DR-Assisted Cluster Analysis

Finding meaningful structure in high-dimensional microscopy data without predefined labels.

View on GitHub

DRCA is a Python toolkit for data-driven feature extraction from multidimensional microscopy data. It combines dimensionality reduction and unsupervised clustering to reveal spatially resolved patterns without predefined labels.

Visual highlights

DRCA dimensionality reduction and clustering workflow
DRCA uses liner/nonlinear dimensionality reduction methods and density-based clustering to make complex microscopy datasets explorable.
DRCA application to plasmonic EELS data
DRCA separates and maps spatially overlapping plasmonic features in EELS data.