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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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“Genes with fold changes ≥ 2, and fdr values 0.01 that adjusted for multiple testing from P -values by the Benjamini–Hochberg adjustment method [11] were identified to be differentially expressed.”
InfluentialDifferential expression analysis using a model-based gene clustering algorithm for RNA-seq data
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“Tang et al. [43] evaluated the performance of 12 DE pipelines available across nine R packages (TCC [14], edgeR [11], DESeq [44], DESeq2 [15], limma [45], samr [46], PoissonSeq [47], baySeq [12], and EBSeq [13]) and recommended the use of TCC when performing three-group count data.”
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“In addition, baySeq (Hardcastle and Kelly, 2010), NOIseq (Tarazona et al., 2012), SAMseq (Tibshirani et al., 2011), and PoissonSeq (Li et al., 2012) are available.”
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“First, CISCA estimates the sequencing depth d j by using the Li's method in [7].”
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Truong, J. K., Li, J., Li, Q., Pachura, K. J., Rao, A., Gumber, S., Fuchs, C. D., Feranchak, A. P., Karpen, S. J., Trauner, M., et al. (2023) · JCI Insight · doi:10.1172/jci.insight.149360 · Cited by 12
Self-citationLi, G., Zhang, T., Zhang, G., Chen, L., Han, W., Dai, G., Xie, K., Zhu, X., YiJun, S., & Wang, J. (2018) · Biochemical and Biophysical Research Communications · doi:10.1016/j.bbrc.2018.11.044 · Cited by 11
Watkins, J. M., Montes, C., Clark, N. M., Song, G., Oliveira, C. C., Mishra, B., Brachova, L., Seifert, C. M., Mitchell, M. S., Yang, J., et al. (2023) · Molecular & Cellular Proteomics · doi:10.1016/j.mcpro.2023.100705 · Cited by 11
“TMT-NEAT was used for sample loading (within-run) and internal reference (between-run) normalization to eliminate batch effects (41) to generate quality control plots such as p - and q -value histograms and to determine differential expression with PoissonSeq (42).”
MethodPre- and Post-Processing Workflow for Affinity Purification Mass Spectrometry Data
Fischer, M., Zilkenat, S., Gerlach, R. G., Wagner, S., & Renard, B. Y. (2014) · Journal of Proteome Research · doi:10.1021/pr401249b · Cited by 11
Liu, Y., Wu, P., Zhou, J., Johnson‐Pais, T. L., Lai, Z., Chowdhury, W. H., Rodríguez, R., & Chen, Y. (2017) · BMC Bioinformatics · doi:10.1186/s12859-017-1803-9 · Cited by 11
Impact of human gene annotations on RNA-seq differential expression analysis
Hamaguchi, Y., Zeng, C., & Hamada, M. (2021) · BMC Genomics · doi:10.1186/s12864-021-08038-7 · Cited by 13
Differential expression analysis for sequence count data via mixtures of negative binomials
Bonafede, E. (2015) · AMS Dottorato Institutional Doctoral Theses Repository (University of Bologna) · doi:10.6092/unibo/amsdottorato/6741 · Cited by 9
Statistical Assessment of Depth Normalization for Small RNA Sequencing
Qin, L., Zou, J., Shi, J., Lee, A., Mihailovic, A., Farazi, T. A., Tuschl, T., & Singer, S. (2020) · JCO Clinical Cancer Informatics · doi:10.1200/cci.19.00118 · Cited by 10
“The former included total count (TC),(2) upper quartile (UQ),(1) median,(2) trimmed mean of M values (TMM),(17) DESeq,(3) and PoissonSeq.(20) The latter included quantile normalization (QN) with and without removing lowcount miRNAs,(21) surrogate variable analysis (SVA),(6) and remove unwanted variation (RUV), with th…”
BackgroundStatistical strategies for microRNAseq batch effect reduction.
Guo, Y. P., Zhao, S., Su, P.-F., Li, C.-I., Ye, F., Flynn, C. R., & Shyr, Y. (2014) · PubMed · doi:10.3978/j.issn.2218-676x.2014.06.05 · Cited by 10
“Batch effect in RNAseq data has been considered widespread and should be properly addressed (12-14).”
BackgroundSystematic benchmarking of statistical methods to assess differential expression of circular RNAs
Buratin, A., Bortoluzzi, S., & Gaffo, E. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbac612 · Cited by 12
Llinas, R. J., Xiong, J. Q., Clark, N. M., Burkhart, S. E., & Bartel, B. (2022) · PLANT PHYSIOLOGY · doi:10.1093/plphys/kiac221 · Cited by 9
“Differential expression was performed using PoissonSeq (Li et al., 2012) with a q-value cutoff of 0.05.”
MethodInfluentialAnalytical tools to assess aging in humans: The rise of geri-omics
da Costa, J. P., Rocha‐Santos, T., & Duarte, A. C. (2015) · TrAC Trends in Analytical Chemistry · doi:10.1016/j.trac.2015.09.011 · Cited by 9
Auxin Induces Widespread Proteome Remodeling in Arabidopsis Seedlings
Clark, N. M., Shen, Z., Briggs, S. P., Walley, J. W., & Kelley, D. R. (2019) · PROTEOMICS · doi:10.1002/pmic.201900199 · Cited by 9
“We then performed statistical analysis to determine which proteins have ratios significantly different than 1 by testing against 100 randomly generated null distributions [18] (see Supplemental Methods).”
MethodRocke, D. M., Ruan, L., Gossett, J. J., Durbin-Johnson, B. P., & Aviran, S. (2015) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/018739 · Cited by 12
“Several methods ([40], [43]) use permutation testing as the basis for analysis.”
MethodDong, Y., Gupta, S., Wargent, J. J., Putterill, J., Macknight, R. C., Gechev, T., Mueller‐Roeber, B., & Dijkwel, P. P. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms241411323 · Cited by 7
“We used edgeR with Trimmed Mean of M-values (TMM) normalisation in all treatments [29].”
MethodMachine Learning Models for Cancer Research: A Narrative Review of Bulk RNA-Seq Applications
Pudova, E. A., Pavlov, V., Guvatova, Z. G., Fedorova, M., Шегай, П. В., Kudryavtseva, A. V., & Snezhkina, A. V. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms262412081 · Cited by 8
“It is evident that contemporary normalization methodologies, including TMM (Trimmed Mean of M-values), DESeq, PoissonSeq and UpperQuartile, are capable of accounting for the distinctive characteristics inherent in RNA-Seq data [16,18,19].”
Li, D., Ge, S., Liu, Y., Pan, M., Wang, X., Han, G., Zou, S., Liu, R., Niu, K., Zhao, C., et al. (2023) · iScience · doi:10.1016/j.isci.2023.108558 · Cited by 8
“With anchor set from Drosophila spike-in RNA, we implemented enONE normalization procedures with five scaling toolsets, including TC, UQ, TMM, DESeq, and PoissonSeq, 17 as well as three regression-based procedures, namely RUVg, RUVs, and RUVse.”
MethodQuickMMCTest: quick multiple Monte Carlo testing
Gandy, A., & Hahn, G. (2016) · Statistics and Computing · doi:10.1007/s11222-016-9656-z · Cited by 8
“…approximate all p values using a conservative p value estimator and finally use these estimates as input for the multiple testing procedure, thus treating them as if they were the p values (Nusinow et al. 2012; Gusenleitner et al. 2012; Rahmatallah et al. 2012; Zhou et al. 2013; Li et al. 2012).”
MethodSMIXnorm: Fast and Accurate RNA-Seq Data Normalization for Formalin-Fixed Paraffin-Embedded Samples
Yin, S., Zhan, X., Yao, B., Xiao, G., Wang, X., & Xie, Y. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.650795 · Cited by 7
“PoissonSeq (PS) (Li et al., 2012) models RNA-seq data by a Poisson log-linear model, where a set of sample-specific parameters is included as offset parameters in the linear predictor to account for different sequencing depths.”
MethodZheng, L., Pan, C., Tian, W., Liang, C., Feng, Y., He, W. A., Yang, Z., Wang, B., Qiu, Q., Li, N., et al. (2023) · Cellular Oncology · doi:10.1007/s13402-023-00797-7 · Cited by 8
Bankers, L., & Neiman, M. (2017) · G3 Genes Genomes Genetics · doi:10.1534/g3.116.037275 · Cited by 6
The Dynamic Change of Gene-Regulated Networks in Cashmere Goat Skin with Seasonal Variation
Hu, S., Li, C., Wu, D., Huo, H., Bai, H., & Wu, J. (2021) · Biochemical Genetics · doi:10.1007/s10528-021-10114-2 · Cited by 7
Garg, T., Weiss, C. R., & Sheth, R. A. (2022) · Cancers · doi:10.3390/cancers14153628 · Cited by 7
Modeling Overdispersion Heterogeneity in Differential Expression Analysis Using Mixtures
Bonafede, E., Picard, F., Robin, S. S., & Viroli, C. (2015) · Biometrics · doi:10.1111/biom.12458 · Cited by 7
“Yu et al. (2013) and Soneson and Delorenzi (2013) evaluated the comparative performance of these approaches, together with other relevant proposals for differential expression analysis of RNA-Seq data, such as NOISeq (Tarazona et al., 2011), PoissonSeq (Li et al., 2012), SAMSeq (Li and Tibshirani, 2013), QuasiSeq (Lund…”
BackgroundZhou, X., & Robinson, M. D. (2015) · Genome biology · doi:10.1186/s13059-015-0781-3 · Cited by 5
“A recent report in Genome Biology from Rapaport and co-authors claimed that some methods, namely PoissonSeq [4] and limma [9], “have improved modeling of genes expressed in one condition”, where they showed a striking difference in the ability to separate differential expression [7].”
MethodModeling and analysis of RNA‐seq data: a review from a statistical perspective
Li, W. V., & Li, J. J. (2018) · Quantitative Biology · doi:10.1007/s40484-018-0144-7 · Cited by 77
Self-citationA Bayesian hierarchical model for analyzing methylated RNA immunoprecipitation sequencing data
Zhang, M., Li, Q., & Xie, Y. (2018) · Quantitative Biology · doi:10.1007/s40484-018-0149-2 · Cited by 7
Guo, J., He, K., Zeng, H., Shi, Y. H., Ye, P., Zhou, Q., Pan, Z., & Long, X. (2019) · Oncology Letters · doi:10.3892/ol.2019.10061 · Cited by 7
“Finally, the FDR was used as the key indicator of differential expression of miRNAs during the screening process (20).”
MethodBayesian Modeling of Microbiome Data for Differential Abundance AnalysisPreprint
Li, Q., Jiang, S., Koh, A. Y., Xiao, G., & Zhan, X. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.08741 · Cited by 10
“Multiplicative characterizations of the NB (or Poisson as a special case) mean are typical in both the frequentist (e.g., Witten 2011, Li et al. 2012, Cameron & Trivedi 2013) and the Bayesian literature (e.g., Banerjee et al. 2014, Airoldi & Bischof 2016) to justify latent heterogeneity and…”
Background
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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