Cited by 370Gene expression and cancer classificationGenomics and Phylogenetic StudiesBioinformatics and Genomic Networks

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Normalization, testing, and false discovery rate estimation for RNA-sequencing data

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Li, J., Witten, D. M., Johnstone, I. M., Tibshirani, R.·2012·Biostatistics

DOI 10.1093/biostatistics/kxr031PMC PMC3372940

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Abstract

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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Method
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Background
This Provisional PDF corresponds to the article as it appeared upon acceptance. Copyedited and fully formatted PDF and full text (HTML) versions will be made available soon. A shared transcriptional program in early breast neoplasias despite genetic and clinical distinctions
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Background
The complement system supports normal postnatal development and gonadal function in both sexesPreprint
Lee, A. S., Rusch, J., Usmani, A., Lima, A. C., Wong, W. S. W., Huang, N., Lepamets, M., Vigh‐Conrad, K. A., Worthington, R. E., Mägi, R., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/233825 · Cited by 2
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Method
Structure learning of undirected graphical models for count dataPreprint
Nguyen, T. K. H., & Chiogna, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.10854 · Cited by 2
Systematic benchmarking of bulk and single-cell RNA-seq statistical methods to assess differential expression of circular RNAsPreprint
Buratin, A., Bortoluzzi, S., & Gaffo, E. (2022) · Research Square · doi:10.21203/rs.3.rs-2018316/v1 · Cited by 2
Epitranscriptome analysis of NAD-capped RNA by spike-in-based normalizationPreprint
Li, D., Ge, S., Liu, Y., Pan, M., Wang, X., Han, G., Zou, S., Liu, R., Niu, K., Zhao, C., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.03.23.534034 · Cited by 2
Bioinformatics for Analyzing the Role of Epigenetics in Plant Disease Resistance
Singh, K., Balyan, H. S., & Gupta, P. K. (2024) · Wiley eBooks · doi:10.1002/9781394209965.ch15 · Cited by 2
Robust differential expression analysis by learning discriminant boundary in multi-dimensional space of statistical attributes
Bei, Y., & Hong, P. (2016) · BMC Bioinformatics · doi:10.1186/s12859-016-1386-x · Cited by 1
“To deal with this so-called over-dispersion problem, PoissonSeq [24] applied a power transformation to make the data distribution look more like Poisson.”
Method
Robust high-dimensional tuning free multiple testing
B, J. F. A., Lou, Z., & Yu, M. (2023) · The Annals of Statistics · doi:10.1214/23-aos2322 · Cited by 1
Analysis of gene expression of Babesia gibsoni cultured with diminazene aceturate using RNA sequencing
MATSUDA, N., Minori, I. T. O., Nukada, Y., TOYOMA, M., Nagai, K., Motegi, T., Morita, T., & Yamasaki, M. (2024) · Journal of Veterinary Medical Science · doi:10.1292/jvms.24-0395 · Cited by 1
Multi‐proteomic profiling indicates potential regulatory signatures underlying rice resistance to Magnaporthe oryzae
Auler, P. A., Montes, C., Arge, L. W., Song, G., Walley, J. W., & Silva-Filho, M. C. (2026) · The Plant Journal · doi:10.1111/tpj.70892 · Cited by 1
“Differential protein accumulation analysis (DAP) between control and inoculated plants was performed using the Pseq algorithm from the PoissonSeq package (Li et al., 2012).”
RNA sequencing data: biases and normalization
Finotello, F., Lavezzo, E., Barzon, L., Fontana, P., Si‐Ammour, A., Toppo, S., & Camillo, B. D. (2012) · EMBnet journal · doi:10.14806/ej.18.a.441 · Cited by 1
Statistical methods for the analysis of RNA sequencing data
Chu, M.-K. (2014) · Scholarship@Western (Western University) · Cited by 1
geneEx - A Novel Tool to Assess Differential Expression from Gene and Exon Sequencing Data
Scicolone, O. M., Paciello, G., & Ficarra, E. (2018) · International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC) · doi:10.5220/0006657501740181 · Cited by 1
“Six of these methods, i.e. Cuffdiff (Trapnell et al., 2013), edgeR (Robinson et al., 2010), DESeq (Love et al., 2014), PoissonSeq (Li et al., 2012), baySeq (Hardcastle and Kelly, 2010) and limma (Smyth, 2005), were ∗ co-first-author recently compared in SEQC (DeLuca et al., 2012) and ENCODE (Consortium et al., 2004) d…”
BackgroundMethod
Inference of Gene Coexpression Networks from Bulk-Based RNA-Sequencing Data
Lamere, A. T. (2021) · Methods in molecular biology · doi:10.1007/978-1-0716-1534-8_2 · Cited by 1
A New ℓ 0 -Regularized Log-Linear Poisson Graphical Model with Applications to RNA Sequencing Data
Li, C. Z., Kawaguchi, E. S., & Li, G. (2021) · Journal of Computational Biology · doi:10.1089/cmb.2020.0558 · Cited by 1
“…for sequencing depth, (2) biological entities (e.g., genes, miRNAs) with low counts or low variances are ﬁltered out, (3) vectors with potential over-dispersion are transformed using a power transformation to transform the data closer to Poisson distribution (Li et al., 2012; Wan et al., 2016).”
Structural inference of time‐varying mixed graphical models
Liu, Q., Zhang, Y., & Ouyang, Z. (2021) · Stat · doi:10.1002/sta4.414 · Cited by 1
“The raw RNA-Seq count data are processed by the function processSeq from the R package XMRF (Allen & Liu, 2012), which adjusted for sequencing depth and overdispersion (Li et al., 2012).”
Method
Integrative Structural Learning of Mixed Graphical Models via Pseudo-likelihood
Liu, Q., & Zhang, Y. (2023) · Statistics in Biosciences · doi:10.1007/s12561-023-09367-9 · Cited by 1
RNA-Seq Data Analysis in Prokaryotes: A Review for Non-experts
Cubillos, A. E. R., Perlaza-Jiménez, L., & Giraldo, A. J. B. (2014) · Acta Biológica Colombiana · doi:10.15446/abc.v19n2.41010 · Cited by 2
Semi-supervised Feature Extraction for RNA-Seq Data Analysis
Liu, J., Xu, Y. D., Gao, Y.-L., Wang, D., Zheng, C.-H., & Shang, J. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-22053-6_70 · Cited by 1
“We compare our methods with several popularly used methods in bioinformatics, such as Sparse LDA(SLDA) [11], SPCA [3], PMD [5] and PS [12].”
Method
RNA-seq data analysis using nonparametric Gaussian process models
Nguyen, T. T., Nahavandi, S., Creighton, D., & Khosravi, A. (2016) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2016.7727870 · Cited by 1
Modifying SAMseq to account for asymmetry in the distribution of effect sizes when identifying differentially expressed genes
Kotoka, E. F., & Orr, M. (2017) · Statistical Applications in Genetics and Molecular Biology · doi:10.1515/sagmb-2016-0037 · Cited by 1
DFseq: Distribution-Free Method to Detect Differential Gene Expression for RNA-Sequencing Data
Yang, S., Wachtel, M. S., & Wu, J. (2018) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2018.2866994 · Cited by 1
“…that the distribution of read counts should be modeled by distributions that consider only non-negative integer values, such as Poisson distribution [1], [2], negative binomial (NB) distribution [3], [4], [5], [6], beta-binomial (BB) distribution [7], and generalized Poisson (GP) distribution [8].”
Background
The new technologies of high-throughput single-cell RNA sequencing
Vodiasova, E. A., Chelebieva, E. S., & Кулешова, О. Н. (2019) · Vavilov Journal of Genetics and Breeding · doi:10.18699/vj19.520 · Cited by 2
Analyzing Gene Pathways from Microarrays to Sequencing Platforms
Miecznikowski, J. C., Wang, D., Ren, X., Wang, J., & Liu, S. (2020) · Springer eBooks · doi:10.1007/978-3-030-34675-1_16 · Cited by 1
Statistical methods for transcriptomics: From microarrays to RNA-seq
Campos, S. T. (2014) · RiuNet (Universitat Politècnica de València) · doi:10.4995/thesis/10251/48485 · Cited by 1
Statistical and Computational Methods for Differential Expression Analysis in High-throughput Gene Expression Data
Shi, Y. (2016) · Deep Blue (University of Michigan) · Cited by 1
Suporte ao desenvolvimento e à composição de serviços web semânticos para a análise de expressão gênica
Guardia, G. D. A. (2016) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/t.95.2016.tde-28102016-101702 · Cited by 1
QuickMMCTest - Higher accuracy for multiple testing correctionsPreprint
Gandy, A., & Hahn, G. (2014) · arXiv (Cornell University) · Cited by 1
A novel antiviral lncRNA EDAL shields a T309 O-GlcNAcylation site to promote EZH2 degradationPreprint
Sui, B.-K., Chen, D., Liu, W., Wu, Q., Tian, B., Hou, J., Li, Y.-Y., Liu, S., Xie, J., Jiang, H., et al. (2019) · bioRxiv · doi:10.1101/824813 · Cited by 2
slim shady is a novel allele of PHYTOCHROME B present in the T-DNA line SALK_015201Preprint
Dash, L., McEwan, R. E., Montes, C., Mejia, L., Walley, J. W., Dilkes, B. P., & Kelley, D. R. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.02.12.430994 · Cited by 1
“QuantSeq reads were mapped to the TAIR10 genome and dif - ferential gene expression analysis was performed using PoissonSeq implemented in R (Li et al., 2012).”
Class Discovery, Comparison, and Prediction Methods for RNA-Seq Data
Cephe, A., Koçhan, N., Zararsız, G. E., Eldem, V., & Zararsız, G. (2023) · IGI Global eBooks · doi:10.4018/978-1-7998-9220-5.ch123 · Cited by 1
Regulatory architecture of disease resistance in maize revealed by multi-omic systems geneticsPreprint
Clark, N. M., Song, G., Kabahuma, M. K., Kolkman, J. M., Christensen, S. A., Montes, C., Malik, S., Nelson, R., & Walley, J. W. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.08.29.610401 · Cited by 1
Análise, via RNAseq, do transcritoma da cana-de-açúcar e identificação de genes expressos em resposta a Sporisorium scitamineum, o agente causal do carvão
Palhares, A. (2014) · doi:10.11606/t.11.2014.tde-11112014-142109
An island-based approach for RNA-SEQ differential expression analysis.
Eteleeb, A. M. (2015) · ThinkIR: The University of Louisville's Institutional Repository (University of Louisville) · doi:10.18297/etd/2072
“PoissonSeq tests for DE by determining the significance of the correlation term Yj in the linear model using a score statistic [126, 91].”
MethodInfluential
Time Course RNA-seq: A Potential Avenue with Somewhat Different Approach in Tandem of Differential Analysis
Oh, S., Zhao, H., & Noonan, J. P. (2012) · ... International Conference on Complex, Intelligent and Software Intensive Systems · doi:10.1109/cisis.2012.204

Authors: Li, J., Witten, D. M., Johnstone, I. M., & Tibshirani, R.
Affiliations: Stanford University (US)
University of Washington (US)
Corresponding author: Li, J.
ORCID: Li, J.
Witten, D. M.
Johnstone, I. M.
Tibshirani, R.
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
DOI: 10.1093/biostatistics/kxr031
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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Normalization, testing, and false discovery rate estimation for RNA-sequencing data

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