ifcb_read_summary: Read and Summarize Classified IFCB Data
In iRfcb: Tools for Managing Imaging FlowCytobot (IFCB) Data

ifcb_read_summary

R Documentation

Read and Summarize Classified IFCB Data

Description

This function reads a MATLAB .mat file containing aggregated and classified IFCB (Imaging FlowCytobot) data generated by the countcells_allTBnew_user_training function from the ifcb-analysis repository (Sosik and Olson 2007), or a list of classified data generated by ifcb_summarize_class_counts. It returns a data frame with species counts and optionally biovolume information based on specified thresholds.

Usage

ifcb_read_summary(
  summary,
  hdr_directory = NULL,
  biovolume = FALSE,
  threshold = "opt",
  use_python = FALSE
)

Arguments

`summary`	A character string specifying the path to the `.mat` summary file or a list generated by `ifcb_summarize_class_counts`.
`hdr_directory`	A character string specifying the path to the directory containing header (.hdr) files. Default is NULL.
`biovolume`	A logical indicating whether the file contains biovolume data. Default is FALSE.
`threshold`	A character string specifying the threshold type for counts and biovolume. Options are "opt" (default), "adhoc", and "none".
`use_python`	Logical. If `TRUE`, attempts to read the `.mat` file using a Python-based method. Default is `FALSE`.

Details

If use_python = TRUE, the function tries to read the .mat file using ifcb_read_mat(), which relies on SciPy. This approach may be faster than the default approach using R.matlab::readMat(), especially for large .mat files. To enable this functionality, ensure Python is properly configured with the required dependencies. You can initialize the Python environment and install necessary packages using ifcb_py_install().

If use_python = FALSE or if SciPy is not available, the function falls back to using R.matlab::readMat().

Value

A data frame containing the summary information including file list, volume analyzed, species counts, optionally biovolume, and other metadata.

References

Sosik, H. M. and Olson, R. J. (2007), Automated taxonomic classification of phytoplankton sampled with imaging-in-flow cytometry. Limnol. Oceanogr: Methods 5, 204–216.

Examples

mat_file <- system.file("exdata/example_summary.mat", package = "iRfcb")

summary_data <- ifcb_read_summary(mat_file, biovolume = FALSE, threshold = "opt")
print(summary_data)

iRfcb documentation built on April 16, 2025, 1:09 a.m.