Type of Document Master's Thesis Author Tang, Tian Author's Email Address ttang3@student.gsu.edu URN etd-11152008-003553 Title Infrared Spectroscopy In Combination With Advanced Statistical Methods For Distinguishing Viral Infected Biological Cells Degree Master of Science Department Mathematics and Statistics Advisory Committee
Advisor Name Title Yu-Sheng Hsu Committee Chair Gary Hastings Committee Member Jiawei Liu Committee Member Keywords
- Cross-validation
- Wilcoxon Rank Sum Test
- Logistic Regression
- Partial Least Square Regression
- Area under the ROC Curve
- Sensitivity and specificity
- Infrared spectroscopy
Date of Defense 2008-10-30 Availability unrestricted Abstract Fourier Transform Infrared (FTIR) microscopy is a sensitive method for detecting difference in the morphology of biological cells. In this study FTIR spectra were obtained for uninfected cells, and cells infected with two different viruses. The spectra obtained are difficult to discriminate visually. Here we apply advanced statistical methods to the analysis of the spectra, to test if such spectra are useful for diagnosing viral infections in cells. Logistic Regression (LR) and Partial Least Squares Regression (PLSR) were used to build models which allow us to diagnose if spectral differences are related to infection state of the cells. A three-fold, balanced cross-validation method was applied to estimate the shrinkages of the area under the receiving operator characteristic curve (AUC), and specificities at sensitivities of 95%, 90% and 80%. AUC, sensitivity and specificity were used to gauge the goodness of the discrimination methods. Our statistical results shows that the spectra associated with different cellular states are very effectively discriminated. We also find that the overall performance of PLSR is better than that of LR, especially for new data validation. Our analysis supports the idea that FTIR microscopy is a useful tool for detection of viral infections in biological cells.Files
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