NormaliseForIC50

pre-process data to return normalise IC50 curves for direct input into PRISM

This script automates the normalization process for neutralization assays performed on a 96-well plate read on the Promega system, and more generally, serial dilution results with columns/rows of negative and positive controls.
This script achieved perfect concordance with the normalization in PRISM (R=1.0, ρ<2.2e-16).

INPUT: The script currently only reads in the .xlsx file outputs from Promega in a given directory.
OUTPUT: 0 to 100 normalized data sheets collated into the same .xlsx file that can be imported/copied and pasted back to PRISM for IC50 curve fitting.

Installation

Install from github using devtools.

install.packages("devtools") # if you have not installed "devtools" package
devtools::install_github("TKMarkCheng/NormaliseForIC50")

To use the script

Method 1

loaded onto R via

if(!require("remotes"))install.packages("remotes",repos="http://cran.us.r-project.org")
remotes::install_github("TKMarkCheng/NormaliseForIC50",dependencies = TRUE, force = TRUE)

A detailed tutorial is available at vignettes/introduction.html.

• It is very important that you change the input_file, input_directory, and output_file path and names to your own.

Method 2

0. Clone/Download the github repository.
1. Move all the Promega read .xlsx files into the same folder (your input_directory)
2. In the normalisation.R script, Change the input_directory, input_file and output_file to appropriate names
   • input_directory=The folder you made on step 2 where you keep all your Promega read .xlsx files
   • input_file=Any file from your input_directory as a sanity check.
   • output_file=What you want to name the new Excel file (default=test.xlsx)
3. Run normalisation.R from start to finish

Rotated Plate

Rotation defaults to 0 (A1 at top left corner).
In cases where the plate was accidentally rotated, or a vertical serial dilution was performed, we can clockwise rotate the read orientation by a multiple of 90° by changing rotate_by.

Automatic detection of anomalies

Thanks to helpful summer student Kate

Proof of work - Validation

Validation set of 10 plates. Further details can be seen in the Validation directory.

Using a Plate Map

A detailed tutorial is available at vignettes/How_to_integrate_a_platemap.html If you're using modifying a prior made plate map, a strict format of the platemap as specified in Validation/validation_output/example_generated_platemap_manual_changes.xlsx must be followed.

Common issues

Error in utils::unzip(zip_path, list = TRUE) :
zip file '\~$.xlsx' cannot be opened`

You Need to close that (and all) excel file in your target directory for R to be able to read it.