The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR
Open accessAbstract
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
R: A Language and Environment for Statistical Computing
R Core Team (2025) · doi:10.32614/r.manuals · Cited by 352,181
Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0506580102 · Cited by 53,625
Robinson, M. D., McCarthy, D. J., & Smyth, G. K. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btp616 · Cited by 41,364
Self-citationlimma powers differential expression analyses for RNA-sequencing and microarray studies
Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W., & Smyth, G. K. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv007 · Cited by 40,458
Self-citationfeatureCounts: an efficient general purpose program for assigning sequence reads to genomic features
Liao, Y., Smyth, G. K., & Shi, W. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btt656 · Cited by 27,219
Self-citationSmyth, G. K. (2004) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1027 · Cited by 11,829
Self-citationA scaling normalization method for differential expression analysis of RNA-seq data
Robinson, M. D., & Oshlack, A. (2010) · Genome Biology · doi:10.1186/gb-2010-11-3-r25 · Cited by 7,876
voom: precision weights unlock linear model analysis tools for RNA-seq read counts
Law, C. W., Chen, Y., Shi, W., & Smyth, G. K. (2014) · Genome Biology · doi:10.1186/gb-2014-15-2-r29 · Cited by 6,155
Self-citationMapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt
Durinck, S., Spellman, P. T., Birney, E., & Huber, W. (2009) · Nature Protocols · doi:10.1038/nprot.2009.97 · Cited by 4,197
Orchestrating high-throughput genomic analysis with Bioconductor
Huber, W., Carey, V. J., Gentleman, R., Anders, S., Carlson, M., Carvalho, B. S., Bravo, H. C., Davis, S., Gatto, L., Girke, T., et al. (2015) · Nature Methods · doi:10.1038/nmeth.3252 · Cited by 3,608
Self-citation
Research works printed but not linked: 2
- 2016, Mus.musculus: Annotation package for the Mus.musculus object., 10.18129/B9.bioc.Mus.musculus · doi:10.18129/b9.bioc.mus.musculus
- 2016, Homo.sapiens: Annotation package for the Homo.sapiens object., 10.18129/B9.bioc.Homo.sapiens · doi:10.18129/b9.bioc.homo.sapiens
Cited by
Shared and distinct transcriptomic cell types across neocortical areas
Tasic, B., Yao, Z., Graybuck, L. T., Smith, K. A., Nguyen, T. N., Bertagnolli, D., Goldy, J., Garren, E., Economo, M. N., Viswanathan, S., et al. (2018) · Nature · doi:10.1038/s41586-018-0654-5 · Cited by 1,746
Fecal microbiota transplant promotes response in immunotherapy-refractory melanoma patients
Baruch, E. N., Youngster, I., Ben-Betzalel, G., Ortenberg, R., Lahat, A., Katz, L., Adler, K., Dick-Necula, D., Raskin, S., Bloch, N., et al. (2021) · Science · doi:10.1126/science.abb5920 · Cited by 1,757
IL4I1 Is a Metabolic Immune Checkpoint that Activates the AHR and Promotes Tumor Progression
Sadik, A., Somarribas Patterson, L. F., Öztürk, S., Mohapatra, S. R., Panitz, V., Secker, P. F., Pfänder, P., Loth, S., Salem, H., Prentzell, M. T., et al. (2020) · Cell · doi:10.1016/j.cell.2020.07.038 · Cited by 559
Rapid and Dynamic Alternative Splicing Impacts the Arabidopsis Cold Response Transcriptome
Calixto, C. P. G., Guo, W., James, A. B., Tzioutziou, N. A., Entizne, J. C., Panter, P. E., Knight, H., Nimmo, H. G., Zhang, R., & Brown, J. W. S. (2018) · The Plant Cell · doi:10.1105/tpc.18.00177 · Cited by 298
Colbert, L. E., El Alam, M. B., Wang, R., Karpinets, T., Lo, D., Lynn, E. J., Harris, T. A., Elnaggar, J. H., Yoshida-Court, K., Tomasic, K., et al. (2023) · Cancer Cell · doi:10.1016/j.ccell.2023.09.012 · Cited by 270
Metadata
- Authors
- Law, C. W., Alhamdoosh, M., Su, S., Dong, X., Tian, L., Smyth, G. K., & Ritchie, M. E.
- Venue
- F1000Research
- Publisher
- F1000 Research Ltd
- Published
- 2018-12-28
- Volume
- 5
- Pages
- 1408
- ISSN
- 2046-1402 (ISSN-L)
- OpenAlex
- W2429031812
- Cited by
- 614
- References
- 24
- FWCI
- 21.19
- Type
- journal article
- Language
- en
- Open access
- gold
- Licence
- creativecommons.org/licenses/by/4.0/ (tdm)
- Funders
- National Health and Medical Research Council (GNT1058892, GNT1054618, GNT1104924)
- Australian Government NHMRC IRIISS
- State Government Operational Infrastructure Support
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR. https://app.cochat.ai/papers/10.12688/f1000research.9005.3
- The paper
- Law, C. W., Alhamdoosh, M., Su, S., Dong, X., Tian, L., Smyth, G. K., & Ritchie, M. E. (2018). RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR. F1000Research. https://doi.org/10.12688/f1000research.9005.3
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
