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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Lu, H.-T., & Hsueh, H. (2013)
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Ye, T. (2016) · doi:10.32657/10356/65955
“Li and others applied a similar idea to estimate the transformation for data exhibiting overdispersion using the Poisson goodness-of-fit statistic [40] .”
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“For example, Li et al. give the following example [9]: Suppose we have 101 genes in two samples, g1 to g101.”
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Author comment: Temporal and spatial auxin responsive networks in maize primary roots — R2/PR11
Walley, J. W. (2022) · Quantitative Plant Biology · doi:10.1017/qpb.2022.17.pr11
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Decision: Temporal and spatial auxin responsive networks in maize primary roots — R2/PR13
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Review: Temporal and spatial auxin responsive networks in maize primary roots — R1/PR8
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Author comment: Temporal and spatial auxin responsive networks in maize primary roots — R0/PR1
Walley, J. W. (2022) · Quantitative Plant Biology · doi:10.1017/qpb.2022.17.pr1
Recommendation: Temporal and spatial auxin responsive networks in maize primary roots — R1/PR9
(2022) · Quantitative Plant Biology · doi:10.1017/qpb.2022.17.pr9
Decision: Temporal and spatial auxin responsive networks in maize primary roots — R1/PR10
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Identify Diabetes-related Targets based on ForgeNet_GPC
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Additional file 2 of Clipper: p-value-free FDR control on high-throughput data from two conditions
Ge, X., Chen, Y. E., Song, D., McDermott, M., Woyshner, K., Manousopoulou, A., Wang, N., Li, W., Wang, L. D., & Li, J. J. (2021) · Figshare · doi:10.6084/m9.figshare.16786728
Self-citationBISON: bi-clustering of spatial omics data with feature selection
Zhu, B., Cassese, A., Vannucci, M., Guindani, M., & Li, Q. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf495
“…and the normalized gene expression level Such a multiplicative formulation of Poisson means is typical in both the frequentist (Witten 2011, Li et al. 2012, Cameron and Trivedi 2013) and the Bayesian literature (Banerjee et al. 2003, Airoldi and Bischof 2016, Li et al. 2017) to…”
Osabe, T., Shimizu, K., & Kadota, K. (2021) · Figshare · doi:10.6084/m9.figshare.16841049
Osabe, T., Shimizu, K., & Kadota, K. (2021) · Figshare · doi:10.6084/m9.figshare.16841061
Osabe, T., Shimizu, K., & Kadota, K. (2021) · Figshare · doi:10.6084/m9.figshare.16841058
Additional file 3 of Impact of human gene annotations on RNA-seq differential expression analysis
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Additional file 1 of Impact of human gene annotations on RNA-seq differential expression analysis
Hamaguchi, Y., Zeng, C., & Hamada, M. (2021) · Figshare · doi:10.6084/m9.figshare.16778408
Osabe, T., Shimizu, K., & Kadota, K. (2021) · Figshare · doi:10.6084/m9.figshare.16841064
Additional file 1 of Clipper: p-value-free FDR control on high-throughput data from two conditions
Ge, X., Chen, Y. E., Song, D., McDermott, M., Woyshner, K., Manousopoulou, A., Wang, N., Li, W., Wang, L. D., & Li, J. J. (2021) · Figshare · doi:10.6084/m9.figshare.16786725
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Osabe, T., Shimizu, K., & Kadota, K. (2021) · Figshare · doi:10.6084/m9.figshare.16841052
Additional file 3 of Clipper: p-value-free FDR control on high-throughput data from two conditions
Ge, X., Chen, Y. E., Song, D., McDermott, M., Woyshner, K., Manousopoulou, A., Wang, N., Li, W., Wang, L. D., & Li, J. J. (2021) · Figshare · doi:10.6084/m9.figshare.16786731
Self-citationOsabe, T., Shimizu, K., & Kadota, K. (2021) · Figshare · doi:10.6084/m9.figshare.16841067
Rodríguez‐Zaccaro, F. D., Moe‐Lange, J., Malik, S., Montes, C., Hamada, N., Groover, A. T., Walley, J. W., & Shabek, N. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2025.12.22.696057
Contextual evaluation of microRNA sequencing data harmonization for sample clustering
Zou, J., Düren, Y., Wang, X., Xiang, Y., Qi, Y., Wang, M., Wu, Y., Singer, S., & Qin, L. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag514
“The former included Total Count (TC; “edgeR::cpm”) [ 7], Upper Quartile (UQ) [ 5], Median (Med) [ 7], Trimmed Mean of M-values (TMM; “edgeR::calcNormFactors(‘TMM’)”) [ 28], DESeq (“DESeq2::estimateSizeFactors”) [ 29], and PoissonSeq (“Poisson-Seq::PS.Est.Depth”) [ 30].”
Controlling False Discovery Rates in RNA-Sequencing Data
Burden, C. J., Qureshi, S. E., & Wilson, S. C. (2013) · ANU Open Research (Australian National University)
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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