Cited by 7,876Gene expression and cancer classificationGenomics and Phylogenetic StudiesSingle-cell and spatial transcriptomics

The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.

A scaling normalization method for differential expression analysis of RNA-seq data

Open access

Robinson, Mark D, Oshlack, Alicia·2010·Genome Biology

DOI 10.1186/gb-2010-11-3-r25PMC PMC2864565

Open this paper in CoChat — free account Open PDF

Abstract

The fine detail provided by sequencing-based transcriptome surveys suggests that RNA-seq is likely to become the platform of choice for interrogating steady state RNA. In order to discover biologically important changes in expression, we show that normalization continues to be an essential step in the analysis. We outline a simple and effective method for performing normalization and show dramatically improved results for inferring differential expression in simulated and publicly available data sets.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1186/gb-2010-11-3-r25

Cites

25 of 25 references verified0 citation errors · 0 not checkable

Statistics

Figure 1 Normalization is required for RNA-seq data. Data from [6] comparing log ratios of (a) technical replicates and (b) liver versus kidney expression levels, after adjusting for the total number of reads in each sample. The green line shows the smoothed distribution of log-fold-changes of the housekeeping genes. (c) An M versus A plot comparing liver and kidney shows a clear offset from zero. Green points indicate 545 housekeeping genes, while the green line signifies the median log-ratio of the housekeeping genes. The red line shows the estimated TMM normalization factor. The smear of orange points highlights the genes that were observed in only one of the liver or kidney tissues. The black arrow highlights the set of prominent genes that are largely attributable for the overall bias in log-fold-changes.CC BY · PubMed Central

Figure 2 Simulations show TMM normalization is robust and outperforms library size normalization. (a) An example of the simulation results showing the need for normalization due to genes expressed uniquely in one sample (orange dots) and asymmetric DE (blue dots). (b) A lower false positive rate is observed using TMM normalization compared with standard normalization.CC BY · PubMed Central

Figure 3 False discovery plots comparing several published methods. The red line depicts the length-normalized moderated t-statistic analysis. The solid and dashed lines show the library size normalized and TMM normalized Poisson model analysis, respectively. The blue and black lines represent the LR test and exact test, respectively. It can be seen that the use of TMM normalization results in a much lower false discovery rate.CC BY · PubMed Central

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

<tt>edgeR</tt> : a Bioconductor package for differential expression analysis of digital gene expression data
Robinson, M. D., McCarthy, D. J., & Smyth, G. K. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btp616 · Cited by 41,364
Self-citation
Mapping and quantifying mammalian transcriptomes by RNA-Seq
Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L., & Wold, B. (2008) · Nature Methods · doi:10.1038/nmeth.1226 · Cited by 13,696
RNA-Seq: a revolutionary tool for transcriptomics
Wang, Z., Gerstein, M., & Snyder, M. (2009) · Nature Reviews Genetics · doi:10.1038/nrg2484 · Cited by 13,174
Bioconductor: open software development for computational biology and bioinformatics
Gentleman, R. C., Carey, V. J., Bates, D. M., Bolstad, B., Dettling, M., Dudoit, S., Ellis, B., Gautier, L., Ge, Y., Gentry, J., et al. (2004) · Genome biology · doi:10.1186/gb-2004-5-10-r80 · Cited by 12,082
Exploration, normalization, and summaries of high density oligonucleotide array probe level data
Irizarry, R. A. (2003) · Biostatistics · doi:10.1093/biostatistics/4.2.249 · Cited by 10,603
A comparison of normalization methods for high density oligonucleotide array data based on variance and bias
Bolstad, B. M., Irizarry, R. A., Åstrand, M., & Speed, T. P. (2003) · Bioinformatics · doi:10.1093/bioinformatics/19.2.185 · Cited by 8,328
Alternative isoform regulation in human tissue transcriptomes
Wang, E. T., Sandberg, R., Luo, S., Khrebtukova, I., Zhang, L., Mayr, C., Kingsmore, S. F., Schroth, G. P., & Burge, C. B. (2008) · Nature · doi:10.1038/nature07509 · Cited by 5,181
Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation.
Yang, Y., Dudoit, S., Luu, P., Lin, D., Peng, V., Ngai, J., & Speed, T. (2002) · Nucleic Acids Research · doi:10.1093/nar/30.4.e15 · Cited by 3,722
RNA-seq: An assessment of technical reproducibility and comparison with gene expression arrays
Marioni, J. C., Mason, C. E., Mane, S. M., Stephens, M., & Gilad, Y. (2008) · Genome Research · doi:10.1101/gr.079558.108 · Cited by 2,771
BioMart and Bioconductor: a powerful link between biological databases and microarray data analysis
Durinck, S., Moreau, Y., Kasprzyk, A., Davis, S. R., Moor, B. D., Brāzma, A., & Huber, W. K. (2005) · Computer applications in the biosciences · doi:10.1093/bioinformatics/bti525 · Cited by 2,327

Show all 25 references

Research works printed but not linked: 3

Bullard JH, Purdom EA, Hansen KD, Durinck S, Dudoit S: Statistical inference in mRNA-Seq: exploratory data analysis and differential expression. UC Berkeley Division of Biostatistics Working Paper Series. 2009, paper 247
Casella G, Berger RL: Statistical Inference. 2002, Pacific Grove, CA: Duxbury Press
Team RDC: R: A Language and Environment for Statistical Computing. 2009

Sorted by citations

Items 101–182 of 182

A second X chromosome contributes to resilience in a mouse model of Alzheimer’s disease
Davis, E. J., Broestl, L., Abdulai-Saiku, S., Worden, K., Bonham, L. W., Miñones-Moyano, E., Moreno, A. J., Wang, D., Chang, K., Williams, G., et al. (2020) · Science Translational Medicine · doi:10.1126/scitranslmed.aaz5677 · Cited by 176
Performance Assessment and Selection of Normalization Procedures for Single-Cell RNA-Seq
Cole, M. B., Risso, D., Wagner, A., DeTomaso, D., Ngai, J., Purdom, E., Dudoit, S., & Yosef, N. (2019) · Cell Systems · doi:10.1016/j.cels.2019.03.010 · Cited by 173
Exercise alters mouse sperm small noncoding RNAs and induces a transgenerational modification of male offspring conditioned fear and anxiety
Short, A. K., Yeshurun, S., Powell, R., Perreau, V. M., Fox, A., Kim, J. H., Pang, T. Y., & Hannan, A. J. (2017) · Translational Psychiatry · doi:10.1038/tp.2017.82 · Cited by 166
Digital Gene Expression Analysis Based on Integrated De Novo Transcriptome Assembly of Sweet Potato [Ipomoea batatas (L.) Lam.]
Tao, X., Gu, Y.-H., Wang, H.-Y., Zheng, W., Li, X., Zhao, C.-W., & Zhang, Y.-Z. (2012) · PLoS ONE · doi:10.1371/journal.pone.0036234 · Cited by 168
AXIN2+ Pericentral Hepatocytes Have Limited Contributions to Liver Homeostasis and Regeneration
Sun, T., Pikiolek, M., Orsini, V., Bergling, S., Holwerda, S., Morelli, L., Hoppe, P. S., Planas-Paz, L., Yang, Y., Ruffner, H., et al. (2020) · Cell Stem Cell · doi:10.1016/j.stem.2019.10.011 · Cited by 166
Neuronal brain-region-specific DNA methylation and chromatin accessibility are associated with neuropsychiatric trait heritability
Rizzardi, L. F., Hickey, P. F., Rodriguez DiBlasi, V., Tryggvadóttir, R., Callahan, C. M., Idrizi, A., Hansen, K. D., & Feinberg, A. P. (2019) · Nature Neuroscience · doi:10.1038/s41593-018-0297-8 · Cited by 146
Integrative proteogenomic characterization of hepatocellular carcinoma across etiologies and stages
Ng, C. K. Y., Dazert, E., Boldanova, T., Coto-Llerena, M., Nuciforo, S., Ercan, C., Suslov, A., Meier, M.-A., Bock, T., Schmidt, A., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-29960-8 · Cited by 165
Proximal recolonization by self-renewing microglia re-establishes microglial homeostasis in the adult mouse brain
Zhan, L., Krabbe, G., Du, F., Jones, I., Reichert, M. C., Telpoukhovskaia, M., Kodama, L., Wang, C., Cho, S.-H., Sayed, F., et al. (2019) · PLOS Biology · doi:10.1371/journal.pbio.3000134 · Cited by 163
“Normalization was performed with the weighted trimmed mean of M-values (TMM) method [48].”
Method
Functional Dynamics of the Gut Microbiome in Elderly People during Probiotic Consumption
Eloe-Fadrosh, E. A., Brady, A., Crabtree, J., Drabek, E. F., Ma, B., Mahurkar, A., Ravel, J., Haverkamp, M., Fiorino, A.-M., Botelho, C., et al. (2015) · mBio · doi:10.1128/mbio.00231-15 · Cited by 160
Mapping the epigenomic and transcriptomic interplay during memory formation and recall in the hippocampal engram ensemble
Marco, A., Meharena, H. S., Dileep, V., Raju, R. M., Davila-Velderrain, J., Zhang, A. L., Adaikkan, C., Young, J. Z., Gao, F., Kellis, M., et al. (2020) · Nature Neuroscience · doi:10.1038/s41593-020-00717-0 · Cited by 159
Cell-specific histone modification maps in the human frontal lobe link schizophrenia risk to the neuronal epigenome
Girdhar, K., Hoffman, G. E., Jiang, Y., Brown, L., Kundakovic, M., Hauberg, M. E., Francoeur, N. J., Wang, Y.-C., Shah, H., Kavanagh, D. H., et al. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0187-0 · Cited by 139
Obesity impairs short-term and working memory through gut microbial metabolism of aromatic amino acids
Arnoriaga-Rodríguez, M., Mayneris-Perxachs, J., Burokas, A., Contreras-Rodríguez, O., Blasco, G., Coll, C., Biarnés, C., Miranda-Olivos, R., Latorre, J., Moreno-Navarrete, J.-M., et al. (2020) · Cell Metabolism · doi:10.1016/j.cmet.2020.09.002 · Cited by 157
Growth differentiation factor 15 protects against the aging‐mediated systemic inflammatory response in humans and mice
Moon, J. S., Goeminne, L. J. E., Kim, J. T., Tian, J. W., Kim, S., Nga, H. T., Kang, S. G., Kang, B. E., Byun, J., Lee, Y., et al. (2020) · Aging Cell · doi:10.1111/acel.13195 · Cited by 141
Self-organization process in newborn skin organoid formation inspires strategy to restore hair regeneration of adult cells
Lei, M., Schumacher, L. J., Lai, Y.-C., Juan, W.-T., Yeh, C.-Y., Wu, P., Jiang, T.-X., Baker, R. E., Widelitz, R. B., Yang, L., et al. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1700475114 · Cited by 142
Gene expression links functional networks across cortex and striatum
Anderson, K. M., Krienen, F. M., Choi, E. Y., Reinen, J. M., Yeo, B. T. T., & Holmes, A. J. (2018) · Nature Communications · doi:10.1038/s41467-018-03811-x · Cited by 138
Transcriptomic analyses reveal rhythmic and CLOCK-driven pathways in human skeletal muscle
Perrin, L., Loizides-Mangold, U., Chanon, S., Gobet, C., Hulo, N., Isenegger, L., Weger, B. D., Migliavacca, E., Charpagne, A., Betts, J. A., et al. (2018) · eLife · doi:10.7554/elife.34114 · Cited by 139
Dysregulation of MicroRNAs and Target Genes Networks in Peripheral Blood of Patients With Sporadic Amyotrophic Lateral Sclerosis.
Liguori, M., Nuzziello, N., Introna, A., Consiglio, A., Licciulli, F., D’Errico, E., Scarafino, A., Distaso, E., & Simone, I. L. (2018) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2018.00288 · Cited by 131
A cell identity switch allows residual BCC to survive Hedgehog pathway inhibition
Biehs, B., Dijkgraaf, G. J. P., Piskol, R., Alicke, B., Boumahdi, S., Peale, F., Gould, S. E., & de Sauvage, F. J. (2018) · Nature · doi:10.1038/s41586-018-0596-y · Cited by 137
Rescuing Over-activated Microglia Restores Cognitive Performance in Juvenile Animals of the Dp(16) Mouse Model of down Syndrome.
Pinto, B., Morelli, G., Rastogi, M., Savardi, A., Fumagalli, A., Petretto, A., Bartolucci, M., Varea, E., Catelani, T., Contestabile, A., et al. (2020) · Neuron · doi:10.1016/j.neuron.2020.09.010 · Cited by 135
Assessing the Influence of Vegan, Vegetarian and Omnivore Oriented Westernized Dietary Styles on Human Gut Microbiota: A Cross Sectional Study
Losasso, C., Eckert, E. M., Mastrorilli, E., Villiger, J., Mancin, M., Patuzzi, I., Di Cesare, A., Cibin, V., Barrucci, F., Pernthaler, J., et al. (2018) · Frontiers in Microbiology · doi:10.3389/fmicb.2018.00317 · Cited by 125
A quantitative framework for characterizing the evolutionary history of mammalian gene expression
Chen, J., Swofford, R., Johnson, J., Cummings, B. B., Rogel, N., Lindblad-Toh, K., Haerty, W., Palma, F. D., & Regev, A. (2019) · Genome Research · doi:10.1101/gr.237636.118 · Cited by 128
How data analysis affects power, reproducibility and biological insight of RNA-seq studies in complex datasets
Peixoto, L., Risso, D., Poplawski, S. G., Wimmer, M. E., Speed, T. P., Wood, M. A., & Abel, T. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv736 · Cited by 105
HMGB1 links chronic liver injury to progenitor responses and hepatocarcinogenesis
Hernandez, C., Huebener, P., Pradere, J.-P., Antoine, D. J., Friedman, R. A., & Schwabe, R. F. (2018) · Journal of Clinical Investigation · doi:10.1172/jci91786 · Cited by 110
VGF-derived peptide TLQP-21 modulates microglial function through C3aR1 signaling pathways and reduces neuropathology in 5xFAD mice
El Gaamouch, F., Audrain, M., Lin, W.-J., Beckmann, N., Jiang, C., Hariharan, S., Heeger, P. S., Schadt, E. E., Gandy, S., Ehrlich, M. E., et al. (2020) · Molecular Neurodegeneration · doi:10.1186/s13024-020-0357-x · Cited by 92
Differential transcriptome analysis of glandular and filamentous trichomes in Artemisia annua
Soetaert, S. S., Van Neste, C. M., Vandewoestyne, M. L., Head, S. R., Goossens, A., Van Nieuwerburgh, F. C., & Deforce, D. L. (2013) · BMC Plant Biology · doi:10.1186/1471-2229-13-220 · Cited by 109
Distinct initiating events underpin the immune and metabolic heterogeneity of KRAS-mutant lung adenocarcinoma
Best, S. A., Ding, S., Kersbergen, A., Dong, X., Song, J.-Y., Xie, Y., Reljic, B., Li, K., Vince, J. E., Rathi, V., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-12164-y · Cited by 108
Whole transcriptome sequencing of the aging rat brain reveals dynamic RNA changes in the dark matter of the genome
Wood, S. H., Craig, T., Li, Y., Merry, B., & de Magalhães, J. P. (2013) · AGE · doi:10.1007/s11357-012-9410-1 · Cited by 105
Benchmarking methods for detecting differential states between conditions from multi-subject single-cell RNA-seq data
Junttila, S., Smolander, J., & Elo, L. L. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac286 · Cited by 109
The circular RNome of primary breast cancer
Smid, M., Wilting, S. M., Uhr, K., Rodríguez-González, F. G., de Weerd, V., Prager-Van der Smissen, W. J. C., van der Vlugt-Daane, M., van Galen, A., Nik-Zainal, S., Butler, A., et al. (2019) · Genome Research · doi:10.1101/gr.238121.118 · Cited by 105
Differential Content of Proteins, mRNAs, and miRNAs Suggests that MDSC and Their Exosomes May Mediate Distinct Immune Suppressive Functions
Geis-Asteggiante, L., Belew, A. T., Clements, V. K., Edwards, N. J., Ostrand-Rosenberg, S., El-Sayed, N. M., & Fenselau, C. (2018) · Journal of Proteome Research · doi:10.1021/acs.jproteome.7b00646 · Cited by 104
Transcription Factor PU.1 Promotes Conventional Dendritic Cell Identity and Function via Induction of Transcriptional Regulator DC-SCRIPT
Chopin, M., Lun, A. T., Zhan, Y., Schreuder, J., Coughlan, H., D’Amico, A., Mielke, L. A., Almeida, F. F., Kueh, A. J., Dickins, R. A., et al. (2019) · Immunity · doi:10.1016/j.immuni.2018.11.010 · Cited by 104
A reanalysis of mouse ENCODE comparative gene expression data
Gilad, Y., & Mizrahi-Man, O. (2015) · F1000Research · doi:10.12688/f1000research.6536.1 · Cited by 100
Biologically informed deep learning to query gene programs in single-cell atlases
Lotfollahi, M., Rybakov, S., Hrovatin, K., Hediyeh-zadeh, S., Talavera-López, C., Misharin, A. V., & Theis, F. J. (2023) · Nature Cell Biology · doi:10.1038/s41556-022-01072-x · Cited by 100
RNA‐seq Data: Challenges in and Recommendations for Experimental Design and Analysis
Williams, A. G., Thomas, S., Wyman, S. K., & Holloway, A. K. (2014) · Current Protocols in Human Genetics · doi:10.1002/0471142905.hg1113s83 · Cited by 93
Taming Human Genetic Variability: Transcriptomic Meta-Analysis Guides the Experimental Design and Interpretation of iPSC-Based Disease Modeling
Germain, P.-L., & Testa, G. (2017) · Stem Cell Reports · doi:10.1016/j.stemcr.2017.05.012 · Cited by 94
Genes, behavior and next‐generation RNA sequencing
Hitzemann, R., Bottomly, D., Darakjian, P., Walter, N., Iancu, O., Searles, R., Wilmot, B., & McWeeney, S. (2013) · Genes, Brain and Behavior · doi:10.1111/gbb.12007 · Cited by 88
FOXC2 controls adult lymphatic endothelial specialization, function, and gut lymphatic barrier preventing multiorgan failure
González-Loyola, A., Bovay, E., Kim, J., Lozano, T. W., Sabine, A., Renevey, F., Arroz-Madeira, S., Rapin, A., Wypych, T. P., Rota, G., et al. (2021) · Science Advances · doi:10.1126/sciadv.abf4335 · Cited by 94
Low-dose rapamycin extends lifespan in a mouse model of mtDNA depletion syndrome
Siegmund, S. E., Yang, H., Sharma, R., Javors, M., Skinner, O., Mootha, V., Hirano, M., & Schon, E. A. (2017) · Human Molecular Genetics · doi:10.1093/hmg/ddx341 · Cited by 92
Cell culture-based profiling across mammals reveals DNA repair and metabolism as determinants of species longevity
Ma, S., Upneja, A., Galecki, A., Tsai, Y.-M., Burant, C. F., Raskind, S., Zhang, Q., Zhang, Z. D., Seluanov, A., Gorbunova, V., et al. (2016) · eLife · doi:10.7554/elife.19130 · Cited by 89
Decreased lipidated ApoE‐receptor interactions confer protection against pathogenicity of ApoE and its lipid cargoes in lysosomes
Guo, J. L., Braun, D., Fitzgerald, G. A., Hsieh, Y.-T., Rougé, L., Litvinchuk, A., Steffek, M., Propson, N. E., Heffner, C. M., Discenza, C., et al. (2025) · Cell · doi:10.1016/j.cell.2024.10.027 · Cited by 91
Benchmarking microbiome transformations favors experimental quantitative approaches to address compositionality and sampling depth biases
Lloréns-Rico, V., Vieira-Silva, S., Gonçalves, P. J., Falony, G., & Raes, J. (2021) · Nature Communications · doi:10.1038/s41467-021-23821-6 · Cited by 73
Evaluating stably expressed genes in single cells
Lin, Y., Ghazanfar, S., Strbenac, D., Wang, A., Patrick, E., Lin, D. M., Speed, T., Yang, J. Y. H., & Yang, P. (2019) · GigaScience · doi:10.1093/gigascience/giz106 · Cited by 84
Determining the ERK-regulated phosphoproteome driving KRAS-mutant cancer
Klomp, J. E., Diehl, J. N., Klomp, J. A., Edwards, A. C., Yang, R., Morales, A. J., Taylor, K. E., Drizyte-Miller, K., Bryant, K. L., Schaefer, A., et al. (2024) · Science · doi:10.1126/science.adk0850 · Cited by 83
Splice-Junction-Based Mapping of Alternative Isoforms in the Human Proteome
Lau, E., Han, Y., Williams, D. R., Thomas, C. T., Shrestha, R., Wu, J. C., & Lam, M. P. Y. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.11.026 · Cited by 67
Acinetobacter baumannii OxyR Regulates the Transcriptional Response to Hydrogen Peroxide
Juttukonda, L. J., Green, E. R., Lonergan, Z. R., Heffern, M. C., Chang, C. J., & Skaar, E. P. (2019) · Infection and Immunity · doi:10.1128/iai.00413-18 · Cited by 75
Microglia from offspring of dams with allergic asthma exhibit epigenomic alterations in genes dysregulated in autism
Vogel Ciernia, A., Careaga, M., LaSalle, J. M., & Ashwood, P. (2018) · Glia · doi:10.1002/glia.23261 · Cited by 69
UBR5 forms ligand-dependent complexes on chromatin to regulate nuclear hormone receptor stability
Tsai, J. M., Aguirre, J. D., Li, Y.-D., Brown, J., Focht, V., Kater, L., Kempf, G., Sandoval, B., Schmitt, S., Rutter, J. C., et al. (2023) · Molecular Cell · doi:10.1016/j.molcel.2023.06.028 · Cited by 68
Male autism spectrum disorder is linked to brain aromatase disruption by prenatal BPA in multimodal investigations and 10HDA ameliorates the related mouse phenotype
Symeonides, C., Vacy, K., Thomson, S., Tanner, S., Chua, H. K., Dixit, S., Mansell, T., O’Hely, M., Novakovic, B., Herbstman, J. B., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-48897-8 · Cited by 66
Primate innate immune responses to bacterial and viral pathogens reveals an evolutionary trade-off between strength and specificity
Hawash, M. B. F., Sanz-Remón, J., Grenier, J.-C., Kohn, J., Yotova, V., Johnson, Z., Lanford, R. E., Brinkworth, J. F., & Barreiro, L. B. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2015855118 · Cited by 56
Intratumoural Heterogeneity Underlies Distinct Therapy Responses and Treatment Resistance in Glioblastoma
Akgül, S., Patch, A.-M., D’Souza, R. C. J., Mukhopadhyay, P., Nones, K., Kempe, S., Kazakoff, S. H., Jeffree, R. L., Stringer, B. W., Pearson, J. V., et al. (2019) · Cancers · doi:10.3390/cancers11020190 · Cited by 57
Conserved Translatome Remodeling in Nematode Species Executing a Shared Developmental Transition
Stadler, M., & Fire, A. (2013) · PLoS Genetics · doi:10.1371/journal.pgen.1003739 · Cited by 53
Nonalcoholic Steatohepatitis and HCC in a Hyperphagic Mouse Accelerated by Western Diet
Ganguly, S., Muench, G. A., Shang, L., Rosenthal, S. B., Rahman, G., Wang, R., Wang, Y., Kwon, H. C., Diomino, A. M., Kisseleva, T., et al. (2021) · Cellular and Molecular Gastroenterology and Hepatology · doi:10.1016/j.jcmgh.2021.05.010 · Cited by 50
A Metabolic Checkpoint for the Yeast-to-Hyphae Developmental Switch Regulated by Endogenous Nitric Oxide Signaling
Koch, B., Barugahare, A. A., Lo, T. L., Huang, C., Schittenhelm, R. B., Powell, D. R., Beilharz, T. H., & Traven, A. (2018) · Cell Reports · doi:10.1016/j.celrep.2018.10.080 · Cited by 50
Meristem micropropagation of cassava (Manihot esculenta) evokes genome-wide changes in DNA methylation
Kitimu, S. R., Taylor, J., March, T. J., Tairo, F., Wilkinson, M. J., & Rodríguez López, C. M. (2015) · Frontiers in Plant Science · doi:10.3389/fpls.2015.00590 · Cited by 50
RNA-Seq Reveals Novel Transcriptional Reorganization in Human Alcoholic Brain
Farris, S. P., & Dayne Mayfield, R. (2014) · International Review of Neurobiology · doi:10.1016/b978-0-12-801105-8.00011-4 · Cited by 49
Sex specific molecular networks and key drivers of Alzheimer’s disease
Guo, L., Cao, J., Hou, J., Li, Y., Huang, M., Zhu, L., Zhang, L., Lee, Y., Duarte, M. L., Zhou, X., et al. (2023) · Molecular Neurodegeneration · doi:10.1186/s13024-023-00624-5 · Cited by 49
“Genes with at least 1 count per million (CPM) in at least one sample were selected, normalized [13] and corrected for covariates such as postmortem interval (PMI), race, RNA integrity number (RIN), rate of exonic reads, and batch using a linear mixed model.”
Method
Genome-wide DNA methylation and gene expression patterns reflect genetic ancestry and environmental differences across the Indonesian archipelago
Natri, H. M., Bobowik, K. S., Kusuma, P., Crenna Darusallam, C., Jacobs, G. S., Hudjashov, G., Lansing, J. S., Sudoyo, H., Banovich, N. E., Cox, M. P., et al. (2020) · PLoS Genetics · doi:10.1371/journal.pgen.1008749 · Cited by 48
A comparison of methods accounting for batch effects in differential expression analysis of UMI count based single cell RNA sequencing
Chen, W., Zhang, S., Williams, J., Ju, B., Shaner, B., Easton, J., Wu, G., & Chen, X. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.03.026 · Cited by 44
RANK links senescence to stemness in the mammary epithelia, delaying tumor onset but increasing tumor aggressiveness
Benítez, S., Cordero, A., Santamaría, P. G., Redondo-Pedraza, J., Rocha, A. S., Collado-Solé, A., Jimenez, M., Sanz-Moreno, A., Yoldi, G., Santos, J. C., et al. (2021) · Developmental Cell · doi:10.1016/j.devcel.2021.04.022 · Cited by 41
Normalization for sampled count data
Booeshaghi, A. S., Hallgrímsdóttir, I. B., Gálvez-Merchán, Á., & Pachter, L. (2022) · doi:10.1101/2022.05.06.490859 · Cited by 39
Protein quality control and regulated proteolysis in the genome‐reduced organism Mycoplasma pneumoniae
Burgos, R., Weber, M., Martinez, S., Lluch‐Senar, M., & Serrano, L. (2020) · Molecular Systems Biology · doi:10.15252/msb.20209530 · Cited by 39
Comparative MRNA-Seq Analysis Reveals the Improved EPS Production Machinery in Streptococcus Thermophilus ASCC 1275 during Optimized Milk Fermentation
Wu, Q., & Shah, N. P. (2018) · Frontiers in Microbiology · doi:10.3389/fmicb.2018.00445 · Cited by 38
Aprepitant, an antiemetic agent, interferes with metal ion homeostasis of Candida auris and displays potent synergistic interactions with azole drugs
Eldesouky, H. E., Lanman, N. A., Hazbun, T. R., & Seleem, M. N. (2020) · Virulence · doi:10.1080/21505594.2020.1838741 · Cited by 37
Nicotinamide mononucleotide reduces melanin production in aged melanocytes by inhibiting cAMP/Wnt signaling
Brito, S., Baek, J.-M., Cha, B., Heo, H., Lee, S.-H., Lei, L., Jung, S. Y., Lee, S. M., Lee, S. H., Kwak, B.-M., et al. (2022) · Journal of Dermatological Science · doi:10.1016/j.jdermsci.2022.05.002 · Cited by 38
A Comprehensive Survey of Statistical Approaches for Differential Expression Analysis in Single-Cell RNA Sequencing Studies
Das, S., Rai, A., Merchant, M. L., Cave, M. C., & Rai, S. N. (2021) · Genes · doi:10.3390/genes12121947 · Cited by 35
Lower DNA methylation levels in CpG island shores of CR1, CLU, and PICALM in the blood of Japanese Alzheimer’s disease patients
Mitsumori, R., Sakaguchi, K., Shigemizu, D., Mori, T., Akiyama, S., Ozaki, K., Niida, S., & Shimoda, N. (2020) · PLOS ONE · doi:10.1371/journal.pone.0239196 · Cited by 34
NPEBseq: nonparametric empirical bayesian-based procedure for differential expression analysis of RNA-seq data
Bi, Y., & Davuluri, R. V. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-262 · Cited by 30
Transcriptional and translational landscape of Candida auris in response to caspofungin
Zamith-Miranda, D., Amatuzzi, R. F., Munhoz da Rocha, I. F., Martins, S. T., Lucena, A. C. R., Vieira, A. Z., Trentin, G., Almeida, F., Rodrigues, M. L., Nakayasu, E. S., et al. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.09.007 · Cited by 29
Evolution of reduced co-activator dependence led to target expansion of a starvation response pathway
He, B. Z., Zhou, X., & O’Shea, E. K. (2017) · eLife · doi:10.7554/elife.25157 · Cited by 27
Selective estrogen receptor (ER)β activation provokes a redistribution of fat mass and modifies hepatic triglyceride composition in obese male mice
González-Granillo, M., Savva, C., Li, X., Ghosh Laskar, M., Angelin, B., Gustafsson, J.-Å., & Korach-André, M. (2020) · Molecular and Cellular Endocrinology · doi:10.1016/j.mce.2019.110672 · Cited by 28
In vivo transcriptional analysis of mice infected with Leishmania major unveils cellular heterogeneity and altered transcriptomic profiling at single-cell resolution
Venugopal, G., Bird, J. T., Washam, C. L., Roys, H., Bowlin, A., Byrum, S. D., & Weinkopff, T. (2022) · PLOS Neglected Tropical Diseases · doi:10.1371/journal.pntd.0010518 · Cited by 26
HSATII RNA is induced via a noncanonical ATM-regulated DNA damage response pathway and promotes tumor cell proliferation and movement
Nogalski, M. T., & Shenk, T. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2017734117 · Cited by 25
Polymerization of ZBTB transcription factors regulates chromatin occupancy
Park, P. M. C., Park, J., Brown, J., Hunkeler, M., Roy Burman, S. S., Donovan, K. A., Yoon, H., Nowak, R. P., Słabicki, M., Ebert, B. L., et al. (2024) · Molecular Cell · doi:10.1016/j.molcel.2024.06.010 · Cited by 22
“Factors were calculated through an approach integrating techniques from the TMM 69 and Median-Ratio 70 methods.”
Bioinformatics analysis of hepatitis C virus genotype 2a-induced human hepatocellular carcinoma in Huh7 cells
Xu, J., Xu, P., Wu, M., Chen, H., Xu, J., Wu, M., Li, M., & Qian, F. (2016) · OncoTargets and Therapy · doi:10.2147/ott.s91748 · Cited by 20
Implications of Hyperoxia over the Tumor Microenvironment: An Overview Highlighting the Importance of the Immune System
Herrera-Campos, A. B., Zamudio-Martinez, E., Delgado-Bellido, D., Fernández-Cortés, M., Montuenga, L. M., Oliver, F. J., & Garcia-Diaz, A. (2022) · Cancers · doi:10.3390/cancers14112740 · Cited by 17
Integrated drug profiling and CRISPR screening identify BCR::ABL1-independent vulnerabilities in chronic myeloid leukemia
Adnan Awad, S., Dufva, O., Klievink, J., Karjalainen, E., Ianevski, A., Pietarinen, P., Kim, D., Potdar, S., Wolf, M., Lotfi, K., et al. (2024) · Cell Reports Medicine · doi:10.1016/j.xcrm.2024.101521 · Cited by 14
A hollow fiber membrane-based liver organoid-on-a-chip model for examining drug metabolism and transport
Myszczyszyn, A., Muench, A., Lehmann, V., Sinnige, T., van Steenbeek, F. G., Bouwmeester, M., Samsom, R.-A., Keuper-Navis, M., van der Made, T. K., Kogan, D., et al. (2025) · Biofabrication · doi:10.1088/1758-5090/adc3ce · Cited by 14
Genetic determinants of plasma protein levels in the Estonian population
Kalnapenkis, A., Jõeloo, M., Lepik, K., Kukuškina, V., Kals, M., Alasoo, K., Estonian Biobank Research Team, Metspalu, A., Milani, L., Nelis, M., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-57966-3 · Cited by 11
Evaluation of chromatin accessibility in prefrontal cortex of schizophrenia cases and controls
Bryois, J., Garrett, M. E., Song, L., Safi, A., Giusti-Rodriguez, P., Johnson, G. D., Demur, A. B., Fullard, J. F., Roussos, P., Sklar, P., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/141986 · Cited by 7
Intergenerational transport of double-stranded RNA in C. elegans can limit heritable epigenetic changes
Shugarts Devanapally, N. M., Sathya, A., Yi, A. L., Chan, W. M., Marre, J. A., & Jose, A. M. (2025) · eLife · doi:10.7554/elife.99149.3 · Cited by 6
Mouse liver sinusoidal endothelial cell responses to the glucocorticoid receptor agonist dexamethasone
Bhandari, S., Kyrrestad, I., Simón-Santamaría, J., Li, R., Szafranska, K. J., Dumitriu, G., Sánchez Romano, J., Smedsrød, B., & Sørensen, K. K. (2024) · Frontiers in Pharmacology · doi:10.3389/fphar.2024.1377136 · Cited by 6
MicroRNA Transcriptomes Reveal Prevalence of Rare and Species-Specific Arm Switching Events During Zebrafish Ontogenesis
Oliveira, A. C. D., Bovolenta, L. A., Figueiredo, L., Ribeiro, A. D. O., Pereira, B. J. A., de Almeida, T. R. A., Campos, V. F., Patton, J. G., & Pinhal, D. (2024) · Evolutionary Bioinformatics · doi:10.1177/11769343241263230 · Cited by 2

Authors: Robinson, M. D., & Oshlack, A.
Affiliations: Garvan Institute of Medical Research (AU)
Walter and Eliza Hall Institute of Medical Research (AU)
Corresponding author: Robinson, Mark D
Oshlack, Alicia
ORCID: Robinson, Mark D
Oshlack, Alicia
Venue: Genome Biology
Publisher: Springer Science and Business Media LLC
Published: 2010-03-02
Volume: 11
Issue: 3
Pages: R25-R25
ISSN: 1465-6906 (ISSN-L)
1474-760X
DOI: 10.1186/gb-2010-11-3-r25
PMC: PMC2864565
OpenAlex: W2107018762
Cited by: 7,876
References: 25
Citations by year: 2026: 452 · 2025: 604 · 2024: 832 · 2023: 700 · 2022: 773 · 2021: 1010 · 2020: 730 · 2019: 610 · 2018: 540 · 2017: 440 · 2016: 348 · 2015: 285 · 2014: 222 · 2013: 153 · 2012: 100
FWCI: 41.77
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/2.0/ (tdm)
creativecommons.org/licenses/by/2.0/ (vor)
Locations: Genome biology (publishedVersion, cc-by)
Europe PMC (PubMed Central) (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
Minerva Access (University of Melbourne) (submittedVersion, cc-by)
Funders: Medical Research Council
National Health and Medical Research Council (490037)
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 › Single-cell and spatial transcriptomics
Keywords: RNA-seq, normalization, differential gene expression, transcriptome, gene expression, read count normalization
MeSH terms: Base Sequence, Computer Simulation, RNA, Models, Statistical, Gene Library, Gene Expression Profiling

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

A scaling normalization method for differential expression analysis of RNA-seq data

Abstract

Overview

Integrity signals

Figures

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Metadata

Record

About these checks