RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR [version 2; referees: 3 approved]
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 res...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
F1000 Research Ltd
2016-11-01
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| Series: | F1000Research |
| Subjects: | |
| Online Access: | https://f1000research.com/articles/5-1408/v2 |
| _version_ | 1811287640162435072 |
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| author | Charity W. Law Monther Alhamdoosh Shian Su Gordon K. Smyth Matthew E. Ritchie |
| author_facet | Charity W. Law Monther Alhamdoosh Shian Su Gordon K. Smyth Matthew E. Ritchie |
| author_sort | Charity W. Law |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T03:22:06Z |
| format | Article |
| id | doaj.art-99978fb5eb7a448a811084730cc521b9 |
| institution | Directory Open Access Journal |
| issn | 2046-1402 |
| language | English |
| last_indexed | 2024-04-13T03:22:06Z |
| publishDate | 2016-11-01 |
| publisher | F1000 Research Ltd |
| record_format | Article |
| series | F1000Research |
| spelling | doaj.art-99978fb5eb7a448a811084730cc521b92022-12-22T03:04:45ZengF1000 Research LtdF1000Research2046-14022016-11-01510.12688/f1000research.9005.210940RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR [version 2; referees: 3 approved]Charity W. Law0Monther Alhamdoosh1Shian Su2Gordon K. Smyth3Matthew E. Ritchie4The Walter and Eliza Hall Institute of Medical Research, Parkville, 3052, AustraliaCSL Limited, Parkville, Victoria, 3010, AustraliaThe Walter and Eliza Hall Institute of Medical Research, Parkville, 3052, AustraliaThe Walter and Eliza Hall Institute of Medical Research, Parkville, 3052, AustraliaThe Walter and Eliza Hall Institute of Medical Research, Parkville, 3052, AustraliaThe 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.https://f1000research.com/articles/5-1408/v2BioinformaticsGenomicsStructure: Transcription & Translation |
| spellingShingle | Charity W. Law Monther Alhamdoosh Shian Su Gordon K. Smyth Matthew E. Ritchie RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR [version 2; referees: 3 approved] F1000Research Bioinformatics Genomics Structure: Transcription & Translation |
| title | RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR [version 2; referees: 3 approved] |
| title_full | RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR [version 2; referees: 3 approved] |
| title_fullStr | RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR [version 2; referees: 3 approved] |
| title_full_unstemmed | RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR [version 2; referees: 3 approved] |
| title_short | RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR [version 2; referees: 3 approved] |
| title_sort | rna seq analysis is easy as 1 2 3 with limma glimma and edger version 2 referees 3 approved |
| topic | Bioinformatics Genomics Structure: Transcription & Translation |
| url | https://f1000research.com/articles/5-1408/v2 |
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