Last updated: 2023-09-28

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Knit directory: Cardiotoxicity/

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library(tidyverse)
library(ggpubr)
library(ggsignif)
library(gprofiler2)
library(paletteer)
library(ggVennDiagram)
library(gridtext)
library(scales)
library(kableExtra)
library(qvalue)
library(data.table)
library(ComplexHeatmap)
toplistall <- readRDS("data/toplistall.RDS")
siglist <- readRDS("data/siglist_final.RDS")
list2env(siglist, envir = .GlobalEnv)
<environment: R_GlobalEnv>
cpmcounts <- readRDS("data/cpmcount.RDS")
DEG_cormotif <- readRDS("data/DEG_cormotif.RDS")
list2env(DEG_cormotif,envir=.GlobalEnv)
<environment: R_GlobalEnv>
drug_pal_fact <- c("#8B006D" ,"#DF707E", "#F1B72B" ,"#3386DD", "#707031","#41B333")
col_fun5 = circlize::colorRamp2(c(0, 5), c("white", "purple"))
## supplement 1
chrom_reg_Seoane <- read_csv(file = "data/Seonane2019supp1.txt",col_types = cols(...1 = col_skip()))
Seoane_2019 <- chrom_reg_Seoane[,2]
names(Seoane_2019) <- "ENTREZID"
chrom_genes <- (unique(Seoane_2019$ENTREZID))

# supplement 4
Sup4seoane <- read.csv("output/Sup4seoane.csv", row.names = 1)
Sup4genes <- Sup4seoane  %>% 
  filter(pval.expAnth<0.05) %>% 
  distinct(entrez, .keep_all = TRUE) %>% 
  dplyr::select(entrez)

Figure 7: AC treatments induce transcriptional variation across individuals over time.

A) Mean gene expression

mean_vardrug1 <- read.csv("data/mean_vardrug1.csv", row.names = 1)


drug_frame<- mean_vardrug1 %>% 
  rownames_to_column(var = "entrezid") %>% 
  pivot_longer(cols = mean.Da.3:var.Ve.24, names_to = "short", values_to = "values") %>% 
  separate(short, into=c("calc","treatment","time")) %>% 
  mutate(treatment= factor(treatment, levels = c("Do","Ep","Da","Mi","Tr","Ve"),labels = c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>% 
  mutate(time= factor(time, levels = c("3","24"), labels= c("3 hours","24 hours"))) %>% 
  group_by(treatment, time, calc) %>% 
  as.data.frame



drug_frame %>% 
  filter(calc!="var") %>% 
  ggplot(., aes(x= treatment, y=values,fill=treatment))+
  geom_boxplot(outlier.shape=NA)+
  geom_signif(comparisons =list(c("TRZ","VEH"),
                                c("MTX","VEH"),
                                c("DNR","VEH"),
                                c("EPI","VEH"),
                                c("DOX","VEH")),
              test= "t.test",
              tip_length = 0.01,
              map_signif_level=FALSE,
              textsize =4,
              y_position=11,
              step_increase = 0.05)+
  facet_wrap(.~time)+
  ggtitle("Means")+
  ylim(0,18)+
 scale_fill_manual(values=drug_pal_fact)+
  theme_bw()+
  xlab(" ")+
  ylab(expression("counts log"[2]~"cpm "))+
  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, "mm"),face = "bold"))

B) Variance in gene expression

drug_pal_fact <- c("#8B006D","#DF707E","#F1B72B", "#3386DD","#707031","#41B333")

drug_frame %>% 
  filter(calc!="mean") %>% 
  ggplot(., aes(x= treatment, y=values, fill=treatment))+
  geom_boxplot(outlier.shape=NA)+
  geom_signif(comparisons =list(c("DOX","VEH"),
                                c("EPI","VEH"),
                                c("DNR","VEH"),
                                c("MTX","VEH"),
                                c("TRZ","VEH")),
              test= "t.test",
              tip_length = 0.01,
              map_signif_level=FALSE,
              textsize =4,
              y_position=0.85,
              step_increase = 0.1)+
  facet_wrap(~time)+
  ggtitle("Variance")+
  coord_cartesian(ylim = c(0,1))+
  # ylim(0,2)+
scale_fill_manual(values=drug_pal_fact)+
  theme_bw()+
  xlab(" ")+
  ylab(expression("variance of log "[2]~" cpm "))+
  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, "mm"),face = "bold"))

Version Author Date
89d0333 reneeisnowhere 2023-08-15
drug_frame %>% 
  filter(calc!="mean") %>%
  ggplot(., aes(x= treatment, y=values))+
  geom_boxplot(outlier.shape=NA)+
  geom_signif(comparisons =list(c("DOX","VEH"),
                                c("EPI","VEH"),
                                c("DNR","VEH"),
                                c("MTX","VEH"),
                                c("TRZ","VEH")),
              test= "t.test",
              map_signif_level=FALSE,
              textsize =4,
               y_position=(.5),
              step_increase = 0.1)+
  facet_wrap(~time)+
  ggtitle("var")#+

Version Author Date
89d0333 reneeisnowhere 2023-08-15
  # ylim(0,0.5)

C) Pairwise correlation of drug response variance

organized_drugframe <- read.csv("data/organized_drugframe.csv",
                                row.names = 1)
Var_test_list3 <- readRDS("data/Var_test_list3.RDS")
#Is the data normally distributed?

set3 <- names(Var_test_list3)
shorterlist <- Var_test_list3[[1]][[13]]
names(shorterlist) <- rownames(organized_drugframe)

statfun3 <- data.frame(ENTREZID=rownames(organized_drugframe))
for (i in 1:length(set3)){
  a <- set3[i]
    s_list<- Var_test_list3[[i]][[13]]
  names(s_list) <- rownames(organized_drugframe)
  hold<- map_df(s_list, ~as.data.frame(.x$statistic), .id="ENTREZID")
   statfun3[paste0(a)] <- hold$`.x$statistic`
 
}

Var_test_list24 <- readRDS("data/Var_test_list24.RDS")
set24 <- names(Var_test_list24) 

statfun24 <- data.frame(ENTREZID=rownames(organized_drugframe))
for (i in 1:length(set24)){
  a <- set24[i]
    s_list<- Var_test_list24[[i]][[13]]
  names(s_list) <- rownames(organized_drugframe)
  hold<- map_df(s_list, ~as.data.frame(.x$statistic), .id="ENTREZID")
   statfun24[paste0(a)] <- hold$`.x$statistic`
 
}


Fstatcor <- statfun24 %>% 
  full_join(., statfun3, by="ENTREZID") %>% 
   column_to_rownames('ENTREZID') %>% 
   # as.matrix(.) %>% 
   # rowwise() %>% 
   cor(., method="spearman")


 col_fun <- circlize::colorRamp2(c(-1,0, 1), c("blue","white", "red"))
 
mat_colF <- data.frame(time= c(rep("24 hours",5), rep("3 hours",5)), class= (c("AC","AC","AC","nAC","nAC","AC","AC","AC","nAC","nAC")),TOP2i=c(rep("yes",4), "no", rep("yes",4),"no"))

rownames(mat_colF) <- colnames(Fstatcor)



mat_colors <- list(time=c("chocolate4", "pink"), class=c("yellow1","lightgreen"), TOP2i =c("darkgreen","goldenrod"))

names(mat_colors$time) <- unique(mat_colF$time)
names(mat_colors$class) <- unique(mat_colF$class)
names(mat_colors$TOP2i) <- unique(mat_colF$TOP2i)
         
ha <- HeatmapAnnotation(df=mat_colF, col=mat_colors)

   Heatmap(Fstatcor, 
           column_title = "3 & 24 hr F stat corr", 
           col=col_fun,
          top_annotation = ha,
           cell_fun = function(j, i, x, y, width, height, fill) {
        grid.text(sprintf("%.2f", Fstatcor[i, j]), x, y, gp = gpar(fontsize = 10))})

Version Author Date
89d0333 reneeisnowhere 2023-08-15

More analysis of the variablity in gene expression can be found at this link.


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] ComplexHeatmap_2.16.0 data.table_1.14.8     qvalue_2.32.0        
 [4] kableExtra_1.3.4      scales_1.2.1          gridtext_0.1.5       
 [7] ggVennDiagram_1.2.3   paletteer_1.5.0       gprofiler2_0.2.2     
[10] ggsignif_0.6.4        ggpubr_0.6.0          lubridate_1.9.2      
[13] forcats_1.0.0         stringr_1.5.0         dplyr_1.1.3          
[16] purrr_1.0.2           readr_2.1.4           tidyr_1.3.0          
[19] tibble_3.2.1          ggplot2_3.4.3         tidyverse_2.0.0      
[22] workflowr_1.7.1      

loaded via a namespace (and not attached):
 [1] rematch2_2.1.2      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      getPass_0.2-2      
[10] png_0.1-8           systemfonts_1.0.4   callr_3.7.3        
[13] vctrs_0.6.3         reshape2_1.4.4      rvest_1.0.3        
[16] shape_1.4.6         pkgconfig_2.0.3     crayon_1.5.2       
[19] fastmap_1.1.1       magick_2.7.5        backports_1.4.1    
[22] labeling_0.4.3      utf8_1.2.3          promises_1.2.1     
[25] rmarkdown_2.24      tzdb_0.4.0          ps_1.7.5           
[28] bit_4.0.5           xfun_0.40           cachem_1.0.8       
[31] jsonlite_1.8.7      RVenn_1.1.0         later_1.3.1        
[34] cluster_2.1.4       broom_1.0.5         parallel_4.3.1     
[37] R6_2.5.1            RColorBrewer_1.1-3  bslib_0.5.1        
[40] stringi_1.7.12      car_3.1-2           jquerylib_0.1.4    
[43] Rcpp_1.0.11         iterators_1.0.14    knitr_1.44         
[46] IRanges_2.34.1      httpuv_1.6.11       splines_4.3.1      
[49] timechange_0.2.0    tidyselect_1.2.0    rstudioapi_0.15.0  
[52] abind_1.4-5         yaml_2.3.7          doParallel_1.0.17  
[55] codetools_0.2-19    processx_3.8.2      plyr_1.8.8         
[58] withr_2.5.0         evaluate_0.21       xml2_1.3.5         
[61] circlize_0.4.15     pillar_1.9.0        carData_3.0-5      
[64] whisker_0.4.1       stats4_4.3.1        foreach_1.5.2      
[67] plotly_4.10.2       generics_0.1.3      vroom_1.6.3        
[70] rprojroot_2.0.3     S4Vectors_0.38.1    hms_1.1.3          
[73] munsell_0.5.0       glue_1.6.2          lazyeval_0.2.2     
[76] tools_4.3.1         webshot_0.5.5       fs_1.6.3           
[79] colorspace_2.1-0    cli_3.6.1           fansi_1.0.4        
[82] viridisLite_0.4.2   svglite_2.1.1       gtable_0.3.4       
[85] rstatix_0.7.2       sass_0.4.7          digest_0.6.33      
[88] BiocGenerics_0.46.0 farver_2.1.1        rjson_0.2.21       
[91] htmlwidgets_1.6.2   htmltools_0.5.6     lifecycle_1.0.3    
[94] httr_1.4.7          GlobalOptions_0.1.2 bit64_4.0.5