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Analyzing High-Dimensional Gene Expression and DNA Methylation Data with R

PIPELINES TO ANALYZE “OMICS” DATA RNA-Seq GENE EXPRESSION IN S2-DRSC CELLS MICROARRAY GENE EXPRESSION IN YEAST CELLS AND IN PROSTATE SAMPLES DNA METHYLATION IN NORMAL AND COLON/RECTAL ADENOCARCINOMA SAMPLES MICROARRAY GENE EXPRESSION DATA Data generation Preprocessing and quality control of microarray data DATA FROM NEXT GENERATION SEQUENCING Data generation Preprocessing and quality control of bulk RNA-Seq data : Genome-scale epigenetic data DATA GENERATION QUALITY CONTROL AND PREPROCESSING OF DNA METHYLATION DATA The control probe adjustment and reduction of global correlation pipeline (CPACOR)Quantile normalization with ComBat CELL TYPE COMPOSITION INFERENCES Reference-based methods Reference-free methods APPENDIX – MODIFIED PROGRAMS IN THE CPACOR WITH AN APPLICATION : Screening genome-scale genetic and epigenetic data SCREENING VIA TRAINING AND TESTING SAMPLES SCREENING INCORPORATING SURROGATE VARIABLES SURE INDEPENDENCE SCREENING Correlation learning NON- AND SEMI-PARAMETRIC SCREENING TECHNIQUES Random forest Support vector machine : Cluster Analysis in Data mining NON-PARAMETRIC CLUSTER ANALYSIS METHODS Distances Partitioning-based methods Hierarchical clustering Hybrids of partitioning-based and hierarchical clustering Examples – clustering to detect gene expression patterns CLUSTER ANALYSES IN LINEAR REGRESSIONS BICLUSTER ANALYSES JOINT CLUSTER ANALYSIS : Methods to select genetic and epigenetic factors based on linear associations FREQUENTIST APPROACHES Elastic net Adaptive LASSO Smoothly clipped absolute deviation (SCAD)BAYESIAN APPROACHES Zellner’s g-prior Extension of Zellner’s g-prior to multi-components G-prior The spike-and-slab prior EXAMPLES – SELECTING IMPORTANT EPIGENETIC FACTORS : Non- and semi-parametric methods to select genetic and epigenetic factors VARIABLE SELECTION BASED ON SPLINES OVERVIEW OF THE ANOVA-BASED APPROACH VARIABLE SELECTION BUILT UPON REPRODUCING KERNELS EXAMPLES Selecting important epigenetic factors Selecting variables with known underlying truth : Network construction and analyses UNDIRECTED NETWORKS The two-stage graphs selection method The GGMselect package and gene expression examples CORRELATION NETWORKS BAYESIAN NETWORKS NETWORK COMPARISONS Comparing undirected networks Comparing Bayesian networks Bibliography

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