| Literature DB >> 26674615 |
Michael I Love1, Simon Anders2, Vladislav Kim3, Wolfgang Huber3.
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.Entities:
Keywords: Bioconductor; RNA-seq; differential expression; gene expression; genomics; high-throughput sequencing; statistical analysis; visualization
Year: 2015 PMID: 26674615 PMCID: PMC4670015 DOI: 10.12688/f1000research.7035.1
Source DB: PubMed Journal: F1000Res ISSN: 2046-1402