Figure 3: TOP2i induce global gene expression changes over 24 hours.

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Last updated: 2024-02-06

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Knit directory: Cardiotoxicity/

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---

library(car)
library(tidyverse)
library(tinytex)
library(BiocGenerics)
library(drc)
library(cowplot)
library(ggsignif)
library(RColorBrewer)
library(broom)
library(edgeR)
library(data.table)

library(broom)
library(limma)
library(corrplot)
library(ggVennDiagram)
library(ComplexHeatmap)
library(gridtext)
library(paletteer)

Figure 3: TOP2i induce global gene expression changes over 24 hours.

A. PCA of log₂ cpm

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

level_order2 <- c('75','87','77','79','78','71')

label_list <- readRDS("data/label_list.RDS")
list2env(label_list,envir =.GlobalEnv)

<environment: R_GlobalEnv>

label <- (interaction(substring(drug, 0, 2), indv, time))
x <- readRDS("data/filtermatrix_x.RDS")

x <- calcNormFactors(x, method = "TMM")
normalized_lib_size <- x$samples$lib.size * x$samples$norm.factors
dat_cpm <- cpm(x$counts, lib.size = normalized_lib_size, log=TRUE)

colnames(dat_cpm) <- label

calc_pca <- function(x) {
  # Performs principal components analysis with prcomp
  # x: a sample-by-gene numeric matrix
  prcomp(x, scale. = TRUE, retx = TRUE)
}
time <- rep((rep(c("3h", "24h"), c(6,6))), 6) 

time <- ordered(time, levels =c("3h", "24h"))

anno <- readRDS("data/annotation_data_frame.RDS")
cpm_per_sample <- cbind(anno, t(dat_cpm)) 
pca_all <- calc_pca(t(dat_cpm))
pca_all_anno <- data.frame(anno, pca_all$x)


PCA_plot_group <- pca_all_anno %>%
  mutate(drug =case_match(drug, "Daunorubicin"~"DNR",
                        "Doxorubicin"~"DOX",
                        "Epirubicin"~"EPI",
                        "Mitoxantrone"~"MTX",
                        "Trastuzumab"~"TRZ",
                        "Vehicle"~"VEH", .default = drug)) %>%
  ggplot(.,aes(x = PC1, y = PC2, col=drug, shape=time))+
  geom_point(size= 5)+
  scale_color_manual(values=drug_pal_fact, name="treatment")+
   ggrepel::geom_text_repel(label=indv)+
   ggtitle(expression("PCA of log"[2]*"(cpm)"))+
  theme_bw()+
  guides( size =4)+
  labs(y = "PC 2 (15.76%)", x ="PC 1 (29.06%)")+
  theme(plot.title=element_text(size= 14,hjust = 0.5),
        axis.title = element_text(size = 12, color = "black"))
PCA_plot_group

Past versions of PCA-1.png

Version	Author	Date
2315be3	reneeisnowhere	2024-02-06
a745db3	reneeisnowhere	2023-09-27
4bb0b80	reneeisnowhere	2023-07-28
c40aced	reneeisnowhere	2023-07-26
513ca48	reneeisnowhere	2023-07-26
27d1916	reneeisnowhere	2023-07-21

More information on overall RNA-seq analysis can be found at this link

toplistall <- readRDS("data/toplistall.RDS")
siglist <- readRDS("data/siglist_final.RDS")
list2env(siglist, envir = .GlobalEnv)

<environment: R_GlobalEnv>

cpmcounts <- readRDS("data/cpmcount.RDS")

B. Pairwise correlation of drug responses in pairwise analysis

efit2 <- readRDS("data/efit2_final.RDS")
FCmatrix <- subset(efit2$coefficients)

colnames(FCmatrix) <-
  c(
    "DNR\n3h",
    "DOX\n3h",
    "EPI\n3h",
    "MTX\n3h",
    "TRZ\n3h",
    "DNR\n24h",
    "DOX\n24h",
    "EPI\n24h",
    "MTX\n24h",
    "TRZ\n24h"
  )


mat_col <-
  data.frame(
    time = c(rep("3 hours", 5), rep("24 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_col) <- colnames(FCmatrix)

mat_colors <-
  list(
    time = c("pink", "chocolate4"),
    class = c("yellow1", "lightgreen"),
    TOP2i = c("darkgreen", "goldenrod")
  )
names(mat_colors$time) <- unique(mat_col$time)
names(mat_colors$class) <- unique(mat_col$class)
names(mat_colors$TOP2i) <- unique(mat_col$TOP2i)
corrFC <- cor(FCmatrix)
ComplexHeatmap::pheatmap(
  corrFC,
  display_numbers = TRUE,
  number_format = "%.2f",
  main = ("Correlation of all expressed FC values, n=14084"),
  annotation_col = mat_col,
  annotation_colors = mat_colors,
  fontsize = 10,
  fontsize_row = 8,
  angle_col = "0",
  treeheight_row = 25,
  fontsize_col = 8,
  treeheight_col = 20
)

Past versions of corrFC-1.png

Version	Author	Date
2315be3	reneeisnowhere	2024-02-06

HMcorr <- ComplexHeatmap::pheatmap(
  corrFC,
  display_numbers = TRUE,
  number_format = "%.2f",
  annotation_col = mat_col,
  annotation_colors = mat_colors,
  fontsize = 10,
  fontsize_row = 8,
  angle_col = "0",
  treeheight_row = 25,
  fontsize_col = 8,
  treeheight_col = 20
)

Additional code and information can be found here

C. Venn Diagram of 3 hour DEGs

total3 <- list(sigVDA3$ENTREZID,sigVDX3$ENTREZID, sigVEP3$ENTREZID,sigVMT3$ENTREZID)
totalin_common3 <- c(sigVDA3$SYMBOL,sigVDX3$SYMBOL, sigVEP3$SYMBOL,sigVMT3$SYMBOL)


fig3hrvenn <- ggVennDiagram::ggVennDiagram(total3,
              category.names = c("DNR    \nn = 532\n",
                                 "DOX\n  n = 19\n",
                                 "EPI\n   n = 210\n",
                                 " MTX\n   n = 75\n"),
              show_intersect = FALSE,
              set_color = "black",
              category_size = c(5,5,5,5),
              label = "count",
              label_percent_digit = 1,
              label_size = 4,
              label_alpha = 0,
              caption_size = 4,
              label_color = "black",
              edge_lty = "solid", set_size = 4.5)+
  scale_x_continuous(expand = expansion(mult = .3))+
  scale_y_continuous(expand = expansion(mult = .2))+
  # scale_color_fish_d(option = "Bodianus_pulchellus", direction = -1)+
  scale_fill_distiller(palette="Spectral", direction = -1, limits=c(0,4500))+
  labs(title = "3 hour",
       caption = paste("n =", length(unique(totalin_common3)),"genes\n "))+
  theme(plot.title = element_text(size = rel(1.5), hjust = 0.5, vjust =1),
        plot.caption=element_text(size=12))
fig3hrvenn 

Past versions of 3_hr_venn-1.png

Version	Author	Date
2315be3	reneeisnowhere	2024-02-06

D. Venn Diagram of 24 hour DEGs

total24 <-
  list(sigVDA24$ENTREZID,
       sigVDX24$ENTREZID,
       sigVEP24$ENTREZID,
       sigVMT24$ENTREZID)
in_common24 <-
  c(sigVDA24$ENTREZID,
    sigVDX24$ENTREZID,
    sigVEP24$ENTREZID,
    sigVMT24$ENTREZID)


fig24hrvenn <- ggVennDiagram::ggVennDiagram(
  total24,
  category.names = c(
    "DNR \nn = 7017  \n ",
    "DOX\n n = 6645\n",
    "EPI\n n = 6328\n",
    " MTX\n   n = 1115\n "
  ),
  show_intersect = FALSE,
  set_color = "black",
  category_size = c(5, 5, 5, 5),
  label = "count",
  label_percent_digit = 1,
  label_size = 4,
  label_alpha = 0,
  label_color = "black",
  edge_lty = "solid",
  set_size = 4
) +
  scale_x_continuous(expand = expansion(mult = .3)) +
  scale_y_continuous(expand = expansion(mult = .2)) +
  # scale_color_paletteer_d(palette = "fishualize::Bodianus_pulchellus") +
  scale_fill_distiller(palette = "Spectral", direction = -1) +
  labs(title = "24 hour",
       caption = paste("n =", length(unique(in_common24)), "genes\n ")) +
  
  theme(
    plot.title = element_text(
      size = rel(1.6),
      hjust = 0.5,
      vjust = 1
    ),
    plot.caption = element_text(size = 12)
  )
fig24hrvenn

Past versions of 24_hr_venn-1.png

Version	Author	Date
2315be3	reneeisnowhere	2024-02-06

More information can be found here.

E. 3 hour top enriched KEGG pathways

kegglistDEG3 <- readRDS("data/kegglistDEG3.RDS")
list2env(kegglistDEG3, envir = .GlobalEnv)

<environment: R_GlobalEnv>

keggDEGlong3 <- list(
  "DOX" = DXdeg3k,
  "EPI" = EPdeg3k,
  "DNR" = DAdeg3k,
  "MTX" = MTdeg3k,
  "Anthracyclines" = DDEdeg3k,
  "TOP2Bi" = DDEMdeg3k
)
col_funkegg = circlize::colorRamp2(c(0, 31), c("white", "darkred"))
keggtable3 <-
  data.table::rbindlist(keggDEGlong3,
                        idcol = "deg",
                        fill = TRUE,
                        use.names = TRUE) #%>% complete(deg = names(keglistDEG3))

kegg_sig_mat3 <- keggtable3 %>%
  dplyr::select(deg, p_value, term_name) %>%
  add_row(deg = "TOP2i",
          p_value = 1,
          term_name = "Herpes simplex virus 1 infection") %>%
  mutate(
    term_name = case_match(
      term_name,
      "Cell cycle" ~ "Cell\ncycle",
      "p53 signaling pathway" ~ "p53\nsig.\npath.",
      "Base excision repair" ~ "Base\nexcision\nrepair",
      "Herpes simplex virus 1 infection" ~ "HSV1\ninfection"    ,
      "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()#

kegg_mat3 <- keggtable3 %>%
  dplyr::select(deg, log_val, term_name) %>%
  add_row(deg = "TOP2i",
          log_val = NA,
          term_name = "Herpes simplex virus 1 infection") %>%
  mutate(
    term_name = case_match(
      term_name,
      "p53 signaling pathway" ~ "p53\nsig.\npath.",
      "Base excision repair" ~ "Base\nexcision\nrepair",
      "Herpes simplex virus 1 infection" ~ "HSV1\ninfection"    ,
      "DNA replication" ~ "DNA\nrep.",
      .default = term_name
    )
  ) %>%
  pivot_wider(id_cols = everything(),
              names_from = "term_name",
              values_from = "log_val") %>%
  column_to_rownames('deg') %>%
  as.matrix()#

draw(
  Heatmap(
    kegg_mat3,
    column_title = "KEGG Pathway -log 10(p value)",
    name = "-log10 (p value)",
    cluster_rows = FALSE,
    cluster_columns = FALSE,
    column_names_rot = 0,
    column_names_centered = TRUE,
    row_names_max_width = max_text_width(rownames(kegg_mat3),
                                         gp = gpar(fontsize = 10)),
    col = col_funkegg,
    na_col = "lightyellow",
    column_labels = gt_render(c("HSV-1 infx.",
                                "p53\nsig.\npath.")),
    cell_fun = function(j, i, x, y, width, height, fill) {
      if (kegg_sig_mat3[i, j] < 0.05)
        grid.text("*", x, y, gp = gpar(fontsize = 20))
    }
  )
)

Past versions of KEGG3-1.png

Version	Author	Date
2315be3	reneeisnowhere	2024-02-06

More information can be found here.

F. 24 hour top enriched KEGG Pathways

library(grid)
kegglistDEG24 <- readRDS("data/kegglistDEG24.RDS")
list2env(kegglistDEG24, envir = .GlobalEnv)

<environment: R_GlobalEnv>

keggDEGlong <- list(
  "DOX" = DXdegk,
  "EPI" = EPdegk,
  "DNR" = DAdegk,
  "MTX" = MTdegk,
  "Anthracyclines" = DDEdegk,
  "TOP2i" = DDEMdegk
)
col_funkegg = circlize::colorRamp2(c(0, 31), c("white", "darkred"))
keggtable <- data.table::rbindlist(keggDEGlong, idcol = "deg")

kegg_sig_mat <- keggtable %>%
  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()#

kegg_mat <- keggtable %>%
  dplyr::select(deg, log_val, 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 = "log_val") %>%
  column_to_rownames('deg') %>%
  as.matrix()#

Heatmap(
  kegg_mat,
  column_title = "KEGG Pathway -log p values",
  name = "-log10 (p value)",
  cluster_rows = FALSE,
  cluster_columns = FALSE,
  column_names_rot = 0,
  column_names_centered = TRUE,
  row_names_max_width = max_text_width(rownames(kegg_mat),
                                       gp = gpar(fontsize = 10)),
  col = col_funkegg,
  na_col = "lightyellow",
  column_labels = gt_render(c(
    "p53\nsig.\npath.",
    "B.E.R.",
    "Cell\ncycle",
    "DNA\nrep."
  )),
  cell_fun = function(j, i, x, y, width, height, fill) {
    if (kegg_sig_mat[i, j] < 0.05)
      grid.text("*", x, y, gp = gpar(fontsize = 20))
  }
)

Past versions of KEGG24-1.png

Version	Author	Date
2315be3	reneeisnowhere	2024-02-06

Further KEGG analysis information and code may be found here

Session information

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] paletteer_1.6.0       gridtext_0.1.5        ComplexHeatmap_2.16.0
 [4] ggVennDiagram_1.5.0   corrplot_0.92         data.table_1.14.8    
 [7] edgeR_3.42.4          limma_3.56.2          broom_1.0.5          
[10] RColorBrewer_1.1-3    ggsignif_0.6.4        cowplot_1.1.1        
[13] drc_3.0-1             MASS_7.3-60           BiocGenerics_0.46.0  
[16] tinytex_0.48          lubridate_1.9.3       forcats_1.0.0        
[19] stringr_1.5.0         dplyr_1.1.3           purrr_1.0.2          
[22] readr_2.1.4           tidyr_1.3.0           tibble_3.2.1         
[25] ggplot2_3.4.4         tidyverse_2.0.0       car_3.1-2            
[28] carData_3.0-5         workflowr_1.7.1      

loaded via a namespace (and not attached):
 [1] rematch2_2.1.2      sandwich_3.1-0      rlang_1.1.2        
 [4] magrittr_2.0.3      clue_0.3-65         GetoptLong_1.0.5   
 [7] git2r_0.32.0        multcomp_1.4-25     matrixStats_1.1.0  
[10] compiler_4.3.1      getPass_0.2-2       png_0.1-8          
[13] callr_3.7.3         vctrs_0.6.4         shape_1.4.6        
[16] pkgconfig_2.0.3     crayon_1.5.2        fastmap_1.1.1      
[19] magick_2.8.1        backports_1.4.1     labeling_0.4.3     
[22] utf8_1.2.4          promises_1.2.1      rmarkdown_2.25     
[25] markdown_1.12       tzdb_0.4.0          ps_1.7.5           
[28] xfun_0.41           cachem_1.0.8        jsonlite_1.8.7     
[31] highr_0.10          later_1.3.1         cluster_2.1.4      
[34] parallel_4.3.1      R6_2.5.1            bslib_0.6.1        
[37] stringi_1.7.12      jquerylib_0.1.4     Rcpp_1.0.11        
[40] iterators_1.0.14    knitr_1.45          zoo_1.8-12         
[43] IRanges_2.34.1      httpuv_1.6.12       Matrix_1.6-2       
[46] splines_4.3.1       timechange_0.2.0    tidyselect_1.2.0   
[49] rstudioapi_0.15.0   abind_1.4-5         yaml_2.3.7         
[52] doParallel_1.0.17   codetools_0.2-19    processx_3.8.2     
[55] lattice_0.22-5      withr_3.0.0         evaluate_0.23      
[58] survival_3.5-7      xml2_1.3.5          circlize_0.4.15    
[61] pillar_1.9.0        whisker_0.4.1       foreach_1.5.2      
[64] stats4_4.3.1        generics_0.1.3      rprojroot_2.0.4    
[67] hms_1.1.3           S4Vectors_0.38.2    commonmark_1.9.1   
[70] munsell_0.5.0       scales_1.3.0        gtools_3.9.4       
[73] glue_1.6.2          tools_4.3.1         locfit_1.5-9.8     
[76] fs_1.6.3            mvtnorm_1.2-3       plotrix_3.8-4      
[79] colorspace_2.1-0    cli_3.6.1           fansi_1.0.5        
[82] gtable_0.3.4        sass_0.4.7          digest_0.6.33      
[85] ggrepel_0.9.4       TH.data_1.1-2       farver_2.1.1       
[88] rjson_0.2.21        htmltools_0.5.7     lifecycle_1.0.4    
[91] httr_1.4.7          GlobalOptions_0.1.2