normalisePlates: Normalising culture fitness by plate

In JulBaer/baQFA: Quantitative Fitness Analysis Version 2

Normalising culture fitness by plate

Description

Sometimes estimated culture fitnesses vary systematically depending on the plate on which they are inoculated. Agar in individual plates could come from different batches, and therefore have slightly different levels of nutrients or water. Plates could be inoculated at different times, and stored at slightly different temperatures for example. Depending on inoculation method, inoculation time specified may be less accurate for individual plates. Any of these issues could effect simulated fitness slightly. This function allows us to normalise culture fitnessses across plates to eliminate such effects. It should only really be used for small differences. In order to preserve real signal, the experimental sources of larger differences should be corrected before analysis instead of by normalising them away. This normalisation function only works if there are more than one plate in the input data.frame as (of course) otherwise there is no normalisation between plates possible.

Usage

normalisePlates(d, column, groupcol = "Treatment", fmin = 1e-06)

Arguments

d	data.frame. Output from qfa.fit2 for normalization
column	String. name of column to normalise (typically the fitness measure or model fit value of interest). See qfa.fit2 and makeFitness2 help files for descriptions of available culture fitness measures
groupcol	String. Name of column labelling group membership of each plate/barcode. Typically this will be the Treatment or Medium column (or some combination of both if there are more than one treatment and more than one medium within the dataframe d)
fmin	Epsilon (very small number greater than zero). Do not scale by dividing fitnesses by a plate-wide median if median is less than fmin

Details

Starting with a data frame describing the output from the qfa.fit function (with optional added columns from the makeFitness function) normalisePlates finds all unique groups in that data frame, calculates a median value from the indicated column for all plates in a given group and then normalises the fitnesses of each culture on each plate so that the median fitness on each plate is equal to the median fitness for all plates in the group. The function returns a vector which can be added to the original data frame or used to over-write the original, raw data.

Typically a "group" will be a description of treatment (e.g. temperature) or of growth medium (e.g. drug added to solid agar) or differenet strains.

Value

Vector. The output vector contains the normalised values of the input column. This can be added to the input data.frame or replace the original values.

Examples

data(qfa.testdata)
#Strip non-experimental edge cultures
qfa.testdata = qfa.testdata[(qfa.testdata$Row!=1) & (qfa.testdata$Col!=1) & (qfa.testdata$Row!=8) & (qfa.testdata$Col!=12),]
# Define which measure of cell density to use
qfa.testdata$Growth = qfa.testdata$Intensity
GmpFit = qfa.fit2(qfa.testdata, inocguess=NULL, detectThresh=0, globalOpt=F, AUCLim=NA, TimeFormat="h", Model="Gmp")
# Construct fitness measures
GmpFit = makeFitness2(GmpFit, AUCLim=NA, plotFitness="no", filename = NA)
#create a mock data.frame with slightly increased MDR values
GmpFit_m = GmpFit
GmpFit_m$MDR = GmpFit_m$MDR + rnorm(length(GmpFit_m$MDR), mean = 0.3, sd = 0.1)
#change barcode of mock data.frame and combine into one
GmpFit_m$Barcode = "Mock"
GmpFit_m = rbind(GmpFit, GmpFit_m)
#Add the normalised values of MDR, indicate that the different treatments are stored within ORF column
GmpFit_m$MDRnorm = normalisePlates(GmpFit_m, "MDR", "ORF")
#create plots
qfaFitnessPlot(GmpFit_m, plotFitness = "MDR")

JulBaer/baQFA documentation built on Feb. 19, 2023, 10:32 p.m.