Multidimensional scaling informed by F-statistic: Visualizing grouped microbiome data with inference

doi:10.1371/journal.pcbi.1014102

Multidimensional scaling informed by F-statistic: Visualizing grouped microbiome data with inference

Fig 2

Majorization algorithm optimizes the F-informed MDS objective function.

(A) Changes in each term comprising the objective function (Eq 5), including raw stress and confirmatory terms, are plotted against the training epochs for different hyperparameter values . A semisynthetic dataset of N = 200, replicate 1, was generated using SparseDOSSA [52] (see the “Semisynthetic data” section of the Methods). (B) The PERMANOVA p-value under two-dimensional representation (p_z) is plotted against epoch and until the stopping criteria are met. (C) Number of epochs until the termination is plotted against the hyperparameter, ranging between 0.2 and 1. Error bars indicate the standard deviation of triplicates.

doi: https://doi.org/10.1371/journal.pcbi.1014102.g002