GCG Super Script.R

In ddebrauske/GCG: Growth Curve Generator: Process and Analyze Plate Reader Experiments

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#  Made By Derek J. Debrauske, 2021
#  ddebrauske@gmail.com
#  ddebrauske@wisc.edu

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#Update, install and attach GCG package.
detach(package:GCG)
remove.packages("GCG")#remove GCG package from R root folder so you can update it.
#remove GCG package from workspace so you can update it.
devtools::install_github("ddebrauske/GCG", force=TRUE)#install GCG package from github
library(GCG) #add package to current R environment


#run "help()" commands to see more information on each function
#run "head()" commands to preview data


#Optional - Set the working directory to the folder you'd like to work from. (If you open a .R file, the wd will by default be the folder you opened from.)
setwd("C:/Users/Derek Debrauske/Dropbox/R/Projects/")



#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
##Act I: Gathering data
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#~~~~~~~~~~~~
#1. Step through plate layout input file, edited from template
#     This function returns a long data frame, combining all of the information from your plate layouts. Each row of this data frame refers to one well
help("PlateLayout")
layout <- PlateLayout("Plate_Layout.csv") #change this path to refer to th elocation of your Plate_layout file.
head(layout, n = 12) #run this to see what the converted format looks like.
unique(layout$Plate)

#~~~~~~~~~~~~
#2. Do the same with the blank input file.
# Returns a similar, long dataframe with wells as rows.
help("PlateBlank")#Converts matrix style blanks from template into long table
blank <- PlateBlank("Plate_Blanks.csv")  #change this path to refer to th elocation of your Plate_blank file.
head(blank, n=12) #check out what the output format looks like
unique(blank$Plate) #check that you got all your plates

#~~~~~~~~~~~~
#3. Combine all of the above information into one table.
help("CombineLayoutBlank")
layout.blanks <- CombineLayoutBlank(layoutDF = layout, blankDF = blank)
head(layout.blanks, n=12)

# ~~~~~~~~~~~~
# Optional step -- if your plate reader data is exported from your plate reader as a workbook with many sheets, you can use this function to separate these sheets into separate .csv files to be used in the next steps. (uncomment to use)
# help("ExcelToCSV")
# ExcelToCSV(path = "", out_dir = "")
# list.files("")

#~~~~~~~~~~~~
#4. Import plate reader data from .csv files into R.
#make if( no blank ){ don't subtract}
help("Import")
Timepoint.data <- Import(path = "Spark Data/", plate.reader.type = "spark", read.interval = 60)
head(Timepoint.data, n=12)

#~~~~~~~~~~~~
#5. attach information from layout and blank to plate reader data. this function back-subtracts the blank values from the OD600 of corresponding wells.
help("TimeseriesLayoutBlank")
# You can run this one of two ways:


#     1. Without back subtracting blanks: use only 
#data.combined.no.blank <- TimeseriesLayoutBlank(timepoint.df = Timepoint.data, layout.df = layout)
#head(data.combined.no.blank)


#     2. With back-subtracting blanks:
data.combined <- TimeseriesLayoutBlank(timepoint.df= Timepoint.data, layout.blank.df = layout.blanks)


#5.1 subset out all strains labeled as "ddH2O" these are my border wells
data.combined <- subset(data.combined, Strain != "ddH2O")
head(data.combined) #you can see now that there are no more "ddH2O" wells, and the row A (border) has been skipped.

#~~~~~~~~~~~~
#6. Summarize technical replicates
#this finds mean, SE of biological replicates. if there are technical replicates, they are averaged first, then mean and SE is calculated from these means.
help("SummarizeTechreps")
Tech.Rep.Summary <- SummarizeTechReps(data.combined)
head(Tech.Rep.Summary)

#~~~~~~~~~~~~
#7. Summarize Biological Replicates
help("SummarizeBioReps")
Bio.Rep.Summary <- SummarizeBioReps(Tech.Rep.Summary)
head(Bio.Rep.Summary)



#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
##Act II: Plotting Curves
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#~~~~~~~~~~~~
# Plot all replicate data as a wrap of all conditions
## NOTE: Use "./" as working directory (this shorthand for working directory in R). we were able to skip this detail in previous sections and simply type "". 
help("GCGplot_wrap")
p <- GCGplot_wrap(Bio.Rep.Summary, out.dir= "./", graphic.title = "ChemGen Validation R2")
print(p)

#~~~~~~~~~~~~
# Plot individual conditions
help("GCGplot_conds")
GCGplot_conds(Tech.Rep.Summary, graphic.title ="ChemGen Validation R2", out.dir= "./")#see results in folder

#~~~~~~~~~~~~
#plot all wells to spot-check plates.
help("GCGplot_matrices")
GCGplot_matrices(data.combined, out.dir = "./" , graphic.title = "ChemGen Validation R2", matrix.columns = 10 )#see results in folder

#~~~~~~~~~~~~
#plot each biological rep as a separate facet_wrap
help("GCGplot_bioreps")
GCGplot_bioreps(data.combined, out.dir = "./", graphic.title = "ChemGen Validation R2")#see results in folder




#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
##Act III: Other analysis
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#1. Find Area Under the Curve
eAUC_data <- SuperAUC(data.combined, TRUE)

#2. Prepare data for analysis with growthcuver
gcr_input <- Growthcurver_convert(data.combined)

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