For this recipe, we'll need a few new packages: caret, class, dplyr, and magrittr. As a dataset, we will use the built-in iris dataset.
- 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
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