Lilikoi: an R package for personalized pathway-based classification modeling using metabolomics data

Abstract Lilikoi (the Hawaiian word for passion fruit) is a new and comprehensive R package for personalized pathway-based classification modeling using metabolomics data. Four basic modules are presented as the backbone of the package: feature mapping module, which standardizes the metabolite names provided by users and maps them to pathways; dimension transformation module, which transforms the metabolomic profiles to personalized pathway-based profiles using pathway deregulation scores; feature selection module, which helps to select the significant pathway features related to the disease phenotypes; and classification and prediction module, which offers various machine learning classification algorithms. The package is freely available under the GPLv3 license through the github repository at: https://github.com/lanagarmire/lilikoi and CRAN: https://cran.r-project.org/web/packages/lilikoi/index.html.


1) Lack of novel contribution.
It is clear from the manuscript that a key contribution of the package is to use the 'pathifier' package to convert from metabolite concentration profiles to pathway profiles based on PDS scores. The authors have shown how such approach was able to obtain novel insights compared to other approaches. However, a) The algorithm is not a novel contribution, as the pathifier is already published package. b) In terms of novel application (or novel biological insights), the authors' group already demonstrated this in their 2016 Genome Medicine paper "Novel personalized pathway-based metabolomics models reveal key metabolic pathways for breast cancer diagnosis".
2) Lack of user friendliness. a) Users need to know R in order to use this package. However, for researchers familiar with R, they can almost do all these tasks without using this pipeline, as almost all its component packages are already available. Such a pipeline should ideally be implemented to have graphical user interface (GUI) so other people (who don't know R) can still use. b) Even as an R package, the standard publication route is CRAN, with built-in unit testing and vignette tutorial. Installing R package from CRAN is very straightforward as compared to GitHub. In addition, publishing on CRAN also facilitates quality assurance and long-term maintenance.
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