Introduction to Bioinformatics with R

Description

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