Multivariate Analysis as a Tool for Quantification of Conformational Transitions in DNA Thin Films
Abstract
:1. Introduction
2. Materials and Methods
2.1. Preparation of DNA Solutions and Thin Films, and Data Collection
2.2. Multivariate Analysis
2.2.1. Principal Component Analysis
2.2.2. Support Vector Machine
2.2.3. Principal Component Regression
3. Results
3.1. Vibrational Signatures of DNA Thin Films in Base Region
3.2. Principal Component Analysis
3.3. Classification by SVM
3.4. Principal Component Regression
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Conflicts of Interest
References
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SVM0 | # Spectra | 1800–650 | ||||
Calibration ds: 3–40 min | 3 min | 11 | 11 | |||
Validation ds: 3–40 min | 5 min | 36 | 36 | |||
10 min | 11 | 11 | ||||
15 min | 4 | 4 | ||||
20 min | 2 | 2 | ||||
25 min | 2 | 2 | ||||
40 min | 9 | 7 | ||||
Success rate: | 97% (73/75) | |||||
SVM1 | # Spectra | 1800–935 | 1800–1550 | 1320–1155 | ||
Calibration ds: 3–40 min | Class 1 | 11 | 11 | 11 | 10 | |
Validation ds: 3–40 min | Class 2 | 36 | 36 | 36 | 35 | |
Class 3 | 28 | 28 | 28 | 27 | ||
Success rate: | 100% | 100% | 96% | |||
SVM2 | # Spectra | 1800–935 | 1800–1550 | 1320–1155 | 1155–990 | |
Calibration ds: 3, 5, 10, 40 min | ||||||
Validation ds: 15, 20, 25 min | Class 3 | 36 | 36 | 36 | 36 | 35 |
Success rate: | 100% | 100% | 100% | 97% | ||
SVM3 | # Spectra | 1800–935 | ||||
Calibration ds: 3, 5, 40 min | ||||||
Validation ds: 10, 15, 20, 25 min | Class 3 | 90 | 22 | |||
Success rate: | 24% | |||||
SVM4 | # Spectra | 1800–935 | ||||
Calibration ds: 3, 5, 15, 40 min | ||||||
Validation ds: 10, 20, 25 min | Class 3 | 72 | 59 | |||
Success rate: | 82% | |||||
SVM5 | # Spectra | 1800–935 | ||||
Calibration ds: 3, 5, 10 min | ||||||
Validation ds: 15, 20, 25, 40 min | Class 3 | 81 | 81 | |||
Success rate: | 100% | |||||
SVM6 | # Spectra | 1800–935 | ||||
Calibration ds: 3, 5, 10 min | ||||||
Validation ds: Class 2 spectra | Class 2 | 3 | 3 | |||
from our DNA library | ||||||
Success rate: | 100% |
PCR1 | 1800–935 | 1800–1550 | 1320–1155 | 1155–990 | |
Calibration ds: 3, 5, 10, 40 min | R2 | 0.922 | 0.875 | 0.838 | 0.847 |
Validation ds: 15, 20, 25 min | RMSEC | 2.88 | 3.76 | 4.25 | 4.19 |
classes: 7 | RMSECV | 3.61 | 4.03 | 4.38 | 4.39 |
3 PCs | RMSEP | 2.99 | 2.46 | 3.33 | 2.36 |
PCR2 | 1800–935 | 1800–1550 | 1320–1155 | 1155–990 | |
Calibration ds: 3, 5, 10, 40 min | R2 | 0.929 | 0.882 | 0.848 | 0.856 |
Validation ds: 15, 20, 25 min | RMSEC | 0.18 | 0.24 | 0.27 | 0.27 |
classes: 3 | RMSECV | 0.23 | 0.26 | 0.28 | 0.28 |
2 PCs | RMSEP | 0.16 | 0.14 | 0.20 | 0.17 |
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Serec, K.; Dolanski Babić, S. Multivariate Analysis as a Tool for Quantification of Conformational Transitions in DNA Thin Films. Appl. Sci. 2021, 11, 5895. https://doi.org/10.3390/app11135895
Serec K, Dolanski Babić S. Multivariate Analysis as a Tool for Quantification of Conformational Transitions in DNA Thin Films. Applied Sciences. 2021; 11(13):5895. https://doi.org/10.3390/app11135895
Chicago/Turabian StyleSerec, Kristina, and Sanja Dolanski Babić. 2021. "Multivariate Analysis as a Tool for Quantification of Conformational Transitions in DNA Thin Films" Applied Sciences 11, no. 13: 5895. https://doi.org/10.3390/app11135895
APA StyleSerec, K., & Dolanski Babić, S. (2021). Multivariate Analysis as a Tool for Quantification of Conformational Transitions in DNA Thin Films. Applied Sciences, 11(13), 5895. https://doi.org/10.3390/app11135895