GOI_plots
ERM
2023-11-06
Last updated: 2023-11-06
Checks: 6 1
Knit directory: Cardiotoxicity/
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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 = factor(time, levels= c("3h","24h"), 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("")+
# ggtitle("24 hours")+
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)
}
pearson_extract <- function(corr_df,ENTREZID) {
ld50_plot <- corr_df %>%
dplyr::filter(entrezgene_id == ENTREZID) %>%
ggplot(., aes(x=LD50, y=counts))+
geom_point(aes(col=indv))+
geom_smooth(method="lm")+
facet_wrap(hgnc_symbol~Drug, scales="free")+
theme_classic()+
stat_cor(method="pearson",
aes(label = paste(..r.label.., ..p.label.., sep = "~`,`~")),
color = "red",
label.x.npc = 0,
label.y.npc=1,
size = 3)
tnni_plot <- corr_df %>%
dplyr::filter(entrezgene_id == ENTREZID) %>%
ggplot(., aes(x=rtnni, y=counts))+
geom_point(aes(col=indv))+
geom_smooth(method="lm")+
facet_wrap(hgnc_symbol~Drug, scales="free")+
theme_classic()+
stat_cor(method="pearson",
aes(label = paste(..r.label.., ..p.label.., sep = "~`,`~")),
color = "red",
label.x.npc = 0,
label.y.npc=1,
size = 3)
ldh_plot <- corr_df %>%
dplyr::filter(entrezgene_id == ENTREZID) %>%
ggplot(., aes(x=rldh, y=counts))+
geom_point(aes(col=indv))+
geom_smooth(method="lm")+
facet_wrap(hgnc_symbol~Drug, scales="free")+
theme_classic()+
stat_cor(method="pearson",
aes(label = paste(..r.label.., ..p.label.., sep = "~`,`~")),
color = "red",
label.x.npc = 0,
label.y.npc=1,
size = 3)
##ggbuild to get model:
ld50_build <- ggplot_build(ld50_plot)
ld50_data <- data.frame('rho_LD50'= c(ld50_build$data[[3]]$r,NA,NA),
'sig_LD50'=c(ld50_build$data[[3]]$p.value,NA,NA),
'rowname'=c("DOX","EPI","DNR","MTX", "TRZ", "VEH"))
tnni_build <- ggplot_build(tnni_plot)
tnni_data <- data.frame('rho_tnni'= c(tnni_build$data[[3]]$r),
'sig_tnni'=c(tnni_build$data[[3]]$p.value),
'rowname'=c("DOX","EPI","DNR","MTX", "TRZ", "VEH"))
ldh_build <- ggplot_build(ldh_plot)
ldh_data <- data.frame('rho_ldh'= c(ldh_build$data[[3]]$r),
'sig_ldh'=c(ldh_build$data[[3]]$p.value),
'rowname'=c("DOX","EPI","DNR","MTX", "TRZ", "VEH"))
results <- cbind(ldh_data,tnni_data[,1:2],ld50_data[,1:2])
return(results)
}library(ComplexHeatmap)
library(tidyverse)
library(ggsignif)
library(RColorBrewer)
library(scales)
library(sjmisc)
library(kableExtra)
library(broom)
library(ggstats)
library(Hmisc)
library(ggpubr)<environment: R_GlobalEnv><environment: R_GlobalEnv>Genes of Interest log2 cpm
TOP2B
Top2a
CDKN1a
ATM
ATR
RARG
KAT6b
cpm_boxplot(cpmcounts,GOI='23522',"Dark2",drug_pal_vehend,
ylab=(expression(atop(" ",italic("KAT6B")~log[2]~"cpm "))))
KDM4b
cpm_boxplot(cpmcounts,GOI='23030',"Dark2",drug_pal_vehend,
ylab=(expression(atop(" ",italic("KDM4B")~log[2]~"cpm "))))
expression correlation:
first, will do expression the following GOIs:
| time | id | ENTREZID | SYMBOL | adj.P.Val | |
|---|---|---|---|---|---|
| 23371…1 | 24_hours | DNR | 23371 | TNS2 | 0.0000011 |
| 23254…2 | 24_hours | DNR | 23254 | KAZN | 0.0000165 |
| 283337…3 | 24_hours | DNR | 283337 | ZNF740 | 0.0001526 |
| 23030…4 | 24_hours | DNR | 23030 | KDM4B | 0.0003429 |
| 51020…5 | 24_hours | DNR | 51020 | HDDC2 | 0.0013039 |
| 10818…6 | 24_hours | DNR | 10818 | FRS2 | 0.0019914 |
| 5916…7 | 24_hours | DNR | 5916 | RARG | 0.0070726 |
| 80010…8 | 24_hours | DNR | 80010 | RMI1 | 0.0403160 |
| 23371…9 | 24_hours | DOX | 23371 | TNS2 | 0.0000007 |
| 10818…10 | 24_hours | DOX | 10818 | FRS2 | 0.0001291 |
| 23254…11 | 24_hours | DOX | 23254 | KAZN | 0.0001461 |
| 23030…12 | 24_hours | DOX | 23030 | KDM4B | 0.0017905 |
| 51020…13 | 24_hours | DOX | 51020 | HDDC2 | 0.0057312 |
| 283337…14 | 24_hours | DOX | 283337 | ZNF740 | 0.0111769 |
| 64078…15 | 24_hours | DOX | 64078 | SLC28A3 | 0.0298732 |
| 80010…16 | 24_hours | DOX | 80010 | RMI1 | 0.0357673 |
| 23371…17 | 24_hours | EPI | 23371 | TNS2 | 0.0000025 |
| 10818…18 | 24_hours | EPI | 10818 | FRS2 | 0.0002169 |
| 23254…19 | 24_hours | EPI | 23254 | KAZN | 0.0003003 |
| 51020…20 | 24_hours | EPI | 51020 | HDDC2 | 0.0014213 |
| 283337…21 | 24_hours | EPI | 283337 | ZNF740 | 0.0071182 |
| 5916…22 | 24_hours | EPI | 5916 | RARG | 0.0147965 |
| 64078…23 | 24_hours | EPI | 64078 | SLC28A3 | 0.0170798 |
| 23030…24 | 24_hours | EPI | 23030 | KDM4B | 0.0209094 |
| 80010…25 | 24_hours | MTX | 80010 | RMI1 | 0.0066041 |
24 hour TNNI to cpm
# # now I have the long dataframe, time to combine with other data snf limit to just the GOIs
# gene_corr_frame <- gene_corr_df %>%
# filter(entrezgene_id %in% GOI_genelist$entrezgene_id) %>%
# left_join(., GOI_join, by=c("Drug","indv")) %>%
# dplyr::select(indv, Drug,entrezgene_id,counts, rldh,rtnni,LD50,tox) %>%
# mutate(Drug=factor(Drug, levels=c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
# full_join(.,GOI_genelist, by="entrezgene_id")
# # #
# # saveRDS(gene_corr_df,"data/gene_corr_df.RDS")
# saveRDS(gene_corr_frame,"data/gene_corr_frame.RDS")
gene_corr_frame <- readRDS("data/gene_corr_frame.RDS")
gene_corr_df <- readRDS("data/gene_corr_df.RDS")
## remove nas!
for (gene in GOI_genelist$entrezgene_id){
gene_plot <- gene_corr_frame %>%
dplyr::filter(entrezgene_id == gene) %>%
ggplot(., aes(x=rtnni, y=counts))+
geom_point(aes(col=indv))+
geom_smooth(method="lm")+
facet_wrap(hgnc_symbol~Drug, scales="free")+
theme_classic()+
xlab("troponin I expression") +
ylab("Gene counts in log2 cpm") +
ggtitle(expression(paste("Correlation between counts and troponin I by drug")))+
scale_color_brewer(palette = "Dark2",name = "Individual", label = c("1","2","3","4","5","6"))+
ggpubr:: stat_cor(method="pearson",
cor.coef.name="rho",
aes(label = paste(..r.label.., ..p.label.., sep = "~`,\n`~")),
color = "red",
label.x.npc = 0.01,
label.y.npc=0.01,
size = 3)+
theme(plot.title = element_text(size = rel(1.5), hjust = 0.5,face = "bold"),
axis.title = element_text(size = 15, color = "black"),
axis.ticks = element_line(size = 1.5),
axis.text = element_text(size = 8, color = "black", angle = 20),
strip.text.x = element_text(size = 12, color = "black", face = "italic"))
print(gene_plot)
}
| Version | Author | Date |
|---|---|---|
| 3359641 | reneeisnowhere | 2023-07-28 |
24 hour LDH to cpm
## ggplot by drug for ldh
for (gene in GOI_genelist$entrezgene_id){
gene_plot <- gene_corr_frame %>%
dplyr::filter(entrezgene_id == gene) %>%
ggplot(., aes(x=rldh, y=counts))+
geom_point(aes(col=indv))+
geom_smooth(method="lm")+
facet_wrap(hgnc_symbol~Drug, scales="free")+
theme_classic()+
xlab("troponin I expression") +
ylab("Gene counts in log2 cpm") +
ggtitle(expression(paste("Correlation between counts and LDH by drug")))+
scale_color_brewer(palette = "Dark2",name = "Individual", label = c("1","2","3","4","5","6"))+
stat_cor(method="pearson",
cor.coef.name="rho",
aes(label = paste(..r.label.., ..p.label.., sep = "~`,`~")),
color = "red",
label.x.npc = 0.01,
label.y.npc=0.01,
size = 3)+
theme(plot.title = element_text(size = rel(1.5), hjust = 0.5,face = "bold"),
axis.title = element_text(size = 15, color = "black"),
axis.ticks = element_line(size = 1.5),
axis.text = element_text(size = 8, color = "black", angle = 20),
strip.text.x = element_text(size = 12, color = "black", face = "italic"))
print(gene_plot)
}
| Version | Author | Date |
|---|---|---|
| 3359641 | reneeisnowhere | 2023-07-28 |
Toxicity for GOI
for (gene in GOI_genelist$entrezgene_id){
gene_plot <- gene_corr_frame %>%
dplyr::filter(entrezgene_id == gene) %>%
ggplot(., aes(x=tox, y=counts))+
geom_point(aes(col=indv))+
geom_smooth(method="lm")+
facet_wrap(hgnc_symbol~Drug, scales="free")+
theme_classic()+
xlab("Toxicity score") +
ylab("Gene counts in log2 cpm") +
ggtitle(expression(paste("Correlation between counts and toxicity by drug")))+
scale_color_brewer(palette = "Dark2",name = "Individual", label = c("1","2","3","4","5","6"))+
stat_cor(method="pearson",
cor.coef.name="rho",
aes(label = paste(..r.label.., ..p.label.., sep = "~`,`~")),
color = "red",
label.x.npc = 0.01,
label.y.npc=0.01,
size = 3)+
theme(plot.title = element_text(size = rel(1.5), hjust = 0.5,face = "bold"),
axis.title = element_text(size = 15, color = "black"),
axis.ticks = element_line(size = 1.5),
axis.text = element_text(size = 8, color = "black", angle = 20),
strip.text.x = element_text(size = 12, color = "black", face = "italic"))
print(gene_plot)
}
| Version | Author | Date |
|---|---|---|
| 3359641 | reneeisnowhere | 2023-07-28 |
Fig9 expression correlation
##add in list of ldh,tnni and ld50
avg_LD50 <- readRDS("data/avg_LD50.RDS")
gene_corr_frame <- readRDS("data/gene_corr_frame.RDS")
gene_corr_df <- readRDS("data/gene_corr_df.RDS")
GOI_join <- RNAnormlist %>%
mutate(indv=factor(indv,levels = level_order2)) %>%
mutate(indv=as.numeric(indv)) %>%
mutate(indv=factor(indv)) %>%
mutate(Drug = factor(Drug, levels = c("DNR",
"DOX",
"EPI",
"MTX",
"TRZ",
"VEH"))) %>%
dplyr::select(indv, Drug,rldh,rtnni) %>%
full_join(.,avg_LD50,by=c("Drug"="sDrug","indv"="indv")) %>%
rename("LD50"="name")
##now we have the frame, time to limit df of GOI_genelist to fig 9 genes
Fig9_genes <- GOI_genelist %>%
filter(hgnc_symbol %in% list("RARG", "ZNF740", "TNS2", "SLC28A3","RMI1"))
##joining to counts
gene_corr_fig9 <- gene_corr_df %>%
filter(entrezgene_id %in% Fig9_genes$entrezgene_id) %>%
left_join(., GOI_join, by=c("Drug","indv")) %>%
mutate(Drug=factor(Drug, levels = c("DOX","EPI","DNR", "MTX", "TRZ", "VEH"))) %>%
full_join(.,GOI_genelist, by="entrezgene_id")
# saveRDS(gene_corr_fig9,"output/gene_corr_fig9.RDS")GOI from variance
DOX examples
Vargenes <- readRDS("data/geneset_24.RDS")
expressedgenes <- read.csv("data/backGL.txt")
top_genes <- sapply(Vargenes,head,4)
top_genes_df<- top_genes %>% as.data.frame() %>%
pivot_longer(everything(),names_to="combo",values_to = "ENTREZID") %>%
separate(combo, into = c("drug",NA,NA)) %>%
mutate(ENTREZID=as.numeric(ENTREZID)) %>%
inner_join(., expressedgenes, by="ENTREZID") %>% as.data.frame()
for (g in seq(1:4)){
datafilter <- top_genes_df %>% filter(drug=="DOX")
a <- datafilter[g,3]
# b <- datafilter[g,1]
cpm_boxplot(cpmcounts,GOI=datafilter[g,2],
"Dark2",
drug_pal_fact,
ylab=bquote(~italic(.(a))~log[2]~"cpm "))
}
EPI examples
for (g in seq(1:4)){
datafilter <- top_genes_df %>% filter(drug =="EPI")
a <- datafilter[g,3]
# b <- datafilter[g,1]
cpm_boxplot(cpmcounts,GOI=datafilter[g,2],
"Dark2",
drug_pal_fact,
ylab=bquote(~italic(.(a))~log[2]~"cpm "))
}
| Version | Author | Date |
|---|---|---|
| da2a305 | reneeisnowhere | 2023-07-21 |
| Version | Author | Date |
|---|---|---|
| da2a305 | reneeisnowhere | 2023-07-21 |
| Version | Author | Date |
|---|---|---|
| da2a305 | reneeisnowhere | 2023-07-21 |
| Version | Author | Date |
|---|---|---|
| da2a305 | reneeisnowhere | 2023-07-21 |
qvalue EPI
cpm_boxplot_time <-function(cpmcounts,timex, 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")) %>%
dplyr::filter(time == paste(timex)) %>%
mutate(time=factor(time, levels =c("3h", "24h"), 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=1.5, alpha=0.5))+
guides(alpha= "none", size= "none")+
scale_color_brewer(palette = brewer_palette, guide = "none")+
scale_fill_manual(values=fill_colors)+
# try(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=10,hjust = 0.5,face="bold"),
axis.title = element_text(size = 10, color = "black"),
axis.ticks = element_line(linewidth = 1.0),
axis.line = element_line(linewidth = 1.0),
axis.text.x = element_blank(),
strip.text.x = element_text(margin = margin(2,0,2,0, "pt"),face = "bold"))
print(plot)
}
storeEPI <- readRDS("data/qvalueEPItemp.RDS")
# EPIgoi <- getBM(attributes=my_attributes,filters ='entrezgene_id',
# values = storeEPI$ENTREZID, mart = ensembl)
set.seed(12345)
sampset <- storeEPI %>%
dplyr::select(ENTREZID) %>%
sample_n(.,4)
for (g in seq(from=1, to=length(sampset$ENTREZID))){
a <- sampset$hgnc_symbol[g]
cpm_boxplot_time(cpmcounts,'24h',GOI=sampset[g,1],"Dark2",drug_pal_fact,
ylab=bquote(~italic(.(a))~log[2]~"cpm "))
}
| Version | Author | Date |
|---|---|---|
| 3359641 | reneeisnowhere | 2023-07-28 |
| Version | Author | Date |
|---|---|---|
| 3359641 | reneeisnowhere | 2023-07-28 |
| Version | Author | Date |
|---|---|---|
| 3359641 | reneeisnowhere | 2023-07-28 |
| Version | Author | Date |
|---|---|---|
| 3359641 | reneeisnowhere | 2023-07-28 |
MTX examples
for (g in seq(1:4)){
datafilter <- top_genes_df %>% filter(drug =="MTX")
a <- datafilter[g,3]
# b <- datafilter[g,1]
cpm_boxplot(cpmcounts,GOI=datafilter[g,2],
"Dark2",
drug_pal_fact,
ylab=bquote(~italic(.(a))~log[2]~"cpm "))
}
| Version | Author | Date |
|---|---|---|
| da2a305 | reneeisnowhere | 2023-07-21 |
| Version | Author | Date |
|---|---|---|
| da2a305 | reneeisnowhere | 2023-07-21 |
| Version | Author | Date |
|---|---|---|
| da2a305 | reneeisnowhere | 2023-07-21 |
| Version | Author | Date |
|---|---|---|
| da2a305 | reneeisnowhere | 2023-07-21 |
TRZ examples
for (g in seq(1:4)){
datafilter <- top_genes_df %>% filter(drug =="TRZ")
a <- datafilter[g,3]
# b <- datafilter[g,1]
cpm_boxplot(cpmcounts,GOI=datafilter[g,2],
"Dark2",
drug_pal_fact,
ylab=bquote(~italic(.(a))~log[2]~"cpm "))
}
| Version | Author | Date |
|---|---|---|
| da2a305 | reneeisnowhere | 2023-07-21 |
| Version | Author | Date |
|---|---|---|
| da2a305 | reneeisnowhere | 2023-07-21 |
| Version | Author | Date |
|---|---|---|
| da2a305 | reneeisnowhere | 2023-07-21 |
| Version | Author | Date |
|---|---|---|
| da2a305 | reneeisnowhere | 2023-07-21 |
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] ggpubr_0.6.0 Hmisc_5.1-1 ggstats_0.5.0
[4] broom_1.0.5 kableExtra_1.3.4 sjmisc_2.8.9
[7] scales_1.2.1 RColorBrewer_1.1-3 ggsignif_0.6.4
[10] lubridate_1.9.3 forcats_1.0.0 stringr_1.5.0
[13] dplyr_1.1.3 purrr_1.0.2 readr_2.1.4
[16] tidyr_1.3.0 tibble_3.2.1 ggplot2_3.4.4
[19] tidyverse_2.0.0 ComplexHeatmap_2.16.0 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] gridExtra_2.3 rlang_1.1.1 magrittr_2.0.3
[4] clue_0.3-65 GetoptLong_1.0.5 git2r_0.32.0
[7] matrixStats_1.0.0 compiler_4.3.1 mgcv_1.9-0
[10] getPass_0.2-2 png_0.1-8 systemfonts_1.0.5
[13] callr_3.7.3 vctrs_0.6.4 rvest_1.0.3
[16] pkgconfig_2.0.3 shape_1.4.6 crayon_1.5.2
[19] fastmap_1.1.1 backports_1.4.1 labeling_0.4.3
[22] utf8_1.2.3 promises_1.2.1 rmarkdown_2.25
[25] tzdb_0.4.0 ps_1.7.5 xfun_0.40
[28] cachem_1.0.8 jsonlite_1.8.7 highr_0.10
[31] later_1.3.1 parallel_4.3.1 cluster_2.1.4
[34] R6_2.5.1 bslib_0.5.1 stringi_1.7.12
[37] car_3.1-2 rpart_4.1.21 jquerylib_0.1.4
[40] Rcpp_1.0.11 iterators_1.0.14 knitr_1.44
[43] base64enc_0.1-3 IRanges_2.34.1 Matrix_1.6-1.1
[46] splines_4.3.1 httpuv_1.6.11 nnet_7.3-19
[49] timechange_0.2.0 tidyselect_1.2.0 abind_1.4-5
[52] rstudioapi_0.15.0 yaml_2.3.7 doParallel_1.0.17
[55] codetools_0.2-19 sjlabelled_1.2.0 processx_3.8.2
[58] lattice_0.22-5 withr_2.5.1 evaluate_0.22
[61] foreign_0.8-85 xml2_1.3.5 circlize_0.4.15
[64] pillar_1.9.0 carData_3.0-5 whisker_0.4.1
[67] checkmate_2.3.0 foreach_1.5.2 stats4_4.3.1
[70] insight_0.19.6 generics_0.1.3 rprojroot_2.0.3
[73] S4Vectors_0.38.2 hms_1.1.3 munsell_0.5.0
[76] glue_1.6.2 tools_4.3.1 data.table_1.14.8
[79] webshot_0.5.5 fs_1.6.3 colorspace_2.1-0
[82] nlme_3.1-163 htmlTable_2.4.2 Formula_1.2-5
[85] cli_3.6.1 fansi_1.0.5 viridisLite_0.4.2
[88] svglite_2.1.2 gtable_0.3.4 rstatix_0.7.2
[91] sass_0.4.7 digest_0.6.33 BiocGenerics_0.46.0
[94] farver_2.1.1 rjson_0.2.21 htmlwidgets_1.6.2
[97] htmltools_0.5.6.1 lifecycle_1.0.3 httr_1.4.7
[100] GlobalOptions_0.1.2