Table 2.

Summary of analysis methods with links to tutorials

Method		Tutorial
Dimensionality reduction methods
1	Principal component analysis (PCA) [43]	PCA tutorial http://www.sthda.com/english/articles/31-principal-component-methods-in-r-practical-guide/112-pca-principal-component-analysis-essentials/
Unsupervised machine learning
1	k-means [44]⁠	k-means tutorial https://www.datanovia.com/en/lessons/k-means-clustering-in-r-algorith-and-practical-examples/
2	Agglomerative hierarchical clustering (AHC) [45]	AHC tutorial https://www.datanovia.com/en/lessons/agglomerative-hierarchical-clustering/
3	Divisive hierarchical clustering (DHC) [45]⁠	DHC tutorial https://www.datanovia.com/en/lessons/divisive-hierarchical-clustering/
4	Latent Dirichlet allocation (LDA) [24]⁠	LDA tutorial https://eight2late.wordpress.com/2015/09/29/a-gentle-introduction-to-topic-modeling-using-r/
Supervised machine learning
1	Support vector machines (SVMs) [46]⁠	SVM tutorial http://www.sthda.com/english/articles/36-classification-methods-essentials/144-svm-model-support-vector-machine-essentials/
2	Random forest (RF) [47]⁠	RF tutorial http://www.sthda.com/english/articles/35-statistical-machine-learning-essentials/140-bagging-and-random-forest-essentials/
3	Neural network (NN) [48]⁠	NN tutorial http://htmlpreview.github.io/?https://github.com/ledell/sldm4-h2o/blob/master/sldm4-deeplearning-h2o.html