Renee_DRC_Code
ERM
2023-04-20
Last updated: 2023-04-20
Checks: 7 0
Knit directory: Cardiotoxicity/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| 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 |
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 (daun) Doxorubicin (doxo) Epirubicin (epi) Mitoxantrone
(mito) Trastuzumab (tras) [ 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)Step 2 is importing the data from the DRC_compilation.xlsx file. Future ref will refer to the table.
Step 3:
The files “should” 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 two 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.
##find the individuals rds Once they are all in a data from, a few functions need to be defined for ggplot: ##functions
Viability
I wanted to graph viability across all samples. Below are the results from a sample of concentrations.
###plot all together!#####
###LD50 extract
###drm extracting
##ld50 condensed
R version 4.2.2 (2022-10-31 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19044)
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] RColorBrewer_1.1-3 ggsignif_0.6.4 cowplot_1.1.1
[4] Hmisc_4.8-0 Formula_1.2-5 survival_3.5-3
[7] lattice_0.20-45 drc_3.0-1 MASS_7.3-58.2
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loaded via a namespace (and not attached):
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