case_images: PMC8060468_01
case_images: The COVID-19 case patient and 20 healthy control subjects plotted in two dimensions following dimensionality reduction by stochastic neighbor embedding. The Purple dot represents the COVID-19 patient, while the yellow dots represent the healthy controls. The dimensionality reduction shows that based on 59 plasma analyte concentrations, the COVID-19 patients is distinct and easily separable. The axes are dimension-less.
case_images: fped-09-597926-g0001.jpg
case_images: PMC8060468_01_fped-09-597926-g0001.jpg
case_images: F1
case_images: The analyte data from the COVID-19 patient and the 20 healthy control subjects were first visualized with a nonlinear dimensionality reduction on the full data matrix using the t-distributed stochastic nearest neighbor (t-SNE) embedding algorithm (Figure 1). t-SNE assumes that the "optimal" representation of the data lies on a manifold with complex geometry, but low dimension, embedded in the full dimensional space of the raw data.