Principal Component Analysis in Application to Brillouin Microscopy Data
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866909067490885632 |
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| author | Mahmodi, Hadi Poulton, Christopher G. Lesley, Mathew N. Oldham, Glenn Ong, Hui Xin Langford, Steven J. Kabakova, Irina V. |
| author_facet | Mahmodi, Hadi Poulton, Christopher G. Lesley, Mathew N. Oldham, Glenn Ong, Hui Xin Langford, Steven J. Kabakova, Irina V. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_04745 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Principal Component Analysis in Application to Brillouin Microscopy Data Mahmodi, Hadi Poulton, Christopher G. Lesley, Mathew N. Oldham, Glenn Ong, Hui Xin Langford, Steven J. Kabakova, Irina V. Quantitative Methods Optics 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. |
| title | Principal Component Analysis in Application to Brillouin Microscopy Data |
| topic | Quantitative Methods Optics |
| url | https://arxiv.org/abs/2401.04745 |