gcplyr: an R package for microbial growth curve data analysis

Abstract

Background: 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.

Results: 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.

Conclusions: 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.

Keywords: Carrying capacity; Doubling time; Growth; Growth curve; Growth rate; Lag time; Microbiology; Modeling; Software; Tidy data.

Fig. 1

Workflow to use gcplyr to analyze microbial growth curve data. Growth curve data are commonly found in one of three layouts: block-shaped, wide-shaped, or tidy-shaped. Block-shaped data are organized to match the physical layout of the multi-well plate. Wide-shaped data contain one column for each well from a plate. Tidy data contain all the observations in a single column. gcplyr functions import data files regardless of the data layout, reshape them into tidy-shaped data, then merge them with experimental design information. Data and designs can then be smoothed and have derivatives calculated before extracting growth curve metrics. Metrics can be easily merged with other non-growth curve experimental data before statistical analyses and visualization

Fig. 2

Example analysis of growth curves with gcplyr. I used gcplyr to calculate several common metrics for two previously-published growth curves of Pseudomonas fluorescens isolates [28]: one ancestral isolate, and one experimentally evolved descendant isolate. A Here I used gcplyr to calculate the maximum bacterial density. B Here I used gcplyr to calculate the total area under the curve. C Here I used gcplyr to calculate cellular growth rate from the slope of log-transformed density with a rolling window five data points (75 min) wide