Skip to contents

gcplyr

What this package can do

gcplyr makes 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 generate them in R, and can merge experimental design information with density data from plate readers.
  • gcplyr makes it easy to analyze growth curve data with tidyverse or dplyr functions, and to plot growth curve data with ggplot2.
  • 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.
  • gcplyr has several methods to deal with noise in growth curve data.

Please send all questions, requests, comments, and bugs to mikeblazanin [at] gmail [dot] com

Installation

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

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 read through the articles series, which breaks down a typical workflow using gcplyr from start to finish, starting with the introduction:

  1. Introduction: vignette("gc01_gcplyr")
  2. Importing and transforming data: vignette("gc02_import_reshape")
  3. Incorporating experimental designs: vignette("gc03_incorporate_designs")
  4. Pre-processing and plotting your data: vignette("gc04_preprocess_plot")
  5. Processing your data: vignette("gc05_process")
  6. Analyzing your data: vignette("gc06_analyze")
  7. Dealing with noise: vignette("gc07_noise")
  8. Best practices and other tips: vignette("gc08_conclusion")
  9. Working with multiple plates: vignette("gc09_multiple_plates")
  10. Using make_design to generate experimental designs: vignette("gc10_using_make_design")

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