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dc.contributor.authorLaw, CW
dc.contributor.authorAlhamdoosh, M
dc.contributor.authorSu, S
dc.contributor.authorDong, X
dc.contributor.authorTian, L
dc.contributor.authorSmyth, GK
dc.contributor.authorRitchie, ME
dc.date.accessioned2021-02-05T00:24:31Z
dc.date.available2021-02-05T00:24:31Z
dc.date.issued2016
dc.identifier.citationLaw, C. W., Alhamdoosh, M., Su, S., Dong, X., Tian, L., Smyth, G. K. & Ritchie, M. E. (2016). RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR.. F1000Res, 5, pp.1408-1408. https://doi.org/10.12688/f1000research.9005.3.
dc.identifier.issn2046-1402
dc.identifier.urihttp://hdl.handle.net/11343/259992
dc.description.abstractThe 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.
dc.languageeng
dc.publisherF1000 Research Ltd
dc.rights.urihttps://creativecommons.org/licenses/by/4.0
dc.titleRNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR.
dc.typeJournal Article
dc.identifier.doi10.12688/f1000research.9005.3
melbourne.affiliation.departmentMedical Biology (W.E.H.I.)
melbourne.affiliation.departmentSchool of Mathematics and Statistics
melbourne.affiliation.facultyMedicine, Dentistry & Health Sciences
melbourne.affiliation.facultyScience
melbourne.source.titleF1000Research
melbourne.source.volume5
melbourne.source.pages1408-1408
dc.rights.licenseCC BY
melbourne.elementsid1085383
melbourne.openaccess.pmchttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC4937821
melbourne.contributor.authorLaw, Charity
melbourne.contributor.authorRitchie, Matthew
melbourne.contributor.authorSmyth, Gordon
dc.identifier.eissn2046-1402
melbourne.accessrightsOpen Access


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