Lactate dehydrogenase, troponin I, and calcium analysis
ERM
2023-09-26
Last updated: 2023-09-26
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Knit directory: Cardiotoxicity/
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##package loading
library(readxl)
library(ggpubr)
library(rstatix)
library(tidyverse)
library(zoo)
library(ggsignif)
library(RColorBrewer)
library(stats)
library(readr)
library(ggalt)48 hour Lactate Dehydrogenase analysis
level_order2 <- c('75','87','77','79','78','71')
drug_pal_fact <- c("#8B006D","#DF707E","#F1B72B", "#3386DD","#707031","#41B333")
RINsamplelist <-read_csv("data/RINsamplelist.txt",col_names = TRUE)
norm_LDH <- read.csv("data/norm_LDH.csv",row.names = 1)
clamp_summary <- read.csv("data/Clamp_Summary.csv", row.names=1)
full_list <- read.csv("data/DRC48hoursdata.csv", row.names = 1)
calcium_data <- read_csv("data/DF_Plate_Peak.csv", col_types = cols(...1 = col_skip()))
k_means <- read.csv("data/K_cluster_kisthree.csv")
# drug_palexpand <- c("#41B333","#8B006D","#DF707E","#F1B72B", "#3386DD","#707031","purple3","darkgreen", "darkblue")
#named colors: dark pink,Red,yellow,blue, dark grey, green(green is always control, may need to move pal around)
48 hour viability to LDH activity correlation
Pearson correlation of 48 hour 0.5 \(\mu\)M viability with LDH 48 hours 0.5 \(\mu\)M (supplemental data S4 Fig)
viability %>%
full_join(., norm_LDH48, by = c("indv","Drug","Conc")) %>%
ggplot(., aes(x=per.live, y=ldh))+
geom_point(aes(col=indv))+
geom_smooth(method="lm")+
facet_wrap("Drug")+
theme_bw()+
xlab("Average viability of cardiomyocytes/100") +
ylab("Average LDH") +
ggtitle("Relative viability and relative LDH release at 48 hours")+
scale_color_brewer(palette = "Dark2",
name = "Individual",
label = c("1","2","3","4","5","6"))+
ggpubr::stat_cor(method="pearson",
aes(label = paste(..r.label.., ..p.label.., sep = "~`,`~")),
color = "red")+
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 = 15,
color = "black",
face = "bold")) 
#24 hour LDH analysis
Data input
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')))
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')))
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')))
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')))
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')))
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')))
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$VDA
Welch Two Sample t-test
data: VE_24_ldh and DA_24_ldh
t = -3.7541, df = 17.12, p-value = 0.001564
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-1.0263033 -0.2880301
sample estimates:
mean of x mean of y
1.012944 1.670111
$VDX
Welch Two Sample t-test
data: VE_24_ldh and DX_24_ldh
t = -3.7427, df = 17.065, p-value = 0.001611
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-1.3902576 -0.3880757
sample estimates:
mean of x mean of y
1.012944 1.902111
$VEP
Welch Two Sample t-test
data: VE_24_ldh and EP_24_ldh
t = -4.285, df = 17.065, p-value = 0.0004969
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-1.5254695 -0.5190861
sample estimates:
mean of x mean of y
1.012944 2.035222
$VMT
Welch Two Sample t-test
data: VE_24_ldh and MT_24_ldh
t = -5.1821, df = 17.085, p-value = 7.383e-05
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-1.5220379 -0.6415176
sample estimates:
mean of x mean of y
1.012944 2.094722
$VTR
Welch Two Sample t-test
data: VE_24_ldh and TR_24_ldh
t = -2.5982, df = 17.112, p-value = 0.01868
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.85356981 -0.08876353
sample estimates:
mean of x mean of y
1.012944 1.484111
$VVEH
Welch Two Sample t-test
data: VE_24_ldh and VE_24_ldh
t = 0, df = 34, p-value = 1
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.02989169 0.02989169
sample estimates:
mean of x mean of y
1.012944 1.012944 #24 hour Troponin I analysis
DA_24_TNNI <- matrix(c(0.790,0.783,1.855,1.693,1.009,1.071,0.736,0.771,
1.035,1.202,1.228,1.151),
ncol =2, nrow =6, byrow =TRUE,
dimnames=list(c('87_0.5','71_0.5','75_0.5','77_0.5','78_0.5','79_0.5')))
DX_24_TNNI <-matrix(c(1.006,1.006,1.295,1.179,1.464,1.493,1.319,1.236,
1.231,1.221,1.342,1.296),
ncol =2, nrow =6, byrow =TRUE,
dimnames=list(c('87_0.5','71_0.5','75_0.5','77_0.5','78_0.5','79_0.5')))
EP_24_TNNI <- matrix(c(0.955,0.822,1.220,1.092,1.459,1.425,1.076,1.222,
1.018,1.269,1.262,1.331),
ncol =2, nrow =6, byrow =TRUE,
dimnames=list(c('87_0.5','71_0.5','75_0.5','77_0.5','78_0.5','79_0.5')))
MT_24_TNNI <- matrix(c(1.529,1.682,1.205,1.138,1.436,1.521,1.694,
1.778,1.115,1.231,1.006,0.957),
ncol =2, nrow =6, byrow =TRUE,
dimnames=list(c('87_0.5','71_0.5','75_0.5','77_0.5','78_0.5','79_0.5')))
TR_24_TNNI<- matrix(c(2.089,1.911,1.245,0.968,1.180,1.168,1.118,
1.014,1.496,1.433,1.388,1.235),
ncol =2, nrow =6, byrow =TRUE,
dimnames=list(c('87_0.5','71_0.5','75_0.5','77_0.5','78_0.5','79_0.5')))
VE_24_TNNI<- matrix(c(1.000,0.783,1.000,1.000,0.917,1.031,1.000,
0.958,1.000,1.000,1.087,1.106),
ncol =3, nrow =6, byrow =TRUE,
dimnames=list(c('87_0.5','71_0.5','75_0.5','77_0.5','78_0.5','79_0.5')))
tnni24hstat <- list('VDAT'=t.test(VE_24_TNNI,DA_24_TNNI),
'VDXT'=t.test(VE_24_TNNI,DX_24_TNNI),
'VEPT'=t.test(VE_24_TNNI,EP_24_TNNI),
'VMTT'=t.test(VE_24_TNNI,MT_24_TNNI),
'VTRT'=t.test(VE_24_TNNI,TR_24_TNNI),
'VVEHT'=t.test(VE_24_TNNI,VE_24_TNNI))
tnni24hstat$VDAT
Welch Two Sample t-test
data: VE_24_TNNI and DA_24_TNNI
t = -1.2565, df = 11.832, p-value = 0.2332
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.36079840 0.09713173
sample estimates:
mean of x mean of y
0.978500 1.110333
$VDXT
Welch Two Sample t-test
data: VE_24_TNNI and DX_24_TNNI
t = -5.8512, df = 15.728, p-value = 2.633e-05
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.3799972 -0.1776694
sample estimates:
mean of x mean of y
0.978500 1.257333
$VEPT
Welch Two Sample t-test
data: VE_24_TNNI and EP_24_TNNI
t = -3.4195, df = 13.915, p-value = 0.004181
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.32673646 -0.07476354
sample estimates:
mean of x mean of y
0.97850 1.17925
$VMTT
Welch Two Sample t-test
data: VE_24_TNNI and MT_24_TNNI
t = -4.4919, df = 12.316, p-value = 0.0006915
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.5625613 -0.1957720
sample estimates:
mean of x mean of y
0.978500 1.357667
$VTRT
Welch Two Sample t-test
data: VE_24_TNNI and TR_24_TNNI
t = -3.7217, df = 11.904, p-value = 0.002956
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.5951287 -0.1553713
sample estimates:
mean of x mean of y
0.97850 1.35375
$VVEHT
Welch Two Sample t-test
data: VE_24_TNNI and VE_24_TNNI
t = 0, df = 34, p-value = 1
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.0574163 0.0574163
sample estimates:
mean of x mean of y
0.9785 0.9785 24 hour TNNI and LDH
mean24ldh <- as.data.frame(rbind(colMeans(t(DA_24_ldh)),
colMeans(t(DX_24_ldh)),
colMeans(t(EP_24_ldh)),
colMeans(t(MT_24_ldh)),
colMeans(t(TR_24_ldh)),
colMeans(t(VE_24_ldh))))
mean24ldh$Drug <- c( "DNR", "DOX", "EPI", "MTX", "TRZ","VEH") ###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( "DNR", "DOX", "EPI", "MTX", "TRZ","VEH")
colnames(mean24tnni) <- gsub("_0.5","",colnames(mean24tnni))
mean24tnni <- pivot_longer(mean24tnni,
col=-Drug,
names_to = 'indv',
values_to = "tnni")
tvl24hour <- full_join(mean24ldh,mean24tnni, by=c("Drug","indv"))
# write.csv(tvl24hour,"output/tvl24hour.txt")Normalization of TNNI and LDH to RNA concentration
RNAnormlist <- RINsamplelist %>%
mutate(Drug=case_match(Drug,"daunorubicin"~"DNR",
"doxorubicin"~"DOX",
"epirubicin"~"EPI",
"mitoxantrone"~"MTX",
"trastuzumab"~"TRZ",
"vehicle"~"VEH", .default= Drug)) %>%
filter(time =="24h") %>%
ungroup() %>%
dplyr::select(indv,Drug,Conc_ng.ul) %>%
mutate(indv= factor(indv,levels= level_order2))
RNAnormlist <- RNAnormlist %>%
full_join(.,tvl24hour,by= c("Drug", "indv")) %>%
mutate(Drug = factor(Drug, levels = c( "DOX",
"DNR",
"EPI",
"MTX",
"TRZ",
"VEH"))) %>%
mutate(rldh= ldh/Conc_ng.ul) %>%
mutate(rtnni=tnni/Conc_ng.ul)
# write.csv(RNAnormlist,"output/TNNI_LDH_RNAnormlist.txt")
ggplot(RNAnormlist, aes(x=rldh, y=rtnni))+
geom_point(aes(col=indv))+
geom_smooth(method="lm")+
ggpubr::stat_cor(label.y.npc=1,
label.x.npc = 0,
method="pearson",
aes(label = paste(..r.label.., ..p.label..,
sep = "~`,`~")), color = "darkred")+
facet_wrap("Drug", scales="free")+
scale_color_brewer(palette = "Dark2")+
scale_color_brewer(palette = "Dark2",
name = "Individual",
label = c("1","2","3","4","5","6"))+
ylab("Troponin I release at 24 hours")+
xlab("Lactate DH release at 24 hours")+
theme_classic()+
theme(strip.background = element_rect(fill = "transparent")) +
theme(plot.title = element_text(size = rel(1.5), hjust = 0.5),
legend.position = "none",
axis.title = element_text(size = 15, color = "black"),
axis.ticks = element_line(linewidth = 1.5),
axis.line = element_line(linewidth = 1.5),
axis.text = element_text(size = 12, color = "black", angle = 0),
strip.text.x = element_text(size = 15, color = "black", face = "bold"))+
ggtitle("Correlation between Troponin I release and LDH activity")
RNAnormlist %>%
mutate(Drug = factor(Drug, levels = c( "DOX",
"EPI",
"DNR",
"MTX",
"TRZ",
"VEH"))) %>%
ggplot(., aes(x=Drug, y=rldh))+
geom_boxplot(position = "identity", fill = drug_pal_fact)+
geom_point(aes(col=indv, size =3,alpha=0.5))+
geom_signif(comparisons =list(c("VEH","DOX"),
c("VEH","EPI"),
c("VEH","DNR"),
c("VEH","MTX"),
c("VEH","TRZ")),
test="t.test",
map_signif_level=TRUE,
textsize =4,
step_increase = 0.1)+
theme_classic()+
guides(size = "none",alpha="none")+
scale_color_brewer(palette = "Dark2", name = "Individual")+
xlab("")+
ylab("Relative LDH activity ")+
ggtitle("Lactate dehydrogenase release at 24 hours")+
theme_classic()+
theme(strip.background = element_rect(fill = "transparent")) +
theme(plot.title = element_text(size = rel(1.5), hjust = 0.5),
legend.position = "none",
axis.title = element_text(size = 15, color = "black"),
axis.ticks = element_line(linewidth = 1.5),
axis.line = element_line(linewidth = 1.5),
axis.text = element_text(size = 12, color = "black", angle = 0),
strip.text.x = element_text(size = 15, color = "black", face = "bold"))
RNAnormlist %>%
mutate(Drug = factor(Drug, levels = c( "DOX",
"EPI",
"DNR",
"MTX",
"TRZ",
"VEH"))) %>%
ggplot(., aes(x=Drug, y=rtnni))+
geom_boxplot(position = "identity", fill = drug_pal_fact)+
geom_point(aes(col=indv, size =3,alpha=0.5))+
geom_signif(comparisons =list(c("VEH","DOX"),
c("VEH","EPI"),
c("VEH","DNR"),
c("VEH","MTX"),
c("VEH","TRZ")),
test="t.test",
map_signif_level=TRUE,
textsize =4,
step_increase = 0.1)+
theme_classic()+
guides(size = "none",alpha="none")+
scale_color_brewer(palette = "Dark2", name = "Individual")+
xlab("")+
ylab("Relative Troponin I levels ")+
ggtitle("Troponin I release at 24 hours")+
theme_classic()+
theme(strip.background = element_rect(fill = "transparent")) +
theme(plot.title = element_text(size = rel(1.5), hjust = 0.5),
legend.position = "none",
axis.title = element_text(size = 15, color = "black"),
axis.ticks = element_line(linewidth = 1.5),
axis.line = element_line(linewidth = 1.5),
axis.text = element_text(size = 12, color = "black", angle = 0),
strip.text.x = element_text(size = 15, color = "black", face = "bold"))
Calcium data at 24 hours
The data and code were given from Omar Johnson (except for the renaming of everything, that is me!) THANK YOU SIMON, FOR YOUR INPUT.
calcium_data <- calcium_data %>%
rename('Treatment' = 'Condition', 'indv' = 'Experiment') %>%
mutate(
Drug = case_match(
Treatment,
"Dau_0.5" ~ "DNR",
"Dau_1" ~ "DNR",
"Dox_0.5" ~ "DOX",
"Dox_1" ~ "DOX",
"Epi_0.5" ~ "EPI",
"Epi_1" ~ "EPI",
"Mito_0.5" ~ "MTX",
"Mito_1" ~ "MTX",
"Tras_0.5" ~ "TRZ",
"Tras_1" ~ "TRZ",
"Control" ~ "VEH",
.default = Treatment
)
) %>%
mutate(Drug = factor(Drug,
levels = c("DOX",
"EPI",
"DNR",
"MTX",
"TRZ",
"VEH"))) %>%
mutate(
Conc = case_match(
Treatment,
"Dau_0.5" ~ "0.5",
"Dau_1" ~ "1.0",
"Dox_0.5" ~ "0.5",
"Dox_1" ~ "1.0",
"Epi_0.5" ~ "0.5",
"Epi_1" ~ "1.0",
"Mito_0.5" ~ "0.5",
"Mito_1" ~ "1.0",
"Tras_0.5" ~ "0.5",
"Tras_1" ~ "1.0",
'Control' ~ '0',
.default = Treatment
)
)
clamp_summary <- clamp_summary %>%
rename('Treatment' = 'Cond', 'indv' = 'Exp') %>%
mutate(
Drug = case_match(
Treatment,
"Dau_0.5" ~ "DNR",
"Dau_1" ~ "DNR",
"Dox_0.5" ~ "DOX",
"Dox_1" ~ "DOX",
"Epi_0.5" ~ "EPI",
"Epi_1" ~ "EPI",
"Mito_0.5" ~ "MTX",
"Mito_1" ~ "MTX",
"Tras_0.5" ~ "TRZ",
"Tras_1" ~ "TRZ",
"Control" ~ "VEH" ,
.default = Treatment
)
) %>%
mutate(Drug = factor(Drug,
levels = c("DOX",
"EPI",
"DNR",
"MTX",
"TRZ",
"VEH"))) %>%
mutate(
Conc = case_match(
Treatment,
"Dau_0.5" ~ "0.5",
"Dau_1" ~ "1.0",
"Dox_0.5" ~ "0.5",
"Dox_1" ~ "1.0",
"Epi_0.5" ~ "0.5",
"Epi_1" ~ "1.0",
"Mito_0.5" ~ "0.5",
"Mito_1" ~ "1.0",
"Tras_0.5" ~ "0.5",
"Tras_1" ~ "1.0",
'Control' ~ '0',
.default = Treatment
)
) %>%
rename(
c(
'Mean_Amplitude' = 'R1S1_Mean..a.u..',
'Rise_Slope' = 'R1S1_Rise_Slope..a.u..ms.',
'FWHM' = 'R1S1_Half_Width..ms.',
'Decay_Slope' = 'R1S1_Decay_Slope..a.u..ms.',
'Decay_Time' = 'Decay_Time..ms.',
'Rise_Time' = 'R1S1_Rise_Time'
)
) %>%
mutate(indv = substr(indv, 1, 2)) %>%
mutate(indv = factor(indv, levels = level_order2)) %>%
filter(Conc == 0 | Conc == 0.5)
saveRDS(calcium_data,"data/calcium_data.RDS")
saveRDS(clamp_summary ,"data/clamp_summary.RDS")Mean amplitude
MA_plot <- clamp_summary %>%
dplyr::select(Drug, Conc, indv, Mean_Amplitude) %>%
ggplot(., aes(Drug, Mean_Amplitude)) +
geom_boxplot(position = "identity", fill = drug_pal_fact) +
geom_point(aes(col = indv, size = 2, alpha = 0.5)) +
guides(size = "none",
alpha = "none",
colour = "none") +
scale_color_brewer(palette = "Dark2",
name = "Individual",
label = c("2", "3", "5")) +
geom_signif(
comparisons = list(
c("VEH", "TRZ"),
c("VEH", "MTX"),
c("VEH", "DNR"),
c("VEH", "EPI"),
c("VEH", "DOX")
),
test = "t.test",
map_signif_level = TRUE,
step_increase = 0.1,
textsize = 4
) +
ggtitle("Mean amplitude") +
ylab("a.u.") +
xlab(" ") +
theme_classic() +
theme(
plot.title = element_text(size = 18, 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),
axis.text = element_text(
size = 12,
color = "black",
angle = 0
),
strip.text.x = element_text(
size = 15,
color = "black",
face = "bold"
)
)
MA_plot
Rising Slope
RS_plot <- clamp_summary %>%
dplyr::select(Drug, Conc, indv, Rise_Slope) %>%
ggplot(., aes(Drug, Rise_Slope)) +
geom_boxplot(position = "identity", fill = drug_pal_fact) +
geom_point(aes(col = indv, size = 2, alpha = 0.5)) +
guides(size = "none",
alpha = "none",
colour = "none") +
scale_color_brewer(palette = "Dark2",
name = "Individual",
label = c("2", "3", "5")) +
geom_signif(
comparisons = list(
c("VEH", "TRZ"),
c("VEH", "MTX"),
c("VEH", "DNR"),
c("VEH", "EPI"),
c("VEH", "DOX")
),
test = "t.test",
map_signif_level = TRUE,
step_increase = 0.1,
textsize = 4
) +
ggtitle(expression(paste("Rising slope"))) +
labs(y = "a.u./sec") +
theme_classic() +
theme(
plot.title = element_text(size = 18, 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),
axis.text = element_text(
size = 12,
color = "black",
angle = 0
),
strip.text.x = element_text(
size = 15,
color = "black",
face = "bold"
)
)
RS_plot
Decay slope
Decay_plot <- clamp_summary %>%
dplyr::select(Drug, Conc, indv, Decay_Slope) %>%
ggplot(., aes(Drug, Decay_Slope)) +
geom_boxplot(position = "identity", fill = drug_pal_fact) +
geom_point(aes(col = indv, size = 2, alpha = 0.5)) +
guides(size = "none",
alpha = "none",
colour = "none") +
scale_color_brewer(palette = "Dark2",
name = "Individual",
label = c("2", "3", "5")) +
geom_signif(
comparisons = list(
c("VEH", "TRZ"),
c("VEH", "MTX"),
c("VEH", "DNR"),
c("VEH", "EPI"),
c("VEH", "DOX")
),
test = "t.test",
map_signif_level = TRUE,
step_increase = 0.1,
textsize = 4
) +
ggtitle(expression(paste("Decay slope "))) +
labs(y = "a.u./sec") +
theme_classic() +
theme(
plot.title = element_text(size = 18, 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),
axis.text = element_text(
size = 12,
color = "black",
angle = 0
),
strip.text.x = element_text(
size = 15,
color = "black",
face = "bold"
)
)
Decay_plot
Full-Width-half max
FWHM_plot <- clamp_summary %>%
dplyr::select(Drug, Conc, indv, FWHM) %>%
ggplot(., aes(Drug, FWHM)) +
geom_boxplot(position = "identity", fill = drug_pal_fact) +
geom_point(aes(col = indv, size = 2, alpha = 0.5)) +
guides(size = "none",
alpha = "none",
colour = "none") +
scale_color_brewer(palette = "Dark2",
name = "Individual",
label = c("2", "3", "5")) +
geom_signif(
comparisons = list(
c("VEH", "TRZ"),
c("VEH", "MTX"),
c("VEH", "DNR"),
c("VEH", "EPI"),
c("VEH", "DOX")
),
test = "t.test",
map_signif_level = TRUE,
step_increase = 0.1,
textsize = 4
) +
ylab("sec") +
xlab(" ") +
theme_classic() +
ggtitle("Full-width at half-max") +
theme(
plot.title = element_text(size = 14, hjust = 0.5),
axis.title = element_text(size = 10, color = "black"),
axis.ticks = element_line(linewidth = 1),
axis.line = element_line(linewidth = 1),
axis.text = element_text(
size = 10,
color = "black",
angle = 0
),
strip.text.x = element_text(
size = 12,
color = "black",
face = "bold"
)
)
FWHM_plot
k means plot
k_means %>% mutate(
Drug = case_match(
Drug_Name,
"Dau_0.5" ~ "DNR",
"Dau_0.5.1" ~ "DNR",
"Dau_0.5.2" ~ "DNR",
"Dox_0.5" ~ "DOX",
"Dox_0.5.1" ~ "DOX",
"Dox_0.5.2" ~ "DOX",
"Epi_0.5" ~ "EPI",
"Epi_0.5.1" ~ "EPI",
"Epi_0.5.2" ~ "EPI",
"Mito_0.5" ~ "MTX",
"Mito_0.5.1" ~ "MTX",
"Mito_0.5.2" ~ "MTX",
"Tras_0.5" ~ "TRZ",
"Tras_0.5.1" ~ "TRZ",
"Tras_0.5.2" ~ "TRZ",
"Control.1" ~ "VEH",
"Control.2" ~ "VEH",
"Control" ~ "VEH",
.default = Drug_Name
)
) %>%
mutate(
Class = case_match(
Drug,
"DOX" ~ "TOP2i",
"DNR" ~ "TOP2i",
"EPI" ~ "TOP2i",
"MTX" ~ "TOP2i",
"TRZ" ~ "not-TOP2i",
"VEH" ~ "not-TOP2i",
.default = Drug
)
) %>%
mutate(Drug = factor(Drug, levels = c("DOX",
"EPI",
"DNR",
"MTX",
"TRZ",
"VEH"))) %>%
ggplot(., aes(
x = PC1,
y = PC2,
col = Drug,
shape = factor(Class)
)) +
geom_point(size = 8) +
scale_shape_manual(values = c(19, 17, 15)) +
scale_color_manual(values = drug_pal_fact) +
# geom_encircle(aes(group=Cluster))+
# annotate("text", label = c("Cluster 1","Cluster 2", "Cluster 3"), x = c(-2,0,1.5),y=c(-0.5,0,0.5))+
ggtitle(expression("PCA of Ca" ^ "2+" ~ "data")) +
theme_bw() +
labs(x = "PC 1 (54 %)", y = "PC 2 (34%)") +
theme(
plot.title = element_text(size = 14, hjust = 0.5),
axis.title = element_text(size = 10, color = "black"),
axis.ticks = element_line(size = 1.5),
axis.text = element_text(
size = 12,
color = "black",
angle = 0
),
strip.text.x = element_text(
size = 15,
color = "black",
face = "bold"
)
)
Beat Rate
BR_plot <- calcium_data %>%
dplyr::select(Drug, Conc, indv, Rate) %>% #Peak_variance,Ave_FF0,
mutate(indv = substr(indv, 1, 2)) %>%
mutate(indv = factor(indv, levels = level_order2)) %>%
mutate(contrl = 0.383) %>%
mutate(norm_rate = Rate / contrl) %>%
filter(Conc == 0 | Conc == 0.5) %>%
ggplot(., aes(x = Drug, y = Rate)) +
geom_boxplot(position = "identity", fill = drug_pal_fact) +
geom_point(aes(col = indv, size = 2, alpha = 0.5)) +
guides(alpha = "none") +
geom_signif(
comparisons = list(
c("VEH", "TRZ"),
c("VEH", "MTX"),
c("VEH", "DNR"),
c("VEH", "EPI"),
c("VEH", "DOX")
),
test = "t.test",
map_signif_level = TRUE,
step_increase = 0.1,
textsize = 4
) +
guides(alpha = "none", size = "none") +
scale_color_brewer(palette = "Dark2",
name = "Individual",
label = c("2", "3", "5")) +
ggtitle("Contraction rate") +
theme_classic() +
guides(size = "none",
colour = guide_legend(override.aes = list(size = 4, alpha = 0.5))) +
labs(y = "avg. beats/sec") +
theme(
plot.title = element_text(size = 18, hjust = 0.5),
axis.title = element_text(size = 14, color = "black"),
axis.ticks = element_line(linewidth = 1.5),
axis.line = element_line(linewidth = 1.5),
axis.text = element_text(
size = 10,
color = "black",
angle = 0
),
strip.text.x = element_text(
size = 12,
color = "black",
face = "bold"
)
)
BR_plot
Line Plots of Calcium data
myfiles <-
list.files(path = "data/CALIMA_Data/78-1/",
pattern = "*.csv",
full.names = TRUE)
myfiles <- myfiles %>% as.tibble() %>%
mutate(filenames = value) %>%
separate(filenames, c(NA, NA, NA, "file"), sep = "/") %>%
separate(file, c("Drug", "indv"))
Normalization_And_Set_File <- function(file_path) {
# Read in the data from the file
CALIMA_obj <- read.csv(file_path)
# Normalize the data
ROI_cut <- CALIMA_obj[, 2:ncol(CALIMA_obj)]
ROI_cut_rowmeans <- rowMeans(ROI_cut)
Intensity <- (ROI_cut_rowmeans / min(ROI_cut_rowmeans))
Final_ROI <-
tibble::as_tibble(cbind(CALIMA_obj[, 1], Intensity, ROI_cut))
Final_ROI$Intensity <- Final_ROI$Intensity - 1
return(Final_ROI)
}
Plot_Line_df <- function(directory) {
holder <- list()
# List CSV files in the folder that is output from CALIMA
file_list <-
list.files(directory, pattern = "*.csv", full.names = TRUE)
file_list <- file_list %>% as.tibble() %>%
mutate(filenames = value) %>%
separate(filenames, c(NA, NA, NA, "file"), sep = "/") %>%
separate(file, c("Drug", "indv"))
# Loop over all files in directory
for (i in 1:length(file_list$value)) {
normalized_data <-
data.frame("indv" = file_list$indv[i], "drug" = file_list$Drug[i])
# Normalize the data from the file
norm_out <- Normalization_And_Set_File(file_list$value[i])
holder[[file_list$Drug[i]]] <-
cbind(normalized_data, norm_out[, 1:2])
# Return the plot
}
return(holder)
}plot_87 <- Plot_Line_df("data/CALIMA_Data/87-1/")
df_87forplot <- plot_87 %>%
bind_rows() %>%
rename("Xaxis" = `CALIMA_obj[, 1]`) %>%
mutate(drug = factor(drug, levels = c("DOX",
"EPI",
"DNR",
"MTX",
"TRZ",
"VEH")))
ggplot(df_87forplot, aes(x = Xaxis, y = Intensity, group = drug)) +
geom_line(size = 1.5, aes(color = drug)) +
xlab("") +
theme_bw() +
ggtitle("Individual 2") +
scale_x_continuous(expand = c(0, 0)) +
scale_color_manual(values = drug_pal_fact) +
theme(
plot.title = element_text(size = 18, hjust = 0.5),
axis.title = element_text(size = 14, color = "black"),
axis.ticks = element_line(linewidth = 1.5),
axis.line = element_line(linewidth = 1.5),
axis.text = element_text(
size = 10,
color = "black",
angle = 0
),
strip.text.x = element_text(
size = 12,
color = "black",
face = "bold"
)
)
| Version | Author | Date |
|---|---|---|
| 90253fc | reneeisnowhere | 2023-07-07 |
plot_78 <- Plot_Line_df("data/CALIMA_Data/78-1/")
df_78forplot <- plot_78 %>%
bind_rows() %>%
rename("Xaxis" = `CALIMA_obj[, 1]`) %>%
mutate(drug = factor(drug, levels = c("DOX",
"EPI",
"DNR",
"MTX",
"TRZ",
"VEH")))
ggplot(df_78forplot, aes(x = Xaxis, y = Intensity, group = as.factor(drug))) +
geom_line(size = 1.5, aes(color = drug)) +
xlab("") +
theme_bw() +
ggtitle("Individual 5") +
scale_x_continuous(expand = c(0, 0)) +
scale_color_manual(values = drug_pal_fact) +
theme(
plot.title = element_text(size = 18, hjust = 0.5),
axis.title = element_text(size = 14, color = "black"),
axis.ticks = element_line(linewidth = 1.5),
axis.line = element_line(linewidth = 1.5),
axis.text = element_text(
size = 10,
color = "black",
angle = 0
),
strip.text.x = element_text(
size = 12,
color = "black",
face = "bold"
)
)
| Version | Author | Date |
|---|---|---|
| 90253fc | reneeisnowhere | 2023-07-07 |
plot_77 <- Plot_Line_df("data/CALIMA_Data/77-1/")
df_77forplot <- plot_77 %>%
bind_rows() %>%
rename("Xaxis" = `CALIMA_obj[, 1]`) %>%
mutate(drug = factor(drug, levels = c("DOX",
"EPI",
"DNR",
"MTX",
"TRZ",
"VEH")))
ggplot(df_77forplot, aes(x = Xaxis, y = Intensity, group = as.factor(drug))) +
geom_line(size = 1.5, aes(color = drug)) +
xlab("") +
theme_bw() +
ggtitle("Individual 3") +
scale_x_continuous(expand = c(0, 0)) +
scale_color_manual(values = drug_pal_fact) +
theme(
plot.title = element_text(size = 18, hjust = 0.5),
axis.title = element_text(size = 14, color = "black"),
axis.ticks = element_line(linewidth = 1.5),
axis.line = element_line(linewidth = 1.5),
axis.text = element_text(
size = 10,
color = "black",
angle = 0
),
strip.text.x = element_text(
size = 12,
color = "black",
face = "bold"
)
)
| Version | Author | Date |
|---|---|---|
| 90253fc | reneeisnowhere | 2023-07-07 |
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] stats graphics grDevices utils datasets methods base
other attached packages:
[1] ggalt_0.4.0 RColorBrewer_1.1-3 ggsignif_0.6.4 zoo_1.8-12
[5] lubridate_1.9.2 forcats_1.0.0 stringr_1.5.0 dplyr_1.1.3
[9] purrr_1.0.2 readr_2.1.4 tidyr_1.3.0 tibble_3.2.1
[13] tidyverse_2.0.0 rstatix_0.7.2 ggpubr_0.6.0 ggplot2_3.4.3
[17] readxl_1.4.3 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] tidyselect_1.2.0 farver_2.1.1 fastmap_1.1.1 ash_1.0-15
[5] promises_1.2.1 digest_0.6.33 timechange_0.2.0 lifecycle_1.0.3
[9] processx_3.8.2 magrittr_2.0.3 compiler_4.3.1 rlang_1.1.1
[13] sass_0.4.7 tools_4.3.1 utf8_1.2.3 yaml_2.3.7
[17] knitr_1.44 labeling_0.4.3 bit_4.0.5 abind_1.4-5
[21] KernSmooth_2.23-22 withr_2.5.0 grid_4.3.1 proj4_1.0-13
[25] fansi_1.0.4 git2r_0.32.0 colorspace_2.1-0 extrafontdb_1.0
[29] scales_1.2.1 MASS_7.3-60 cli_3.6.1 rmarkdown_2.24
[33] crayon_1.5.2 generics_0.1.3 rstudioapi_0.15.0 httr_1.4.7
[37] tzdb_0.4.0 cachem_1.0.8 splines_4.3.1 maps_3.4.1
[41] parallel_4.3.1 cellranger_1.1.0 vctrs_0.6.3 Matrix_1.6-1
[45] jsonlite_1.8.7 carData_3.0-5 car_3.1-2 callr_3.7.3
[49] hms_1.1.3 bit64_4.0.5 jquerylib_0.1.4 glue_1.6.2
[53] ps_1.7.5 stringi_1.7.12 gtable_0.3.4 later_1.3.1
[57] extrafont_0.19 munsell_0.5.0 pillar_1.9.0 htmltools_0.5.6
[61] R6_2.5.1 rprojroot_2.0.3 vroom_1.6.3 evaluate_0.21
[65] lattice_0.21-8 backports_1.4.1 broom_1.0.5 httpuv_1.6.11
[69] bslib_0.5.1 Rcpp_1.0.11 nlme_3.1-163 Rttf2pt1_1.3.12
[73] mgcv_1.9-0 whisker_0.4.1 xfun_0.40 fs_1.6.3
[77] getPass_0.2-2 pkgconfig_2.0.3