title: RNA-Seq workflow: gene-level exploratory analysis and differential expression.
abstract: Here we walk through an end-to-end gene-level RNA-Seq differential expression workflow using Bioconductor packages. We will start from the FASTQ files, show how these were aligned to the reference genome, and prepare a count matrix which tallies the number of RNA-seq reads/fragments within each gene for each sample. We will perform exploratory data analysis (EDA) for quality assessment and to explore the relationship between samples, perform differential gene expression analysis, and visually explore the results.
authors.affiliation: Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, Massachusetts, USA.
authors.forename: Michael I
authors.initials: MI
authors.lastname: Love
authors.affiliation: Institute for Molecular Medicine Finland, Helsinki, Finland ; European Molecular Biology Laboratory, Heidelberg, Germany.
authors.forename: Simon
authors.initials: S
authors.lastname: Anders
authors.affiliation: European Molecular Biology Laboratory, Heidelberg, Germany.
authors.forename: Vladislav
authors.initials: V
authors.lastname: Kim
authors.affiliation: European Molecular Biology Laboratory, Heidelberg, Germany.
authors.forename: Wolfgang
authors.initials: W
authors.lastname: Huber
keywords: Bioconductor; RNA-seq; differential expression; gene expression; genomics; high-throughput sequencing; statistical analysis; visualization