The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
Normalization, testing, and false discovery rate estimation for RNA-sequencing data
Open accessAbstract
We discuss the identification of genes that are associated with an outcome in RNA sequencing and other sequence-based comparative genomic experiments. RNA-sequencing data take the form of counts, so models based on the Gaussian distribution are unsuitable. Moreover, normalization is challenging because different sequencing experiments may generate quite different total numbers of reads. To overcome these difficulties, we use a log-linear model with a new approach to normalization. We derive a novel procedure to estimate the false discovery rate (FDR). Our method can be applied to data with quantitative, two-class, or multiple-class outcomes, and the computation is fast even for large data sets. We study the accuracy of our approaches for significance calculation and FDR estimation, and we demonstrate that our method has potential advantages over existing methods that are based on a Poisson or negative binomial model. In summary, this work provides a pipeline for the significance analysis of sequencing data.
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“Normalization methods that take this into account are TMM [47], DESeq [48], PoissonSeq [49] and UpperQuartile [45], which ignore highly variable and/or highly expressed features.”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [49]
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Cites
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482
Robinson, M. D., McCarthy, D. J., & Smyth, G. K. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btp616 · Cited by 41,364
Differential expression analysis for sequence count data
Anders, S., & Huber, W. (2010) · Genome Biology · doi:10.1186/gb-2010-11-10-r106 · Cited by 15,581
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
In silico prediction of protein-protein interactions in human macrophages
Souiai, O., Guerfali, F. Z., Miled, S. B., Brun, C., & Benkahla, A. (2014) · BMC Research Notes · doi:10.1186/1756-0500-7-157 · Cited by 10,785
Significance analysis of microarrays applied to the ionizing radiation response
Tusher, V. G., Tibshirani, R., & Chu, G. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.091062498 · Cited by 10,568
Self-citationStatistical significance for genomewide studies
Storey, J. D., & Tibshirani, R. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1530509100 · Cited by 9,692
Self-citationHapeman, D. E., & Agresti, A. (1991) · Technometrics · doi:10.1080/00401706.1991.10484817 · Cited by 9,407
A 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
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Cited by
Sorted by citations
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
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
“This article compares the performance of the limma-based pipelines to edgeR [20,34], DESeq [13], baySeq [21], TSPM [25], PoissonSeq [26] and DSS [22], all of which are based on NB or over-dispersed Poisson distributions.”
MethodInfluentialA survey of best practices for RNA-seq data analysis
Conesa, A., Madrigal, P., Tarazona, S., Gomez-Cabrero, D., Cervera, A., McPherson, A., Szcześniak, M. W., Gaffney, D. J., Elo, L. L., Zhang, X., et al. (2016) · Genome Biology · doi:10.1186/s13059-016-0881-8 · Cited by 2,794
“Normalization methods that take this into account are TMM [47], DESeq [48], PoissonSeq [49] and UpperQuartile [45], which ignore highly variable and/or highly expressed features.”
MethodSingle-cell RNA sequencing technologies and bioinformatics pipelines
Hwang, B., Lee, J. H., & Bang, D. (2018) · Experimental & Molecular Medicine · doi:10.1038/s12276-018-0071-8 · Cited by 1,866
“To overcome the inherent problems in within-sample normalization methods, alternative approaches have been developed 52 – 54 .”
MethodDifferential analysis of RNA-seq incorporating quantification uncertainty
Pimentel, H., Bray, N. L., Puente, S., Melsted, P., & Pachter, L. (2017) · Nature Methods · doi:10.1038/nmeth.4324 · Cited by 1,534
Metadata
- Authors
- Li, J., Witten, D. M., Johnstone, I. M., & Tibshirani, R.
- Affiliations
- Stanford University (US)
- University of Washington (US)
- Corresponding author
- Li, J.
- Venue
- Biostatistics
- Publisher
- Oxford University Press (OUP)
- Published
- 2011-10-14
- Volume
- 13
- Issue
- 3
- Pages
- 523-538
- ISSN
- 1465-4644 (ISSN-L)
- 1468-4357
- PMC
- PMC3372940
- OpenAlex
- W2142165994
- Cited by
- 369
- References
- 40
- Citations by year
- 2026: 3 · 2025: 10 · 2024: 12 · 2023: 22 · 2022: 22 · 2021: 49 · 2020: 30 · 2019: 24 · 2018: 24 · 2017: 33 · 2016: 29 · 2015: 39 · 2014: 39 · 2013: 19 · 2012: 12
- FWCI
- 9.36
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Locations
- Biostatistics (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (n01hv28183-13-0-0)
- National Heart, Lung, and Blood Institute (N01-HV-28 183)
- National Institute of Biomedical Imaging and Bioengineering (R01EB1988, R01EB001988)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- RNA sequencing, read count normalization, false discovery rate estimation, log-linear models, significance testing, count data, Poisson model, negative binomial regression
- MeSH terms
- Data Interpretation, Statistical, Humans, RNA, Messenger, Models, Statistical, Sequence Analysis, DNA, Reverse Transcriptase Polymerase Chain Reaction
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