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LDH_analysis.Rmd
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LDH_analysis.Rmd
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---
title: "LDH_analysis"
author: "ERM"
date: "`r Sys.Date()`"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
## R Markdown
```{r setup}
knitr::opts_chunk$set(warning = FALSE, message = FALSE)
library(readxl)
library(ggpubr)
library(rstatix)
library(tidyverse)
library(zoo)
library(ggplot2)
library(ggsignif)
```
## Including Plots
```{r ldh input, echo=FALSE}
norm_LDH <- read.csv("data/norm_LDH.csv")
norm_LDH <- norm_LDH[,-1]##get rid of line numbers
norm_LDH$Drug <- factor(norm_LDH$Drug, levels = c( "Daunorubicin", "Doxorubicin", "Epirubicin", "Mitoxantrone", "Trastuzumab","Control"))
ggplot(norm_LDH, aes(x = as.factor(Conc), y = norm_val, group = Conc)) +
geom_boxplot(aes(fill = as.factor(Conc))) +
#geom_point(aes(color= indv))+
# stat_compare_means(method="t.test", ref.group=6, label="p",angle = 75,hide.ns = TRUE, label.y=c(4.5),label.x= c(1.5,2.5,3.5,4.5,5.5,6.5,7.5),aes(angle=90))+
scale_fill_manual(values = c("#F3BABD","#EAA2A5","#E28A8E",
"#DA7277","#CF5157","#C43138","#982124","#6B1210" ),
aesthetics = c("Conc", "fill")) +
guides(fill = "none") +
geom_hline(yintercept = 1,lty =3) +
theme_bw() +
xlab(expression(paste("Drug [", mu, "M]"
))) +
ylab("Relative LDH release") +
labs(fill = "Individual") +
facet_wrap("Drug",) +
ylim(0, 5)+
theme(strip.background = element_rect(fill = "transparent")) +
theme(
axis.title = element_text(size = 15, color = "black"),
axis.ticks = element_line(size = 1.5),
axis.text = element_text(size = 9, color = "black", angle = 20),
strip.text.x = element_text(size = 15, color = "black", face = "bold"))
```
```{r ldh 24h results, echo=FALSE, message=FALSE, warning=FALSE}
# ldh24means <- data.frame('',nrow=6)
DA_24_ldh <- matrix(c(1.188,1.222,1.195,1.030,1.074,1.064,1.298,1.282,1.262,
1.901,1.975,1.970,3.131,3.246,3.080,1.339,1.438,1.367),
ncol =3, nrow =6, byrow =TRUE,
dimnames=list(c('87_0.5','79_0.5','75_0.5','77_0.5','78_0.5','71_0.5')))
#ldh24means$Daunorubicin <- rollmean(t(DA_24_ldh),3)
DX_24_ldh <-matrix(c(0.981,0.974,0.978,1.253,1.233,1.292,2.098,2.153,
2.114,2.214,2.244,2.239,3.808,3.825,3.735,1.037,1.030,1.030),
ncol =3, nrow =6, byrow =TRUE,
dimnames=list(c('87_0.5','79_0.5','75_0.5','77_0.5','78_0.5','71_0.5')))
#ldh24means$Doxorubicin <- rollmean(t(DX_24_ldh),3)
EP_24_ldh <- matrix(c(1.504,1.320,1.469,1.536,1.301,1.531,1.562,1.541,1.558,
3.414,3.103,3.236,3.588,3.398,3.611,1.013,0.958,0.991),
ncol =3, nrow =6, byrow =TRUE,
dimnames=list(c('87_0.5','79_0.5','75_0.5','77_0.5','78_0.5','71_0.5')))
#ldh24means$Epirubicin <- rollmean(t(EP_24_ldh),3)
MT_24_ldh <- matrix(c(1.508,1.467,1.391,1.493,1.468,1.483,2.010,1.820,1.911,
3.089,2.936,2.921,3.623,3.377,3.560,1.222,1.211,1.215),
ncol =3, nrow =6, byrow =TRUE,
dimnames=list(c('87_0.5','79_0.5','75_0.5','77_0.5','78_0.5','71_0.5')))
#ldh24means$Mitoxantrone <- rollmean(t(MT_24_ldh),3)
TR_24_ldh<- matrix(c(0.941,0.891,0.953,0.743,0.774,0.812,1.514,1.225,1.252,
2.391,1.989,2.172,3.040,2.622,2.613,0.970,0.917,0.895),
ncol =3, nrow =6, byrow =TRUE,
dimnames=list(c('87_0.5','79_0.5','75_0.5','77_0.5','78_0.5','71_0.5')))
#ldh24means$Trastuzumab <- rollmean(t(TR_24_ldh),3)
VE_24_ldh<- matrix(c(1.000,1.000,0.977,1.000,1.100,1.096,1.000,0.938,0.951,
1.000,1.027,1.038,1.000,1.058,1.062,1.000,1.011,0.975),
ncol =3, nrow =6, byrow =TRUE,
dimnames=list(c('87_0.5','79_0.5','75_0.5','77_0.5','78_0.5','71_0.5')))
#ldh24means$Vehicle <- rollmean(t(VE_24_ldh),3)
LDH24hstat <- list('VDA'=t.test(VE_24_ldh,DA_24_ldh),
'VDX'=t.test(VE_24_ldh,DX_24_ldh),
'VEP'=t.test(VE_24_ldh,EP_24_ldh),
'VMT'=t.test(VE_24_ldh,MT_24_ldh),
'VTR'=t.test(VE_24_ldh,TR_24_ldh),
'VVEH'=t.test(VE_24_ldh,VE_24_ldh))
LDH24hstat
```
```{r TNNI using relative to vehicle information}
DA_24_TNNI <- matrix(c(0.790,0.783,1.855,1.693,1.009,1.071,0.736,0.771,
1.035,1.202,1.228,1.151),
ncol =2, nrow =6, byrow =TRUE,
dimnames=list(c('87_0.5','71_0.5','75_0.5','77_0.5','78_0.5','79_0.5')))
DX_24_TNNI <-matrix(c(1.006,1.006,1.295,1.179,1.464,1.493,1.319,1.236,
1.231,1.221,1.342,1.296),
ncol =2, nrow =6, byrow =TRUE,
dimnames=list(c('87_0.5','71_0.5','75_0.5','77_0.5','78_0.5','79_0.5')))
EP_24_TNNI <- matrix(c(0.955,0.822,1.220,1.092,1.459,1.425,1.076,1.222,
1.018,1.269,1.262,1.331),
ncol =2, nrow =6, byrow =TRUE,
dimnames=list(c('87_0.5','71_0.5','75_0.5','77_0.5','78_0.5','79_0.5')))
MT_24_TNNI <- matrix(c(1.529,1.682,1.205,1.138,1.436,1.521,1.694,
1.778,1.115,1.231,1.006,0.957),
ncol =2, nrow =6, byrow =TRUE,
dimnames=list(c('87_0.5','71_0.5','75_0.5','77_0.5','78_0.5','79_0.5')))
TR_24_TNNI<- matrix(c(2.089,1.911,1.245,0.968,1.180,1.168,1.118,
1.014,1.496,1.433,1.388,1.235),
ncol =2, nrow =6, byrow =TRUE,
dimnames=list(c('87_0.5','71_0.5','75_0.5','77_0.5','78_0.5','79_0.5')))
VE_24_TNNI<- matrix(c(1.000,0.783,1.000,1.000,0.917,1.031,1.000,
0.958,1.000,1.000,1.087,1.106),
ncol =3, nrow =6, byrow =TRUE,
dimnames=list(c('87_0.5','71_0.5','75_0.5','77_0.5','78_0.5','79_0.5')))
tnni24hstat <- list('VDAT'=t.test(VE_24_TNNI,DA_24_TNNI),
'VDXT'=t.test(VE_24_TNNI,DX_24_TNNI),
'VEPT'=t.test(VE_24_TNNI,EP_24_TNNI),
'VMTT'=t.test(VE_24_TNNI,MT_24_TNNI),
'VTRT'=t.test(VE_24_TNNI,TR_24_TNNI),
'VVEHT'=t.test(VE_24_TNNI,VE_24_TNNI))
tnni24hstat
library(Biobase)
apple.tnniest <- t(as.data.frame(subListExtract(tnni24hstat,'estimate')))##this pulls out a specific thing and puts into datafram as a vector for later manipulation (think pvalue or mean)
```
```{r combining ldh and tnni data at 24 hours}
mean24ldh <- as.data.frame(rbind(colMeans(t(DA_24_ldh)),
colMeans(t(DX_24_ldh)),
colMeans(t(EP_24_ldh)),
colMeans(t(MT_24_ldh)),
colMeans(t(TR_24_ldh)),
colMeans(t(VE_24_ldh))))
mean24ldh$Drug <- c( "Daunorubicin", "Doxorubicin", "Epirubicin", "Mitoxantrone", "Trastuzumab","Control") ###add drug name then take out the 0.5 thing
colnames(mean24ldh) <- gsub("_0.5","",colnames(mean24ldh))
##now use pivot longer and join the frames
mean24ldh <- mean24ldh %>% pivot_longer(.,col=-Drug, names_to = 'indv', values_to = "ldh")
mean24tnni <- as.data.frame(rbind(colMeans(t(DA_24_TNNI)),
colMeans(t(DX_24_TNNI)),
colMeans(t(EP_24_TNNI)),
colMeans(t(MT_24_TNNI)),
colMeans(t(TR_24_TNNI)),
colMeans(t(VE_24_TNNI))))
mean24tnni$Drug <- c( "Daunorubicin", "Doxorubicin", "Epirubicin", "Mitoxantrone", "Trastuzumab","Control")
colnames(mean24tnni) <- gsub("_0.5","",colnames(mean24tnni))
mean24tnni <- pivot_longer(mean24tnni,
col=-Drug,
names_to = 'indv',
values_to = "tnni")
ggplot(mean24ldh, aes(x=Drug,y=ldh))+
geom_boxplot() +
geom_point(aes(col=indv,shape=indv, size =3))+
geom_signif(comparisons =list(c("Control","Daunorubicin"),
c("Control","Doxorubicin"),
c("Control","Epirubicin"),
c("Control","Mitoxantrone"),
c("Control","Trastuzumab")),
map_signif_level=FALSE,
textsize =6,
tip_length = .1,
vjust = 0.2, step_increase = 0.1)+
theme_bw()+
ggtitle("Relative LDH release in media")+
theme(plot.title = element_text(size = rel(1.5), hjust = 0.5),
axis.title = element_text(size = rel(0.8)))
ggplot(mean24tnni, aes(x=Drug,y=tnni))+
geom_boxplot() +
geom_point(aes(col=indv, shape=indv, size =3))+
geom_signif(comparisons =list(c("Control","Daunorubicin"),
c("Control","Doxorubicin"),
c("Control","Epirubicin"),
c("Control","Mitoxantrone"),
c("Control","Trastuzumab")),
map_signif_level=FALSE,
textsize =6,
tip_length = .1,
vjust = 0.2, step_increase = 0.1)+
theme_bw()+
ggtitle("Relative Troponin I release in media")+
theme(plot.title = element_text(size = rel(1.5), hjust = 0.5),
axis.title = element_text(size = rel(0.8)))
cor(mean24ldh$ldh, mean24tnni$tnni)
tvl24hour <- inner_join(mean24ldh,mean24tnni, by=c("Drug","indv"))
ggplot(tvl24hour, aes(x=ldh,y=tnni))+
geom_point(aes(col=indv))+ geom_smooth(method="lm")+
facet_wrap("Drug", scales= "free")+theme_bw()+
stat_cor(aes(label = after_stat(rr.label)), color = "red", geom = "label")+
ggtitle("ldh verses cTNNI 24 hour by Drug")
#_____________________________________________________________________________________
#
# model <- lm(ldh~tnni, data = tvl24hour)
#
# res <- resid(model)
# plot(fitted(model), res)
#
#
#
# qqnorm(res)
# qqline(res)
# plot(density(res))
#
# summary(model)
#
#
# model <- lm(tnni~ldh+indv+Drug, data = tvl24hour)
#
# res <- resid(model)
# plot(fitted(model), res)
#
#
#
# qqnorm(res)
# qqline(res)
# plot(density(res))
#
# hist(residuals(model), col = "steelblue")
```
```{r checking numbers}
ggplot(mean24ldh, aes(x=indv, y=ldh))+geom_point()+ facet_wrap("Drug")
ggplot(mean24tnni, aes(x=indv, y=tnni))+geom_point()+ facet_wrap("Drug")
```