Lactate dehydrogenase, troponin I, and calcium analysis
ERM
2023-09-27
Last updated: 2023-09-27
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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)
| Version | Author | Date |
|---|---|---|
| df6d78a | reneeisnowhere | 2023-09-26 |
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")
| Version | Author | Date |
|---|---|---|
| df6d78a | reneeisnowhere | 2023-09-26 |
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"))
| Version | Author | Date |
|---|---|---|
| df6d78a | reneeisnowhere | 2023-09-26 |
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"))
| Version | Author | Date |
|---|---|---|
| df6d78a | reneeisnowhere | 2023-09-26 |
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