TITLE: 96-well Growth Curve Analysis
AUTHORS: Norman van Rhijn, Panagiotis Papastamoulis, Takanori Furukawa, Elaine Bignell, Mike Bromley, Magnus Rattray

[DESCRIPTION]
<div class = "text-blocks"><div class = "text-block"><span>Generally, growth assays for filamentous fungi have been performed on solid media, either as dilution series or spot tests. However, the solid media environment does not accurately mimic the environment encountered during infection (ie the mammalian lung). Previously, we have developed a methodology to perform liquid growth assays in time for </span><span style = "font-style:italic;">A. fumigatus </span><span>and other filamentous fungi including analysis via mathematical modelling.</span></div><div class = "text-block"><span>This protocol is designed for </span><span style = "font-style:italic;">Aspergillus </span><span>species (and other filamentous fungi) to generate growth curves in liquid media in a 96-well plate. This can be done in high-throughput to generate 96 growth curves per run.</span></div></div>

[STEPS]
?. [Pre-processing]
Import the file from the previous protocol into R studio.
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?. [Pre-processing]
You can find the script below that you need in order to read your raw data-files, format the file names like this:plate1-Rep1.txt  plate1-Rep3.txt  plate2-Rep2.txt  plate3-Rep1.txt  plate3-Rep3.txt  plate4-Rep2.txt  plate5-Rep1.txt  plate5-Rep3.txtplate1-Rep2.txt  plate2-Rep1.txt  plate2-Rep3.txt  plate3-Rep2.txt  plate4-Rep1.txt  plate4-Rep3.txt  plate5-Rep2.txt
Make sure the different replicates are in different files. However, the location in the plate should be exactly the same (ie. A5 should be A5, B2 should be B2). If this is not the case or replicates are on one plate, transform the original files to match this format. Furthermore, titles of the wells should exactly be the same!
?. [Pre-processing]
Apply the code below. Change the number of reps and plates to your experiment (top two lines). Make sure the data is formatted as above.The final object returned after applying the code is the list `myDataList`.
?. [Run Beast]
Install BEAST software from CRAN into R studio.https://rdrr.io/cran/beast/man/beast-package.htmlManual can be found: https://cran.r-project.org/web/packages/beast/beast.pdf
?. [Run Beast]
Run beast. The top is an example for data included in the package (don't run if you want to run your own data). Example dataset is attached to run.If you want an example first, run the top example and reload your data with the code above.For your own data run, only run the code from Run MCMC sampler and Print and Plot output lines.Assume that the observed data consists of N time-series, each one consisting of R variables (or replicates) measured at T consecutive time-points. The input of the main function beast should be given in the form of a list myDataList with the following attributes:	• length(myDataList) should be equal to R, that is, the number of variables (or replicates)	• dim(myDataList)[[1]],..., dim(myDataList)[[R]] should be all T×N, that is, rows and columns should correspond to time-points and different series, respectively.Then, a basic usage of the package consists of the following commands:	• beastRun 	• print(beastRun)	• plot(beastRun)which correspond to running the MCMC sampler, printing and plotting output summaries, respectively. Below a toy-example is given which reproduces Figure 1 of the paper. > library('beast')        	  # load package> data("FungalGrowthDataset")     # load dataset> myIndex #                                 specific subset of time-series> set.seed(1)              	  # optional# 	Run MCMC sampler with the default number of iterations (nIter =70000):> run_mcmc                      zeroNormalization = TRUE) # Print output:> print(run_mcmc)# Plot output to file: "beast_plot.pdf"> plot(run_mcmc, fileName = "beast_plot_fungal_data.pdf", timeScale=1/6, xlab = "hours", ylab = "growth")The complexity prior distribution with parameter gammaParameter = 2 is the default prior assumption imposed on the number of change-points.  Smaller (larger) values of gammaParameter will a-priori support larger (respectively: smaller) number of change-points. Full documentation of the package is given in https://cran.r-project.org/web/packages/beast/beast.pdf.
?. [Run Beast]
This should result in a PDF file containing all curves combined clustered in groups with different change-points and individual curves for each isolate.