What is it about?
In this work, we develop a statistical pipeline to infer fitness values from tracking relative abundances of microbial strains via high-throughput sequencing. In other words, experimentally, we can track the dynamics of a growing culture with many uniquely-identified strains as they evolve. From this tracking information, we want to learn the relative fitness of each of these strains. Here, we use Bayesian statistics not only to extract these fitness values but also to report the level of uncertainty we have about these inferred parameters.
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Why is it important?
When extracting quantitative information from experimental assays via fitting a model, there are two main things to consider: 1. The mathematical model we use to describe how we think the data was generated. 2. The statistical analysis we deploy to fit the model's parameters. Addressing the second point, it becomes evident that extracting the values of the parameters we are interested in and quantifying the confidence we should assign to them is of the utmost importance. In essence, we need to communicate the level of uncertainty in our estimated parameters, considering the inherent noise in our measurements. This is where Bayesian statistics comes to our aid with its apt mathematical formalism. In this work, we generate a computational tool for researchers working on experimental evolution to deploy a Bayesian inference pipeline on their experimental data, helping the field to improve their data analysis workflow.
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This page is a summary of: Bayesian inference of relative fitness on high-throughput pooled competition assays, PLoS Computational Biology, March 2024, PLOS,
DOI: 10.1371/journal.pcbi.1011937.
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