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- Herausgeber: Packt Publishing
- Sprache: Englisch
Handling biological data effectively requires an in-depth knowledge of machine learning techniques and computational skills, along with an understanding of how to use tools such as edgeR and DESeq. With the R Bioinformatics Cookbook, you’ll explore all this and more, tackling common and not-so-common challenges in the bioinformatics domain using real-world examples.
This book will use a recipe-based approach to show you how to perform practical research and analysis in computational biology with R. You will learn how to effectively analyze your data with the latest tools in Bioconductor, ggplot, and tidyverse. The book will guide you through the essential tools in Bioconductor to help you understand and carry out protocols in RNAseq, phylogenetics, genomics, and sequence analysis. As you progress, you will get up to speed with how machine learning techniques can be used in the bioinformatics domain. You will gradually develop key computational skills such as creating reusable workflows in R Markdown and packages for code reuse.
By the end of this book, you’ll have gained a solid understanding of the most important and widely used techniques in bioinformatic analysis and the tools you need to work with real biological data.
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Veröffentlichungsjahr: 2019
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R Bioinformatics Cookbook
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Contributors
About the author
Professor Dan MacLean has a Ph.D. in molecular biology from the University of Cambridge and gained postdoctoral experience in genomics and bioinformatics at Stanford University in California. Dan is now a Honorary Professor in the School of Computing Sciences at the University of East Anglia. He has worked in bioinformatics and plant pathogenomics, specializing in R and Bioconductor and developing analytical workflows in bioinformatics, genomics, genetics, image analysis, and proteomics at The Sainsbury Laboratory since 2006. Dan has developed and published software packages in R, Ruby, and Python with over 100,000 downloads combined.
About the reviewer
Cho-Yi Chen is an Olympic swimmer, a bioinformatician, and a computational biologist. He majored in computer science, and later devoted himself to biomedical research. He received his MS and Ph.D. degrees in bioinformatics, genomics, and systems biology from National Taiwan University. He was a founding member of the Taiwan Society of Evolution and Computational Biology. He is now a postdoctoral research fellow in the Department of Biostatistics and Computational Biology at the Dana-Farber Cancer Institute, Harvard University. He is an active scientist and software developer, striving to advance our understanding of cancer and other human diseases.
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Table of Contents
Title Page
Copyright and Credits
R Bioinformatics Cookbook
About Packt
Why subscribe?
Contributors
About the author
About the reviewer
Packt is searching for authors like you
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Download the color images
Conventions usedpacktpub.com/.../9781789950694_ColorImages.pdf
Sections
Getting ready
How to do it...
How it works...
There's more...
See also
Get in touch
Reviews
Performing Quantitative RNAseq
Technical requirements
Estimating differential expression with edgeR
Getting ready
How to do it...
Using edgeR from a count table
Using edgeR from an ExpressionSet object
How it works...
Using edgeR from a count table
Using edgeR from an ExpressionSet object
Estimating differential expression with DESeq2
Getting ready
How to do it...
Using DESeq2 from a count matrix
Using DESeq2 from an ExpressionSet object
How it works...
Using DESeq2 from a count matrix
Using DESeq2 from an ExpressionSet object
Power analysis with powsimR
Getting ready
How to do it...
How it works...
There's more...
Finding unannotated transcribed regions
Getting ready
How to do it...
How it works...
There's more...
Finding regions showing high expression ab initio with bumphunter
Getting ready...
How to do it...
How it works...
There's more...
Differential peak analysis
Getting ready
How to do it...
How it works...
Estimating batch effects using SVA
Getting ready
How to do it...
How it works...
Finding allele-specific expressions with AllelicImbalance
Getting ready
How to do it...
How it works...
There's more...
Plotting and presenting RNAseq data
Getting ready
How to do it...
How it works...
Finding Genetic Variants with HTS Data
Technical requirements
Finding SNPs and indels from sequence data using VariantTools
Getting ready
How to do it...
How it works...
There's more...
See also
Predicting open reading frames in long reference sequences
Getting ready
How to do it...
How it works...
There's more...
Plotting features on genetic maps with karyoploteR
Getting ready
How to do it...
How it works...
There's more...
See also
Selecting and classifying variants with VariantAnnotation
Getting ready
How to do it...
How it works...
See also
Extracting information in genomic regions of interest
Getting ready
How to do it...
How it works...
There's more...
Finding phenotype and genotype associations with GWAS
Getting ready
How to do it...
How it works...
Estimating the copy number at a locus of interest
Getting ready
How to do it...
How it works...
See also
Searching Genes and Proteins for Domains and Motifs
Technical requirements
Finding DNA motifs with universalmotif
Getting ready
How to do it...
How it works...
There's more...
Finding protein domains with PFAM and bio3d
Getting ready
How to do it...
How it works...
There's more...
Finding InterPro domains
Getting ready
How to do it...
How it works...
There's more...
See also...
Performing multiple alignments of genes or proteins
Getting ready
How to do it...
How it works...
There's more...
Aligning genomic length sequences with DECIPHER
Getting ready
How to do it...
How it works...
Machine learning for novel feature detection in proteins
Getting ready
How to do it...
How it works...
3D structure protein alignment with bio3d
Getting ready
How to do it...
How it works...
There's More...
Phylogenetic Analysis and Visualization
Technical requirements
Reading and writing varied tree formats with ape and treeio
Getting ready
How to do it...
How it works...
See also
Visualizing trees of many genes quickly with ggtree
Getting ready
How to do it...
How it works...
There's more...
Quantifying differences between trees with treespace
Getting ready
How to do it...
How it works...
There's more...
Extracting and working with subtrees using ape
Getting ready
How to do it...
How it works...
There's more...
Creating dot plots for alignment visualization
Getting ready
How to do it...
How it works...
Reconstructing trees from alignments using phangorn
Getting ready
How to do it...
How it works...
Metagenomics
Technical requirements
Loading in hierarchical taxonomic data using phyloseq
Getting ready
How to do it...
How it works...
There's more...
See also
Rarefying counts and correcting for sample differences using metacoder
Getting ready
How to do it...
How it works...
There's more...
Reading amplicon data from raw reads with dada2
Getting ready
How to do it...
How it works...
See also
Visualizing taxonomic abundances with heat trees in metacoder
Getting ready
How to do it...
How it works...
Computing sample diversity with vegan
Getting ready
How to do it...
How it works...
See also...
Splitting sequence files into OTUs
Getting ready
How to do it...
How it works...
Proteomics from Spectrum to Annotation
Technical requirements
Representing raw MS data visually
Getting ready
How to do it...
How it works...
Viewing proteomics data in a genome browser
Getting ready
How to do it...
How it works...
There's more...
Visualizing distributions of peptide hit counts to find thresholds
Getting ready
How to do it...
How it works...
Converting MS formats to move data between tools
Getting ready
How to do it...
How it works...
Matching spectra to peptides for verification with protViz
Getting ready
How to do it...
How it works...
Applying quality control filters to spectra
Getting ready
How to do it...
How it works...
There's more...
Identifying genomic loci that match peptides
Getting ready
How to do it...
How it works...
Producing Publication and Web-Ready Visualizations
Technical requirements
Visualizing multiple distributions with ridgeplots
Getting ready
How to do it...
How it works...
Creating colormaps for two-variable data
Getting ready
How to do it...
How it works...
See also
Representing relational data as networks
Getting ready
How to do it...
How it works...
There's more...
Creating interactive web graphics with plotly
Getting ready
How to do it...
How it works...
Constructing three-dimensional plots with plotly
Getting ready
How to do it...
How it works...
Constructing circular genome plots of polyomic data
Getting ready
How to do it...
How it works...
Working with Databases and Remote Data Sources
Technical requirements
Retrieving gene and genome annotation from BioMart
Getting ready
How to do it...
How it works...
Retrieving and working with SNPs
Getting ready
How to do it...
How it works...
There's more...
See also
Getting gene ontology information
Getting ready
How to do it...
How it works...
Finding experiments and reads from SRA/ENA
Getting ready
How to do it...
How it works...
There's more...
Performing quality control and filtering on high-throughput sequence reads
Getting ready
How to do it...
How it works...
Completing read-to-reference alignment with external programs
Getting ready...
How to do it...
How it works...
Visualizing the quality control of read-to-reference alignments
Getting ready...
How to do it...
How it works...
Useful Statistical and Machine Learning Methods
Technical requirements
Correcting p-values to account for multiple hypotheses
Getting ready
How to do it...
How it works...
Generating a simulated dataset to represent a background
Getting ready
How to do it...
How it works...
Learning groupings within data and classifying with kNN
Getting ready
How to do it...
How it works...
Predicting classes with random forests
Getting ready
How to do it...
How it works...
There's more
Predicting classes with SVM
Getting ready
How to do it...
How it works...
Learning groups in data without prior information
Getting ready
How to do it...
How it works...
There's more
Identifying the most important variables in data with random forests
Getting ready
How to do it...
How it works...
Identifying the most important variables in data with PCA
Getting ready
How to do it...
How it works...
Programming with Tidyverse and Bioconductor
Technical requirements
Making base R objects tidy
Getting ready
How to do it...
How it works...
Using nested dataframes
Getting ready
How it works...
How it works...
There's more...
Writing functions for use in dplyr::mutate()
Getting ready
How to do it...
How it works...
Working programmatically with Bioconductor classes
Getting ready
How to do it...
How it works...
Developing reusable workflows and reports
Getting ready
How to do it...
How it works...
Making use of the apply family of functions
Getting ready
How to do it...
How it works...
Building Objects and Packages for Code Reuse
Technical requirements
Creating simple S3 objects to simplify code
Getting ready
How to do it...
How it works...
Taking advantage of generic object functions with S3 classes
Getting ready
How to do it...
How it works...
Creating structured and formal objects with the S4 system
Getting ready
How to do it...
How it works
See also
Simple ways to package code for sharing and reuse
Getting ready
How to do it...
How it works...
Using devtools to host code from GitHub
Getting ready
How to do it...
How it works...
Building a unit test suite to ensure that functions work as you intend
Getting ready
How to do it...
How it works...
Using continuous integration with Travis to keep code tested and up to date
Getting ready
How to do it...
How it works...
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Preface
In R Bioinformatics Cookbook, you will encounter common and not-so-common challenges in the bioinformatics domain using real-world examples.
This book will use a recipe-based approach to help you perform practical research and analysis in computational biology with R. You will gain an understanding of your data through the analysis of Bioconductor, ggplot, and the tidyverse library in bioinformatics. You will be introduced to a number of essential tools in Bioconductor so that you can understand and carry out protocols in RNAseq, phylogenetics, genomics, and sequence analysis. You will also learn how machine learning techniques can be used in the bioinformatics domain. You will develop key computational skills, such as developing workflows in R Markdown and designing your own packages for efficient and reproducible code reuse.
By the end of this book, you'll have a solid understanding of the most important and widely used techniques in bioinformatic analysis, as well as the tools you'll need to work with real biological data.
Who this book is for
This book is for data scientists, bioinformatics analysts, researchers, and R developers who want to address intermediate-to-advanced biological and bioinformatics problems using a recipe-based approach. Working knowledge of the R programming language and some basic understanding of bioinformatics are mandatory.
What this book covers
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 asde novoDNA motifsand 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.
To get the most out of this book
Good knowledge of R and its various libraries is mandatory for this book.
Download the example code files
You can download the example code files for this book from your account at www.packt.com. If you purchased this book elsewhere, you can visit www.packtpub.com/support and register to have the files emailed directly to you.
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The code bundle for the book is also hosted on GitHub athttps://github.com/PacktPublishing/R-Bioinformatics-Cookbook. In case there's an update to the code, it will be updated on the existing GitHub repository.
We also have other code bundles from our rich catalog of books and videos available athttps://github.com/PacktPublishing/. Check them out!
Download the color images
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here: http://www.packtpub.com/sites/default/files/downloads/9781789950694_ColorImages.pdf.
Conventions usedpacktpub.com/.../9781789950694_ColorImages.pdf
There are a number of text conventions used throughout this book.
CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "We'll look at using the ape and treeio packages to get tree data into and out of R. "
A block of code is set as follows:
if (!requireNamespace("BiocManager")) install.packages("BiocManager") BiocManager::install()
Bold: Indicates a new term, an important word, or words that you see on screen. For example, words in menus or dialog boxes appear in the text like this. Here is an example: "Some of the dependencies rely on encapsulated Java code, so you'll need to install a Java Runtime Environment (JRE) for your system."
Sections
In this book, you will find several headings that appear frequently (Getting ready, How to do it..., How it works..., There's more..., and See also).
To give clear instructions on how to complete a recipe, use these sections as follows:
Getting ready
This section tells you what to expect in the recipe and describes how to set up any software or any preliminary settings required for the recipe.
How to do it...
This section contains the steps required to follow the recipe.
How it works...
This section usually consists of a detailed explanation of what happened in the previous section.
There's more...
This section consists of additional information about the recipe in order to make you more knowledgeable about the recipe.
See also
This section provides helpful links to other useful information for the recipe.
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Feedback from our readers is always welcome.
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Performing Quantitative RNAseq
The technology of RNAseq has revolutionized the study of transcript abundances, bringing high-sensitivity detection and high-throughput analysis. Bioinformatic analysis pipelines using RNAseq data typically start with a read quality control step followed by either alignment to a reference or the assembly of sequence reads into longer transcriptsde novo. After that, transcript abundances are estimated with read counting and statistical models and differential expression between samples is assessed. Naturally, there are many technologies available for all steps of this pipeline. The quality control and read alignment steps will usually take place outside of R, so analysis in R will begin with a file of transcript or gene annotations (such as GFF and BED files) and a file of aligned reads (such as BAM files).
The tools in R for performing analysis are powerful and flexible. Many of them are part of the Bioconductor suite and, as such, integrate together very nicely. The key question researchers wish to answer with RNAseq is usually: Which transcripts are differentially expressed? In this chapter, we'll look at some recipes for that in standard cases where we already know the genomic positions of genes we're interested in, and in cases where we need to find unannotated transcripts. We'll also look at other important recipes that help answer the questions How many replicates are enough? and Which allele is expressed more?
In this chapter, we will cover the following recipes:
Estimating differential expression with edgeR
Estimating differential expression with DESeq2
Power analysis with powsimR
Finding unannotated transcribed regions with GRanges objects
Finding regions showing high expression ab initio with bumphunter
Differential peak analysis
Estimating batch effects using SVA
Finding allele-specific expression with AllelicImbalance
Plotting and presenting RNAseq data
Technical requirements
The sample data you'll need is available from this book's GitHub repository: https://github.com/PacktPublishing/R-Bioinformatics_Cookbook. If you want to use the code examples as they are written, then you will need to make sure that this data is in a sub-directory of whatever your working directory is.
Here are the R packages that you'll need. Most of these will install with install.packages(); others are a little more complicated:
Bioconductor
AllelicImbalance
bumphunter
csaw
DESeq
edgeR
IRanges
Rsamtools
rtracklayer
sva
SummarizedExperiment
VariantAnnotation
dplyr
extRemes
forcats
magrittr
powsimR
readr
Bioconductor is huge and has its own installation manager. You can install it with the following code:
if (!requireNamespace("BiocManager")) install.packages("BiocManager") BiocManager::install()
Normally, in R, a user will load a library and use the functions directly by name. This is great in interactive sessions but it can cause confusion when many packages are loaded. To clarify which package and function I'm using at a given moment, I will occasionally use the packageName::functionName() convention.
letters[1:5]
This will give us output as follows:
## a b c d e
Note that the output lines are prefixed with ##.
Estimating differential expression with edgeR
edgeR is a widely used and powerful package that implements negative binomial models suitable for sparse count data such as RNAseq data in a general linear model framework, which are powerful for describing and understanding count relationships and exact tests for multi-group experiments. It uses a weighted style normalization called TMM, which is the weighted mean of log ratio between sample and control, after removal of genes with high counts and outlying log ratios. The TMM value should be close to one, but can be used as a correction factor to be applied to overall library sizes
In this recipe, we'll look at some options from preparing read counts for annotated regions in some object to identifying the differentially expressed features in a genome. Usually, there is an upstream step requiring us to take high-throughput sequence reads, align them to a reference and produce files describing those alignments, such as .bam files. With those files prepared, we'd fire up R and start to analyze. So that we can concentrate on the differential expression analysis part of the process, we'll use a prepared dataset for which all of the data is ready. Chapter 8, Working with Databases and Remote Data Sources, shows you how to go from raw data to this stage if you're looking for how to do that step.
As there are many different tools and methods for getting those alignments of reads, we will look at starting the process with two common input object types. We'll use a count table, like that we would have if we were loading from a text file and we'll use an ExpressionSet (eset) object, which is an object type common in Bioconductor.
Our prepared dataset will be themodencodeflydata from the NHGRI encyclopedia of DNA elements project for the model organism,Drosophila melanogaster. You can read about this project atwww.modencode.org. The dataset contains 147 different samples forD. melanogaster, a fruit fly with an approximately 110 Mbp genome, annotated with about 15,000 gene features.
Getting ready
The data is provided as both a count matrix and an ExpressionSet object and you can see the Appendix at the end of this book for further information on these object types. The data is in this book's code and data repository at https://github.com/PacktPublishing/R_Bioinformatics_Cookbookunderdatasets/ch1/modencodefly_eset.RData, datasets/ch1/modencodefly_count_table.txt, and datasets/ch1/modencodelfy_phenodata.txt . We'll also use theedgeR (from Bioconductor), readr, and magrittr libraries.
How to do it...
We will see two ways of estimating differential expressions with edgeR.
How it works...
We saw two ways of estimating differential expression with edgeR. In the first half of this recipe, we used edgeR starting with our data in a text file.
Using edgeR from a count table
In step 1, we use the read_tsv() function in the readr package to load the tab delimited text file of counts into a dataframe called count_dataframe. Then, from that, we extract the 'gene' column to a new variable, genes, and erase it from count_dataframe, by assigning NULL. This is all done so we can easily convert into the count_matrixmatrix with the base as.matrix() function and add the gene information back as rownames. Finally, we load the phenotype data we'll need from file using the readr read_table2() function.
Step 2 is concerned with working out which columns in count_matrix we want to use. We define a variable, experiments_of_interest, which holds the column names we want and then use the %in% operator and which() functions to create a binary vector that matches the number of columns. If, say, the third column of the columns_of_interest vector is TRUE it indicates the name was in the experiments_of interest variable.
Step 3 begins with loading the magrittr package to get the %>% operator, which will allow piping. We then use R indexing with the binary columns_of_interest factor to select the names of columns we want and send it to the forcats as_factor() function to get a factor object for our grouping variable. Sample grouping information is basically a factor that tells us which samples are replications of the same thing and it's important for the experimental design description. We need to create a grouping vector, each index of which refers to a column in the counts table. So, in the following example, the first three columns in the data would be replicates of one sample, the second three columns in the counts table would be replicates of a different replicate, and so on.We can use any symbols in the grouping vector to represent the groups. The more complicated the grouping vector, the more complicated the experiment design can be. In the recipe here, we'll use a simple test/control design:
numeric_groups <- c(1,1,1,2,2,2)letter_groups <- c("A","A","A", "B","B","B")
A simple vector like this will do, but you can also use afactor object. The factor is R's categorical data type and is implemented as a vector of integers that have associated name labels, called levels. When a factor is displayed, the name labels are taken instead of the integers. The factor object has a memory of sorts, and even when a subset of levels is used, all of the levels that could have been used are retained so that when, for example, the levels are used as categories, empty levels can still be displayed.
In Step 4, we use indexing to extract the columns of data we want to actually analyze.
By Step 5, our preparatory work is done and we can build the DGEList object we need to do differential analysis. To start, we load the edgeR library and use the DGEList() function on counts_of_interest and our grouping object.
In Step 6, with DGEList, we can go through the edgeR process. First, we create the experimental design descriptor design object with the base model.matrix() function. A model design is required to tell the functions how to compare samples; this is a common thing in R and so has a base function. We use the grouping variable we created. We must estimate the dispersions of each gene with the estimateDisp() function, then we can use that measure of variability in tests. Finally, a generalized linear model is fit and the quasi-likelihood F-test is applied with the two uses of glmQLFTest(), first with the dispersal estimates, eset_dge, then with the resulting fit object.
We can use the topTags() function to see the details of differentially expressed genes. We get the following output:
## Coefficient: groupingL2Larvae ## logFC logCPM F PValue FDR ## FBgn0027527 6.318665 11.14876 42854.72 1.132951e-41 1.684584e-37 ## [ reached 'max' / getOption("max.print") -- omitted 9 rows ]
The columns show the gene name, the logFC value of the gene, the F value, the P value and the False Detection Rate (FDR). Usually, the column we want to make statistical conclusions from is FDR.
Using edgeR from an ExpressionSet object
In Step 1, we are looking at using edgeR from our prepared eset object. We first load that in, using the base R function as it is stored in a standard Rdata format file.
In Step 2, we prepare the vector of experiments of interest. This works as in step 2, except that we don't need to look at the pheno_data object we created from a file; instead, we can use the eset function, phenoData(), to extract the phenotype data straight from the eset object (note that this is one of the major differences between eset and the count matrix—see this book's Appendix for further information).
In Step 3, we create the grouping factor. Again, this can be done by using the phenoData() extraction function, but, as it returns a factor, we need to drop the levels that aren't selected using the droplevels() function (see the How it works... section in the Estimating differential expression with edgeR recipe, step 3
