| Autor: |
Mahmodi, Hadi, Poulton, Christopher G., Lesley, Mathew N., Oldham, Glenn, Ong, Hui Xin, Langford, Steven J., Kabakova, Irina V. |
| Rok vydání: |
2024 |
| Předmět: |
|
| Druh dokumentu: |
Working Paper |
| Popis: |
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. |
| Databáze: |
arXiv |
| Externí odkaz: |
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