (gene in GOI_genelist$entrezgene_id){
    gene_plot <- gene_corr_frame %>% 
      dplyr::filter(entrezgene_id == gene) %>%
      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("troponin I expression") +
      ylab("Gene counts in log2 cpm") +
      ggtitle(expression(paste("Correlation between counts and troponin I by drug")))+
      scale_color_brewer(palette = "Dark2",name = "Individual", label = c("1","2","3","4","5","6"))+
     ggpubr:: stat_cor(method="pearson",
                       cor.coef.name="rho",
               aes(label = paste(..r.label.., ..p.label.., sep = "~`,\n`~")),
               color = "red",
               label.x.npc = 0.01,
               label.y.npc=0.01, 
               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(gene_plot)
   
  }

24 hour LDH to cpm

## ggplot by drug for ldh
 
  
  for (gene in GOI_genelist$entrezgene_id){
    gene_plot <- gene_corr_frame %>% 
      dplyr::filter(entrezgene_id == gene) %>%
      ggplot(., aes(x=rldh, y=counts))+
      geom_point(aes(col=indv))+
      geom_smooth(method="lm")+
      facet_wrap(hgnc_symbol~Drug, scales="free")+
      theme_classic()+
      xlab("troponin I expression") +
      ylab("Gene counts in log2 cpm") +
      ggtitle(expression(paste("Correlation between counts and LDH by drug")))+
      scale_color_brewer(palette = "Dark2",name = "Individual", label = c("1","2","3","4","5","6"))+
      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(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(gene_plot)
   
  }

Toxicity for GOI

 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")+
      facet_wrap(hgnc_symbol~Drug, scales="free")+
      theme_classic()+
      xlab("Toxicity score") +
      ylab("Gene counts in log2 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"))+
      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(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(gene_plot)
   
 }

Fig9 expression correlation

##add in list of ldh,tnni and ld50
avg_LD50 <- readRDS("data/avg_LD50.RDS")

gene_corr_frame <- readRDS("data/gene_corr_frame.RDS")
   gene_corr_df <- readRDS("data/gene_corr_df.RDS")



GOI_join <- RNAnormlist %>% 
  mutate(indv=factor(indv,levels = level_order2)) %>% 
  mutate(indv=as.numeric(indv)) %>%
  mutate(indv=factor(indv)) %>% 
  mutate(Drug = factor(Drug, levels = c("DNR", 
                                       "DOX",
                                       "EPI",
                                       "MTX",
                                       "TRZ",
                                       "VEH"))) %>% 
  dplyr::select(indv, Drug,rldh,rtnni) %>% 
  full_join(.,avg_LD50,by=c("Drug"="sDrug","indv"="indv")) %>% 
  rename("LD50"="name")
##now we have the frame, time to limit df of GOI_genelist to fig 9 genes
Fig9_genes <- GOI_genelist %>% 
  filter(hgnc_symbol %in% list("RARG", "ZNF740", "TNS2", "SLC28A3","RMI1"))
##joining to counts

gene_corr_fig9 <- gene_corr_df %>%
    filter(entrezgene_id %in% Fig9_genes$entrezgene_id) %>%
    left_join(., GOI_join, by=c("Drug","indv")) %>%
    mutate(Drug=factor(Drug, levels = c("DOX","EPI","DNR", "MTX", "TRZ", "VEH"))) %>%
    full_join(.,GOI_genelist, by="entrezgene_id")
  
  # saveRDS(gene_corr_fig9,"output/gene_corr_fig9.RDS")

GOI from variance

Vargenes <- readRDS("data/geneset_24.RDS")
expressedgenes <- read.csv("data/backGL.txt")
top_genes <- sapply(Vargenes,head,4)
top_genes_df<-  top_genes %>% as.data.frame() %>% 
pivot_longer(everything(),names_to="combo",values_to = "ENTREZID") %>% 
  separate(combo, into = c("drug",NA,NA)) %>% 
  mutate(ENTREZID=as.numeric(ENTREZID)) %>% 
  inner_join(., expressedgenes, by="ENTREZID") %>% as.data.frame()


for (g in seq(1:20)){
  a <- top_genes_df$SYMBOL[g]
  b <- top_genes_df$drug[g]
  cpm_boxplot(cpmcounts,GOI=top_genes_df[g,2],
              "Dark2",
              drug_pal_fact,
              ylab=bquote(~italic(.(a))~log[2]~"cpm "))+
    ggtitle(expression(paste0("Top 10 in ",b," var genes")))
}

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] 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_2.16.0 workflowr_1.7.0      

loaded via a namespace (and not attached):
  [1] gridExtra_2.3       rlang_1.1.1         magrittr_2.0.3     
  [4] clue_0.3-64         GetoptLong_1.0.5    git2r_0.32.0       
  [7] matrixStats_1.0.0   compiler_4.3.1      mgcv_1.9-0         
 [10] getPass_0.2-2       png_0.1-8           systemfonts_1.0.4  
 [13] callr_3.7.3         vctrs_0.6.3         rvest_1.0.3        
 [16] pkgconfig_2.0.3     shape_1.4.6         crayon_1.5.2       
 [19] fastmap_1.1.1       backports_1.4.1     labeling_0.4.2     
 [22] utf8_1.2.3          promises_1.2.0.1    rmarkdown_2.23     
 [25] tzdb_0.4.0          ps_1.7.5            xfun_0.39          
 [28] cachem_1.0.8        jsonlite_1.8.7      highr_0.10         
 [31] later_1.3.1         parallel_4.3.1      cluster_2.1.4      
 [34] R6_2.5.1            bslib_0.5.0         stringi_1.7.12     
 [37] car_3.1-2           rpart_4.1.19        jquerylib_0.1.4    
 [40] Rcpp_1.0.11         iterators_1.0.14    knitr_1.43         
 [43] base64enc_0.1-3     IRanges_2.34.1      Matrix_1.6-0       
 [46] splines_4.3.1       httpuv_1.6.11       nnet_7.3-19        
 [49] timechange_0.2.0    tidyselect_1.2.0    abind_1.4-5        
 [52] rstudioapi_0.15.0   yaml_2.3.7          doParallel_1.0.17  
 [55] codetools_0.2-19    sjlabelled_1.2.0    processx_3.8.1     
 [58] lattice_0.21-8      withr_2.5.0         evaluate_0.21      
 [61] foreign_0.8-84      xml2_1.3.5          circlize_0.4.15    
 [64] pillar_1.9.0        carData_3.0-5       whisker_0.4.1      
 [67] checkmate_2.2.0     foreach_1.5.2       stats4_4.3.1       
 [70] insight_0.19.3      generics_0.1.3      rprojroot_2.0.3    
 [73] S4Vectors_0.38.1    hms_1.1.3           munsell_0.5.0      
 [76] glue_1.6.2          tools_4.3.1         data.table_1.14.8  
 [79] webshot_0.5.5       fs_1.6.2            colorspace_2.1-0   
 [82] nlme_3.1-162        htmlTable_2.4.1     Formula_1.2-5      
 [85] cli_3.6.1           fansi_1.0.4         viridisLite_0.4.2  
 [88] svglite_2.1.1       gtable_0.3.3        rstatix_0.7.2      
 [91] sass_0.4.6          digest_0.6.33       BiocGenerics_0.46.0
 [94] farver_2.1.1        rjson_0.2.21        htmlwidgets_1.6.2  
 [97] htmltools_0.5.5     lifecycle_1.0.3     httr_1.4.6         
[100] GlobalOptions_0.1.2

GOI_plots

ERM

2023-07-20