RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR

F1000Research
Charity W LawMatthew E Ritchie

Abstract

The ability to easily and efficiently analyse RNA-sequencing data is a key strength of the Bioconductor project. Starting with counts summarised at the gene-level, a typical analysis involves pre-processing, exploratory data analysis, differential expression testing and pathway analysis with the results obtained informing future experiments and validation studies. In this workflow article, we analyse RNA-sequencing data from the mouse mammary gland, demonstrating use of the popular edgeR package to import, organise, filter and normalise the data, followed by the limma package with its voom method, linear modelling and empirical Bayes moderation to assess differential expression and perform gene set testing. This pipeline is further enhanced by the Glimma package which enables interactive exploration of the results so that individual samples and genes can be examined by the user. The complete analysis offered by these three packages highlights the ease with which researchers can turn the raw counts from an RNA-sequencing experiment into biological insights using Bioconductor.

References

Oct 4, 2005·Proceedings of the National Academy of Sciences of the United States of America·Aravind SubramanianJill P Mesirov
May 2, 2006·Statistical Applications in Genetics and Molecular Biology·Gordon K Smyth
Jan 30, 2009·Bioinformatics·Davis J McCarthy, Gordon K Smyth
Mar 4, 2010·Genome Biology·Mark D Robinson, Alicia Oshlack
Jul 9, 2010·Bioinformatics·Di WuGordon K Smyth
May 29, 2012·Nucleic Acids Research·Di Wu, Gordon K Smyth
Apr 6, 2013·Nucleic Acids Research·Yang LiaoWei Shi
Feb 4, 2014·Genome Biology·Charity W LawGordon K Smyth
Jan 22, 2015·Nucleic Acids Research·Matthew E RitchieGordon K Smyth
Jan 31, 2015·Nature Methods·Wolfgang HuberMartin Morgan
Jan 7, 2016·The Bone & Joint Journal·E P Su, S Su
Mar 17, 2016·Genomics Data·Ruijie LiuMatthew E Ritchie
Feb 17, 2017·Bioinformatics·Shian SuMatthew E Ritchie

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Citations

Apr 6, 2018·Cellular & Molecular Immunology·Xiangxiang JiangHongmei Wang
Nov 8, 2017·Diabetes & Vascular Disease Research : Official Journal of the International Society of Diabetes and Vascular Disease·Chenyu LiYan Xu
Jan 16, 2018·F1000Research·Monther AlhamdooshMatthew E Ritchie
May 26, 2017·Journal of Cellular Biochemistry·Chenyu LiYan Xu
Oct 19, 2017·F1000Research·Leonardo Collado-TorresAndrew E Jaffe
Mar 6, 2018·BMC Bioinformatics·GunHwan KoByungwook Lee
Apr 18, 2019·Investigative Ophthalmology & Visual Science·Michal KabzaMarzena Gajecka
Apr 5, 2020·Genes·Magdalena Ewa PawełkowiczWojciech Pląder
Jun 13, 2020·F1000Research·Stuart LeeMichael I Love
Jun 14, 2020·The Journal of Biological Chemistry·Andrei V ChernovVeronica I Shubayev
Aug 21, 2020·Science Translational Medicine·Nicholas A J DawsonMegan K Levings

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Datasets Mentioned

BETA
GSE63310

Methods Mentioned

BETA
RNA-seq
MDS
an
interactive
RNAseq123

Software Mentioned

EList
Refseq
RNAseq123
voom
DGEList
biomaRt
glMDPlot
BLAS
Bioconductor
NGenes

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