The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
It’s DE-licious: A Recipe for Differential Expression Analyses of RNA-seq Experiments Using Quasi-Likelihood Methods in edgeR
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Citations
Cites
featureCounts: 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-citationHTSeq—a Python framework to work with high-throughput sequencing data
Anders, S., Pyl, P. T., & Huber, W. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btu638 · Cited by 21,464
Mapping and quantifying mammalian transcriptomes by RNA-Seq
Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L., & Wold, B. (2008) · Nature Methods · doi:10.1038/nmeth.1226 · Cited by 13,696
RNA-Seq: a revolutionary tool for transcriptomics
Wang, Z., Gerstein, M., & Snyder, M. (2009) · Nature Reviews Genetics · doi:10.1038/nrg2484 · Cited by 13,174
Smyth, G. K. (2004) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1027 · Cited by 11,829
Self-citationModerated statistical tests for assessing differences in tag abundance
Robinson, M. D., & Smyth, G. K. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btm453 · Cited by 856
Self-citation
Research works printed but not linked: 12
- Shendure J, Aiden EL (2012) The expanding scope of DNA sequencing. Nat Biotechnol 30.11:1084–1094 · DOI · Paper page
- Liao Y, Smyth GK, Shi W (2013) The Subread aligner: fast, accurate and scalable read mapping by seed-and-vote. Nucleic Acids Res 41.10:e108 · DOI · Paper page
- Robinson MD, McCarthy DJ, Smyth GK (2010) edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26.1:139–140 · DOI · Paper page
- McCarthy DJ, Chen Y, Smyth GK (2012) Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Res 40.10:4288–4297 · DOI · Paper page
- Lund et al SP (2012) Detecting differential expression in RNA-sequence data using quasi-likelihood with shrunken dispersion estimates. Stat Appl Genet Mol Biol 11.5:Article 8 · DOI · Paper page
- Robinson MD, Smyth GK (2008) Small-sample estimation of negative binomial dispersion, with applications to SAGE data. Biostatistics 9.2:321–332 · DOI · Paper page
- Anders S et al (2013) Count-based differential expression analysis of RNA sequencing data using R and Bioconductor. Nat Protoc 8:1765–1786 · DOI · Paper page
- Fu NY, Rios A, Pal B, Soetanto R, Lun ATL, Liu K, Beck T, Best S, Vaillant F, Bouillet P, Strasser A, Preiss T, Smyth GK, Lindeman G, Visvader J (2015) EGF-mediated induction of Mcl-1 at the switch to lactation is essential for alveolar cell survival. Nat Cell Biol 17.4:365–375 · DOI
- Huber W et al (2015) Orchestrating high-throughput genomic analysis with Bioconductor. Nat Methods 12.2:115–121 · DOI · Paper page
- Trapnell C, Pachter L, Salzberg SL (2009) TopHat: discovering splice junctions with RNA-seq. Bioinformatics 25.9:1105–1111 · DOI · Paper page
- Robinson MD, Oshlack A (2010) A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol 11.3:R25 · DOI · Paper page
- Wu D et al (2010) ROAST: rotation gene set tests for complex microarray experiments. Bioinformatics 26.17:2176–2182 · DOI · Paper page
Cited by
Chen, Y., Lun, A. T. L., & Smyth, G. K. (2016) · F1000Research · doi:10.12688/f1000research.8987.1 · Cited by 1,287
Self-citationConfronting false discoveries in single-cell differential expression
Squair, J. W., Gautier, M., Kathe, C., Anderson, M. A., James, N. D., Hutson, T. H., Hudelle, R., Qaiser, T., Matson, K. J. E., Barraud, Q., et al. (2021) · doi:10.1101/2021.03.12.435024 · Cited by 1,086
Root microbiota drive direct integration of phosphate stress and immunity
Castrillo, G., Teixeira, P. J. P. L., Paredes, S. H., Law, T. F., de Lorenzo, L., Feltcher, M. E., Finkel, O. M., Breakfield, N. W., Mieczkowski, P., Jones, C. D., et al. (2017) · Nature · doi:10.1038/nature21417 · Cited by 956
Bias, robustness and scalability in single-cell differential expression analysis
Soneson, C., & Robinson, M. D. (2018) · Nature Methods · doi:10.1038/nmeth.4612 · Cited by 770
Crowell, H. L., Soneson, C., Germain, P.-L., Calini, D., Collin, L., Raposo, C., Malhotra, D., & Robinson, M. D. (2020) · Nature Communications · doi:10.1038/s41467-020-19894-4 · Cited by 515
Metadata
- Authors
- Lun, A. T. L., Chen, Y., & Smyth, G. K.
- Venue
- Methods in Molecular Biology
- Publisher
- Springer New York
- Published
- 2016
- Volume
- 1418
- Pages
- 391-416
- ISSN
- 1064-3745 (ISSN-L)
- 1940-6029
- OpenAlex
- W2429188508
- Cited by
- 418
- References
- 6
- FWCI
- 38.84
- Type
- book chapter
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: It’s DE-licious: A Recipe for Differential Expression Analyses of RNA-seq Experiments Using Quasi-Likelihood Methods in edgeR. https://app.cochat.ai/papers/10.1007/978-1-4939-3578-9_19
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- Lun, A. T. L., Chen, Y., & Smyth, G. K. (2016). It’s DE-licious: A Recipe for Differential Expression Analyses of RNA-seq Experiments Using Quasi-Likelihood Methods in edgeR. Methods in Molecular Biology. https://doi.org/10.1007/978-1-4939-3578-9_19
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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.
