Obtaining maximum information from colony counts with REPOP
Bacterial counts from native environments, such as soil or the animal gut, often show substantial variability across replicate samples. This heterogeneity is typically attributed to genetic or environmental factors. A common approach to estimating bacterial populations involves successive dilution and plating, followed by multiplying colony counts by dilution factors. This method, however,…
Standard bacterial population estimation techniques, like successive dilution and plating followed by colony counting, typically result in overestimation of heterogeneity. This is due to the combination of genetic or environmental factors and inherent uncertainties in sampling. These methods overlook potential subpopulations within the data, particularly when observing multimodal patterns in bacterial samples from sources like Caenorhabditis elegans gut microbiota.
To address this, researchers have developed REPOP, a PyTorch-based library that reconstructs bacterial populations using a Bayesian framework. REPOP explicitly accounts for the uncertainty introduced by dilution and plating processes, correcting biases that arise when excluding plates considered too crowded to resolve individual colonies.
Demonstrations using both simulated and experimental datasets reveal that REPOP effectively recovers the underlying multimodality, where conventional methods fail. REPOP’s capabilities and available on GitHub: https://github.com/LabPresse/REPOP.
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