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Full Screen- Title
- CERTIFICATE
- DECLARATION
- ACKNOWLEDGEMENT
- ABSTRACT
- CONTENTS
- List of Tables
- List of Figures
- List of Abbreviations
- 1. Introduction
- 1.1 Biological background
- 1.2 Microarrays
- 1.3 Statistical Issues
- 1.4 Research Focus
- 1.5 Organization of the thesis
- References
- 2. Review of Literature
- 2.1 Representation of gene expression data
- 2.2 Data preprocessing
- 2.3 Visualization tools
- 2.4 Analysis of Comparative Experiments
- 2.5 Clustering and Classification
- References
- 3. Analysis of Differential Gene Expression
- 3.1 Introduction
- 3.2 Methods
- 3.2.1 The Lognormal Distribution
- 3.2.2 t--test (unequal variance)
- 3.2.3 Moderated t--test
- 3.2.4 Bootstrap t--test
- 3.2.5 Generalized p--value technique (GP)
- 3.2.6 Generalized p--value with shrinkage (GPS)
- 3.2.7 Empirical estimate of false discovery rate
- Results and Discussion
- 3.3.1 SpikeIn Experiment
- 3.3.2 ApoAI Knockout Experiment
- 3.3.3 Leukemia dataset
- 3.4 Conclusion
- References
- 4. Clustering and Dimension Reduction Strategies
- 4.1 Introduction
- 4.2 Materials and Methods
- 4.2.1 Dataset and Algorithms
- 4.2.2 Singular Value Decomposition
- 4.2.3 Partial Least Squares
- 4.2.4 Clustering algorithms
- 4.2.5 Cluster validity measure - The Adjusted Rand Index
- 4.3 Results and Discussion
- 4.3.1 Singular value decomposition
- 4.3.2 Simulation Study
- 4.3.3 Partial Least Squares
- 4.4 Conclusion
- References
- 5. Bayesian variable selection
- 5.1 Methods
- 5.1.1 Gene selection
- 5.1.2 Simulation study
- 5.2 Results and Discussion
- 5.2.1 Leukemia dataset
- 5.2.2 ApoAI Knockout Experiment
- 5.2.3 Simulation Study
- 5.3 Conclusion
- References
- 6. Distribution of Microarray Intensities
- 6.1 Introduction
- 6.2 Random number generation
- 6.3 Estimation of parameters
- 6.4 Results and Discussion
- 6.5 An Autoregressive Model for microarray data
- 6.6 Conclusion
- References
- APPENDIX
- Datasets
- Algorithms