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 transcripts de 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
