Highly variable genes
ERM
2023-07-21
Last updated: 2023-07-21
Checks: 7 0
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$..values..[[9]] ##4435
length(intersect(AC_share_var,AC_shared_sig)) #(88 overlap)[1] 88length(intersect(not_AC_shared,AC_shared_sig)) #4347[1] 4347#
# stored <-
expressedgenes %>%
mutate(var_AC= if_else(ENTREZID %in% AC_share_var, "y","no")) %>%
mutate(not_AC=if_else(ENTREZID %in% not_AC_shared,"y","no")) %>%
mutate(AC_shared_sig=if_else(ENTREZID %in% AC_shared_sig,"y","no")) %>%
# group_by(AC_shared_sig,not_AC) %>%
summarise("var_share-n" = sum(not_AC=="no"&AC_shared_sig=="no"), "var_share-y"=sum(not_AC=="no"&AC_shared_sig=="y"),"not_var-n"=sum(not_AC=="y"&AC_shared_sig=="no"), "not_var-y"=sum(not_AC=="y"&AC_shared_sig=="y")) %>%
pivot_longer(everything(), names_to = "set", values_to = "count") %>%
separate(set, into = c("set", "color"),sep="-" ) %>%
mutate(color, factor(color, levels = c("y","no"))) %>%
mutate(set=case_match(set,"not_var"~"not AC shared\nvar. gene","var_share"~"AC shared\nvar. gene")) %>%
ggplot( ., aes(x=set, y=count, group=color,fill=color))+
geom_col(position='fill')+
theme_classic()+
scale_color_manual(values=drug_palc)+
scale_fill_manual(values=c("y"="cornflowerblue","n"="darkblue"),
labels=c("not an AC-sp gene","shared with\nAC sp"))+
scale_y_continuous(expand=expansion(0.001))+
theme(strip.text=element_text(size=10, face = "bold"),
axis.text.x = element_text(margin = margin(2,0,2,0, "pt"), size=12),
strip.background = element_rect (linetype=1, linewidth = 0.5))
testmat <- matrix(c(88,4347,115,9534),nrow = 2,byrow = TRUE)
chisq.test(testmat)#$p.value
Pearson's Chi-squared test with Yates' continuity correction
data: testmat
X-squared = 12.877, df = 1, p-value = 0.0003326Top2i shared
TOP2i_share_var <- venn24part$..values..[[17]] # 32
not_T2_shared <- setdiff(expressedgenes$ENTREZID,TOP2i_share_var)#14052
TOP2i_shared_sig <- sig_24_venn$..values..[[1]] ##882
length(intersect(TOP2i_share_var,TOP2i_shared_sig)) #(3 overlap)[1] 3length(intersect(not_T2_shared,TOP2i_shared_sig)) #879[1] 879# stored <-
expressedgenes %>%
mutate(var_t2= if_else(ENTREZID %in% TOP2i_share_var, "y","no")) %>%
mutate(not_t2= if_else(ENTREZID %in% not_T2_shared,"y","no")) %>%
mutate(T2_shared_sig=if_else(ENTREZID %in% TOP2i_shared_sig,"y","no")) %>%
# group_by(AC_shared_sig,not_t2) %>%
summarise("var_share-n" = sum(not_t2=="no"&T2_shared_sig=="no"), "var_share-y"=sum(not_t2=="no"&T2_shared_sig=="y"),"not_var-n"=sum(not_t2=="y"&T2_shared_sig=="no"), "not_var-y"=sum(not_t2=="y"&T2_shared_sig=="y"))%>%
pivot_longer(everything(), names_to = "set", values_to = "count") %>%
separate(set, into = c("set", "color"),sep="-" ) %>%
mutate(color, factor(color, levels = c("y","no"))) %>%
mutate(set=case_match(set,"not_var"~"not TOP2i shared\nvar. gene","var_share"~"TOP2i shared\nvar. gene")) %>%
ggplot( ., aes(x=set, y=count, group=color,fill=color))+
geom_col(position='fill')+
theme_classic()+
xlab(" ")+
ggtitle("TOP2i variable genes in TOP2i specific n= 4435")+
scale_color_manual(values=drug_palc)+
scale_fill_manual(values=c("y"="cornflowerblue","n"="darkblue"),
labels=c("not a TOP2i-sp gene","shared with\nTOP2i-sp"))+
scale_y_continuous(expand=expansion(0.001))+
theme(strip.text=element_text(size=10, face = "bold"),
axis.text.x = element_text(margin = margin(2,0,2,0, "pt"), size=12),
strip.background = element_rect (linetype=1, linewidth = 0.5))
testmatTOP <- matrix(c(3,879,29,13173),nrow = 2,byrow = TRUE)
chisq.test(testmatTOP)#$p.value
Pearson's Chi-squared test with Yates' continuity correction
data: testmatTOP
X-squared = 0.13128, df = 1, p-value = 0.7171GO 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
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