Comparison of principal component analysis and biochemical component analysis in Raman spectroscopy for the discrimination of apoptosis and necrosis in K562 leukemia cells
Raman spectroscopy has been explored as a promising label-free technique in discriminating apoptosis and necrosis induced cell death in leukemia cells. In addition to Principal component analysis (PCA) as commonly employed in Raman data analysis, another less commonly used but powerful method is Bio...
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Main Authors: | , , |
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Format: | Article |
Language: | English |
Published: |
2013
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Online Access: | https://hdl.handle.net/10356/95434 http://hdl.handle.net/10220/9093 |
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Institution: | Nanyang Technological University |
Language: | English |
Summary: | Raman spectroscopy has been explored as a promising label-free technique in discriminating apoptosis and necrosis induced cell death in leukemia cells. In addition to Principal component analysis (PCA) as commonly employed in Raman data analysis, another less commonly used but powerful method is Biochemical Component Analysis (BCA). In BCA, a Raman spectrum is decomposed into the contributions from several known basic biochemical components, such as proteins, lipid, nucleic acids and glycogen groups etc. The differences in terms of classification accuracy and interpretability of resulting data between these two methods in Raman spectroscopy have not been systematically investigated to our knowledge. In this study, we utilized both methods to analyze the Raman spectra measured from live cells, apoptotic and necrotic leukemia cells. The comparison indicates that two methods yield comparable accuracy in sample classification when the numbers of basic components are equal. The changes in the contributions of biochemical components in BCA can be interpreted by cell biology principles in apoptosis and necrosis. In contrast, the contributions of most principle components in PCA are difficult to interpret except the first one. The capability of BCA to unveil fine biochemical changes in cell spectra and excellent accuracy in classification can impel the broad application of Raman spectroscopy in biological research. |
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