PCA and clustering for scientific datasets

Explore multidimensional datasets with PCA, hierarchical clustering, K-means and self-organizing maps. Examine major axes of variation, outliers and sample relationships. Normalization, scaling and parameters affect interpretation; apparent clusters are hypotheses, not proof.

Try J-Express

Open J-Express to explore available demo datasets. Read the method documentation for limitations and supported workflows.

For exploratory research. Validate scientific conclusions independently.