Wrangle and Analyze Growth Curve Data

Easy wrangling and model-free analysis of microbial growth curve data, as commonly output by plate readers. Tools for reshaping common plate reader outputs into 'tidy' formats and merging them with design information, making data easy to work with using 'gcplyr' and other packages. Also streamlines common growth curve processing steps, like smoothing and calculating derivatives, and facilitates model-free characterization and analysis of growth data. See methods at < https://mikeblazanin.github.io/gcplyr/>.

gcplyr

What this package can do

gcplyr was created to make it easier to import, wrangle, and do model-free analyses of microbial growth curve data, as commonly output by plate readers.

• gcplyr can flexibly import all the common data formats output by plate readers and reshape them into ‘tidy’ formats for analyses.
• gcplyr can import experimental designs from files or directly in R, then merge this design information with density data.
• This merged tidy-shaped data is then easy to work with and plot using functions from gcplyr and popular packages dplyr and ggplot2.
• gcplyr can calculate plain and per-capita derivatives of density data.
• gcplyr has several methods to deal with noise in density or derivatives data.
• gcplyr can extract parameters like growth rate/doubling time, maximum density (carrying capacity), lag time, area under the curve, diauxic shifts, extinction, and more without fitting an equation for growth to your data.

Please send all questions, requests, comments, and bugs to [email protected]

Installation

You can install the version most-recently released on CRAN by running the following line in R:

install.packages("gcplyr")

You can install the most recently-released version from GitHub by running the following lines in R:

install.packages("devtools")
devtools::install_github("mikeblazanin/gcplyr")

Getting Started

The best way to get started is to check out the online documentation, which includes examples of all of the most common gcplyr functions and walks through how to import, reshape, and analyze growth curve data using gcplyr from start to finish.

This documentation is also available as a series of pdf vignette files:

1. Introduction
2. Importing and transforming data
3. Incorporating experimental designs
4. Pre-processing and plotting data
5. Processing data
6. Analyzing data
7. Dealing with noise
8. Best practices and other tips
9. Working with multiple plates
10. Using make_design to generate experimental designs

Citation

Please cite software as:

Blazanin, M. gcplyr: an R package for microbial growth curve data analysis. BMC Bioinformatics 25, 232 (2024). https://doi.org/10.1186/s12859-024-05817-3