Table 2:

Selected open source (R/Bioconductor/Web-based) tools for supervised learning algorithms.

Method	Source	Reference
PLS-DA	Bioconductor (ropls)	[1]
PLS-DA, RF and SVM	Bioconductor (biosigner)	[2]
SVM, RF	Bioconductor (MLSeq)	[3]
RF, SVM, PLS-DA	Metaboanalyst http://www.metaboanalyst.ca/	[4]
PCA, PLS-DA, RF	Bioconductor (statTarget)	[5]
Feature selection, Metric evaluation	Bioconductor (OmicsMarker)	[6]
Sparse PLS-DA	Bioconductor (mixOmics)	[7]
Feature selection, Metric evaluation	CRAN (lilikoi)	[8]
Probabilistic Principal Component Analysis	CRAN (MetabolAnalyze)	[9]
Kernel-based Metabolite Differential Analysis	CRAN (KMDA)	[10]
PLS-DA, OPLS-DA	CRAN (muma)	[11]
RF	CRAN (RFmarkerDetector)	[12]
RF, SVM, PLS-DA	CRAN (caret)	[13]

References

^1.Thévenot, E.A.: ropls: PCA, PLS (-DA) and OPLS (-DA) for multivariate analysis and feature selection of omics data. (2016).

^2.Rinaudo, P., Boudah, S., Junot, C., Thévenot, E.A.: Biosigner: a new method for the discovery of significant molecular signatures from omics data. Frontiers in molecular biosciences 3, 26 (2016).

^3.Zararsiz, G., Goksuluk, D., Korkmaz, S., Eldem, V., Duru, I.P., Unver, T., Ozturk, A., Zararsiz, M.G., klaR, M., biocViews Sequencing, R.: Package ‘MLSeq’. (2014).

^4.Xia, J., Psychogios, N., Young, N., Wishart, D.S.: MetaboAnalyst: a web server for metabolomic data analysis and interpretation. Nucleic acids research 37(suppl_2), W652-W660 (2009).

^5.Luan, H., Ji, F., Chen, Y., Cai, Z.: statTarget: A streamlined tool for signal drift correction and interpretations of quantitative mass spectrometry-based omics data. Analytica chimica acta 1036, 66–72 (2018).

^6.Determan Jr, C.E., Determan Jr, M.C.E.: Package ‘OmicsMarkeR’. (2015).

^7.Rohart, F., Gautier, B., Singh, A., Le Cao, K.-A.: mixOmics: An R package for ‘omics feature selection and multiple data integration. PLoS computational biology 13(11), e1005752 (2017).

^8.Al-Akwaa, F.M., Yunits, B., Huang, S., Alhajaji, H., Garmire, L.X.: Lilikoi: an R package for personalized pathway-based classification modeling using metabolomics data. GigaScience 7(12), giy136 (2018).

^9.Gift, N., Gormley, I.C., Brennan, L., Gormley, M.C.: Package ‘MetabolAnalyze’. (2010).

^10.Zhan, X., Patterson, A.D., Ghosh, D.: Kernel approaches for differential expression analysis of mass spectrometry-based metabolomics data. BMC bioinformatics 16(1), 77 (2015).

^11.Gaude, E., Chignola, F., Spiliotopoulos, D., Spitaleri, A., Ghitti, M., Garcìa-Manteiga, J.M., Mari, S., Musco, G.: muma, An R package for metabolomics univariate and multivariate statistical analysis. Current Metabolomics 1(2), 180–189 (2013).

^12.Palla, P.: Information management and multivariate analysis techniques for metabolomics data. Universita’degli Studi di Cagliari (2015)

^13.Kuhn, M.: Building predictive models in R using the caret package. Journal of statistical software 28(5), 1–26 (2008).