Dose-Response analysis

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Last updated: 2023-09-26

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Knit directory: Cardiotoxicity/

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File	Version	Author	Date	Message
Rmd	6fefb5a	reneeisnowhere	2023-09-26	wflow_publish("analysis/DRC_analysis.Rmd")
Rmd	62286c3	reneeisnowhere	2023-07-28	Updateing figure code
Rmd	06800c9	reneeisnowhere	2023-07-26	Commits to small changes and edits
html	ae4d18d	reneeisnowhere	2023-07-19	Build site.
Rmd	8d042ad	reneeisnowhere	2023-07-19	added ld50 calc of averaged viability
html	d404b2c	reneeisnowhere	2023-07-19	Build site.
Rmd	b3abdbf	reneeisnowhere	2023-07-19	added ld50 calc of averaged viability
Rmd	7755b30	reneeisnowhere	2023-07-19	added ld50 calc of averaged viability
Rmd	f88c9e4	reneeisnowhere	2023-07-18	adding in ld50 for average
Rmd	130918a	reneeisnowhere	2023-07-14	rearrage and update codes
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Rmd	e3409d1	reneeisnowhere	2023-07-03	axis change on LD50 corr
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Rmd	0f1c868	reneeisnowhere	2023-07-03	updated ld50 correlation, adde the plots
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Rmd	c3fd0fe	reneeisnowhere	2023-06-30	adding in LD50 comparisons
Rmd	f4bd5e1	reneeisnowhere	2023-06-27	checking code changes overtime
Rmd	c1d667f	reneeisnowhere	2023-06-23	updating the codes at Friday start.
Rmd	1e75bd0	reneeisnowhere	2023-06-19	update ld50 and slope plot
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Rmd	3d4ca64	reneeisnowhere	2023-06-16	updates on Friday
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Rmd	5ede159	reneeisnowhere	2023-06-13	finalized plots and curves for slope and LD50 and index updata
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Rmd	850d883	reneeisnowhere	2023-06-06	adding slope with t-test
Rmd	293585d	reneeisnowhere	2023-06-06	updated to slope code
Rmd	e0e109c	reneeisnowhere	2023-05-30	adding slope plot, working on individual plots
Rmd	25d32da	reneeisnowhere	2023-05-26	Adding 3 hour and chisq test to populations
Rmd	8d87a52	reneeisnowhere	2023-05-17	end of day
html	a0ffcd6	reneeisnowhere	2023-04-20	Build site.
Rmd	64f64df	reneeisnowhere	2023-04-20	ld50 DRC image added
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Rmd	33ec579	reneeisnowhere	2023-04-20	ld50 DRC image added
html	2cd8dbe	reneeisnowhere	2023-04-20	Build site.
Rmd	79c0445	reneeisnowhere	2023-04-20	adding DRC analysis to github
html	3ff1a00	reneeisnowhere	2023-04-20	Build site.
Rmd	7c0c02a	reneeisnowhere	2023-04-20	adding DRC analysis to github
html	bea5c80	reneeisnowhere	2023-04-20	Build site.
Rmd	2faf972	reneeisnowhere	2023-04-20	adding DRC analysis to github
Rmd	6d925a2	reneeisnowhere	2023-04-16	updating cormotif with updated RNAseq counts
Rmd	575fd81	reneeisnowhere	2023-04-11	updating cormotif
Rmd	4e52216	reneeisnowhere	2023-03-31	End of week updates
Rmd	3a26d52	reneeisnowhere	2023-03-22	Wed poster analysis changes
Rmd	945460e	reneeisnowhere	2023-03-19	Updating go plot with reorder
Rmd	69b5d53	reneeisnowhere	2023-03-17	updated DRC 6 plot with color for indivd
Rmd	11a2ab4	reneeisnowhere	2023-03-03	updates
Rmd	49191f8	reneeisnowhere	2023-03-03	more tracking and updates
Rmd	90a0227	reneeisnowhere	2023-02-27	monday2-27
Rmd	accc241	reneeisnowhere	2023-02-10	updates for the week
Rmd	1fc5c19	reneeisnowhere	2023-02-09	Git update
Rmd	8c41736	reneeisnowhere	2023-02-07	update with corrMotif

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This is my Dose-Response curve analysis and data with summaries.
I hope to explain what and how I did things for future analysis, so that the code and data are reproducible. All data was created by treating cardiomyocytes at ~day 27 of diff with 0.01-50 \(\mu\)M concentrations of the following drugs:
Daunorubicin (DNR)
Doxorubicin (DOX)
Epirubicin (EPI)
Mitoxantrone (MTX)
Trastuzumab (TRZ)
[ note: TRZ could only be used at concentrations of 10 \(\mu\)M and lower]
Vehicle (VEH)

VEH is effectively the same media, however water is added in volumes equivalent to the volume used to dilute the drugs in a 10 mM stock concentration. This is why it has values at different concentrations and why I analyze the data this way.

Cell viability was assessed using Presto Blue reagent according to manufacturer’s protocols.
90 uL of Galactose media + 10 uL of Presto Blue reagent were added to each well of the 96 well DRC plate for one hour. The cell media was then extracted to a black, clear bottom plate and wrapped in foil and stored at 4 degrees overnight. A plate reader was used to measure florescence intensity and the relative fluorescent (RFLU) values were exported to an excel file.

Analysis was done as follows:

1. The background wells containing media + reagent only were averaged and the average value was subtracted from every well on the plate. Because the wells on the plate were randomized, matching the treatment with the wells is critical and was done inefficiently in an excel file for each experiment.

2. Percent Viability was calculated by averaging the RFLU from the vehicle at each concentration, and dividing each drug’s RFLU at that concentration by the Vehicle control average RFLU to give a ratio. All values less than 0 were turned into zero in the excel document, and a final compilation for each differentiation and dose curve treatment was stored in an document called DRC_compilation.xlsx. ( note: I spelled incorrectly in my computer)

3. For calculation to control for plate to plate variance, the “Empty_Blank” well RFLU2 values from each plate within an experiment were averaged.
   eg:

   Step 1. plate 1= empty average RFLU2 = 29000,

   Step 2. plate 2 empty average RFLU2 =31000 normalized ratio plate 1/plate 2 or 29000/31000 =0.9355

   Step 3. This normalization ratio then was divided into every RFLU2 value on plate 1. Plate 2 would be divided by 1 to keep the formulas consistent (31000/31000 or plate2/plate2) to create a column that contained the adjusted RFLU2 values, called “adj”.

4. Calculations then proceed as described originally. The first viability value is called “Percent”. The adjusted value for intra-plate differences is called “Padj” although this use was later deprecated.

Part 2 of the analysis of data begins by entering data from the excel compilation to R. The data is stored as an excel file, which was then stored as an .RDS object for analysis retrieval.

Step 1 in R is loading the libraries needed for analysis:

library(car)
library(tidyverse)
library(tinytex)
library(BiocGenerics)
library(data.table)
library(drc)
library(Hmisc)
library(cowplot)
library(grid)
library(ggsignif)
library(RColorBrewer)
library(broom)

Step 2 is importing the data from the DRC_compilation.xlsx file. The data was imported and then stored as a data table in R.

Step 3:

The files have a list of similar names: 

• ‘Drug’ which is the short name of drug used

• ‘Conc’ which is the concentration of drug added (in microMolar);

• ‘Sname’ the abbreviated name of Drug and Conc;

• ‘Well’ letter with two number format ‘Row’ # 1-8 for A-G ‘Column’ numbers 1-12;

• ‘Plate’ will vary between one and three;

• ‘RFLU’ the RFLU from the 0 hour reading with background subtracted (not all experiments have them);

• ‘RFLU2’ the RFLU from the 48 hour reading with background subtracted.

I streamlined the data into more simple formats:

• Individual 1 is cell line 75-1 (D04_75_1,D05_75_1 )

• Individual 2 is cell line 87-1 (D04_87_1, D05_87_1)

• Individual 3 is cell line 77-1 (D02_77_1, D03_77_1)

• Individual 4 is 79-1 (D04_79_1, D05_79_1)

• Individual 5 is 78-1 (D01_78_1, D03_78_1)

• Individual 6 is 71-1 (D02_71_1, DJAG_71_1)

  • As of 6-2-22 I am implementing a new naming for R handles:
    ind1a, ind1b etc, to make sure I process out the “empty_blanks”

    I am also converting ‘Conc’ column to numeric

Step 4: File cleanup first subsets each file imported by taking out the ‘Empty’ samples in the Drug column and then renaming the file to individual names and DRC a and b

A data-frame called DRC48hoursdata.csv is complied from each of the experiments above and is the “final” table stored in the data folder.

Loading the data into R

drug_pal_fact <- c("#8B006D","#DF707E","#F1B72B", "#3386DD","#707031","#41B333")
full_list <- read.csv("data/DRC48hoursdata.csv", row.names = 1)

Once they are all in a data frame, a few thing need to be defined for other function like ggplot.

daunls <- list() ###daunorubicin list
doxols <- list() ### doxorubicin list
epils <- list() ###epirubicin list
mitols <- list() ###mitoxantrone list
trasls <- list() ###trastuzmab list
vehls <- list() ###vehicle list
# name <- c( "Veh","Daun", "Doxo","Epi", "Mito", "Tras")### text list of drugs

Viability

### to extract parameters for mapping

averageFL <-   full_list %>%
  na.omit()%>% 
    mutate(sDrug=Drug) %>% 
    mutate(sDrug=case_match(Drug,"Daun"~"DNR",
                            "Doxo"~"DOX",
                            "Epi"~"EPI",
                            "Mito"~"MTX",
                            "Tras"~"TRZ",
                            "Veh"~ "VEH", .default= Drug)) %>%
    group_by(SampleID, sDrug,Conc) %>%
    mutate(sDrug=factor(sDrug, levels = c('DOX','EPI','DNR','MTX','TRZ','VEH'))) %>% 
    dplyr::summarize(Mean = mean(Percent))
# saveRDS(averageFL,"data/averageviabilitytable.RDS")

  averageFL %>%
    ungroup() %>% 
    mutate(indv=substr(SampleID,4,4)) %>%
    mutate(indv=factor(indv, levels= c('1','2','3','4','5','6'))) %>% 
    filter(Conc <5) %>% 
    mutate(Conc= factor(as.numeric(Conc))) %>% 
    group_by(indv,sDrug,Conc) %>% 
    dplyr::summarize(Viability=mean(Mean)) %>%
    ungroup() %>% 
    ggplot(.,  aes(x=sDrug, y= Viability*100 )) +
    geom_boxplot(position="dodge", outlier.colour = "transparent",
                 aes(fill=sDrug))+
    geom_point(aes(color=indv))+
    guides(alpha = "none")+
    ylim(0,150.5)+
    scale_color_brewer(palette = "Dark2",
                       guide="legend",
                       name ="Individual", 
                       labels(c(1,2,3,4,5,6)))+
    scale_fill_manual(values=drug_pal_fact, name ="Treatment")+
    theme_classic() +
    # geom_hline(yintercept = 1,lty = 4)+
    ylab("Viability") +
    facet_wrap(~Conc)+ 
    ggtitle("Viablity across concentrations at 48 hours")+
    theme(axis.title=element_text(size=10),
          axis.ticks=element_line(size =2),
          axis.text=element_text(size=9, face = "bold"),
          panel.grid.major = element_line(colour = 'darkgrey'),
          panel.border=element_rect(fill = NA, size = 2),
          plot.title = element_text(hjust = 0.5, size =15, face = "bold"))

Past versions of "Viability data-1.png"

Version	Author	Date
d404b2c	reneeisnowhere	2023-07-19
8b862b3	reneeisnowhere	2023-06-13
bea5c80	reneeisnowhere	2023-04-20

averageFL %>% 
  ungroup() %>% 
  mutate(indv=substr(SampleID,4,4)) %>%
  mutate(indv=factor(as.numeric(indv))) %>% 
  filter(Conc<5) %>% 
  group_by(indv,sDrug,Conc) %>% 
  dplyr::summarize(Viability=mean(Mean)) %>% ungroup() %>% 
  ggplot(.,  aes(x=as.factor(Conc), y= Viability*100 )) +
  geom_boxplot(position="dodge",outlier.colour="transparent", aes(fill=sDrug))+
  geom_point(aes(color=indv))+
  guides(alpha = "none")+
  ylim(0,150.5)+
  scale_color_brewer(palette = "Dark2",guide="legend",
                     name = "Individual",labels(c(1,2,3,4,5,6)))+
  scale_fill_manual(values=drug_pal_fact, name = "Treatment")+
  theme_classic() +
  xlab(expression(paste("Concentration [", mu, "M]")))+
  ylab("Viability") +
  facet_wrap(~sDrug)+ 
  ggtitle("Viablity across drugs at 48 hours")+
  theme(axis.title=element_text(size=10),
        axis.ticks=element_line(size =2),
        axis.text=element_text(size=9, face = "bold"),
        panel.grid.major = element_line(colour = 'darkgrey'),
        panel.border=element_rect(fill = NA, size = 2),
        plot.title = element_text(hjust = 0.5, size =15, face = "bold"))

Past versions of "viablity by drug-1.png"

Version	Author	Date
d404b2c	reneeisnowhere	2023-07-19
8b862b3	reneeisnowhere	2023-06-13
bea5c80	reneeisnowhere	2023-04-20

The above codes can be altered to include ALL concentrations.

Ploting the DRC

Making the Dose-response curve involved averaging the quadruplicates at each concentration for each curve analyzed.

I broke up the full list into treatments and replicates. DA, DX, EP, MT, TR, and VE objects stored the mean viability tables, and

DA2, DX2, EP2, MT2, TR2, and VE2 contain all the data points for generation of the confidence intervals later in the analysis.

avg_via <- averageFL %>%
    ungroup() %>% 
    mutate(indv=substr(SampleID,4,4)) %>%
    mutate(indv=factor(indv, levels= c('1','2','3','4','5','6'))) %>% 
    group_by(indv,sDrug,Conc) %>% 
    dplyr::summarize(Viability=mean(Mean))

updated_list <- full_list %>%
  na.omit()%>% 
  mutate(sDrug=Drug) %>% 
  mutate(sDrug=case_match(Drug,"Daun"~"DNR",
                            "Doxo"~"DOX",
                            "Epi"~"EPI",
                            "Mito"~"MTX",
                            "Tras"~"TRZ",
                            "Veh"~ "VEH", .default= Drug)) %>%
    group_by(SampleID, sDrug,Conc) %>%
    mutate(indv=substr(SampleID,4,4)) %>%
    mutate(indv=factor(as.numeric(indv))) %>% 
    mutate(sDrug=factor(sDrug, levels = c('DOX','EPI','DNR','MTX','TRZ','VEH'))) 



DA <- avg_via %>% filter(sDrug == "DNR")
DX <- avg_via %>% filter(sDrug =="DOX")
EP <- avg_via %>% filter(sDrug =="EPI")
MT <- avg_via %>% filter(sDrug =="MTX")
TR <- avg_via %>% filter(sDrug =="TRZ")
VE <- avg_via%>% filter(sDrug =="VEH")

DA2 <- updated_list %>% filter(sDrug == "DNR")
DX2 <- updated_list %>% filter(sDrug =="DOX")
EP2 <- updated_list %>% filter(sDrug =="EPI")
MT2 <- updated_list %>% filter(sDrug =="MTX")
TR2 <- updated_list %>% filter(sDrug =="TRZ")
VE2 <- updated_list%>% filter(sDrug =="VEH")

The code below is an explanation of the ggplot code I used to plot the DRC by individual. This uses the two mean viability values from DRC1 and biological replicate DRC2, as input to generate the Dose-response curve for each treatment/individual combination.

ggplot(DA, 
       aes(x=Conc, y= Viability, color =indv,alpha =0.4)) +
  guides(color="none", alpha = "none")+  #guides turned off for group plotting
  stat_smooth(method = "drm", ## This code calls DRM inside ggplot2
              method.args = list(fct = L.4(c(NA,NA,1,NA))), se = FALSE)+  #note: fct means function, and L.4 stands for 4 parameter log-logistic curve  I have set at 1 for upper-limit and left the rest 'free'
  ylim(0,1.5)+ #setting the limits so everything with graph
  scale_color_brewer(palette = "Dark2")+ #adding color of choice
  scale_x_log10() +  # Change the x-axis scale to log 10 scale
  theme_classic() +  # looks pretty
  xlab(expression(paste("Concentration [", mu, "M]")))+
  ylab("Percent viability/100") +
  ggtitle("Daunorubicin")+
  theme(plot.title = element_text(hjust = 0.5, size =15, face = "bold"),
        axis.title=element_text(size=10),
        axis.ticks=element_line(linewidth =2),
        axis.text=element_text(size=10, face = "bold"),
        panel.grid.major = element_line(colour = 'lightgrey'),
        panel.border=element_rect(fill = NA, linewidth = 2),
        plot.background = element_rect(fill = "white", colour = NA))

The code above is used multiple times and output values are stored into an R object, so I can later combine the group into one full plot.

daplot <-
  ggplot(DA,aes(x=Conc, y= Viability, 
                 color = indv)) +
  stat_smooth(method = "drm", 
                method.args = list(fct = L.4(c(NA,NA,1,NA))), 
              se = FALSE)+
  ylim(0,1.5)+
  scale_color_brewer(palette = "Dark2")+
  guides(color="none")+
      scale_x_log10() +  # Change the x-axis scale to log 10 scale
      theme_classic() +
      xlab(NULL)+#expression(paste("Concentration [", mu, "M]")))+
    ylab(NULL) +
    ggtitle("DNR")+
    theme(plot.title = element_text(hjust = 0.5, size =15, face = "bold"),
          axis.title=element_text(size=10),
          axis.ticks=element_line(linewidth =2),
          axis.text=element_text(size=10, face = "bold"),
          panel.grid.major = element_line(colour = 'lightgrey'),
          panel.border=element_rect(fill = NA, linewidth = 2),
          plot.background = element_rect(fill = "white", colour = NA))

dxplot <-
  ggplot(DX,aes(x=Conc, y= Viability, 
                 color = indv)) +
  stat_smooth(method = "drm", 
                method.args = list(fct = L.4(c(NA,NA,1,NA))), 
              se = FALSE)+
  ylim(0,1.5)+
  scale_color_brewer(palette = "Dark2")+
  guides(color=guide_legend(title="Treatment"))+
  guides(color="none")+
      scale_x_log10() +  # Change the x-axis scale to log 10 scale
      theme_classic() +
      xlab(NULL)+
      ylab(NULL) +
      theme(plot.title = element_text(hjust = 0.5, size =15, face ="bold"))+
      ggtitle("DOX")+
      theme(axis.title=element_text(size=10),
            axis.ticks=element_line(linewidth = 2),
            axis.text=element_text(size=10, face = "bold"),
            panel.grid.major = element_line(colour = 'lightgrey'),
            panel.border=element_rect(fill = NA, linewidth = 2),
            plot.background = element_rect(fill = "white", colour = NA))

epplot <- 
  ggplot(EP, 
         aes(x=Conc, y= Viability, color = indv)) +
  stat_smooth(method = "drm",
              method.args = list(fct = L.4(c(NA,NA,1,NA))), se = FALSE)+
  ylim(0,1.5)+
  scale_color_brewer(palette = "Dark2")+
  guides(color="none")+
  scale_x_log10() +  
  theme_classic() +
  scale_color_brewer(palette = "Dark2")+
  xlab(NULL)+
  ylab(NULL) +
  theme(plot.title = element_text(hjust = 0.5, size =15, face ="bold"))+
  ggtitle("EPI")+
  theme(axis.title=element_text(size=10),
        axis.ticks=element_line(linewidth = 2),
        axis.text=element_text(size=10, face = "bold"),
        panel.grid.major = element_line(colour = 'lightgrey'),
        panel.border=element_rect(fill = NA, linewidth = 2),
        plot.background = element_rect(fill = "white", colour = NA))

mtplot <- 
  ggplot(MT, 
         aes(x=Conc, y= Viability, color = indv)) +
  stat_smooth(method = "drm",
              method.args = list(fct = L.4(c(NA,NA,1,NA))), se = FALSE)+
  ylim(0,1.5)+
  scale_color_brewer(palette = "Dark2")+
  guides(color="none")+
  scale_x_log10() +
  scale_color_brewer(palette = "Dark2")+
  theme_classic() +
  xlab(NULL)+
  ylab(NULL) +
  theme(plot.title = element_text(hjust = 0.5, size =15, face="bold"))+
  ggtitle("MTX")+
  theme(axis.title=element_text(size=10),
        axis.ticks=element_line(linewidth = 2),
        axis.text=element_text(size=10, face = "bold"),
        panel.grid.major = element_line(colour = 'lightgrey'),
        panel.border=element_rect(fill = NA, linewidth =  2),
        plot.background = element_rect(fill = "white", colour = NA))

##creating legend for full plot

leg <- ggplot(TR, aes(x=Conc, y= Viability, color = indv, alpha = 0.6)) +
          geom_line( size=2)+
          theme_classic() +
  guides(alpha = "none")+
      scale_color_brewer(palette = "Dark2",labels = c("1", "2", "3","4","5","6"))+
        labs(color = "Individual", linewidth=3)
    
trplot <-
  ggplot(TR, 
        aes(x=Conc, y= Viability, color = indv)) +
  stat_smooth(method = "drm", 
              method.args = list(fct = L.4(c(NA,1,NA,NA))), se = FALSE)+
  ylim(0,1.5)+
  scale_color_brewer(palette = "Dark2")+
  guides(color="none")+
  scale_x_log10() + 
  theme_classic() +
  scale_color_brewer(palette = "Dark2")+
  xlab(expression(paste("Concentration ", mu, "M")))+
  ylab(NULL) +
  theme(plot.title = element_text(hjust = 0.5, size =15, face = "bold"))+
  ggtitle("TRZ")+
  theme(axis.title=element_text(size=10),
        axis.ticks=element_line(size =2),
        axis.text=element_text(size=10, face = "bold"),
        panel.grid.major = element_line(colour = 'lightgrey'),
        panel.border=element_rect(fill = NA, size = 2),
        plot.background = element_rect(fill = "white", colour = NA))

veplot <- 
  ggplot(VE,
         aes(x=Conc, y= Viability, color = indv)) +
  stat_smooth(method = "drm", 
              method.args = list(fct = L.4(c(NA,NA,NA,NA))), se = FALSE)+
  ylim(0,1.5)+
  scale_color_brewer(palette = "Dark2")+
  guides(color="none")+
  scale_x_log10() +  
  theme_classic() +
  xlab(expression(paste("Concentration ", mu, "M"))) +
  ylab(NULL) +
  ggtitle("VEH") +
  theme(plot.title = element_text(hjust = 0.5, size = 15, face = "bold"))+
  theme(axis.title = element_text(size = 10),
        axis.ticks = element_line(linewidth =  2),
        axis.text = element_text(size = 10, face = "bold"),
        panel.grid.major = element_line(colour = 'lightgrey'),
        panel.border = element_rect(fill = NA, linewidth = 2),
        panel.background = element_rect(fill = "white"),
        plot.background = element_rect(fill = "white", colour = NA))

plot all together!

## take a legend from a plot
legend_b <- ggpubr::get_legend(leg)

ggpubr::as_ggplot(legend_b)

Past versions of "plot together-1.png"

Version	Author	Date
d404b2c	reneeisnowhere	2023-07-19

plan2 <-  plot_grid(daplot,dxplot,epplot,mtplot, trplot,veplot,legend_b,ncol =4)

plan2

Past versions of "plot together-2.png"

Version	Author	Date
d404b2c	reneeisnowhere	2023-07-19

LD50 and slope extraction (First try)

Now I want to extract from the model, the slope and LD50 calculated by the dose-response model. I make several lists to store the info into.

daunls <- list() ###daunorubicin list
dnr_sl <- list() ##slope
doxols <- list() ### doxorubicin list
dox_sl <- list()
epils <- list() ###epirubicin list
epi_sl <- list()
mitols <- list() ###mitoxantrone list
mtx_sl <- list()
trasls <- list() ###trastuzmab list
trz_sl <- list()
vehls <- list() ###vehicle list
veh_sl <- list()
ID <- c("ind1a",
        "ind1b",
        "ind2a",
        "ind2b",
        "ind3a",
        "ind3b",
        "ind4a",
        "ind4b",
        "ind5a",
        "ind5b",
        "ind6a",
        "ind6b")

Then I run the complete samples without averaging, to get the LD50s from each replicate and individual. The values are stored in several files. Slope values are in the *_sl dataframes, LD50 are in the *ls dataframes and confidence intervals are in the *_intv dataframes.

da_confint <- list()
for(k in ID){
thingda <-  filter(DA2, SampleID==k)
  thingda.m <- drm(Percent~Conc, data = thingda, fct=LL.4(c(NA, NA,1, NA),names=c("Slope", "Lower Limit","Upper Limit","ED50")))
  dnr_sl[k] <- thingda.m$fit$par[1]
  daunls[k] <- thingda.m$fit$par[3]
  da_confint[paste0(k)]<-list(predict(thingda.m, interval="confidence"))
}
DNR_intv <- map_df(da_confint, ~as.data.frame(.x), .id="indv")
DNR_intv$Conc <- DA2$Conc

 dx_confint<-list()

for(k in ID){  
thingdx <-  filter(DX2, SampleID==k)
  thingdx.m <- drm(Percent~Conc, data = thingdx, fct=LL.4(c(NA, NA,1, NA),names=c("Slope","Lower Limit","Upper Limit","ED50")))
  dox_sl[k] <- thingdx.m$fit$par[1]
  doxols[k] <-thingdx.m$fit$par[3]
  dx_confint[paste0(k)]<-list(predict(thingdx.m,interval="confidence"))
}

DOX_intv <- map_df(dx_confint, ~as.data.frame(.x), .id="indv")
DOX_intv$Conc <- DX2$Conc

 ep_confint<-list()
 for(k in ID){ 
 thingep <-  filter(EP2, SampleID==k)
  thingep.m <- drm(Percent~Conc, data = thingep, fct=LL.4(c(NA, NA,1, NA),names=c("Slope", "Lower Limit","Upper Limit","ED50")))
  epi_sl[k] <- thingep.m$fit$par[1]
  # onione <- ED(thingep.m, c(50), interval = "delta")
  epils[k] <- thingep.m$fit$par[3]
  # print(paste0(k, " Epirubicin"))
  ep_confint[paste0(k)]<-list(predict(thingep.m,interval="confidence"))
 }

EPI_intv <- map_df(ep_confint, ~as.data.frame(.x), .id="indv")
EPI_intv$Conc <- EP2$Conc

 mt_confint<-list()
for(k in ID){
  thingmt <-  filter(MT2, SampleID==k)
  thingmt.m <- drm(Percent~Conc, data = thingmt, fct=LL.4(c(NA,NA,1, NA),names=c("Slope","Lower Limit","Upper Limit", "ED50")))
  mtx_sl[k] <- thingmt.m$fit$par[1]
  # onionm <- ED(thingmt.m, c(50), interval = "delta")
  mitols[k] <- thingmt.m$fit$par[3]
  # print(paste0(k, " Mitoxantrone"))
  mt_confint[paste0(k)]<-list(predict(thingmt.m,interval="confidence"))
}
MTX_intv <- map_df(mt_confint, ~as.data.frame(.x), .id="indv")
MTX_intv$Conc<- MT2$Conc 

Note: Could not get numbers to plot for TRZ and VEH

 tr_confint<-list()
for(k in ID){
  thingtr <-  filter(TR2, SampleID==k)
  try(thingtr.m <- drm(Percent~Conc, 
                       data = thingtr, fct=L.4(c(NA, NA,1,NA), names=c("Slope","Lower Limit","Upper Limit", "ED50")))) 
  try(trz_sl[k] <- thingtr.m$fit$par[1])
  try(trasls[k] <- thingtr.m$fit$par[3])
  try(tr_confint[paste0(k)]<-list(predict(thingtr.m,interval="confidence")))
         }
 
TRZ_intv <- map_df(tr_confint, ~as.data.frame(.x), .id="indv")
TRZ_intv$Conc <- TR2$Conc

 ve_confint<-list()
for(k in ID){
  thingve <-  filter(VE2, SampleID==k)
  try(thingve.m <- tryCatch(drm(Percent~Conc, data = thingve, fct=L.4(c(0, NA,NA,NA),names=c("Slope","Lower Limit","Upper Limit", "ED50")))))
  try(veh_sl[k] <- thingve.m$fit$par[1])
  try(vehls[k] <- thingve.m$fit$par[3])
  try(ve_confint[paste0(k)]<-list(predict(thingve.m,interval="confidence")))
}
VEH_intv <- map_df(ve_confint, ~as.data.frame(.x), .id="indv")
VEH_intv$Conc <- VE2$Conc

DRC replicates

How reproducible are each of the DRCs? here I plot several to see how the overlap is working. Each replicate for each Treatment set is ploted

pull_drc <- data.frame(individual=c('ind1','ind2','ind3','ind4','ind5','ind6'))
pull_drc2 <- data.frame("ind1", "ind2","ind3","ind4","ind5","ind6")
doubl_plot <- data.frame("ind3a", "ind3b", "ind5a", "ind5b")

for (each in 1:6){
  newdata <- DNR_intv %>% 
   filter(indv==paste0(pull_drc2[[each]],'a')|
                         indv==paste0(pull_drc2[[each]],'b')) %>% 
    mutate(indv=factor(indv,levels=c(paste0(pull_drc2[[each]],'a'),paste0(pull_drc2[[each]],'b'))))
     f <- DA2 %>% filter(SampleID==paste0(pull_drc2[[each]],'a')|
                         SampleID==paste0(pull_drc2[[each]],'b')) %>% 
   ggplot(., aes(x=Conc, y= Percent, color = SampleID,alpha =0.6)) +
  guides(color="none", alpha = "none")+
      geom_ribbon(data = newdata, aes(x = Conc, y = Prediction, ymin = Lower, ymax = Upper, fill=indv),
              alpha = 0.1, color = "white")+
     stat_smooth(method = "drm",
                  method.args = list(fct = L.4(c(NA,NA,1,NA))),
                 se = FALSE)+
      ylim(-.4,1.5)+
      scale_color_brewer(palette = "Dark2")+
       scale_fill_brewer(palette = "Dark2")+
       scale_x_log10() +  # Change the x-axis scale to log 10 scale
      theme_classic() +
      xlab(NULL)+
      ylab(NULL) +
        # scale_y_continuous( expand = expansion(mult = .1)) +
      theme(plot.title = element_text(hjust = 0.5, size =15, face ="bold"))+
      ggtitle(paste0("Daunorubicin individual ",each))+
      theme(axis.title=element_text(size=10),
            axis.ticks=element_line(linewidth = 2),
            axis.text=element_text(size=10, face = "bold"),
            panel.grid.major = element_line(colour = 'lightgrey'),
            panel.border=element_rect(fill = NA, linewidth = 2),
            plot.background = element_rect(fill = "white", colour = NA))
  print(f)
} 

Past versions of "pulldrc individuals-1.png"

Version	Author	Date
d404b2c	reneeisnowhere	2023-07-19
8b862b3	reneeisnowhere	2023-06-13

Past versions of "pulldrc individuals-2.png"

Version	Author	Date
d404b2c	reneeisnowhere	2023-07-19
8b862b3	reneeisnowhere	2023-06-13

Past versions of "pulldrc individuals-3.png"

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d404b2c	reneeisnowhere	2023-07-19
8b862b3	reneeisnowhere	2023-06-13

Past versions of "pulldrc individuals-4.png"

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d404b2c	reneeisnowhere	2023-07-19
8b862b3	reneeisnowhere	2023-06-13

Past versions of "pulldrc individuals-5.png"

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d404b2c	reneeisnowhere	2023-07-19
8b862b3	reneeisnowhere	2023-06-13

Past versions of "pulldrc individuals-6.png"

Version	Author	Date
d404b2c	reneeisnowhere	2023-07-19
8b862b3	reneeisnowhere	2023-06-13

Past versions of DXpulls-1.png

Version	Author	Date
d404b2c	reneeisnowhere	2023-07-19
8b862b3	reneeisnowhere	2023-06-13

Past versions of DXpulls-2.png

Version	Author	Date
d404b2c	reneeisnowhere	2023-07-19
8b862b3	reneeisnowhere	2023-06-13

Past versions of DXpulls-3.png

Version	Author	Date
d404b2c	reneeisnowhere	2023-07-19
8b862b3	reneeisnowhere	2023-06-13

Past versions of DXpulls-4.png

Version	Author	Date
d404b2c	reneeisnowhere	2023-07-19
8b862b3	reneeisnowhere	2023-06-13

Past versions of DXpulls-5.png

Version	Author	Date
d404b2c	reneeisnowhere	2023-07-19
8b862b3	reneeisnowhere	2023-06-13

Past versions of DXpulls-6.png

Version	Author	Date
d404b2c	reneeisnowhere	2023-07-19
8b862b3	reneeisnowhere	2023-06-13

for (each in 1:6){
  
  newdata <- EPI_intv %>%
    filter(indv==paste0(pull_drc2[[each]],'a')|
                         indv==paste0(pull_drc2[[each]],'b')) %>% 
    mutate(indv=factor(indv,levels=c(paste0(pull_drc2[[each]],'a'),paste0(pull_drc2[[each]],'b'))))
  
  ep<- EP2 %>% 
    filter(SampleID==paste0(pull_drc2[[each]],'a')|
                         SampleID==paste0(pull_drc2[[each]],'b')) %>% 
    ggplot(., aes(x=Conc, y= Percent, color = SampleID,alpha =0.6)) +
    geom_ribbon(data = newdata, aes(x = Conc, y = Prediction, ymin = Lower, ymax = Upper, fill=indv),
              alpha = 0.1, color = "white")+
     stat_smooth(method = "drm",
                  method.args = list(fct = L.4(c(NA,NA,1,NA))),
                 se = FALSE)+
      ylim(-.4,1.5)+
    guides(color="none", alpha = "none", fill="none")+
      scale_color_brewer(palette = "Dark2")+
     scale_fill_brewer(palette = "Dark2")+
      scale_x_log10() +  # Change the x-axis scale to log 10 scale
      theme_classic() +
      xlab(NULL)+
      ylab(NULL) +
      theme(plot.title = element_text(hjust = 0.5, size =15, face ="bold"))+
      ggtitle(paste0("Epirubicin individual ",each))+
      theme(axis.title=element_text(size=10),
            axis.ticks=element_line(linewidth = 2),
            axis.text=element_text(size=10, face = "bold"),
            panel.grid.major = element_line(colour = 'lightgrey'),
            panel.border=element_rect(fill = NA, linewidth = 2),
            plot.background = element_rect(fill = "white", colour = NA))
  print(ep)
} 

Past versions of "epi plots-1.png"

Version	Author	Date
d404b2c	reneeisnowhere	2023-07-19

Past versions of "epi plots-2.png"

Version	Author	Date
d404b2c	reneeisnowhere	2023-07-19

Past versions of "epi plots-3.png"

Version	Author	Date
d404b2c	reneeisnowhere	2023-07-19

Past versions of "epi plots-4.png"

Version	Author	Date
d404b2c	reneeisnowhere	2023-07-19

Past versions of "epi plots-5.png"

Version	Author	Date
d404b2c	reneeisnowhere	2023-07-19

Past versions of "epi plots-6.png"

Version	Author	Date
d404b2c	reneeisnowhere	2023-07-19

for (each in 1:6){
  
  newdata <- MTX_intv %>%
    filter(indv==paste0(pull_drc2[[each]],'a')|
                         indv==paste0(pull_drc2[[each]],'b')) %>% 
    mutate(indv=factor(indv,levels=c(paste0(pull_drc2[[each]],'a'),paste0(pull_drc2[[each]],'b'))))
  
  mt<-MT2 %>% 
    filter(SampleID==paste0(pull_drc2[[each]],'a')|
                         SampleID==paste0(pull_drc2[[each]],'b')) %>% 
    ggplot(., aes(x=Conc, y= Percent, color = SampleID,alpha =0.6)) +
    geom_ribbon(data = newdata, aes(x = Conc, y = Prediction, ymin = Lower, ymax = Upper, fill=indv),
              alpha = 0.1, color = "white")+
     stat_smooth(method = "drm",
                  method.args = list(fct = L.4(c(NA,NA,1,NA))),
                 se = FALSE)+
      ylim(-.4,1.5)+
    guides(color="none", alpha = "none", fill="none")+
      scale_color_brewer(palette = "Dark2")+
     scale_fill_brewer(palette = "Dark2")+
      scale_x_log10() +  # Change the x-axis scale to log 10 scale
      theme_classic() +
      xlab(NULL)+
      ylab(NULL) +
      theme(plot.title = element_text(hjust = 0.5, size =15, face ="bold"))+
      ggtitle(paste0("Mitoxantrone individual ",each))+
      theme(axis.title=element_text(size=10),
            axis.ticks=element_line(linewidth = 2),
            axis.text=element_text(size=10, face = "bold"),
            panel.grid.major = element_line(colour = 'lightgrey'),
            panel.border=element_rect(fill = NA, linewidth = 2),
            plot.background = element_rect(fill = "white", colour = NA))
  print(mt)
} 

Past versions of mtrep-1.png

Version	Author	Date
d404b2c	reneeisnowhere	2023-07-19

Past versions of mtrep-2.png

Version	Author	Date
d404b2c	reneeisnowhere	2023-07-19

Past versions of mtrep-3.png

Version	Author	Date
d404b2c	reneeisnowhere	2023-07-19

Past versions of mtrep-4.png

Version	Author	Date
d404b2c	reneeisnowhere	2023-07-19

Past versions of mtrep-5.png

Version	Author	Date
d404b2c	reneeisnowhere	2023-07-19

Past versions of mtrep-6.png

Version	Author	Date
d404b2c	reneeisnowhere	2023-07-19

for (each in 1:6){
   newdata <- TRZ_intv %>%
    filter(indv==paste0(pull_drc2[[each]],'a')|
                         indv==paste0(pull_drc2[[each]],'b'))%>% 
    mutate(indv=factor(indv,levels=c(paste0(pull_drc2[[each]],'a'),paste0(pull_drc2[[each]],'b'))))
  
  tr<-TR2 %>% 
    filter(SampleID==paste0(pull_drc2[[each]],'a')|
                         SampleID==paste0(pull_drc2[[each]],'b')) %>% 
    ggplot(., aes(x=Conc, y= Percent, color = SampleID,alpha =0.6)) +
    geom_ribbon(data = newdata, aes( y = Prediction, ymin = Lower, ymax = Upper, fill=indv),
              alpha = 0.1, color = "white")+
     stat_smooth(method = "drm",
                  method.args = list(fct = L.4(c(NA,NA,1,NA))),
                 se = FALSE)+
      ylim(-.4,1.5)+
    guides(color="none", alpha = "none", fill="none")+
      scale_color_brewer(palette = "Dark2")+
      scale_x_log10() +  # Change the x-axis scale to log 10 scale
      theme_classic() +
      xlab(NULL)+
      ylab(NULL) +
      theme(plot.title = element_text(hjust = 0.5, size =15, face ="bold"))+
      ggtitle(paste0("Trastuzumab individual ",each))+
      theme(axis.title=element_text(size=10),
            axis.ticks=element_line(linewidth = 2),
            axis.text=element_text(size=10, face = "bold"),
            panel.grid.major = element_line(colour = 'lightgrey'),
            panel.border=element_rect(fill = NA, linewidth = 2),
            plot.background = element_rect(fill = "white", colour = NA))
  print(tr)
} 

Past versions of "tras rep-1.png"

Version	Author	Date
d404b2c	reneeisnowhere	2023-07-19

Past versions of "tras rep-2.png"

Version	Author	Date
d404b2c	reneeisnowhere	2023-07-19

Past versions of "tras rep-3.png"

Version	Author	Date
d404b2c	reneeisnowhere	2023-07-19

Past versions of "tras rep-4.png"

Version	Author	Date
d404b2c	reneeisnowhere	2023-07-19

Past versions of "tras rep-5.png"

Version	Author	Date
d404b2c	reneeisnowhere	2023-07-19

Past versions of "tras rep-6.png"

Version	Author	Date
d404b2c	reneeisnowhere	2023-07-19

for (each in 1:6){
  newdata <- VEH_intv %>%
    filter(indv==paste0(pull_drc2[[each]],'a')|
                         indv==paste0(pull_drc2[[each]],'b'))%>% 
    mutate(indv=factor(indv,levels=c(paste0(pull_drc2[[each]],'a'),paste0(pull_drc2[[each]],'b'))))
  
  ve<-VE2 %>% 
    filter(SampleID==paste0(pull_drc2[[each]],'a')|
                         SampleID==paste0(pull_drc2[[each]],'b')) %>% 
    ggplot(., aes(x=Conc, y= Percent, color = SampleID,alpha =0.6)) +
    geom_ribbon(data = newdata, aes( y = Prediction, ymin = Lower, ymax = Upper, fill=indv),
              alpha = 0.1, color = "white")+
     stat_smooth(method = "drm",
                  method.args = list(fct = L.4(c(NA,NA,1,NA))),
                 se = FALSE)+
      ylim(-.4,1.5)+
    guides(color="none", alpha = "none", fill="none")+
      scale_color_brewer(palette = "Dark2")+
      scale_x_log10() +  # Change the x-axis scale to log 10 scale
      theme_classic() +
      xlab(NULL)+
      ylab(NULL) +
      theme(plot.title = element_text(hjust = 0.5, size =15, face ="bold"))+
      ggtitle(paste0("Vehicle individual ",each))+
      theme(axis.title=element_text(size=10),
            axis.ticks=element_line(linewidth = 2),
            axis.text=element_text(size=10, face = "bold"),
            panel.grid.major = element_line(colour = 'lightgrey'),
            panel.border=element_rect(fill = NA, linewidth = 2),
            plot.background = element_rect(fill = "white", colour = NA))
  print(ve)
 } 

Past versions of "veh rep-1.png"

Version	Author	Date
d404b2c	reneeisnowhere	2023-07-19

Past versions of "veh rep-2.png"

Version	Author	Date
d404b2c	reneeisnowhere	2023-07-19

Past versions of "veh rep-3.png"

Version	Author	Date
d404b2c	reneeisnowhere	2023-07-19

Past versions of "veh rep-4.png"

Version	Author	Date
d404b2c	reneeisnowhere	2023-07-19

Past versions of "veh rep-5.png"

Version	Author	Date
d404b2c	reneeisnowhere	2023-07-19

Past versions of "veh rep-6.png"

Version	Author	Date
d404b2c	reneeisnowhere	2023-07-19

LD50 correlation

##EC50 of breast cancer cell lines

###combining all values into a larger dataframe

library(RColorBrewer)
drug_palc <- c("#8B006D","#DF707E","#F1B72B", "#3386DD","#707031","#41B333")

BC_cell_lines <- read.csv("data/BC_cell_lines.csv",row.names = 1)

  
graphBC <- BC_cell_lines %>%
    mutate(Cell_line= factor(Cell_line)) %>% 
  pivot_longer(.,col=!Cell_line,names_to = 'Treatment',values_to = 'LD50') %>% 
  ggplot(., (aes(x = (Treatment), y = log10(LD50)))) +
  geom_boxplot(position = "identity",aes(fill=Treatment))+
  geom_point(aes(color = Cell_line,
                 size = 5,alpha = 0.5)) +
  ggtitle(expression("Breast cancer cell line reported  LD"[50]*"s"))+
  xlab("")+
  ylab(bquote('Log'[10]~ 'LD'[50]~'in '*mu*M))+
  scale_color_brewer(palette = "Spectral",
                     name = "Cell lines")+
          scale_fill_manual(values=drug_palc)+
  ylim(-2,2)+
  theme_bw() + 
  theme(plot.title = element_text(hjust =0.5, size = 18))+
  guides(alpha ="none", size = "none", fill= "none")+
  #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"))   
  graphBC

Past versions of "combine LD50 values-1.png"

Version	Author	Date
d404b2c	reneeisnowhere	2023-07-19
9a6ec64	reneeisnowhere	2023-07-03
bea5c80	reneeisnowhere	2023-04-20

LD50 by avg of viability:

This is calculating the avg viability for each replicate first, then using those two points to model the dose-response curve on for the final values.

coll_avg_ld50 <- list(DOX_50_avg,EPI_50_avg,DNR_50_avg,MTX_50_avg)
names(coll_avg_ld50) <- c("DOX","EPI","DNR","MTX")
 new_ld50avg <- map_df(coll_avg_ld50, ~as.data.frame(.x), .id="Treatment")
  # saveRDS(new_ld50avg, "data/new_ld50avg.RDS")

  new_ld50avg %>%   
  mutate(Treatment= factor(Treatment, levels = c("DOX","EPI","DNR","MTX"))) %>% 
  mutate(indv= factor(indv, levels = c('1','2','3','4','5','6'))) %>% 
  ggplot(., (aes(x = (Treatment), y = log10(Estimate)))) +
  geom_boxplot(position = "identity",aes(fill=Treatment))+
  geom_point(aes(color = indv,
                 size = 5,alpha = 0.5)) +
  ggtitle(expression("Experimentally-derived LD"[50]*"s from treated cardiomyocytes"))+
  xlab("Treatment")+
  ylab(bquote('Log'[10]~ 'LD'[50]~'in '*mu*M))+
  scale_color_brewer(palette = "Dark2",
                     name = "Individual")+
  ylim(-2,2)+
  scale_fill_manual(values=drug_pal_fact)+
  theme_bw() + 
  theme(plot.title = element_text(hjust =0.5, size = 18))+
  guides(alpha ="none", size = "none")+
  #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"))   

Past versions of "disply new ld50 avg-1.png"

Version	Author	Date
d404b2c	reneeisnowhere	2023-07-19

Replication of DRC in two individuals

This code is to test plotting two individuals on one plot with VE2 code (tried to loop but did not get to work yet)

doubl_plot <- data.frame("ind3a", "ind3b", "ind5a", "ind5b")
trt <- data.frame("DOX","EPI", "DNR", "MTX", "TRZ", "VEH")
plotloop_list <- list("DOX_intv","EPI_intv","DNR_intv", "MTX_intv","TRZ_intv","VEH_intv")

for (each in 1:6){
  newdata <- VEH_intv %>%
    filter(indv%in% doubl_plot)
  
  ve<-VE2 %>% 
    filter(SampleID==paste0(pull_drc2[[each]],'a')|
                         SampleID==paste0(pull_drc2[[each]],'b')) %>% 
    ggplot(., aes(x=Conc, y= Percent, color = SampleID,alpha =0.6)) +
    geom_ribbon(data = newdata, aes( y = Prediction, ymin = Lower, ymax = Upper, fill=indv),
              alpha = 0.1, color = "white")+
     stat_smooth(method = "drm",
                  method.args = list(fct = L.4(c(NA,NA,1,NA))),
                 se = FALSE)+
      ylim(-.4,1.5)+
    guides(color="none", alpha = "none", fill="none")+
      scale_color_brewer(palette = "Dark2")+
      scale_x_log10() +  # Change the x-axis scale to log 10 scale
      theme_classic() +
      xlab(NULL)+
      ylab(NULL) +
      theme(plot.title = element_text(hjust = 0.5, size =15, face ="bold"))+
      ggtitle(paste0("Vehicle individual ",each))+
      theme(axis.title=element_text(size=10),
            axis.ticks=element_line(linewidth = 2),
            axis.text=element_text(size=10, face = "bold"),
            panel.grid.major = element_line(colour = 'lightgrey'),
            panel.border=element_rect(fill = NA, linewidth = 2),
            plot.background = element_rect(fill = "white", colour = NA))
  print(ve)
 } 

Session information

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] broom_1.0.5         RColorBrewer_1.1-3  ggsignif_0.6.4     
 [4] cowplot_1.1.1       Hmisc_5.1-1         drc_3.0-1          
 [7] MASS_7.3-60         data.table_1.14.8   BiocGenerics_0.46.0
[10] tinytex_0.46        lubridate_1.9.2     forcats_1.0.0      
[13] stringr_1.5.0       dplyr_1.1.3         purrr_1.0.2        
[16] readr_2.1.4         tidyr_1.3.0         tibble_3.2.1       
[19] ggplot2_3.4.3       tidyverse_2.0.0     car_3.1-2          
[22] carData_3.0-5       workflowr_1.7.1    

loaded via a namespace (and not attached):
 [1] tidyselect_1.2.0  farver_2.1.1      fastmap_1.1.1     TH.data_1.1-2    
 [5] promises_1.2.1    digest_0.6.33     rpart_4.1.19      timechange_0.2.0 
 [9] lifecycle_1.0.3   cluster_2.1.4     survival_3.5-7    processx_3.8.2   
[13] magrittr_2.0.3    compiler_4.3.1    rlang_1.1.1       sass_0.4.7       
[17] tools_4.3.1       plotrix_3.8-2     utf8_1.2.3        yaml_2.3.7       
[21] knitr_1.44        labeling_0.4.3    htmlwidgets_1.6.2 multcomp_1.4-25  
[25] abind_1.4-5       withr_2.5.0       foreign_0.8-85    nnet_7.3-19      
[29] fansi_1.0.4       ggpubr_0.6.0      git2r_0.32.0      colorspace_2.1-0 
[33] scales_1.2.1      gtools_3.9.4      cli_3.6.1         mvtnorm_1.2-3    
[37] rmarkdown_2.24    generics_0.1.3    rstudioapi_0.15.0 httr_1.4.7       
[41] tzdb_0.4.0        cachem_1.0.8      splines_4.3.1     base64enc_0.1-3  
[45] vctrs_0.6.3       Matrix_1.6-1      sandwich_3.0-2    jsonlite_1.8.7   
[49] callr_3.7.3       hms_1.1.3         rstatix_0.7.2     Formula_1.2-5    
[53] htmlTable_2.4.1   jquerylib_0.1.4   glue_1.6.2        codetools_0.2-19 
[57] ps_1.7.5          stringi_1.7.12    gtable_0.3.4      later_1.3.1      
[61] munsell_0.5.0     pillar_1.9.0      htmltools_0.5.6   R6_2.5.1         
[65] rprojroot_2.0.3   evaluate_0.21     lattice_0.21-8    backports_1.4.1  
[69] httpuv_1.6.11     bslib_0.5.1       Rcpp_1.0.11       nlme_3.1-163     
[73] gridExtra_2.3     checkmate_2.2.0   mgcv_1.9-0        whisker_0.4.1    
[77] xfun_0.40         fs_1.6.3          zoo_1.8-12        getPass_0.2-2    
[81] pkgconfig_2.0.3