Abstract
Premise of the StudyDespite 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 ResultsflowPloidy 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.ConclusionsflowPloidy 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.
Highlights
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
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
Summary
AND RESULTS: flowPloidy builds on the existing statistical tools provided with the R environment. FlowPloidy provides nonlinear- regression-based analysis of FCM histograms This technique has only been available in expensive proprietary software, contributing to its low adoption in the botanical community. FCM histogram analysis requires the user to visually isolate histogram peaks from which to extract the parameters used to calculate genome size and associated statistics, including cell counts and the coefficient of variation (CV). This process is inherently subjective; the CV can be lowered by narrowing the gate, negating its value in quality control. Smith et al.—flowPloidy R package 2 of 4 freely available as part of the open source R Statistical Computing Environment (R Core Team, 2017); (2) as part of the R environment, flowPloidy provides results that can be directly incorporated into further analyses within that system, without requiring users to export or convert to different formats; (3) the source code is available for inspection and modification, enabling advanced users to further customize or extend their analyses; and (4) the workflow is simplified relative to other programs, focusing only on those features necessary for genome size assessment
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