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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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QUALITY CONTROL AND PREPROCESSING OF DNA METHYLATION DATA
Different methods have been proposed for DNA methylation data quality control and preprocessing. We focus on a pipeline proposed by Lehne et al. [95] and the method included in an R package minfi [42]. Both approaches can be applied to DNA methylation data from HumanMethy- lation450 BeadChips as well...
(Analyzing High-Dimensional Gene Expression and DNA Methylation Data with R)
Test to Analyze Sperm DNA Methylation at Single Base-Pair Resolution
There are different approaches to analyze DNA methylation.18 Nevertheless, the most commonly used approaches to analyze sperm DNA methylation are those that identify 5mC at single base-pair resolution. In this section, we will examine some general considerations that apply to this methodology. General...
(Practical Guide to Sperm Analysis: Basic Andrology in Reproductive Medicine)
EXAMPLE OF A HIGH-DIMENSIONAL MULTIPLE REGRESSION: REGRESSING GENE EXPRESSION LEVELS ON TRANSCRIPTION FACTOR BINDING SITES
To illustrate an elegant application of multiple regression in genomics, let’s try to build a statistical model to predict genome-wide expression levels from DNA sequences. A key idea in molecular biology is that gene expression levels are due in part to the presence of consensus binding sites for...
(Statistical modeling and machine learning for molecular biology)
How is DNA methylation regulated at specific genes?
Neuronal activity, brain development, memory formation, and cognitive disease states are all associated with reorganization of DNA methylation patterns at specific genes with defined temporal dynamics (Day et al., 2013; De Jager et al., 2014; Guo, Ma, et al., 2011; Lim et al., 2014; Lister et al., 2013;...
(DNA Modifications in the Brain. Neuroepigenetic Regulation of Gene Expression)
Gene Expression Microarray Data as a Source of Protein Biomarkers
Gene expression microarrays have been in use for more than a decade ago, and microarray studies have been used to study changes in mRNA transcripts in disease- related tissues. Considerable microarray data have been deposited into international repositories including the Gene Expression Omnibus (GEO)...
(The handbook of biomarkers)
Biological Pathway of Gene Expression and Omic Data
Genomic association mechanisms and the associated omic data are linked to different stages of the gene-expression pathway. In the gene-expression process (Figure 4.2), a particular segment of DNA is enzymatically processed, or transcribed, into an RNA molecule. Then, a specific product of RNA, namely...
(Optimization of Trustworthy Biomolecular Quantitative Analysis Using Cyber-Physical Microfluidic Platforms)
Genome-scale gene expression data
For genetic data, we mainly focus on gene expression data produced via Sanger sequencing and next generation sequencing. For epigenetic data, which will be discussed in the next chapter, we focus on DNA rnethy- lation of CpG sites. Single nucleotide polymorphisms in genomewide association studies will...
(Analyzing High-Dimensional Gene Expression and DNA Methylation Data with R)
MICROARRAY GENE EXPRESSION DATA
Data generation Different techniques are available to measure genome-scale gene expressions, for example, cDNA spotted arrays and oligonucleotide arrays (Figure 2.1). For cDNA spotted array, it is the first type of DNA microarray technology developed in the Brown and Botstein Labs at the Stanford...
(Analyzing High-Dimensional Gene Expression and DNA Methylation Data with R)
DNA Methylation Patterns in Disease-Associated Genes in AD
The studies addressing DNA methylation status in AD began in 1995 when West et al. reported an AD-specific hypomethylation of the APP promoter in a single patient (West et al. 1995). Subsequently, evidence for epigenetic changes in AD pathogenesis has been extensively investigated, using various...
(Phytomedicine and Alzheimer’s Disease)
DNA Methylation Patterns in Other Genes Involved in AD
Recently, several genome-wide association studies have revealed Triggering Receptor Expressed on Myeloid cells 2 (TREM2) to be a susceptibility gene associated with AD and other neurodegenera- tive diseases. TREM2 expression was upregulated in the hippocampus of AD patients compared with...
(Phytomedicine and Alzheimer’s Disease)