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F1000Research, F1000Research, (4), p. 1070, 2016

DOI: 10.12688/f1000research.7035.2

F1000Research, F1000Research, (4), p. 1070, 2015

DOI: 10.12688/f1000research.7035.1

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RNA-Seq workflow: gene-level exploratory analysis and differential expression

Journal article published in 2015 by Michael I. Love ORCID, Simon Anders ORCID, Vladislav Kim, Wolfgang Huber ORCID
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Postprint: archiving forbidden
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Data provided by SHERPA/RoMEO

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.