Premise of the study: Despite advantages in terms of reproducibility, histogram analysis based on nonlinear regression is rarely used in genome size assessments in plant biology. This is due in part to the lack of a freely available program to implement the procedure. We have developed such a program, the R package flowPloidy.
Methods and results: flowPloidy builds on the existing statistical tools provided with the R environment. This base provides tools for importing flow cytometry data, fitting nonlinear regressions, and interactively visualizing data. flowPloidy adds tools for building flow cytometry models, fitting the models to histogram data, and producing visual and tabular summaries of the results.
Conclusions: flowPloidy fills an important gap in the study of plant genome size. This package will enable plant scientists to apply a more powerful statistical technique for assessing genome size. flowPloidy improves on existing software options by providing a no-cost workflow streamlined for genome size and ploidy determination.
Keywords: R package; flow cytometry; genome size; polyploidy.