Self-Organizing Maps are unsupervised machine learning algorithms used primarily for clustering and dimensionality reduction. They map high-dimensional data for improved interpretability using a competitive learning approach. Each data point is mapped with some distance to a point on the map. These distances, called activations, show underlying trajectories in the data that can be explored. This is done in two studies.
The first study seeks vulnerabilities in public data by using self-organizing maps to bring people's sensitive attributes to the surface. This can reveal sensitive attributes with low correlation to the data are recoverable, thus leaving people's personal data at risk.
The second study looks into finding underlying trajectories in cell types. Cell type trajectory inference, also called pseudotime analysis, maps developmental and state changes in cells. Using trajectory inference to order single-cell omics data is used in stem cell differentiation, disease progression, and cell response to stimuli among other things.
These studies open the door to new research into applications of self-organizing maps from a 3rd dimension.
