gcplyr: an R package for microbial growth curve data analysis

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 addr...

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Veröffentlicht in:BMC bioinformatics Jg. 25; H. 1; S. 232 - 10
1. Verfasser: Blazanin, Michael
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London BioMed Central 09.07.2024
BioMed Central Ltd
Springer Nature B.V
BMC
Schlagworte:
Algorithms
Applications software
Bacteria
Bioinformatics
Biomedical and Life Sciences
Carrying capacity
Computational Biology - methods
Computational Biology/Bioinformatics
Computer Appl. in Life Sciences
Data Analysis
Design
Diauxie
Doubling time
Electronic data processing
Growth
Growth curve
Growth curves
Growth rate
Imports
Lag time
Life Sciences
Mathematics
Metadata
Methods
Microarrays
Microbiological research
Microbiology
Microorganisms
Population growth
R (Programming language)
Software
Statistical analysis
Technology application
Visualization
Visualization (Computers)
United States
Carrying capacity
Microbiology
Growth rate
Doubling time
Growth
Lag time
Software
Tidy data
Modeling
Growth curve
ISSN:1471-2105, 1471-2105
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
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ISSN:1471-2105
1471-2105
DOI:10.1186/s12859-024-05817-3