Chapter 1, Performing Quantitative RNASeq, teaches you how to process raw RNA sequence read data, perform quality checks, and estimate expression levels for differential gene expression detection and analysis. The chapter will also describe important statistical methods and steps for estimating experimental power—an important part of determining whether particular effects can be detected. All the recipes in this chapter will be based on the most popular Bioconductor tools, including Limma, edgeR, DESeq, and more.
Chapter 2, Finding Genetic Variants with HTS Data, introduces you to a range of techniques for performing next-generation genetic variants, including calling SNPs and Indels, using them in analysis, and creating genetic visualizations. All the recipes in this chapter will be based on the most popular and powerful tools of the Bioconductor package.
Chapter 3, Searching Genes and Proteins for Domains and Motifs, teaches you to analyze sequences for features of functional interest, such as de novo DNA motifs and known domains from widely used databases. In this section, we'll learn about some packages for kernel-based machine learning to find protein sequence features. You will also learn some large-scale alignment techniques for many, or very long, sequences. You will use Bioconductor and other statistical learning packages.
Chapter 4, Phylogenetic Analysis and Visualization, shows us how to use Bioconductor and other R phylogenetic packages such as ape to build and manipulate trees of gene and protein sequences. You will also look at how to compare trees with tree metrics and complete genome-scale visualizations.
Chapter 5, Metagenomics, explores importing data from popular metagenomics packages into R for analysis and learning a variety of effective visualizations. You will use packages such as otu, Metacoder, and DADA in Bioconductor and beyond in order to achieve an end-to-end metagenomics workflow.
Chapter 6, Proteomics from Spectrum to Annotation, teaches us how to import mass spectra and view this in external genome browsers along with genomic data. You will develop diagnostic plots and quality control procedures, and learn how to convert between various formats from different platforms.
Chapter 7, Producing Publication and Web-Ready Visualizations, teaches us how to develop high-quality visualizations that can represent large amounts of data and variables in compact and meaningful ways. You will study extensions to ggplot and the plotly package for interactive visualizations for the web and develop visualizations in the Shiny web environment.
Chapter 8, Working with Databases and Remote Data Sources, teaches us how to use web resources remotely by pulling data from commonly used data repositories. You will also examine the objects representing data in R. Methods in the Bioconductor package are heavily used in this chapter. We will also see how downloaded NGS datasets can be quality controlled for downstream use.
Chapter 9, Useful Statistical and Machine Learning Methods, demonstrates how to implement a range of approaches underlying some advanced statistical techniques including simulating data and performing multiple hypothesis tests. You will also learn some supervised and unsupervised machine learning methods to group and cluster data and samples.
Chapter 10, Programming with Tidyverse and Bioconductor, explains how to write code within tidyverse and integrate standard R functions to create pipelines that can analyze diverse datasets. You will use the biobroom package from Bioconductor and the broom package to reformat objects for use in tidy pipelines. The tidyverse set of packages will be used in functional programming and for creating reproducible, literate workflows.
Chapter 11, Building Objects and Packages for Code Reuse, demonstrates how to take developed code and apply R's object-oriented programming systems to simplify usability. You will also learn how to create a simple R package and how to share your code from GitHub so that other researchers can easily find and use what you have built.
