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 TMM, DESeq, PoissonSeq, and UpperQuartile take heterogeneous transcript distributions into account by ignoring highly variable and/or highly expressed features.
“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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Björnsdotter, E., Nadzieja, M., Chang, W., Escobar-Herrera, L. A., Mancinotti, D., Angra, D., Xia, X., Tacke, R., Khazaeı, H., Crocoll, C., et al. (2021) · Nature Plants · doi:10.1038/s41477-021-00950-w · Cited by 67 · see citing works
Single-cell profiling of transcriptome and histone modifications with EpiDamID
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Applications of Omics Technology for Livestock Selection and Improvement
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Clark, N. M., Buckner, E., Fisher, A., Nelson, E. C., Nguyen, T. T., Simmons, A. R., Balaguer, M. A. D. L., Butler-Smith, T., Sheldon, P. J., Bergmann, D. C., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-13132-2 · Cited by 62 · see citing works
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Multi-Omics Data Integration in Extracellular Vesicle Biology—Utopia or Future Reality?
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“Popular algorithmic implementations of the correction methods required by differential gene expression analysis are TMM [110], PoissonSeq [111], and UpperQuartile [112].”
MethodBayesian modeling of spatial molecular profiling data via Gaussian process
Li, Q., Zhang, M., Xie, Y., & Xiao, G. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab455 · Cited by 62 · see citing works
“…at sample i. Note that such a multiplicative characterization of the NB of Poisson mean is 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…”
BackgroundError estimates for the analysis of differential expression from RNA-seq count data
Burden, C. J., Qureshi, S. E., & Wilson, S. R. (2014) · PeerJ · doi:10.7717/peerj.576 · Cited by 62 · see citing works
“Although variability in library size can have a significant effect on method performance (Lund et al., 2012), correlation between genes under simulation schemes like ours does not (Li et al., 2012).”
BackgroundMethodInfluentialFarazi, T. A., Brown, M., Morozov, P. S., ten Hoeve, J. J., Ben‐Dov, I. Z., Hovestadt, V., Hafner, M., Renwick, N. M., Mihailović, A., Wessels, L. F. A., et al. (2012) · Methods · doi:10.1016/j.ymeth.2012.07.020 · Cited by 61 · see citing works
On Graphical Models via Univariate Exponential Family DistributionsPreprint
Yang, E., Ravikumar, P., Allen, G. I., & Liu, Z. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1301.4183 · Cited by 61 · see citing works
“Univariate count data is typically modeled using Poisson or negative binomial distributions (Li et al. 2011).”
BackgroundMethodWang, P., Nolan, T. M., Clark, N. M., Jiang, H., Montes, C., Guo, H., Bassham, D. C., Walley, J. W., & Yin, Y. (2021) · The Plant Cell · doi:10.1093/plcell/koab210 · Cited by 60 · see citing works
“For differential expression analysis, statistics was performed using the PoissonSeq package in R (Li et al., 2012).”
MethodCurrent and Future Methods for mRNA Analysis: A Drive Toward Single Molecule Sequencing
Bayega, A., Fahiminiya, S., Oikonomopoulos, S., & Ragoussis, J. (2018) · Methods in molecular biology · doi:10.1007/978-1-4939-7834-2_11 · Cited by 58 · see citing works
A comprehensive catalogue of the coding and non-coding transcripts of the human inner ear
Schrauwen, I., Hasin-Brumshtein, Y., Corneveaux, J. J., Ohmen, J. D., White, C., Allen, A. N., Lusis, A. J., Camp, G. V., Huentelman, M. J., & Friedman, R. A. (2015) · Hearing Research · doi:10.1016/j.heares.2015.08.013 · Cited by 57 · see citing works
“When correcting for multiple testing, we identified 420 protein coding genes that reach genome wide significance with both DESeq2 and PoissonSeq analysis that are expressed at a higher level in the inner ear compared to other tissues (Figure 4, Table S2 in Additional file 4).”
BackgroundDetection of Test Speededness Using Change-Point Analysis
Shao, C., Li, J., & Cheng, Y. (2015) · Psychometrika · doi:10.1007/s11336-015-9476-7 · Cited by 57 · see citing works
Self-citationMLSeq: Machine learning interface for RNA-sequencing data
Göksülük, D., Zararsız, G., Korkmaz, S., Eldem, V., Zararsız, G. E., Özçetin, E., Ozturk, A. B., & Karaağaoğlu, A. E. (2019) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2019.04.007 · Cited by 57 · see citing works
Assefa, A. T., Paepe, K. D., Everaert, C., Mestdagh, P., Thas, O., & Vandesompele, J. (2018) · Genome biology · doi:10.1186/s13059-018-1466-5 · Cited by 59 · see citing works
“2) (1) Default, uses poisson log-linear model [46] 92”
MethodInfluentialFrerich, C. A., Brayer, K. J., Painter, B., Kang, H., Mitani, Y., El‐Naggar, A. K., & Ness, S. A. (2017) · Oncotarget · doi:10.18632/oncotarget.23641 · Cited by 55 · see citing works
Sui, B., Chen, D., Liu, W., Wu, Q. D., Tian, B., Li, Y., Hou, J., Liu, S., Xie, J., Jiang, H., et al. (2020) · Genome biology · doi:10.1186/s13059-020-02150-9 · Cited by 54 · see citing works
Statistical and Bioinformatics Analysis of Data from Bulk and Single-Cell RNA Sequencing Experiments
Yu, X., Aghababazadeh, F. A., Chen, Y. A., & Fridley, B. L. (2020) · Methods in molecular biology · doi:10.1007/978-1-0716-0849-4_9 · Cited by 54 · see citing works
“…where log-linear or generalized linear model were proposed to model the mean difference between samples along with using test statistics such as likelihood ratio test, exact test, and score test for hypothesis testing are implemented in DEGseq [67], Myrna [68], and PoissonSeq [69] packages.”
MethodA Log-Linear Graphical Model for inferring genetic networks from high-throughput sequencing data
Allen, G. I., & Liu, Z. (2012) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2012.6392619 · Cited by 54 · see citing works
“The RNA sequencing expression values are non-negative integers; thus, many have advocated to model this count data using the Poisson distribution [9], [10], [11].”
BackgroundMethodInfluentialWang, P., Clark, N. M., Nolan, T. M., Song, G., Bartz, P. M., Liao, C., Montes, C., Katz, E., Polko, J. K., Kieber, J. J., et al. (2022) · The Plant Cell · doi:10.1093/plcell/koac111 · Cited by 53 · see citing works
“Then, statistical analysis was performed using the PoissonSeq package in R (Li et al., 2012).”
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) · Genome biology · doi:10.1186/s13059-021-02506-9 · Cited by 52 · see citing works
Self-citationTroester, M. A., Hoadley, K. A., D’Arcy, M. E., Cherniack, A. D., Stewart, C., Koboldt, D. C., Robertson, A. G., Mahurkar‐Joshi, S., Shen, H., Wilkerson, M. D., et al. (2016) · npj Breast Cancer · doi:10.1038/npjbcancer.2016.7 · Cited by 47 · see citing works
Benchmarking RNA-seq differential expression analysis methods using spike-in and simulation data
Baik, B., Yoon, S., & Nam, D. (2020) · PLoS ONE · doi:10.1371/journal.pone.0232271 · Cited by 47 · see citing works
“Voom methods, edgeR.ql, and SAMseq exhibited overall quite good control of true FDRs, while PoissonSeq and baySeq showed large true FDRs. ROTS, edgeR, and DESeq exhibited poor true FDRs for three sample case and small DE proportions (pDE� 10%), but they were dramatically improved when pDE or sample size was increased.”
InfluentialGene Ranking of RNA-Seq Data via Discriminant Non-Negative Matrix Factorization
Jia, Z., Zhang, X., Guan, N., Bo, X., Barnes, M. R., & Luo, Z. (2015) · PLoS ONE · doi:10.1371/journal.pone.0137782 · Cited by 43 · see citing works
The Brassicaceae Family Displays Divergent, Shoot-Skewed NLR Resistance Gene Expression
Munch, D., Gupta, V., Bachmann, A., Busch, W., Kelly, S. J., Mun, T., & Andersen, S. U. (2017) · PLANT PHYSIOLOGY · doi:10.1104/pp.17.01606 · Cited by 43 · see citing works
Kolshus, E., Ryan, K. M., Blackshields, G., Smyth, P. C., Sheils, O. M., & McLoughlin, D. M. (2017) · Acta Psychiatrica Scandinavica · doi:10.1111/acps.12821 · Cited by 43 · see citing works
Rather, M. A., Agarwal, D., Bhat, T. A., Khan, I. A., Zafar, I., Kumar, S., Amin, A., Sundaray, J. K., & Qadri, T. (2023) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2023.123549 · Cited by 41 · see citing works
Montes, C., Wang, P., Liao, C., Nolan, T. M., Song, G., Clark, N. M., Elmore, J. M., Guo, H., Bassham, D. C., Yin, Y., et al. (2022) · New Phytologist · doi:10.1111/nph.18404 · Cited by 41 · see citing works
“Differential expression was assessed using the PoissonSeq R package (Li et al., 2012).”
MethodZhao, J., Li, H., Yin, Y., An, W., Qin, X., Wang, Y., Li, Y., Fan, Y., & Cao, Y. (2020) · Scientific Reports · doi:10.1038/s41598-020-61064-5 · Cited by 40 · see citing works
Gene expression variability and the analysis of large-scale RNA-seq studies with the MDSeq
Ran, D., & Daye, Z. J. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx456 · Cited by 38 · see citing works
Zhang, Y., Wu, K., Wang, L., Wang, Z., Han, W., Chen, D., Wei, Y., Su, R., Wang, R., Liu, Z., et al. (2019) · Genomics · doi:10.1016/j.ygeno.2019.02.013 · Cited by 40 · see citing works
Self-citationZERO-INFLATED NEGATIVE BINOMIAL REGRESSION FOR DIFFERENTIAL ABUNDANCE TESTING IN MICROBIOME STUDIES
Zhang, X., Mallick, H., & Yi, N. (2021) · DOAJ (DOAJ: Directory of Open Access Journals) · doi:10.18454/jbg.2016.2.2.1 · Cited by 39 · see citing works
Hammoudeh, S. M., Hammoudeh, A. M., Bhamidimarri, P. M., AlSafar, H. S., Mahboub, B. H., Künstner, A., Busch, H. S., Halwani, R., Hamid, Q. A., Rahmani, M., et al. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.595150 · Cited by 38 · see citing works
microRNAs and the mammary gland: a new understanding of gene expression
Gigli, I., & Maizon, D. O. (2013) · Genetics and Molecular Biology · doi:10.1590/s1415-47572013005000040 · Cited by 37 · see citing works
“For the next generation of sequencing technology, normalization will be challenging because different sequencing experiments may generate quite different total numbers of reads (Li et al., 2012b).”
BackgroundMethodResultInfluentialMcKinney, B. A., White, B. C., Grill, D. E., Li, P. W., Kennedy, R. B., Poland, G. A., & Oberg, A. L. (2013) · PLoS ONE · doi:10.1371/journal.pone.0081527 · Cited by 37 · see citing works
Brunner, A. L., Li, J., Guo, X., Sweeney, R. T., Varma, S., Zhu, S., Li, R., Tibshirani, R. J., & West, R. B. (2014) · Genome biology · doi:10.1186/gb-2014-15-5-r71 · Cited by 37 · see citing works
“Briefly, the counts from the data were transformed using Anscombe transformation [37] to stabilize variance, and then normalized using sequencing depths estimated by PoissonSeq [38].”
Self-citationTranscriptomics, proteomics and metabolomics driven biomarker discovery in COPD: an update
Ghosh, N., Dutta, M., Singh, B. K., Banerjee, R., Bhattacharyya, P., & Chaudhury, K. (2016) · Expert Review of Molecular Diagnostics · doi:10.1080/14737159.2016.1198258 · Cited by 37 · see citing works
Choi, S. H., Labadorf, A. T., Myers, R. H., Lunetta, K. L., Dupuis, J., & DeStefano, A. L. (2017) · BMC Bioinformatics · doi:10.1186/s12859-017-1498-y · Cited by 38 · see citing works
“Rapaport et al. [7] assessed commonly used analysis packages (Cuffdiff, edgeR, DESeq, PoissonSeq [20], baySeq, and limma) for RNA-Seq data.”
MethodBeckers, M., Mohorianu, I. I., Stocks, M. B., Applegate, C., Dalmay, T., & Moulton, V. (2017) · RNA · doi:10.1261/rna.059360.116 · Cited by 36 · see citing works
“Other options are rank-based quantile normalization adapted for sRNA-seq data (Bolstad et al. 2003) and subsampling normalization (Li et al. 2012).”
Bankers, L., Fields, P. D., McElroy, K. E., Boore, J. L., Logsdon, J. M., Jr., & Neiman, M. (2017) · Molecular Ecology · doi:10.1111/mec.14146 · Cited by 38 · see citing works
Lee, A. S., Rusch, J., Lima, A. C., Usmani, A., Huang, N., Lepamets, M., Vigh‐Conrad, K. A., Worthington, R. E., Mägi, R., Wu, X., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-12522-w · Cited by 37 · see citing works
Evaluating statistical analysis models for RNA sequencing experiments
Reeb, P., & Steibel, J. P. (2013) · Frontiers in Genetics · doi:10.3389/fgene.2013.00178 · Cited by 36 · see citing works
“…generalized Poisson (GPseq) (Srivastava and Chen, 2010), mixed Poisson (TSPM) (Auer and Doerge, 2011), Poisson log-linear (PoissonSeq) (Li et al., 2012) and negative binomial (edgeR, DESeq, baySeq, NBPSeq) (Anders and Huber, 2010; Hardcastle and Kelly, 2010; Robinson et al., 2010; Di…”
MethodTechnical and biological variance structure in mRNA-Seq data: life in the real world
Oberg, A. L., Bot, B. M., Grill, D. E., Poland, G. A., & Therneau, T. M. (2012) · BMC Genomics · doi:10.1186/1471-2164-13-304 · Cited by 36 · see citing works
“Application of a power transformation to the data in order to use Poisson modeling assumptions has recently been proposed [17].”
MethodPeterson, J. R., Thor, S., Kohler, L., Kohler, P. R. A., Metcalf, W. W., & Luthey-Schulten, Z. A. (2016) · BMC Genomics · doi:10.1186/s12864-016-3219-8 · Cited by 36 · see citing works
RNA-seq analysis in forest tree species: bioinformatic problems and solutions
de Heredia, U. L., & Vázquez‐Poletti, J. L. (2016) · Tree Genetics & Genomes · doi:10.1007/s11295-016-0995-x · Cited by 35 · see citing works
“…et al. 2010),DEGESmulti-step normalization (Kadota et al. 2012), L Lowess locally weighted least squares (Yang et al. 2002), TSM two-step normalization (Li et al. 2012), ARPK averaged reads per kilobase (Dou et al. 2015), GCC normalization by GC content, τ tau parameter (Wang et al. 2014), NP…”
BackgroundAliakbari, M., Cohen, S. P., Lindlöf, A., & Shamloo‐Dashtpagerdi, R. (2021) · Plant Physiology and Biochemistry · doi:10.1016/j.plaphy.2021.02.016 · Cited by 35 · see citing works
Network-Based Identification of Biomarkers Coexpressed with Multiple Pathways
Guo, N. L., & Wan, Y. (2014) · Cancer Informatics · doi:10.4137/cin.s14054 · Cited by 35 · see citing works
◾ Differential Expression Analysis
Korpelainen, E., Tuimala, J., Somervuo, P., Huss, M., & Wong, G. (2014) · Chapman & Hall/CRC eBooks · doi:10.1201/b17457-12 · Cited by 46 · see citing works
Ueti, M. W., Johnson, W. C., Kappmeyer, L. S., Herndon, D. R., Mousel, M. R., Reif, K. E., Taus, N. S., Ifeonu, O. O., da Silva, J. C. F., Suarez, C. E., et al. (2020) · International Journal for Parasitology · doi:10.1016/j.ijpara.2020.08.006 · Cited by 33 · see citing works
Bi, Y., & Davuluri, R. V. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-262 · Cited by 30 · see citing works
“The normalization constant d can be inferred from some previous available methods, for example the methods proposed in DESeq or PoissonSeq [31].”
MethodA new multivariate EWMA control chart via multiple testing
Park, J., & Jun, C. (2015) · Journal of Process Control · doi:10.1016/j.jprocont.2015.01.007 · Cited by 32 · see citing works
A joint-L2,1-norm-constraint-based semi-supervised feature extraction for RNA-Seq data analysis
Liu, J., Wang, D., Gao, Y.-L., Zheng, C.-H., Shang, J., Liu, F., & Xu, Y. D. (2016) · Neurocomputing · doi:10.1016/j.neucom.2016.09.083 · Cited by 32 · see citing works
Differential expression analysis for paired RNA-seq data
Chung, L. M., Ferguson, J. P., Zheng, W., Qian, F., Bruno, V. M., Montgomery, R. R., & Zhao, H. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-110 · Cited by 32 · see citing works
“In particular, Li et al. (2012) proposed a permutation-based approach to generate the null distribution [14].”
- 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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