  Multidimensional scaling (MDS) is an unsupervised learning technique that
preserves pairwise distances between observations and is commonly used for
analyzing multivariate biological datasets. Recent advances in MDS have
achieved successful classification results, but the configurations heavily
depend on the choice of hyperparameters, limiting its broader application.
Here, we present a self-supervised MDS approach informed by the dispersions of
observations that share a common binary label ($F$-ratio). Our visualization
accurately configures the $F$-ratio while consistently preserving the global
structure with a low data distortion compared to existing dimensionality
reduction tools. Using an algal microbiome dataset, we show that this new
method better illustrates the community's response to the host, suggesting its
potential impact on microbiology and ecology data analysis.
