gcplyr: an R package for microbial growth curve data analysis

TL;DR

gcplyr is an R package that simplifies the analysis of microbial growth curve data using a tidy framework and model-free methods.

Contribution

gcplyr introduces a flexible and extensible R package for analyzing microbial growth curves without requiring parametric assumptions.

Findings

gcplyr supports importing and reshaping growth curve data in a tidy format for custom analyses.

The package enables model-free extraction of traits like growth rate, doubling time, and carrying capacity.

gcplyr integrates with visualization and statistical analysis tools for streamlined microbial data analysis.

Abstract

Characterization of microbial growth is of both fundamental and applied interest. Modern platforms can automate collection of high-throughput microbial growth curves, necessitating the development of computational tools to handle and analyze these data to produce insights.

To address this need, here I present a newly-developed R package: gcplyr. gcplyr can flexibly import growth curve data in common tabular formats, and reshapes it under a tidy framework that is flexible and extendable, enabling users to design custom analyses or plot data with popular visualization packages. gcplyr can also incorporate metadata and generate or import experimental designs to merge with data. Finally, gcplyr carries out model-free (non-parametric) analyses. These analyses do not require mathematical assumptions about microbial growth dynamics, and gcplyr is able to extract a broad range of important traits, including growth rate, doubling time, lag time, maximum density and carrying capacity, diauxie, area under the curve, extinction time, and more.

gcplyr makes scripted analyses of growth curve data in R straightforward, streamlines common data wrangling and analysis steps, and easily integrates with common visualization and statistical analyses.

The online version contains supplementary material available at 10.1186/s12859-024-05817-3.

Figures