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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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“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
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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.”
MethodSingle-cell RNA sequencing technologies and bioinformatics pipelines
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MethodDifferential analysis of RNA-seq incorporating quantification uncertainty
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Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data
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“• PoissonSeq (v.1.1.2): No minimum expression mean was applied and the number of permutations was 500.”
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“([14]) did a detailed comparison of many methods on simulated data sets, including (1) their own method, PoissonSeq, (2) SAM (Significance Analysis of Microarrays, [31]) applied to the square root of normalized data, (3) The Poisson distribution based method proposed by [16], which is implemented in an R package DEGSe…”
BackgroundMethodInfluentialSelf-citationSelecting between-sample RNA-Seq normalization methods from the perspective of their assumptions
Evans, C., Hardin, J., & Stoebel, D. M. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbx008 · Cited by 368
“Normalization by testing: PoissonSeq [30] uses an iterative process that alternates between estimating a set of non-differentially expressed genes, and estimating the scaling factor for each sample using that set.”
MethodA Comparative Study of Techniques for Differential Expression Analysis on RNA-Seq Data
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Advanced Applications of RNA Sequencing and Challenges
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A broken promise: microbiome differential abundance methods do not control the false discovery rate
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Measuring differential gene expression with RNA-seq: challenges and strategies for data analysis
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Genome-wide Measurement of RNA Folding Energies
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“In brief, the data were normalized by the library sizes estimated by PoissonSeq (Li et al., 2012).”
Self-citationVOLCANO PLOTS IN ANALYZING DIFFERENTIAL EXPRESSIONS WITH mRNA MICROARRAYS
Li, W. (2012) · Journal of Bioinformatics and Computational Biology · doi:10.1142/s0219720012310038 · Cited by 216
Classification and clustering of sequencing data using a Poisson model
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“nt in Section 2.1.] The models (3) and (4) have been used to identify features that are differentially expressed between conditions [Marioni et al. (2008), Bullard et al. (2010), Witten et al. (2010), Li et al. (2011), Robinson, McCarthy and Smyth (2010), Anders and Huber (2010)]. Though the question of how best to ide…”
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Transforming RNA-Seq Data to Improve the Performance of Prognostic Gene Signatures
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Resolving the backbone of the Brassicaceae phylogeny for investigating trait diversity
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RNA Biomarkers: Frontier of Precision Medicine for Cancer
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“In addition, considering the fluctuation of RNA abundance in bio-fluids and difference of total reads generated between experiments, data normalization is an essential part for the following advanced analyses [98].”
BackgroundRegional Variation in RBM20 Causes a Highly Penetrant Arrhythmogenic Cardiomyopathy
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“Several software packages exist that perform differential gene expression analysis of RNA-seq data 40,45–48 .”
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Computational Pathology: A Path Ahead
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Graphical Models via Generalized Linear Models
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The Impact of Normalization Methods on RNA-Seq Data Analysis
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“Finally, we also tested a normalization method (PS) proposed in [15], implemented in the PoissonSeq package.”
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“…study, we used the single-cell tagged reverse transcription (STRT) method (Islam et al., 2012) to investigate key issues for statistical tests, in several control experiments, and then implement and demonstrate a new concept for differential expression test on single-cell transcriptome profiles.”
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Graphical Models via Univariate Exponential Family Distributions.
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A Local Poisson Graphical Model for Inferring Networks From Sequencing Data
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“Given this, many have sought to model sequencing data via the Poisson [13], [10], [11] or negative binomial [15], [16] distributions whose support is on the set of non-negative integers.”
BackgroundMethodResultInfluentialROTS: reproducible RNA-seq biomarker detector—prognostic markers for clear cell renal cell cancer
Seyednasrollah, F., Rantanen, K., Jaakkola, P., & Elo, L. L. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv806 · Cited by 75
Evaluation of methods for differential expression analysis on multi-group RNA-seq count data
Tang, M., Sun, J., Shimizu, K., & Kadota, K. (2015) · BMC Bioinformatics · doi:10.1186/s12859-015-0794-7 · Cited by 83
“A possible explanation of these results is that EBSeq was originally developed to detect DE isoforms (not DEGs) [41] and the large body of methodology in voom is for microarray data (not RNA-seq count data) [38].”
ResultZero-Inflated Beta Regression for Differential Abundance Analysis with Metagenomics Data
Peng, X., Li, G., & Liu, Z. (2015) · Journal of Computational Biology · doi:10.1089/cmb.2015.0157 · Cited by 82
“Those analytical tools including edgeR and DESeq (Robinson et al., 2010; Anders and Huber, 2010; Li et al., 2012; Young et al., 2012; Mortazavi et al., 2008) may be applicable to metagenomic data analysis, because both data are from sequencing-based technologies.”
MethodComparison of differential accessibility analysis strategies for ATAC-seq data
Gontarz, P. M., Fu, S., Xing, X., Liu, S., Miao, B., Bazylianska, V., SHARMA, A. K., Madden, P. A. F., Cates, K. L., Yoo, A. S. J., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-66998-4 · Cited by 79
Analysis of the Expression of Repetitive DNA Elements in Osteosarcoma
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“The Package performs sample comparison and it can adjust the P-value to overcome problems of multiple testing (Li et al., 2012; McCarthy et al., 2012; Anders et al., 2013).”
Local compartment changes and regulatory landscape alterations in histone H1-depleted cells
Geeven, G., Zhu, Y. K., Kim, B. J., Bartholdy, B., Yang, S.-M., Macfarlan, T. S., Gifford, W. D., Pfaff, S. L., Verstegen, M. J. A. M., Pinto, H. B., et al. (2015) · Genome biology · doi:10.1186/s13059-015-0857-0 · Cited by 72
“For differential DNase-seq analysis between wild type and TKO, we used the PoissonSeq R package [48].”
MethodSimmons, D. B. D., Benskin, J. P., Cosgrove, J. R., Duncker, B. P., Ekman, D. R., Martyniuk, C. J., & Sherry, J. P. (2015) · Environmental Toxicology and Chemistry · doi:10.1002/etc.3002 · Cited by 71
Differential expression analysis for RNAseq using Poisson mixed models
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“More recent methods also take into account over-dispersion across biological replicates (22,23) by replacing Poisson models with negative binomial models (15,16,24–28) or other related approaches (18,29–32).”
BackgroundMethodNovel Data Transformations for RNA-seq Differential Expression Analysis
Zhang, Z., Yu, D., Seo, M., Hersh, C. P., Weiss, S. T., & Qiu, W. (2019) · Scientific Reports · doi:10.1038/s41598-019-41315-w · Cited by 70
VC1 catalyses a key step in the biosynthesis of vicine in faba bean
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Single-cell profiling of transcriptome and histone modifications with EpiDamID
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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 58
“…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 65
“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
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 68
“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
“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
A comprehensive catalogue of the coding and non-coding transcripts of the human inner ear
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“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
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
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“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
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
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 55
“…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
“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
“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 47
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 53
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
“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 42
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 46
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
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
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 44
“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
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 40
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 41
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 40
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
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
“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
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
“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
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 37
“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
“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
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 42
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
“…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
“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
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 40
“…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
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
◾ 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
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
A 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
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
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
“In particular, Li et al. (2012) proposed a permutation-based approach to generate the null distribution [14].”
DEXUS: identifying differential expression in RNA-Seq studies with unknown conditions
Klambauer, G., Unterthiner, T., & Hochreiter, S. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt834 · Cited by 31
“Alongside DEXUS, we included DESeq (24), edgeR (25), baySeq (26), SAMSeq (27), DEGSeq (28), PoissonSeq (29), NOISeq (30) and DSS (31).”
Influential
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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