Figure 5
ERM
2023-07-07
Last updated: 2023-07-07
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Knit directory: Cardiotoxicity/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 63d054a | reneeisnowhere | 2023-07-07 | Various changes to graphs. pending Monday-renames |
| html | 0e3d22d | reneeisnowhere | 2023-07-06 | Build site. |
| Rmd | 6a6c9af | reneeisnowhere | 2023-07-06 | adding fig 5 and changing fig9 heatmap |
| Rmd | a95c83e | reneeisnowhere | 2023-07-06 | update code |
| html | 8f66a2d | reneeisnowhere | 2023-06-29 | Build site. |
| Rmd | 060305d | reneeisnowhere | 2023-06-29 | update in data |
| html | f77112b | reneeisnowhere | 2023-06-27 | Build site. |
| Rmd | ceea93e | reneeisnowhere | 2023-06-27 | adding fig 5 |
library(car)
library(tidyverse)
library(BiocGenerics)
library(data.table)
library(cowplot)
library(ggsignif)
library(RColorBrewer)
library(broom)
library(limma)
library(corrplot)
library(ggVennDiagram)toplistall <- readRDS("data/toplistall.RDS")
siglist <- readRDS("data/siglist_final.RDS")
list2env(siglist, envir = .GlobalEnv)<environment: R_GlobalEnv>cpmcounts <- readRDS("data/cpmcount.RDS")Figure 5
Gene expression responses to AC treatments converge over time
A. Differentially expressed genes by treatment
drug_palNoVeh <- c("#8B006D" ,"#DF707E", "#F1B72B" ,"#3386DD", "#707031")
drug_pal_fact <- c("#8B006D" ,"#DF707E", "#F1B72B" ,"#3386DD", "#707031","#41B333")
toplistall %>%
mutate(id=factor(id, levels = c('DOX', 'EPI', 'DNR', 'MTX', 'TRZ','VEH'))) %>%
mutate(time= factor(time,
levels=c("3_hours","24_hours"),
label=c("3 hours","24 hours"))) %>%
group_by(time, id) %>%
mutate(sigcount = if_else(adj.P.Val < 0.05,'sig','notsig'))%>%
count(sigcount) %>%
pivot_wider(id_cols = c(time,id), names_from=sigcount, values_from=n) %>%
mutate(prop = sig/(sig+notsig)*100) %>%
mutate(prop=if_else(is.na(prop),0,prop)) %>%
ggplot(., aes(x=id, y= prop))+
geom_col(aes(fill=id))+
geom_text(aes(label = sprintf("%.2f",prop)),
position=position_dodge(0.9),vjust=-.2 )+
scale_fill_manual(values =drug_pal_fact)+
guides(fill=guide_legend(title = "Treatment"))+
facet_wrap(id~time)+#labeller = (time = facettimelabel) )+
theme_bw()+
xlab("")+
ylab("Percentage of expressed genes")+
theme_bw()+
facet_wrap(~time)+
ggtitle("Percent DEGs (adj. P value <0.05)")+
scale_y_continuous(expand=expansion(c(0.02,.2)))+
theme(plot.title = element_text(size = rel(1.5), 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),
strip.background = element_rect(fill = "transparent"),
axis.text.x = element_text(size = 8, color = "white", angle = 0),
axis.text.y = element_text(size = 8, color = "black", angle = 0),
strip.text.x = element_text(size = 12, color = "black", face = "bold"))
ggsave("output/Figures/Percent_DEG.eps",width = 6, height =4, units = "in")B. Fold change correlation across all expressed genes n=14084
efit2 <- readRDS("data/efit2_final.RDS")
FCmatrix <- subset(efit2$coefficients)
is.matrix(subset(FCmatrix))[1] TRUEcolnames(FCmatrix) <- c("DNR\n3h","DOX\n3h", "EPI\n3h","MTX\n3h", "TRZ\n3h","DNR\n24h","DOX\n24h", "EPI\n24h","MTX\n24h", "TRZ\n24h")
mat_col <- data.frame(time= c(rep("3 hours",5), rep("24 hours",5)))
rownames(mat_col) <- colnames(FCmatrix)
# list(group=c("#C25757", "#A77E69")
# names(mat_colors$group) <-c("3 hours", "24 hours")
mat_colors <- list(time=c("#C25757", "#524014"))
names(mat_colors$time) <- unique(mat_col$time)
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)
ggsave("output/Figures/FC_cor_plot.eps",width = 7, height =5, units = "in")C. Venn Diagram of 3 hour DEGs
library(paletteer)
total3 <- list(sigVDA3$ENTREZID,sigVDX3$ENTREZID, sigVEP3$ENTREZID,sigVMT3$ENTREZID)
totalin_common3 <- c(sigVDA3$SYMBOL,sigVDX3$SYMBOL, sigVEP3$SYMBOL,sigVMT3$SYMBOL)
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_paletteer_d(palette = "fishualize::Bodianus_pulchellus")+
scale_fill_distiller(palette="Spectral", direction = -1)+
labs(title = "3 hour comparison of DE genes p.adj. <0.05",
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))
ggsave("output/Figures/3_hrvenn.eps")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)
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 comparison of DE genes p.adj. <0.05",
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))
ggsave("output/Figures/24_hrvenn.eps")E. KEGG Pathway analysis
# kegglistDEG <- list(DDEdegk,DDEMdegk,DAdegk,DXdegk,EPdegk, MTdegk)
# names(kegglistDEG) <-c("DDEdegk","DDEMdegk","DAdegk","DXdegk","EPdegk", "MTdegk")
# saveRDS(kegglistDEG,"data/kegglistDEG.RDS")
kegglistDEG <- readRDS("data/kegglistDEG.RDS")
list2env(kegglistDEG, envir=.GlobalEnv)<environment: R_GlobalEnv>library(ComplexHeatmap)
keggDEGlong <-list("DOX"=DXdegk,
"EPI"=EPdegk,
"DNR"=DAdegk,
"MTX"=MTdegk,
"Athracyclines"=DDEdegk,
"TOP2Bi"=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\nreplication",.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\nreplication",.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 = "-log (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.",
"Base\nexcision\nrepair",
"Cell\ncycle",
"DNA\nreplication")),
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))
})
ggsave("output/Figures/Kegg_path.eps")F. Log2 cpm of CDKN1a
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 = case_match(time,"24h"~"24 hours",
"3h"~"3 hours")) %>%
mutate(time=factor(time, levels= c("3 hours","24 hours"),
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("")+
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)
}
cpm_boxplot(cpmcounts,GOI='1026',"Dark2",drug_pal_fact,
ylab=(expression(atop(" ",italic("CDKN1A")~log[2]~"cpm "))))
ggsave("output/Figures/CDKN1a_cpm.eps",width = 6, height =3, units = "in")
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] ComplexHeatmap_2.12.1 paletteer_1.5.0 ggVennDiagram_1.2.2
[4] corrplot_0.92 limma_3.52.4 broom_1.0.5
[7] RColorBrewer_1.1-3 ggsignif_0.6.4 cowplot_1.1.1
[10] data.table_1.14.8 BiocGenerics_0.42.0 lubridate_1.9.2
[13] forcats_1.0.0 stringr_1.5.0 dplyr_1.1.2
[16] purrr_1.0.1 readr_2.1.4 tidyr_1.3.0
[19] tibble_3.2.1 ggplot2_3.4.2 tidyverse_2.0.0
[22] car_3.1-2 carData_3.0-5 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] colorspace_2.1-0 rjson_0.2.21 class_7.3-22
[4] rprojroot_2.0.3 circlize_0.4.15 markdown_1.7
[7] GlobalOptions_0.1.2 fs_1.6.2 gridtext_0.1.5
[10] clue_0.3-64 rstudioapi_0.14 proxy_0.4-27
[13] farver_2.1.1 fansi_1.0.4 xml2_1.3.4
[16] codetools_0.2-19 doParallel_1.0.17 cachem_1.0.8
[19] knitr_1.43 jsonlite_1.8.5 cluster_2.1.4
[22] png_0.1-8 compiler_4.2.2 httr_1.4.6
[25] backports_1.4.1 fastmap_1.1.1 cli_3.6.1
[28] later_1.3.1 htmltools_0.5.5 tools_4.2.2
[31] gtable_0.3.3 glue_1.6.2 Rcpp_1.0.10
[34] jquerylib_0.1.4 vctrs_0.6.3 iterators_1.0.14
[37] xfun_0.39 ps_1.7.5 timechange_0.2.0
[40] lifecycle_1.0.3 getPass_0.2-2 scales_1.2.1
[43] ragg_1.2.5 hms_1.1.3 promises_1.2.0.1
[46] parallel_4.2.2 rematch2_2.1.2 prismatic_1.1.1
[49] yaml_2.3.7 sass_0.4.6 stringi_1.7.12
[52] highr_0.10 S4Vectors_0.34.0 foreach_1.5.2
[55] e1071_1.7-13 shape_1.4.6 commonmark_1.9.0
[58] rlang_1.1.1 pkgconfig_2.0.3 systemfonts_1.0.4
[61] matrixStats_1.0.0 evaluate_0.21 sf_1.0-13
[64] labeling_0.4.2 processx_3.8.1 tidyselect_1.2.0
[67] magrittr_2.0.3 R6_2.5.1 IRanges_2.30.1
[70] generics_0.1.3 DBI_1.1.3 pillar_1.9.0
[73] whisker_0.4.1 withr_2.5.0 units_0.8-2
[76] abind_1.4-5 crayon_1.5.2 KernSmooth_2.23-21
[79] utf8_1.2.3 RVenn_1.1.0 tzdb_0.4.0
[82] rmarkdown_2.22 GetoptLong_1.0.5 callr_3.7.3
[85] git2r_0.32.0 digest_0.6.31 classInt_0.4-9
[88] httpuv_1.6.11 textshaping_0.3.6 stats4_4.2.2
[91] munsell_0.5.0 bslib_0.5.0