This DigitalMicrograph plugin makes Multivariate Statistical Analysis of STEM EELS or EDX data cubes. The core of the package is Principal Component Analysis (PCA) that extracts several components with the highest variance from raw data. In this way the dimensionality of data is greatly reduced and data sets are denoised. For better interpretation, obtained PCA components can be further rotated in the factor space using Varimax or ICA methods. PCA results can also be casted in endmembers spectra. These easy-to-interpret spectra are found by the manupulations in the factor space. For datasets with the complicated internal data distribution the package offers clustering data in the factor space.
Lecture introducing into the MSA stuff delivered in the EELS school in July 2019:
Multivariate Statistical Analysis to denoise EELS spectrum-images