I ‘R’ FlowCytobot: Tools for analyzing and processing data from the Imaging FlowCytobot (IFCB)

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Overview

The iRfcb R package provides tools for working with Imaging FlowCytobot (IFCB) data, including quality control, particle size distribution analysis, and handling of annotated image data. This package facilitates the processing, analysis, and preparation of IFCB images and data for publication. It is especially useful for researchers using, or partly using, the MATLAB ifcb-analysis package (Sosik and Olson 2007).

Key Features

  • Data Management: Comprehensive functions for reading IFCB files, counting and summarizing annotated and classified image data, and correcting annotated data.
  • Quality Control: Tools for analyzing particle size distribution and ensuring high data quality (Hayashi et al. in prep).
  • Geospatial Analysis: Functions to determine if the IFCB is near land or within specific marine basins.
  • Image Extraction: Tools to extract and prepare images for publication.
  • Image Gallery: Interactive gallery for viewing and selecting IFCB images.
  • Taxonomical Data: Tools for handling and analyzing taxonomic data and calculating biomass concentration from image data (Menden-Deuer and Lessard 2000).

Installation

You can install the package from GitHub using the devtools package:

# install.packages("devtools")
devtools::install_github("EuropeanIFCBGroup/iRfcb", dependencies = TRUE)

Some functions in iRfcb require Python. You can download Python from the official website: python.org/downloads. For more details, please visit the project’s webpage.

Documentation and Tutorials

Tutorial

Reference

Repository

For more details and the latest updates, visit the GitHub repository.

License

This package is licensed under the MIT License.

References

  • Hayashi, K., Walton, J., Lie, A., Smith, J., and Kudela, M. Using particle size distribution (PSD) to automate imaging flow cytobot (IFCB) data quality in coastal California, USA. In prep.
  • Menden-Deuer, S., & Lessard, E. J. (2000). Carbon to volume relationships for dinoflagellates, diatoms, and other protist plankton. Limnology and Oceanography, 45(3), 569-579. doi: 10.4319/lo.2000.45.3.0569.
  • Sosik, H. M., & Olson, R. J. (2007). Automated taxonomic classification of phytoplankton sampled with imaging-in-flow cytometry. Limnology and Oceanography: Methods, 5, 204-216.