Sicegar: R package for sigmoidal and double-sigmoidal curve fitting
- Published
- Accepted
- Subject Areas
- Bioinformatics, Computational Biology, Mathematical Biology, Statistics, Data Science
- Keywords
- R package, curve fitting, sigmoidal growth, double-sigmoidal growth
- Copyright
- © 2017 Caglar et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2017. Sicegar: R package for sigmoidal and double-sigmoidal curve fitting. PeerJ Preprints 5:e3116v1 https://doi.org/10.7287/peerj.preprints.3116v1
Abstract
Sigmoidal and double-sigmoidal dynamics are commonly observed in many areas of biology. Here we present sicegar, an R package for the automated fitting and classification of sigmoidal and double-sigmodial data. The package categorizes data into one of three categories, "no signal", "sigmodial", or "double sigmoidal", by rigorously fitting a series of mathematical models to the data. The data is labeled as "ambiguous" if neither the sigmoidal nor double-sigmoidal model fit the data well. In addition to performing the classification, the package also reports a wealth of metrics as well as biologically meaningful parameters describing the sigmoidal or double-sigmoidal curves. In extensive simulations, we find that the package performs well, can recover the original dynamics even under fairly high noise levels, and will typically classify curves as "ambiguous" rather than misclassifying them. The package is available on CRAN and comes with extensive documentation and usage examples.
Author Comment
This preprint describes the sicegar R package available on CRAN at: https://cran.r-project.org/package=sicegar