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The uploaded code should work on any system able to execute R code and install the tidyverse, drc, reshape, and Hmisc packages. Calls to install these packages are at the head of the R script, which can/should be commented out once required packages are installed. We have also included the Jupyter notebook code given the popularity of the platform, though setup of Jupyter to run R scripts is considered out side the scope of this documentation.
The existing scripts have been tested on R version 4.1.1 and later with the i386 architecture, and do not have specific hardware requirements. Assuming a properly installed R console, installation of the required packages typically takes 3-10 depending on internet connection and processing power. The script itself requires no other installation and should typically have a fairly rapid run time with datasets of under 200 plates.
The following code takes a folder containing a series of CSV files that contain the quantification of foci from an FRNT assay, parses the data in the CSV files, attempts a L3 fit for each paring, and produces a ggplot for each grouping. To avoid fits that are less appropriate, if a given fit produces a Hill Constant that is less than 0.5 or greater than 2, the code will force a Hill Constant of 1, and then attempt a fit with the other two parameters.
The structure of the CSV should be as described in the HeaderDetails.txt file. Generally the code is currently setup to handle the data where the first row contains metadata, and the following 8 rows contain the count of foci in a 96-well format. The code is currently setup to process one virus strain per plate and two serum samples across seven dilutions (6 replicates per sera/antibody per dilution and a no-serum positive control). This could be modified as needed to cover different plate layouts, but this layout covers our standard FRNT protocols of 6 replicates per pair and dilution.
For convenience the EC50, EC60, EC70, and EC80 are calculated, and (1/EC50) is given for the reported FRNT50 value. In other words, the value to answer the question, "what dilution ratio of sera will one need to achieve to neutralize 50% of the viral readout for this virus," or how should one dilute a particular sera or antibody sample to achieve a 50% reduction in foci for a particular virus.
Given a folder of correct input CSV files (set in a combination of lines 21 and 29) the output should be a single csv file named "final_ec50.csv" written in an output folder set in line 25. There will also be a single normalized graph produced per "Graph Group" designated in the collection of input CSV files.
All that is required to run the code in a proper environment is to set a working directory, (line 21), an input subdirectory of the working directory (line 29), and an output subdirectory of the working directory (line 25). After this, and assuming valid plates CSV files in the input directory, the code can be run simply by coping and pasting the whole code into the R Console. There are, of course, many ways to execute R code, but this simple method should work.
When the provided example plate is run, it should produce three files, as seen in the Expected Results folder.
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