Last updated: 2023-06-16

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Knit directory: Cardiotoxicity/

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This is my Dose Response Curve Code 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 26 of diff with 0.01-50 \(\mu\)M concentrations of the drugs: Daunorubicin (DNR) Doxorubicin (DOX) Epirubicin (EPI) Mitoxantrone (MTX) Trastuzumab (TRZ) [ note: Trastuzumab could only be used at concentrations of 10 \(\mu\)M and lower] Vehicle (VEH)

Vehicle is the control and is effectively treated by water 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 (to allow bubbles to go away). A plate reader was used to measure florescence and the RFLU values were exported to an excel file.

Analysis was done as follows: The background wells 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.
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) For calculation to control for plate to plate variance, the “Empty_Blank” well RFLU2 values from each plate within an experiment were averaged.
eg:
plate 1= empty average RFLU2 = 29000, plate 2 empty average RFLU2 =31000 normalized ratio plate 1/plate 2 or 29000/31000 =0.9355 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”

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 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 one in R is loading the libraries needed for analysis:

library(car)
#library(dr4pl) no longer used
library(tidyverse)
library(tinytex)
# library(readxl)
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 will first streamline 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 Part 4: ###file cleanup###

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

I then need to convert the concentrations to as.numeric and select the 5 columns that I really need for analysis, followed by dropping any wells that were NA in the Percent column.
The next part of the code is to make analysis files and groups and things for the iterations.

The data-frame called individuals is complied from each of the dataframes wrangled above and will be the “final” R object stored in the project folder.

Loading the data into R

Once they are all in a data frame, a few functions need to be defined for ggplot.

Viability

I wanted to graph viability across all samples. Below are the results from a sample of concentrations.

The above codes can be altered to include ALL concentrations.

Ploting the DRC

The code above is used multiple times and stored into an R object, so I can later combine the group into one full plot. This keeps the graphs in the same proportion to each other. I do alter each individual graph to either add axis titles or remove repetition.

plot all together!

Now to extract all the data from drm.

DRC replicates

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
trx_sl <- list()
vehls <- list() ###vehicle list
veh_sl <- list()
ID <- c("ind1a",
        'ind1b',
        'ind2a',
        'ind2b',
        "ind3a",
        "ind3b",
        "ind4a",
        "ind4b",
        "ind5a",
        "ind5b",
        "ind6a",
        "ind6b")

for(k in ID){
thingda <-  filter(DA, 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]
  # onion <- ED(thingda.m, c(50), interval = "delta")
  daunls[k] <- thingda.m$fit$par[3]
  # print(paste0(k, " Daunorubicin"))
}

for(k in ID){  
thingdx <-  filter(DX, 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]
  # onionx <- ED(thingdx.m, c(50), interval = "delta")
  doxols[k] <-thingdx.m$fit$par[3]
  # print(paste0(k, " Doxorubicin"))
}

 for(k in ID){ 
 thingep <-  filter(EP, 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"))
 }

for(k in ID){
  thingmt <-  filter(MT, 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"))
  
}

LD50 and slope extract

##ld50 condensed

# A tibble: 4 × 2
  Treatment median
  <chr>      <dbl>
1 Daun       0.800
2 Doxo       2.69 
3 Epi        2.49 
4 Mito       1.64 


R version 4.2.2 (2022-10-31 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    

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_5.1-0         drc_3.0-1          
 [7] MASS_7.3-60         data.table_1.14.8   BiocGenerics_0.42.0
[10] tinytex_0.45        lubridate_1.9.2     forcats_1.0.0      
[13] stringr_1.5.0       dplyr_1.1.2         purrr_1.0.1        
[16] readr_2.1.4         tidyr_1.3.0         tibble_3.2.1       
[19] ggplot2_3.4.2       tidyverse_2.0.0     car_3.1-2          
[22] carData_3.0-5       workflowr_1.7.0    

loaded via a namespace (and not attached):
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 [5] tools_4.2.2       backports_1.4.1   bslib_0.5.0       utf8_1.2.3       
 [9] R6_2.5.1          rpart_4.1.19      mgcv_1.8-42       colorspace_2.1-0 
[13] nnet_7.3-19       withr_2.5.0       tidyselect_1.2.0  gridExtra_2.3    
[17] processx_3.8.1    compiler_4.2.2    git2r_0.32.0      cli_3.6.1        
[21] htmlTable_2.4.1   sandwich_3.0-2    labeling_0.4.2    sass_0.4.6       
[25] scales_1.2.1      checkmate_2.2.0   mvtnorm_1.2-2     callr_3.7.3      
[29] digest_0.6.31     foreign_0.8-84    rmarkdown_2.22    base64enc_0.1-3  
[33] pkgconfig_2.0.3   htmltools_0.5.5   plotrix_3.8-2     highr_0.10       
[37] fastmap_1.1.1     htmlwidgets_1.6.2 rlang_1.1.1       rstudioapi_0.14  
[41] farver_2.1.1      jquerylib_0.1.4   generics_0.1.3    zoo_1.8-12       
[45] jsonlite_1.8.5    gtools_3.9.4      magrittr_2.0.3    Formula_1.2-5    
[49] Matrix_1.5-4.1    Rcpp_1.0.10       munsell_0.5.0     fansi_1.0.4      
[53] abind_1.4-5       lifecycle_1.0.3   stringi_1.7.12    multcomp_1.4-24  
[57] whisker_0.4.1     yaml_2.3.7        promises_1.2.0.1  lattice_0.21-8   
[61] splines_4.2.2     hms_1.1.3         knitr_1.43        ps_1.7.5         
[65] pillar_1.9.0      ggpubr_0.6.0      codetools_0.2-19  glue_1.6.2       
[69] evaluate_0.21     getPass_0.2-2     vctrs_0.6.3       tzdb_0.4.0       
[73] httpuv_1.6.11     gtable_0.3.3      cachem_1.0.8      xfun_0.39        
[77] rstatix_0.7.2     later_1.3.1       survival_3.5-5    cluster_2.1.4    
[81] timechange_0.2.0  TH.data_1.1-2