Last updated: 2023-07-21

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Knit directory: Cardiotoxicity/

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File Version Author Date Message
Rmd b94104b reneeisnowhere 2023-07-21 first plot update

Goals for this page:

I will examine the AC-shared variable genes withing AC-shared response genes

library(tidyverse)
library(VennDiagram)
library(paletteer)
library(ggVennDiagram)
library(gridtext)
library(scales)
library(kableExtra)
library(ComplexHeatmap)
library(data.table)
drug_palc <- c("#8B006D","#DF707E","#F1B72B", "#3386DD","#707031","#41B333")
Vargenes <- readRDS("data/geneset_24.RDS")
expressedgenes <- read.csv("data/backGL.txt")
venn24part <- VennDiagram::get.venn.partitions(Vargenes)



toplistall <- readRDS("data/toplistall.RDS")
  #get sig files made with 0.05 data this way(data created on run)
siglist <- readRDS("data/siglist_final.RDS")


siglist24 <- siglist[6:9]
test <- siglist24[1]["ENTREZID"]
sig_24_ENTREZID <- sapply(siglist24,"[[",1)
sig_24_venn<- VennDiagram::get.venn.partitions(sig_24_ENTREZID)

AC shared

AC_share_var <- venn24part$..values..[[25]] #203
not_AC_shared <- setdiff(expressedgenes$ENTREZID,AC_share_var)
AC_shared_sig <- sig_24_venn$..values..[[9]] ##4435
length(intersect(AC_share_var,AC_shared_sig))  #(88 overlap)
[1] 88
length(intersect(not_AC_shared,AC_shared_sig)) #4347
[1] 4347
# 
# stored <-
  expressedgenes %>% 
  mutate(var_AC= if_else(ENTREZID %in% AC_share_var, "y","no")) %>% 
  mutate(not_AC=if_else(ENTREZID %in% not_AC_shared,"y","no")) %>% 
  mutate(AC_shared_sig=if_else(ENTREZID %in% AC_shared_sig,"y","no")) %>% 
  # group_by(AC_shared_sig,not_AC)  %>% 
    summarise("var_share-n" = sum(not_AC=="no"&AC_shared_sig=="no"), "var_share-y"=sum(not_AC=="no"&AC_shared_sig=="y"),"not_var-n"=sum(not_AC=="y"&AC_shared_sig=="no"), "not_var-y"=sum(not_AC=="y"&AC_shared_sig=="y")) %>% 
    pivot_longer(everything(), names_to = "set", values_to = "count") %>% 
    separate(set, into = c("set", "color"),sep="-" ) %>% 
    mutate(color, factor(color, levels = c("y","no"))) %>% 
    mutate(set=case_match(set,"not_var"~"not AC shared\nvar. gene","var_share"~"AC shared\nvar. gene")) %>% 
   ggplot( ., aes(x=set, y=count, group=color,fill=color))+
     geom_col(position='fill')+
      theme_classic()+
   scale_color_manual(values=drug_palc)+
     scale_fill_manual(values=c("y"="cornflowerblue","n"="darkblue"), 
                    labels=c("not an AC-sp gene","shared with\nAC sp"))+
       scale_y_continuous(expand=expansion(0.001))+
    theme(strip.text=element_text(size=10, face = "bold"),
          axis.text.x = element_text(margin = margin(2,0,2,0, "pt"), size=12),
  strip.background = element_rect (linetype=1, linewidth = 0.5))

testmat <- matrix(c(88,4347,115,9534),nrow = 2,byrow = TRUE)

chisq.test(testmat)#$p.value

    Pearson's Chi-squared test with Yates' continuity correction

data:  testmat
X-squared = 12.877, df = 1, p-value = 0.0003326

Top2i shared

TOP2i_share_var <- venn24part$..values..[[17]] # 32
not_T2_shared <- setdiff(expressedgenes$ENTREZID,TOP2i_share_var)#14052
TOP2i_shared_sig <- sig_24_venn$..values..[[1]] ##882
length(intersect(TOP2i_share_var,TOP2i_shared_sig))  #(3 overlap)
[1] 3
length(intersect(not_T2_shared,TOP2i_shared_sig)) #879
[1] 879
# stored <-
  expressedgenes %>% 
  mutate(var_t2= if_else(ENTREZID %in% TOP2i_share_var, "y","no")) %>% 
  mutate(not_t2= if_else(ENTREZID %in% not_T2_shared,"y","no")) %>% 
  mutate(T2_shared_sig=if_else(ENTREZID %in% TOP2i_shared_sig,"y","no")) %>% 
  # group_by(AC_shared_sig,not_t2)  %>%
    summarise("var_share-n" = sum(not_t2=="no"&T2_shared_sig=="no"), "var_share-y"=sum(not_t2=="no"&T2_shared_sig=="y"),"not_var-n"=sum(not_t2=="y"&T2_shared_sig=="no"), "not_var-y"=sum(not_t2=="y"&T2_shared_sig=="y"))%>% 
    pivot_longer(everything(), names_to = "set", values_to = "count") %>% 
    separate(set, into = c("set", "color"),sep="-" ) %>% 
    mutate(color, factor(color, levels = c("y","no"))) %>% 
    mutate(set=case_match(set,"not_var"~"not TOP2i shared\nvar. gene","var_share"~"TOP2i shared\nvar. gene")) %>% 
   ggplot( ., aes(x=set, y=count, group=color,fill=color))+
     geom_col(position='fill')+
      theme_classic()+
    xlab(" ")+
    ggtitle("TOP2i variable genes in TOP2i specific n= 4435")+
   scale_color_manual(values=drug_palc)+
     scale_fill_manual(values=c("y"="cornflowerblue","n"="darkblue"), 
                    labels=c("not a TOP2i-sp gene","shared with\nTOP2i-sp"))+
       scale_y_continuous(expand=expansion(0.001))+
    theme(strip.text=element_text(size=10, face = "bold"),
          axis.text.x = element_text(margin = margin(2,0,2,0, "pt"), size=12),
  strip.background = element_rect (linetype=1, linewidth = 0.5))

testmatTOP <- matrix(c(3,879,29,13173),nrow = 2,byrow = TRUE)

chisq.test(testmatTOP)#$p.value

    Pearson's Chi-squared test with Yates' continuity correction

data:  testmatTOP
X-squared = 0.13128, df = 1, p-value = 0.7171

GO heatmaps

DOX_var24gost <- readRDS("data/DEG-GO/var/DOX_var24gost.RDS")
DOX_table <- DOX_var24gost$result %>% 
  dplyr::select(c(source, term_id, term_name,intersection_size, term_size, p_value))
EPI_var24gost <- readRDS("data/DEG-GO/var/EPI_var24gost.RDS")
EPI_table <- EPI_var24gost$result %>% 
  dplyr::select(c(source, term_id, term_name,intersection_size, term_size, p_value))
DNR_var24gost <- readRDS("data/DEG-GO/var/DNR_var24gost.RDS")
DNR_table <- DNR_var24gost$result %>% 
  dplyr::select(c(source, term_id, term_name,intersection_size, term_size, p_value))
MTX_var24gost <- readRDS("data/DEG-GO/var/MTX_var24gost.RDS")
MTX_table <- MTX_var24gost$result %>% 
  dplyr::select(c(source, term_id, term_name,intersection_size, term_size, p_value))
TRZ_var24gost <- readRDS("data/DEG-GO/var/TRZ_var24gost.RDS")
TRZ_table <- TRZ_var24gost$result %>% 
  dplyr::select(c(source, term_id, term_name,intersection_size, term_size, p_value))
AC_share_var24gost <- readRDS("data/DEG-GO/var/AC_share_var24gost.RDS")
AC_share_var24_table <- AC_share_var24gost$result %>% 
  dplyr::select(c(source, term_id, term_name,intersection_size, term_size, p_value))
TOP2i_var24gost <- readRDS("data/DEG-GO/var/TOP2i_var24gost.RDS")
TOP2i_var24_table <- TOP2i_var24gost$result %>% 
  dplyr::select(c(source, term_id, term_name,intersection_size, term_size, p_value))
terms <- list()
terms[1] <- DOX_table %>% filter(source=="GO:BP") %>% slice_min(.,p_value, n=3) %>% list()
terms[2] <- EPI_table %>% filter(source=="GO:BP") %>%slice_min(.,p_value, n=3) %>% list()
terms[3] <- DNR_table %>% filter(source=="GO:BP") %>%slice_min(.,p_value, n=3) %>% list()
terms[4] <- MTX_table %>% filter(source=="GO:BP") %>%slice_min(.,p_value, n=3) %>% list()
terms[5] <- TRZ_table %>% filter(source=="GO:BP") %>%slice_min(.,p_value, n=3) %>% list()
terms[6] <- AC_share_var24_table %>% filter(source=="GO:BP") %>%slice_min(.,p_value, n=3) %>% list()
terms[7] <- TOP2i_var24_table %>% filter(source=="GO:BP") %>%slice_min(.,p_value, n=3) %>% list()
names(terms) <- c("DOX", "EPI","DNR", "MTX", "TRZ", "AC_shared", "TOP2i_shared")
termlist <- rbindlist(terms)
termlistid <- c("GO:0010867","GO:0043508","GO:0070932")
P_valueterm <- list()
P_valueterm[1] <- DOX_table %>% dplyr::filter(term_id %in% termlistid) %>% list()
P_valueterm[2] <- EPI_table %>% dplyr::filter(term_id %in% termlistid) %>% list()
P_valueterm[3] <- DNR_table %>% dplyr::filter(term_id %in% termlistid) %>% list()
P_valueterm[4] <- MTX_table %>% dplyr::filter(term_id %in% termlistid) %>% list()
P_valueterm[5] <- TRZ_table %>% dplyr::filter(term_id %in% termlistid) %>% list()
P_valueterm[6] <- AC_share_var24_table %>% dplyr::filter(term_id %in% termlistid) %>% list()
P_valueterm[7] <- TOP2i_var24_table %>% dplyr::filter(term_id %in% termlistid) %>% list()

names(P_valueterm) <- c("DOX", "EPI","DNR", "MTX", "TRZ", "AC_shared", "TOP2i_shared")

GO_heatmapdata <- rbindlist(P_valueterm,idcol= "deg")
col_funkegg= circlize::colorRamp2(c(0, 5), c("white", "darkred"))


GO_sig_mat <- GO_heatmapdata %>% 
  dplyr::select(deg,p_value,term_name) %>%
# mutate(term_name= case_match(term_name,"Cell cycle"~"Cell\ncycle","p53 signaling pathway"~"p53\nsig.\npath.","Base excision repair"~"Base\nexcision\nrepair", 
# "DNA replication"~"DNA\nrep.",.default = term_name)) %>% 
  pivot_wider(id_cols = everything(),
              names_from="term_name",
              values_from="p_value",
              values_fill = list(p_value = 1)) %>% 
  column_to_rownames('deg') %>% 
  as.matrix()# 

GO_mat<- GO_heatmapdata%>%
  mutate(log_val= (-log10(p_value))) %>% 
  dplyr::select(deg,log_val,term_name) %>%
  mutate(term_name= case_match(term_name,"histone H3 deacetylation"~"histone H3\n deacetylation","negative regulation of JUN kinase activity
"~"neg. reg. of\nJUN kinase\nactivity","positive regulation of triglyceride biosynthetic process"~"pos. reg.\nof triglyceride\nbiosynthetic\nprocess",.default = term_name)) %>%
  pivot_wider(id_cols = everything(),
              names_from="term_name",values_from="log_val") %>% 
  column_to_rownames('deg') %>% 
  as.matrix()# 

Heatmap(GO_mat,
        column_title = "GO -log10 p values",
        name = "-log10 (p value)",
        cluster_rows = FALSE, 
        cluster_columns = FALSE,
        column_names_rot = 0,
        column_dend_side = "bottom",
        
        column_names_max_height = unit(12,"cm"),
        column_names_centered = TRUE,
        row_names_max_width = max_text_width(
        rownames(GO_mat), 
        gp = gpar(fontsize = 10)),
        col = col_funkegg,
        na_col="lightyellow",
        column_labels = paste0(c("histone H3\n deacetylation",
                                    "neg. reg. of\nJUN kinase\nactivity",
                                    "pos. reg.of\ntriglyceride\nbiosynthetic\nprocess")),
        cell_fun = function(j, i, x, y, width, height, fill) {
        if(GO_sig_mat[i, j]< 0.05)
            grid.text("*", x, y, gp = gpar(fontsize = 20))
})


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] data.table_1.14.8     ComplexHeatmap_2.16.0 kableExtra_1.3.4     
 [4] scales_1.2.1          gridtext_0.1.5        ggVennDiagram_1.2.2  
 [7] paletteer_1.5.0       VennDiagram_1.7.3     futile.logger_1.4.3  
[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       workflowr_1.7.0      

loaded via a namespace (and not attached):
 [1] tidyselect_1.2.0     viridisLite_0.4.2    farver_2.1.1        
 [4] fastmap_1.1.1        promises_1.2.0.1     digest_0.6.33       
 [7] timechange_0.2.0     lifecycle_1.0.3      cluster_2.1.4       
[10] processx_3.8.1       magrittr_2.0.3       compiler_4.3.1      
[13] rlang_1.1.1          sass_0.4.6           tools_4.3.1         
[16] utf8_1.2.3           yaml_2.3.7           knitr_1.43          
[19] lambda.r_1.2.4       labeling_0.4.2       xml2_1.3.5          
[22] RColorBrewer_1.1-3   withr_2.5.0          BiocGenerics_0.46.0 
[25] stats4_4.3.1         fansi_1.0.4          git2r_0.32.0        
[28] colorspace_2.1-0     iterators_1.0.14     cli_3.6.1           
[31] rmarkdown_2.23       crayon_1.5.2         generics_0.1.3      
[34] rstudioapi_0.15.0    httr_1.4.6           tzdb_0.4.0          
[37] rjson_0.2.21         cachem_1.0.8         rvest_1.0.3         
[40] parallel_4.3.1       formatR_1.14         matrixStats_1.0.0   
[43] vctrs_0.6.3          webshot_0.5.5        jsonlite_1.8.7      
[46] callr_3.7.3          IRanges_2.34.1       hms_1.1.3           
[49] GetoptLong_1.0.5     S4Vectors_0.38.1     clue_0.3-64         
[52] systemfonts_1.0.4    foreach_1.5.2        jquerylib_0.1.4     
[55] glue_1.6.2           rematch2_2.1.2       codetools_0.2-19    
[58] ps_1.7.5             shape_1.4.6          stringi_1.7.12      
[61] gtable_0.3.3         RVenn_1.1.0          later_1.3.1         
[64] munsell_0.5.0        pillar_1.9.0         htmltools_0.5.5     
[67] circlize_0.4.15      R6_2.5.1             doParallel_1.0.17   
[70] rprojroot_2.0.3      evaluate_0.21        highr_0.10          
[73] futile.options_1.0.1 png_0.1-8            httpuv_1.6.11       
[76] bslib_0.5.0          Rcpp_1.0.11          svglite_2.1.1       
[79] whisker_0.4.1        xfun_0.39            fs_1.6.2            
[82] getPass_0.2-2        pkgconfig_2.0.3      GlobalOptions_0.1.2