Highly variable genes
ERM
2024-02-05
Last updated: 2024-02-05
Checks: 7 0
Knit directory: Cardiotoxicity/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | df08393 | reneeisnowhere | 2024-02-05 | updates to scripts |
| Rmd | 62286c3 | reneeisnowhere | 2023-07-28 | Updateing figure code |
| Rmd | 06800c9 | reneeisnowhere | 2023-07-26 | Commits to small changes and edits |
| html | ee8be4c | reneeisnowhere | 2023-07-21 | Build site. |
| Rmd | b94104b | reneeisnowhere | 2023-07-21 | first plot update |
Goals for this page:
I will examine the AC-shared variable genes within AC-shared response genes
library(tidyverse)
library(VennDiagram)
library(paletteer)
library(ggVennDiagram)
library(gridtext)
library(scales)
library(kableExtra)
library(ComplexHeatmap)
library(data.table)
# library(tidyverse)
library(ggpubr)
library(ggsignif)
# library(paletteer)
# library(ggVennDiagram)
# library(gridtext)
# library(scales)
# library(kableExtra)
library(qvalue)
# library(data.table)
# library(ComplexHeatmap)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)
backGL <- read.csv("data/backGL.txt", row.names = 1)
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$..values..[[9]] ##4435
length(intersect(AC_share_var,AC_shared_sig)) #(88 overlap)[1] 88length(intersect(not_AC_shared,AC_shared_sig)) #4347[1] 4347#
# stored <-
expressedgenes %>%
mutate(var_AC= if_else(ENTREZID %in% AC_share_var, "y","no")) %>%
mutate(not_AC=if_else(ENTREZID %in% not_AC_shared,"y","no")) %>%
mutate(AC_shared_sig=if_else(ENTREZID %in% AC_shared_sig,"y","no")) %>%
# group_by(AC_shared_sig,not_AC) %>%
summarise("var_share-n" = sum(not_AC=="no"&AC_shared_sig=="no"), "var_share-y"=sum(not_AC=="no"&AC_shared_sig=="y"),"not_var-n"=sum(not_AC=="y"&AC_shared_sig=="no"), "not_var-y"=sum(not_AC=="y"&AC_shared_sig=="y")) %>%
pivot_longer(everything(), names_to = "set", values_to = "count") %>%
separate(set, into = c("set", "color"),sep="-" ) %>%
mutate(color, factor(color, levels = c("y","no"))) %>%
mutate(set=case_match(set,"not_var"~"not AC shared\nvar. gene","var_share"~"AC shared\nvar. gene")) %>%
ggplot( ., aes(x=set, y=count, group=color,fill=color))+
geom_col(position='fill')+
theme_classic()+
scale_color_manual(values=drug_palc)+
scale_fill_manual(values=c("y"="cornflowerblue","n"="darkblue"),
labels=c("not an AC-sp gene","shared with\nAC sp"))+
scale_y_continuous(expand=expansion(0.001))+
theme(strip.text=element_text(size=10, face = "bold"),
axis.text.x = element_text(margin = margin(2,0,2,0, "pt"), size=12),
strip.background = element_rect (linetype=1, linewidth = 0.5))
| Version | Author | Date |
|---|---|---|
| ee8be4c | reneeisnowhere | 2023-07-21 |
testmat <- matrix(c(88,4347,115,9534),nrow = 2,byrow = TRUE)
chisq.test(testmat)#$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$..values..[[1]] ##882
length(intersect(TOP2i_share_var,TOP2i_shared_sig)) #(3 overlap)[1] 3length(intersect(not_T2_shared,TOP2i_shared_sig)) #879[1] 879# stored <-
expressedgenes %>%
mutate(var_t2= if_else(ENTREZID %in% TOP2i_share_var, "y","no")) %>%
mutate(not_t2= if_else(ENTREZID %in% not_T2_shared,"y","no")) %>%
mutate(T2_shared_sig=if_else(ENTREZID %in% TOP2i_shared_sig,"y","no")) %>%
# group_by(AC_shared_sig,not_t2) %>%
summarise("var_share-n" = sum(not_t2=="no"&T2_shared_sig=="no"), "var_share-y"=sum(not_t2=="no"&T2_shared_sig=="y"),"not_var-n"=sum(not_t2=="y"&T2_shared_sig=="no"), "not_var-y"=sum(not_t2=="y"&T2_shared_sig=="y"))%>%
pivot_longer(everything(), names_to = "set", values_to = "count") %>%
separate(set, into = c("set", "color"),sep="-" ) %>%
mutate(color, factor(color, levels = c("y","no"))) %>%
mutate(set=case_match(set,"not_var"~"not TOP2i shared\nvar. gene","var_share"~"TOP2i shared\nvar. gene")) %>%
ggplot( ., aes(x=set, y=count, group=color,fill=color))+
geom_col(position='fill')+
theme_classic()+
xlab(" ")+
ggtitle("TOP2i variable genes in TOP2i specific n= 4435")+
scale_color_manual(values=drug_palc)+
scale_fill_manual(values=c("y"="cornflowerblue","n"="darkblue"),
labels=c("not a TOP2i-sp gene","shared with\nTOP2i-sp"))+
scale_y_continuous(expand=expansion(0.001))+
theme(strip.text=element_text(size=10, face = "bold"),
axis.text.x = element_text(margin = margin(2,0,2,0, "pt"), size=12),
strip.background = element_rect (linetype=1, linewidth = 0.5))
| Version | Author | Date |
|---|---|---|
| ee8be4c | reneeisnowhere | 2023-07-21 |
testmatTOP <- matrix(c(3,879,29,13173),nrow = 2,byrow = TRUE)
chisq.test(testmatTOP)#$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 %>% dplyr::filter(source=="GO:BP") %>% slice_min(.,p_value, n=3) %>% list()
terms[2] <- EPI_table %>% dplyr::filter(source=="GO:BP") %>%slice_min(.,p_value, n=3) %>% list()
terms[3] <- DNR_table %>% dplyr::filter(source=="GO:BP") %>%slice_min(.,p_value, n=3) %>% list()
terms[4] <- MTX_table %>% dplyr::filter(source=="GO:BP") %>%slice_min(.,p_value, n=3) %>% list()
terms[5] <- TRZ_table %>% dplyr::filter(source=="GO:BP") %>%slice_min(.,p_value, n=3) %>% list()
terms[6] <- AC_share_var24_table %>% dplyr::filter(source=="GO:BP") %>%slice_min(.,p_value, n=3) %>% list()
terms[7] <- TOP2i_var24_table %>% dplyr::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))
})
| Version | Author | Date |
|---|---|---|
| ee8be4c | reneeisnowhere | 2023-07-21 |
Seoane data investigation of variability
I will examine the Seoane gene sets and overlap with variable data
list2env(Vargenes, envir = .GlobalEnv)<environment: R_GlobalEnv>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"
Sup1seoane <- (unique(Seoane_2019$ENTREZID))
Sup4genes <- read.csv("output/Sup4seoane.csv", row.names = 1)
Sup4seoane <- Sup4genes %>%
dplyr::filter(pval.expAnth<0.05) %>%
distinct(entrez, .keep_all = TRUE) %>%
dplyr::select(entrez) %>%
rename("ENTREZID"='entrez')intersect(Sup1seoane,AC_share_var)[1] "387893" "79723" "86" "1105" "8473" "3012" #
intersect(Sup4seoane$ENTREZID,as.numeric(AC_share_var))numeric(0)intersect(Sup4seoane$ENTREZID,as.numeric(not_AC_shared)) [1] 11176 10284 8819 23522 7786 2146 4297 79913 8242 51780 6872 23135
[13] 6877 23030 64324 79885 10847 51773 5253 9126 3054 9734 53335 27350
[25] 6601 1108 8289 890 64151 10445 7150 8110 54531 51409 27097 9739
[37] 6595 9555 22823 54556 10592 7528 9031 51377 7799 6602 8202 51564
[49] 79858 10856# Vargenes[[length(Vargenes)+1]] <- list(Sup4seoane$ENTREZID)
library(paletteer)
assignInNamespace(x="plot_venn", value=plot_venn, ns="ggVennDiagram")
sup1overlap <- list(as.numeric(DOX_24_var),as.numeric(EPI_24_var),as.numeric(DNR_24_var),as.numeric(MTX_24_var), Sup1seoane)
# re_incommon <- c(DOXreQTLs$ENTREZID,sigVDA24$ENTREZID, sigVEP24$ENTREZID, sigVMT24$ENTREZID)
#
# names(reQTL_overlapDE24) <- c("Dox_reQTLS", "DNR DEGs","EPI DEGs","MTX DEGs")
ggVennDiagram::ggVennDiagram(sup1overlap,
category.names = c("DOX var",
"EPI var",
"DNR var",
"MTX var",
"Seoane S1"),
show_intersect = FALSE,
set_color = "black",
# category_size = c(6,6,6,6),
label = "count",
# color = c("DOX\negenes" = "yellow","DNR DEGs" ="steelblue","EPI DEGs" = 'red', "MTX DEGs" = 'black') ,
label_percent_digit = 1,
label_size = 4,
label_alpha = 0,
label_color = "black",
edge_lty = "solid", set_size = 4.5)+
scale_x_continuous(expand = expansion(mult = .3))+
scale_y_continuous(expand = expansion(mult = .2))+
# scale_color_paletteer_d(palette = "fishualize::Bodianus_pulchellus")+
# scale_colour_gradientn(colours = cols,
# values = rescale(c(0, 20, 75, 1000, 5000)),
# guide = "colorbar", limits=c(0, 100)) +
scale_fill_distiller(palette="Spectral", direction = -1, limits= c(1,200),oob=scales::squish)#+
# scale_fill_manual(values = cbp1)+
labs(title = "24 hour DOX egenes in other DEG sets")+
theme(plot.title = element_text(size = rel(1.6), hjust = 0.5, vjust =1))NULLsup4overlap <- list(as.numeric(DOX_24_var),as.numeric(EPI_24_var),as.numeric(DNR_24_var),as.numeric(MTX_24_var), as.numeric(Sup4seoane$ENTREZID))
ggVennDiagram::ggVennDiagram(sup4overlap,
category.names = c("DOX var",
"EPI var",
"DNR var",
"MTX var",
"Seoane S4"),
show_intersect = FALSE,
set_color = "black",
# category_size = c(6,6,6,6),
label = "count",
# color = c("DOX\negenes" = "yellow","DNR DEGs" ="steelblue","EPI DEGs" = 'red', "MTX DEGs" = 'black') ,
label_percent_digit = 1,
label_size = 4,
label_alpha = 0,
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_colour_gradientn(colours = cols,
# values = rescale(c(0, 20, 75, 1000, 5000)),
# guide = "colorbar", limits=c(0, 100)) +
scale_fill_distiller(palette="Spectral", direction = -1, limits= c(1,200),oob=scales::squish)#+
# scale_fill_manual(values = cbp1)+
labs(title = "24 hour DOX egenes in other DEG sets")+
theme(plot.title = element_text(size = rel(1.6), hjust = 0.5, vjust =1))NULLDEG <- Vargenes
siglist24 <- siglist[6:9]
test <- siglist24[1]["ENTREZID"]
sig_24_ENTREZID <- sapply(siglist24,"[[",1)
all_unique_DEG <- unique(rbindlist(lapply(sig_24_ENTREZID, as.data.table))) ##8188
all_unique_var <- unique(rbindlist((lapply(Vargenes, as.data.table))))
length(intersect(all_unique_DEG$V1,all_unique_var$V1))##2190[1] 2190DEG_var <- intersect(all_unique_DEG$V1,all_unique_var$V1)
non_DEG_var <- setdiff(expressedgenes$ENTREZID,DEG_var)
ggVennDiagram::ggVennDiagram(list(all_unique_var$V1, all_unique_DEG$V1),
category.names = c("all var genes", "all DEGs"),
show_intersect = FALSE,
set_color = "black",
label = "count",
label_percent_digit = 1,
label_size = 4,
label_alpha = 0,
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_colour_gradientn(colours = cols,
# values = rescale(c(0, 20, 75, 1000, 5000)),
# guide = "colorbar", limits=c(0, 100)) +
scale_fill_distiller(palette="Spectral", direction = -1, limits= c(1000,8000),oob=scales::squish)#+
holdlist <- list(DEG_var, as.numeric(non_DEG_var), Sup1seoane)
ggVennDiagram::ggVennDiagram(list( as.numeric(DEG_var), as.numeric(non_DEG_var), Sup1seoane),
category.names = c("DEG var", "non DEG var","Seoane supp 1"),
show_intersect = FALSE,
set_color = "black",
label = "count",
label_percent_digit = 1,
label_size = 4,
label_alpha = 0,
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_colour_gradientn(colours = cols,
# values = rescale(c(0, 20, 75, 1000, 5000)),
# guide = "colorbar", limits=c(0, 100)) +
scale_fill_distiller(palette="Spectral", direction = -1, limits= c(1000,8000),oob=scales::squish)#+
testS1 <- matrix(c(59,271,2131,11623),nrow = 2,byrow = TRUE)
chisq.test(testS1)
Pearson's Chi-squared test with Yates' continuity correction
data: testS1
X-squared = 1.2204, df = 1, p-value = 0.2693Burridge data
DOXreQTLs <- readRDS("output/DOXreQTLs.RDS")
burr_genes <- readRDS("data/BurridgeDOXTOX.RDS")
# BurridgeDOXTOX <- getBM(attributes=my_attributes,filters ='hgnc_symbol',
# values = burr_genes, mart = ensembl)
# BurridgeDOXTOX <- BurridgeDOXTOX %>% distinct(entrezgene_id, .keep_all = TRUE)
# saveRDS(BurridgeDOXTOX, "data/BurridgeDOXTOX.RDS")
storeEPI <- readRDS("data/qvalueEPItemp.RDS")
VennDiagram::get.venn.partitions(list(storeEPI$ENTREZID, DOXreQTLs$ENTREZID)) X1 X2 ..set.. ..values.. ..count..
1 TRUE TRUE X1∩X2 5119, 93.... 4
2 FALSE TRUE (X2)∖(X1) 29965, 2.... 138
3 TRUE FALSE (X1)∖(X2) 49856, 2.... 504intersect(DOXreQTLs$ENTREZID, burr_genes$entrezgene_id)character(0)# DOX_reqtls <-$ENTREZID %>% as.integer()
DOXreQTLs %>% dplyr::filter(ENTREZID %in% storeEPI$ENTREZID) %>% tally( ) n
1 4 burr_genes %>%
dplyr::filter(entrezgene_id%in% backGL$ENTREZID) %>%
dplyr::filter(entrezgene_id %in% storeEPI$ENTREZID)[1] entrezgene_id ensembl_gene_id hgnc_symbol
<0 rows> (or 0-length row.names)### now for burr genes and epi508
VennDiagram::get.venn.partitions(list(storeEPI$ENTREZID, burr_genes$entrezgene_id)) X1 X2 ..set.. ..values.. ..count..
1 TRUE TRUE X1∩X2 0
2 FALSE TRUE (X2)∖(X1) 220, 790.... 22
3 TRUE FALSE (X1)∖(X2) 49856, 2.... 508### looking at the overlap of egenes and EPI 508 var
storeEPI %>%
dplyr::filter (ENTREZID %in% DOXreQTLs$ENTREZID) %>%
left_join(., backGL, by=c("ENTREZID")) ENTREZID EPI.VEH.24 qvalues SYMBOL ensembl_gene_id hgnc_symbol
1 5119 0.001617342 0.07437143 CHMP1A ENSG00000131165 CHMP1A
2 93134 0.002341605 0.08331969 ZNF561 ENSG00000171469 ZNF561
3 132001 0.002319133 0.08331969 TAMM41 ENSG00000279643 TAMM41
4 10592 0.003429113 0.08968566 SMC2 ENSG00000136824 SMC2looking at heatmaps
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.framev_DNR_24 <- drug_frame %>%
dplyr::filter(calc !="mean", treatment =="Da", time=="24") %>%
select(entrezid,values)%>%
rename("DNR"="values")
v_DOX_24 <- drug_frame %>%
dplyr::filter(calc !="mean", treatment =="Do", time=="24") %>%
select(entrezid,values)%>%
rename("DOX"="values")
v_EPI_24 <- drug_frame %>%
dplyr::filter(calc !="mean", treatment =="Ep", time=="24") %>%
select(entrezid,values)%>%
rename("EPI"="values")
v_MTX_24 <- drug_frame %>%
dplyr::filter(calc !="mean", treatment =="Mi", time=="24") %>%
select(entrezid,values)%>%
rename("MTX"="values")
v_TRZ_24 <- drug_frame %>%
dplyr::filter(calc !="mean", treatment =="Tr", time=="24") %>%
select(entrezid,values)%>%
rename("TRZ"="values")
v_VEH_24 <- drug_frame %>%
dplyr::filter(calc !="mean", treatment =="Ve", time=="24") %>%
select(entrezid,values)%>%
rename("VEH"="values")
val_mat_24 <- left_join(v_DNR_24,v_DOX_24,
by=c("entrezid"))%>%
left_join(.,v_EPI_24, by=c("entrezid")) %>%
left_join(.,v_MTX_24, by=c("entrezid")) %>%
left_join(.,v_TRZ_24, by=c("entrezid")) %>%
left_join(.,v_VEH_24, by=c("entrezid")) %>%
column_to_rownames("entrezid") %>%
as.matrix
summary(val_mat_24) DNR DOX EPI MTX
Min. : 0.000902 Min. : 0.00139 Min. : 0.001492 Min. : 0.000579
1st Qu.: 0.057038 1st Qu.: 0.07892 1st Qu.: 0.124501 1st Qu.: 0.045321
Median : 0.117373 Median : 0.16358 Median : 0.266216 Median : 0.095530
Mean : 0.249433 Mean : 0.32816 Mean : 0.488970 Mean : 0.235354
3rd Qu.: 0.252505 3rd Qu.: 0.35258 3rd Qu.: 0.571324 3rd Qu.: 0.230010
Max. :28.118788 Max. :33.54419 Max. :26.410869 Max. :27.443180
TRZ VEH
Min. : 0.00076 Min. : 0.00057
1st Qu.: 0.04751 1st Qu.: 0.05105
Median : 0.10385 Median : 0.11627
Mean : 0.27199 Mean : 0.30214
3rd Qu.: 0.24733 3rd Qu.: 0.28361
Max. :32.76541 Max. :30.87884 col_fun = circlize::colorRamp2(c(0, 0.4, 2), c("#377EB8", "white", "#E41A1C"))
Heatmap(val_mat_24, col=col_fun,
show_row_names = FALSE,
# width = unit(5, "mm"),
cluster_columns = FALSE)
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] qvalue_2.32.0 ggsignif_0.6.4 ggpubr_0.6.0
[4] data.table_1.14.8 ComplexHeatmap_2.16.0 kableExtra_1.3.4
[7] scales_1.3.0 gridtext_0.1.5 ggVennDiagram_1.5.0
[10] paletteer_1.6.0 VennDiagram_1.7.3 futile.logger_1.4.3
[13] lubridate_1.9.3 forcats_1.0.0 stringr_1.5.0
[16] dplyr_1.1.3 purrr_1.0.2 readr_2.1.4
[19] tidyr_1.3.0 tibble_3.2.1 ggplot2_3.4.4
[22] tidyverse_2.0.0 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] formatR_1.14 rematch2_2.1.2 rlang_1.1.2
[4] magrittr_2.0.3 clue_0.3-65 GetoptLong_1.0.5
[7] git2r_0.32.0 matrixStats_1.1.0 compiler_4.3.1
[10] getPass_0.2-2 png_0.1-8 systemfonts_1.0.5
[13] callr_3.7.3 vctrs_0.6.4 reshape2_1.4.4
[16] rvest_1.0.3 pkgconfig_2.0.3 shape_1.4.6
[19] crayon_1.5.2 fastmap_1.1.1 magick_2.8.1
[22] backports_1.4.1 labeling_0.4.3 utf8_1.2.4
[25] promises_1.2.1 rmarkdown_2.25 tzdb_0.4.0
[28] ps_1.7.5 bit_4.0.5 xfun_0.41
[31] cachem_1.0.8 jsonlite_1.8.7 highr_0.10
[34] later_1.3.1 broom_1.0.5 parallel_4.3.1
[37] cluster_2.1.4 R6_2.5.1 bslib_0.6.1
[40] stringi_1.7.12 RColorBrewer_1.1-3 car_3.1-2
[43] jquerylib_0.1.4 Rcpp_1.0.11 iterators_1.0.14
[46] knitr_1.45 IRanges_2.34.1 splines_4.3.1
[49] httpuv_1.6.12 timechange_0.2.0 tidyselect_1.2.0
[52] rstudioapi_0.15.0 abind_1.4-5 yaml_2.3.7
[55] doParallel_1.0.17 codetools_0.2-19 processx_3.8.2
[58] plyr_1.8.9 withr_3.0.0 evaluate_0.23
[61] lambda.r_1.2.4 xml2_1.3.5 circlize_0.4.15
[64] pillar_1.9.0 carData_3.0-5 whisker_0.4.1
[67] foreach_1.5.2 stats4_4.3.1 generics_0.1.3
[70] vroom_1.6.5 rprojroot_2.0.4 S4Vectors_0.38.2
[73] hms_1.1.3 munsell_0.5.0 glue_1.6.2
[76] tools_4.3.1 webshot_0.5.5 fs_1.6.3
[79] colorspace_2.1-0 cli_3.6.1 futile.options_1.0.1
[82] fansi_1.0.5 viridisLite_0.4.2 svglite_2.1.2
[85] gtable_0.3.4 rstatix_0.7.2 sass_0.4.7
[88] digest_0.6.33 BiocGenerics_0.46.0 farver_2.1.1
[91] rjson_0.2.21 htmltools_0.5.7 lifecycle_1.0.4
[94] httr_1.4.7 GlobalOptions_0.1.2 bit64_4.0.5