Abstract: Methods and systems for generating visual representations of variation of disease-relevant classification are disclosed. Training data is received that comprises sample data units from subjects that represent information about a biological sample via an N-dimensional set of values. A dimensionality reduction algorithm represents each sample data unit as a respective point in a reduced dimension parameter space. Distributions of points from the dimensionality reduction are used to derive a probability density distribution for each of a plurality of disease-relevant classifications in the reduced dimension parameter space. A visual representation of each of the derived probability density distributions in the reduced dimension parameter space is generated to provide a visual representation of disease-relevant classification variation over the parameter space.