Multidimensional scaling informed by F-statistic: Visualizing grouped microbiome data with inference
Fig 4
Different quality metrics confirm consistent preservation of the semisynthetic data pattern with F-informed MDS.
Six dimension reduction methods were evaluated to test their preservation of (A) local and (B) global structure by calculating trustworthiness and continuity using two nearest-neighbor numbers, k = 14 (local), k = 150 (global). The methods were also evaluated using (C) global distortion metrics (Stress-1 and Shepard diagram correlation) and (D) statistical inference metrics, including the ratio in statistical significance (F-rank-ratio) and correlation in F-ratios (F-correlation) using a randomly permuted label set. The following hyperparameters were applied to each method: (F-MDS), number of neighbors n (UMAP, supervised (-S) and unsupervised (-U)), perplexity “perp” (t-SNE), and the number of shortest dissimilarities n (Isomap). Three semisynthetic datasets of N = 200 were generated as described in section “Semisynthetic data” of Methods. The standard deviations were calculated and displayed with error bars.