Introduction to Bioinformatics with R: A Practical Guide for Biologists

Cover
Half Title
Series Page
Title Page
Copyright Page
Contents
Acknowledgements
1. Introduction
	1.1 Why informatics is important for biologists
	1.2 How to use this book
2. Introduction to R
	2.1 Obtaining R
		2.1.1 Downloading R
		2.1.2 Installing R
	2.2 R console
		2.2.1 Starting the R console
	2.3 The R workspace
		2.3.1 Creating/deleting objects
		2.3.2 The working directory
	2.4 Data handling
		2.4.1 Basic data types
		2.4.2 Vectors
		2.4.3 Arrays
		2.4.4 Lists
		2.4.5 Data frames
		2.4.6 Data input/output
	2.5 More advanced concepts: Scripts and functions
		2.5.1 Simple scripts
		2.5.2 Functions
		2.5.3 Using `apply\'
			2.5.3.1 apply
			2.5.3.2 sapply
			2.5.3.3 lapply
			2.5.3.4 mapply
	2.6 Plots
		2.6.1 Simple scatterplot
		2.6.2 Arguments of plot()
		2.6.3 Multiple plots on one graph
		2.6.4 Scatterplots of multiple variables
		2.6.5 Box plots
		2.6.6 Saving images to file
	2.7 More advanced graphics with ggplot2
	2.8 Using R help
3. An Introduction to LINUX for Biological Research
	3.1 UNIX
	3.2 Linux survival guide
	3.3 Useful dependencies and programs
4. Statistical Methods for Data Analysis
	4.1 What are statistical methods, and why do we use them in biological research?
		4.1.1 A worked example
		4.1.2 A brief summary
	4.2 What do I need to understand statistics?
		4.2.1 Probability
			4.2.1.1 Random variables
			4.2.1.2 Probability distributions
			4.2.1.3 Hypothesis testing
		4.2.2 Linear algebra
		4.2.3 Summary
	4.3 Normalization: Removing technical variation
		4.3.1 Centering and scaling
		4.3.2 An illustrative example
		4.3.3 Quantile normalization
		4.3.4 Batch effects
	4.4 Correlation
		4.4.1 Pearson correlation coefficient
		4.4.2 Spearman\'s rank correlation
		4.4.3 Examples
	4.5 Clustering
		4.5.1 Clustering illustration using R
	4.6 Linear regression models
		4.6.1 Limma
			4.6.1.1 Installing limma
			4.6.1.2 Categorical explanatory variables
			4.6.1.3 Continuous explanatory variables
	4.7 Multiple hypothesis testing
	4.8 Survival analysis
		4.8.1 Kaplan-Meier plots
		4.8.2 Cox proportional hazards regression models
	4.9 Projection methods
		4.9.1 PCA
		4.9.2 PLS
	4.10 Resampling: Permutation tests and the bootstrap
	4.11 Stability and robustness
	4.12 Summary
5. Analyzing Generic Tabular Numeric Datasets in R
	5.1 Introduction
	5.2 Loading data into R
	5.3 Data visualisation
		5.3.1 Scatter plots
		5.3.2 Box plots
		5.3.3 Bar charts
	5.4 Correlation and clustering
		5.4.1 Correlation
		5.4.2 Clustering
		5.4.3 Heatmaps
	5.5 Statistical analysis using linear models
		5.5.1 Comparison of two groups
		5.5.2 Alternative models
	5.6 Summary
6. Functional Enrichment Analysis
	6.1 Introduction
	6.2 Loading gene sets into R
	6.3 Over-representation
		6.3.1 Online tools
		6.3.2 Testing gene sets in R
	6.4 Systematic enrichment
		6.4.1 Online tools
		6.4.2 Testing gene sets in R
	6.5 Summary
7. Integrating Multiple Datasets in R
	7.1 Introduction
	7.2 Data import
	7.3 Exploratory data analysis
	7.4 Integrating multiple datasets
		7.4.1 Survival analysis
	7.5 Multiple molecular endpoints
	7.6 Summary
8. Analyzing Microarray Data in R
	8.1 Bioconductor
	8.2 Accessing microarray data from GEO
	8.3 Single-channel array analysis
	8.4 Loading data
	8.5 Data visualisation
		8.5.1 Image plots
		8.5.2 MA plots
		8.5.3 Scatterplots
		8.5.4 Box plots
	8.6 Normalizing data
	8.7 Differential expression (linear models)
		8.7.1 Design matrix
		8.7.2 Fitting linear models
		8.7.3 Making use of the results
		8.7.4 Postscript: Assumptions
	8.8 Clustering and correlation
		8.8.1 Expression profiles
		8.8.2 Correlation
	8.9 Clustering
		8.9.1 Filtering
	8.10 Survival analysis
		8.10.1 Kaplan-Meier plots
		8.10.2 Cox proportional hazards regression
	8.11 Footnote: Correlation to explore associated functions
9. Analyzing DNA Methylation Microarray Data in R
	9.1 Introduction
	9.2 Importing raw data
	9.3 Quality control
	9.4 Normalization and estimating methylation level
	9.5 Analyzing beta values
	9.6 Using previously preprocessed data
	9.7 Further analyses using minfi
10. DNA Analysis with Microarrays
	10.1 Introduction
	10.2 Genotyping
		10.2.1 Normalization
		10.2.2 Genotype calling
		10.2.3 Downstream analysis: Genome-wide association tests
	10.3 Copy number analysis
		10.3.1 Normalization
		10.3.2 Copy number estimation
		10.3.3 Segmentation
			10.3.3.1 Hidden Markov model
			10.3.3.2 Circular binary segmentation
		10.3.4 Downstream analysis
			10.3.4.1 Mapping CNA data to genes
			10.3.4.2 Finding frequently-mutated genes
	10.4 Summary
11. Working with Sequencing Data
	11.1 Introduction
	11.2 Sequence data analysis tasks
	11.3 Quality control
		11.3.1 Base call quality filtering
		11.3.2 Adapter trimming
	11.4 Alignment
		11.4.1 Bowtie
		11.4.2 BWA
		11.4.3 Post-alignment filtering
		11.4.4 Removing duplicate reads
	11.5 Obtaining sequencing data from the SRA
12. Genomic Sequence Profiling
	12.1 Introduction
	12.2 SNV: Single nucleotide variants
	12.3 Variant filtering and annotation
	12.4 Indels: Short insertions and deletions
	12.5 SV: Structural variants
	12.6 Making use of variant calls
	12.7 Summary
13. ChIP-seq
	13.1 Introduction
	13.2 Cross-correlation
	13.3 Filtering blacklisted reads
	13.4 Peak calling
	13.5 Peak annotation
	13.6 Quantitative comparisons of ChIP-seq libraries
	13.7 Summary
14. RNA-seq
	14.1 Introduction
	14.2 Obtaining RNA-seq data from GEO
	14.3 Transcript quantification via pseudoalignment
		14.3.1 Building a transcript index
		14.3.2 Quantifying transcripts using reads
		14.3.3 Downstream analysis
	14.4 Analysis with transcriptome assembly
		14.4.1 Building the transcriptome directly
		14.4.2 Transcript quantification
		14.4.3 Downstream analysis
	14.5 Summary
15. Bisulphite Sequencing
	15.1 Introduction
	15.2 Alignment and methylation calls
	15.3 Downstream analysis
	15.4 Summary
16. Final Notes
Index