Quantitative Fitness Analysis (QFA) is a complementary series of experimental and computational methods for estimating the fitness of thousands of microbial cultures in parallel. QFA is suitable for focussed, high-quality studies of the effect of genetic mutations or drug interventions on growth in model microbial organisms such as brewer's yeast. Culture growth is observed by time-lapse photography of solid agar plates inoculated with cultures in rectangular arrays. Growth curves are constructed by analysing image series using Colonyzer image analysis software (http://research.ncl.ac.uk/colonyzer) which converts images to arrays of cell density estimates. This R package is for a) fitting the generalised logistic model to potentially thousands of parallel growth curves, b) using inferred parameter values to calculate fitnesses for each culture and c) comparing fitnesses between QFA experiments with different genetic backgrounds or treatments to deduce interaction strengths. This package facilitates quantifying the fitness of thousands of independent microbial strains and tracking them throughout growth curve experiments. With appropriately designed experiments, qfa can also estimate genetic interaction strengths and produce epistasis plots.
|Author||Conor Lawless <firstname.lastname@example.org>, with contributions from Alexander Young <email@example.com> and Darren Wilkinson <firstname.lastname@example.org>|
|Date of publication||2017-02-23 16:59:13|
|Maintainer||Conor Lawless <email@example.com>|
|Package repository||View on R-Forge|
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