Principal Component Analysis in Application to Brillouin Microscopy Data
Abstract
Brillouin microscopy has recently emerged as a new bio-imaging modality that provides information on the micromechanical properties of biological materials, cells and tissues. The data collected in a typical Brillouin microscopy experiment represents the high-dimensional set of spectral information. Its analysis requires non-trivial approaches due to subtlety in spectral variations as well as spatial and spectral overlaps of measured features. This article offers a guide to the application of Principal Component Analysis (PCA) for processing Brillouin imaging data. Being unsupervised multivariate analysis, PCA is well-suited to tackle processing of complex Brillouin spectra from heterogeneous biological samples with minimal a priori information requirements. We point out the importance of data pre-processing steps in order to improve outcomes of PCA. We also present a strategy where PCA combined with k-means clustering method can provide a working solution to data reconstruction and deeper insights into sample composition, structure and mechanics.
Keywords
Cite
@article{arxiv.2401.04745,
title = {Principal Component Analysis in Application to Brillouin Microscopy Data},
author = {Hadi Mahmodi and Christopher G. Poulton and Mathew N. Lesley and Glenn Oldham and Hui Xin Ong and Steven J. Langford and Irina V. Kabakova},
journal= {arXiv preprint arXiv:2401.04745},
year = {2024}
}