flan: an R package for inference on mutation models.
Open accessAbstract
This paper describes flan, a package providing tools for fluctuation analysis of mutant cell counts.It includes functions dedicated to the distribution of final numbers of mutant cells.Parametric estimation and hypothesis testing are also implemented, enabling inference on different sorts of data with several possible methods.An overview of the subject is proposed.The general form of mutation models is described, including the classical models as particular cases.Estimating from a model, when the data have been generated by another, induces different possible biases, which are identified and discussed.The three estimation methods available in the package are described, and their mean squared errors are compared.Finally, implementation is discussed, and a few examples of usage on real data sets are given.
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The inability to estimate w in the examples given by Mazoyer et al. (2017) is indicative of practical nonidentifiability.
“The inability to estimate w in the examples given by Mazoyer et al. (2017) is indicative of practical nonidentifiability.”
From rSalvador: An R Package for the Fluctuation Experiment (2017) · cites this paper as Mazoyer et al. (2017)
The User interface section identifies w as the Winsorization cutoff for the ML method, set to 1024 by default, not as a parameter the examples try and fail to estimate. The paper discusses failures to estimate ρ under some conditions, but does not present an inability to estimate w as evidence of practical nonidentifiability. Page numbers are unavailable in our copy. (LLM, conf=0.85)
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Experimental — accuracy not yet measuredThe C.I.s that extend to zero in the examples given by Mazoyer et al. (2017) are a symptom of practical nonidentifiability.
“In addition, the C.I.s that extend to zero in these examples are a symptom of the same problem.”
From rSalvador: An R Package for the Fluctuation Experiment (2017) · cites this paper as Mazoyer et al. (2017)
In the David-data example (page unavailable), all three reported 95% confidence intervals have lower bounds of zero, and the paper says the intervals are quite large because the sample is small. It does not identify those intervals as a symptom of practical nonidentifiability. (LLM, conf=0.86)
Experimental — accuracy not yet measuredMazoyer et al. (2017) proposed a new method based on asymptotic normality of estimated mutation rates.
“Mazoyer et al. (2017) proposed a new method based on asymptotic normality of estimated mutation rates.”
From rSalvador: An R Package for the Fluctuation Experiment (2017) · cites this paper as Mazoyer et al. (2017)
The Introduction says the estimators used by flan are asymptotically normal, and the User interface section says flan.test uses that property for hypothesis testing. But the paper describes a package implementing three existing estimation methods (P0, ML and GF), not the proposal of a new mutation-rate estimation method based on asymptotic normality. (LLM, conf=0.88)
Experimental — accuracy not yet measuredAt first glance the plating efficiency in Werngren and Hoffner (2003) may seem to be 0.2, as was assumed by Mazoyer et al. (2017).
“But at first glance the plating efficiency may seem to be 0.2, as was assumed by Mazoyer et al. (2017).”
From rSalvador: An R Package for the Fluctuation Experiment (2017) · cites this paper as Mazoyer et al. (2017)
At least three versions of the GF method are available: Hamon and Ycart (2012), bz-rates (Gillet-Markowska et al. 2015), and flan (Mazoyer et al. 2017).
“At least three versions of the GF method are available: that given by Hamon and Ycart (2012) via the R code attendant to their paper, that given by the web tool bz-rates (Gillet-Markowska et al. 2015), and that given by the R package flan (Mazoyer et al. 2017).”
From rSalvador: An R Package for the Fluctuation Experiment (2017) · cites this paper as Mazoyer et al. 2017
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References 34 · Cited by 33
Research works printed but not linked: 1
- B. Ycart. Fluctuation analysis with cell deaths. J. Appl. Probab. Statist, 9(1):13-29, 2014. URL https: //arxiv.org/abs/1207.4375. [p335, 337]
- Authors
- Mazoyer, A., Drouilhet, R., Despréaux, S., & Ycart, B.
- Affiliations
- Laboratoire Jean Kuntzmann (FR)
- Venue
- The R Journal
- Publisher
- The R Foundation
- Published
- 2017
- Volume
- 9
- Issue
- 1
- Pages
- 334
- ISSN
- 2073-4859 (ISSN-L)
- OpenAlex
- W2610250630
- Cited by
- 33
- References
- 34
- Citations by year
- 2026: 1 · 2025: 6 · 2024: 6 · 2023: 7 · 2022: 3 · 2020: 2 · 2019: 2 · 2018: 4 · 2017: 2
- FWCI
- 1.91
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- The R Journal (publishedVersion, cc-by)
- Lincoln (University of Nebraska) (submittedVersion, cc-by)
- HAL (submittedVersion, other-oa)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Probability and Statistical Research
- Physical Sciences › Computer Science › Artificial Intelligence › Data Analysis with R
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Evolution and Genetic Dynamics
- Keywords
- mutation models, fluctuation analysis, parameter estimation, hypothesis testing, estimation bias, mean squared error comparison, R package
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