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_Cookbook under datasets/ch1/modencodefly_eset.RData, datasets/ch1/modencodefly_count_table.txt, and datasets/ch1/modencodelfy_phenodata.txt . We'll also use the edgeR (from Bioconductor), readr, and magrittr libraries.
- Title Page
- Copyright and Credits
- About Packt
- Contributors
- Preface
- Performing Quantitative RNAseq
- Technical requirements
- Estimating differential expression with edgeR
- Estimating differential expression with DESeq2
- Power analysis with powsimR
- Finding unannotated transcribed regions
- Finding regions showing high expression ab initio with bumphunter
- Differential peak analysis
- Estimating batch effects using SVA
- Finding allele-specific expressions with AllelicImbalance
- Plotting and presenting RNAseq data
- Finding Genetic Variants with HTS Data
- Technical requirements
- Finding SNPs and indels from sequence data using VariantTools
- Predicting open reading frames in long reference sequences
- Plotting features on genetic maps with karyoploteR
- Selecting and classifying variants with VariantAnnotation
- Extracting information in genomic regions of interest
- Finding phenotype and genotype associations with GWAS
- Estimating the copy number at a locus of interest
- Searching Genes and Proteins for Domains and Motifs
- Technical requirements
- Finding DNA motifs with universalmotif
- Finding protein domains with PFAM and bio3d
- Finding InterPro domains
- Performing multiple alignments of genes or proteins
- Aligning genomic length sequences with DECIPHER
- Machine learning for novel feature detection in proteins
- 3D structure protein alignment with bio3d
- Phylogenetic Analysis and Visualization
- Technical requirements
- Reading and writing varied tree formats with ape and treeio
- Visualizing trees of many genes quickly with ggtree
- Quantifying differences between trees with treespace
- Extracting and working with subtrees using ape
- Creating dot plots for alignment visualization
- Reconstructing trees from alignments using phangorn
- Metagenomics
- Technical requirements
- Loading in hierarchical taxonomic data using phyloseq
- Rarefying counts and correcting for sample differences using metacoder
- Reading amplicon data from raw reads with dada2
- Visualizing taxonomic abundances with heat trees in metacoder
- Computing sample diversity with vegan
- Splitting sequence files into OTUs
- Proteomics from Spectrum to Annotation
- Technical requirements
- Representing raw MS data visually
- Viewing proteomics data in a genome browser
- Visualizing distributions of peptide hit counts to find thresholds
- Converting MS formats to move data between tools
- Matching spectra to peptides for verification with protViz
- Applying quality control filters to spectra
- Identifying genomic loci that match peptides
- Producing Publication and Web-Ready Visualizations
- Technical requirements
- Visualizing multiple distributions with ridgeplots
- Creating colormaps for two-variable data
- Representing relational data as networks
- Creating interactive web graphics with plotly
- Constructing three-dimensional plots with plotly
- Constructing circular genome plots of polyomic data
- Working with Databases and Remote Data Sources
- Technical requirements
- Retrieving gene and genome annotation from BioMart
- Retrieving and working with SNPs
- Getting gene ontology information
- Finding experiments and reads from SRA/ENA
- Performing quality control and filtering on high-throughput sequence reads
- Completing read-to-reference alignment with external programs
- Visualizing the quality control of read-to-reference alignments
- Useful Statistical and Machine Learning Methods
- Technical requirements
- Correcting p-values to account for multiple hypotheses
- Generating a simulated dataset to represent a background
- Learning groupings within data and classifying with kNN
- Predicting classes with random forests
- Predicting classes with SVM
- Learning groups in data without prior information
- Identifying the most important variables in data with random forests
- Identifying the most important variables in data with PCA
- Programming with Tidyverse and Bioconductor
- Building Objects and Packages for Code Reuse
- Technical requirements
- Creating simple S3 objects to simplify code
- Taking advantage of generic object functions with S3 classes
- Creating structured and formal objects with the S4 system
- Simple ways to package code for sharing and reuse
- Using devtools to host code from GitHub
- Building a unit test suite to ensure that functions work as you intend
- Using continuous integration with Travis to keep code tested and up to date
- Other Books You May Enjoy
Getting ready
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