Cited by 617Gene expression and cancer classificationGenomics and Phylogenetic StudiesSingle-cell and spatial transcriptomics

The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.

Removing technical variability in RNA-seq data using conditional quantile normalization

Open access

Hansen, K. D., Irizarry, R. A., WU, Z.·2012·Biostatistics

DOI 10.1093/biostatistics/kxr054PMC PMC3297825

Open this paper in CoChat — free account

Abstract

The ability to measure gene expression on a genome-wide scale is one of the most promising accomplishments in molecular biology. Microarrays, the technology that first permitted this, were riddled with problems due to unwanted sources of variability. Many of these problems are now mitigated, after a decade's worth of statistical methodology development. The recently developed RNA sequencing (RNA-seq) technology has generated much excitement in part due to claims of reduced variability in comparison to microarrays. However, we show that RNA-seq data demonstrate unwanted and obscuring variability similar to what was first observed in microarrays. In particular, we find guanine-cytosine content (GC-content) has a strong sample-specific effect on gene expression measurements that, if left uncorrected, leads to false positives in downstream results. We also report on commonly observed data distortions that demonstrate the need for data normalization. Here, we describe a statistical methodology that improves precision by 42% without loss of accuracy. Our resulting conditional quantile normalization algorithm combines robust generalized regression to remove systematic bias introduced by deterministic features such as GC-content and quantile normalization to correct for global distortions.

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 · 10.1093/biostatistics/kxr054

Cites

38 of 38 references verified0 citation errors · 0 not checkable

Statistics

Fig. 1. Exploratory plots. (a) The points show the frequency of counts in the bins shown on the x-axis. The 3 colors represent 3 samples (NA12812, NA12874, and NA11993) from the Montgomery data. (b) log2-RPKM values are stratified by GC-content for 2 biological replicates from the Montgomery data (NA11918 and NA12761) and are summarized by boxplots. The 2 samples are distinguished by the 2 colors (colors can be seen in the online version). Genes with average (across all 60 samples) log2-RPKM values below 2 are not shown. (c) Log fold changes between RPKM values from the 2 samples and the same genes shown in (b) were computed and are plotted against GC-content. Red is used to show the genes with the 10% highest GC-content and blue is used to show the genes with the 10% lowest GC-content. (d) RPKM log fold changes are plotted against average log2 counts for the samples and genes shown in (b), with the same color coding as in (c). (e) As (d) but from values corrected using the method proposed by Pickrell and others (2010). (f) As (d) but for values normalized using our approach (see Section 4).CC BY-NC · PubMed Central

Fig. 2. Empirical distributions. (a) Empirical density estimates of log(Yg,i)−f^i,j(Xg,j) are shown for 6 samples from the Montgomery data. (b) A histogram of counts in a single sample for genes with a GC-content of 45 ± 1% and with a length between 500 and 2000 bp is shown.CC BY-NC · PubMed Central

Fig. 3. Results from normalizing 60 samples. In these plots, we only show genes with a length greater than 100 bp and an average (across all 60 samples) standard log2-RPKM of 2 or greater. (a) Empirical density estimates of log2-RPKM for 5 different biological replicates from the Montgomery data are shown. (b) As (a) but CQN-normalized expression values on the log2-scale are shown. (c) The estimated GC-content effect are shown as curves for all 60 biological replicates in the Montgomery study. We created a 5 versus 5 comparison using the samples highlighted in blue (group 1) and red (group 2) (colors can be seen in the online version). (d) As (c) but curves are shown for the gene length effect instead of GC-content. (e) Average log fold change is plotted against GC-content. Here, we used RPKM values and compared group 2 to group 1. (f) Average log fold change is plotted against GC-content using CQN-normalized expression measures.CC BY-NC · PubMed Central

Fig. 4. Improved precision provided by CQN on comparisons across studies. (a) We show boxplots of the estimated log fold change between the 2 groups of 5 samples (the same 2 groups as in Figure 3) from the Montgomery data using standard RPKM, expression values normalized by TMM (trimmed median of M-values, the method proposed in Robinson and Oshlack, 2010), the method proposed in Pickrell and others (2010), and CQN with and without quantile normalization. We show genes with length greater than 100 bp and average (across all samples) log2-RPKM greater or equal to 2. (b) We normalized the 29 samples assayed in both Montgomery and Cheung. For each gene, we computed the mean squared difference between the expression measure based on the Montgomery and the Cheung data. The boxplots show the distribution of these precision measures for the highly expressed genes, for each of the 4 choices of normalization: standard RPKM, TMM, the method proposed in Pickrell and others (2010), and CQN. We show genes with length greater than 100 bp and average (across all samples) log2-RPKM greater or equal to 2. (c) For the MicroArray Quality Control data, we obtained fold change estimates between UHR and brain based on RNA-Seq and microarrays. For RNA-seq, we used 2 samples. For the microarrays, we used a 5 versus 5 comparison. The microarray data were normalized using Robust Multiarray Analysis, and the RNA-seq data were normalized by CQN.CC BY-NC · 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
Ultrafast and memory-efficient alignment of short DNA sequences to the human genome.
Langmead, B., Trapnell, C., Pop, M., & Salzberg, S. L. (2009) · Genome Biology · doi:10.1186/gb-2009-10-3-r25 · Cited by 21,849
Differential expression analysis for sequence count data
Anders, S., & Huber, W. (2010) · Genome Biology · doi:10.1186/gb-2010-11-10-r106 · Cited by 15,581
Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation
Trapnell, C., Williams, B. A., Pertea, G., Mortazavi, A., Kwan, G., van Baren, M. J., Salzberg, S. L., Wold, B. J., & Pachter, L. (2010) · Nature Biotechnology · doi:10.1038/nbt.1621 · Cited by 15,556
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
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
Self-citation
A scaling normalization method for differential expression analysis of RNA-seq data
Robinson, M. D., & Oshlack, A. (2010) · Genome Biology · doi:10.1186/gb-2010-11-3-r25 · Cited by 7,876
The International HapMap Project
†The International HapMap Consortium, Genotyping centres: Baylor College of Medicine and ParAllele BioScience, Gibbs, R. A., Belmont, J. W., Hardenbol, P., Willis, T. D., Yu, F., Chinese HapMap Consortium, Yang, H., Ch'ang, L.-Y., et al. (2003) · Nature · doi:10.1038/nature02168 · Cited by 6,057
Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation
McCarthy, D. J., Chen, Y., & Smyth, G. K. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks042 · Cited by 5,195

Show all 38 references

Research works printed but not linked: 1

2011, SUPPL, 1), D800

Sorted by citations

Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
Love, M. I., Huber, W., & Anders, S. (2014) · Genome Biology · doi:10.1186/s13059-014-0550-8 · Cited by 95,106
limma powers differential expression analyses for RNA-sequencing and microarray studies
Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W., & Smyth, G. K. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv007 · Cited by 40,458
GSVA: gene set variation analysis for microarray and RNA-Seq data
Hänzelmann, S., Castelo, R., & Guinney, J. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-7 · Cited by 15,440
Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation
McCarthy, D. J., Chen, Y., & Smyth, G. K. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks042 · Cited by 5,195
Normalization of RNA-seq data using factor analysis of control genes or samples
Risso, D., Ngai, J., Speed, T. P., & Dudoit, S. (2014) · Nature Biotechnology · doi:10.1038/nbt.2931 · Cited by 2,052
Cellular senescence mediates fibrotic pulmonary disease
Schafer, M. J., White, T. A., Iijima, K., Haak, A. J., Ligresti, G., Atkinson, E. J., Oberg, A. L., Birch, J., Salmonowicz, H., Zhu, Y., et al. (2017) · Nature Communications · doi:10.1038/ncomms14532 · Cited by 1,613
“Conditional quantile normalization using the cqn Bioconductor package 48 was applied to the RNAseq data, to reduce variability introduced by GC content, gene size and total gene counts per sample.”
Functional genomic landscape of acute myeloid leukaemia
Tyner, J. W., Tognon, C. E., Bottomly, D., Wilmot, B., Kurtz, S. E., Savage, S. L., Long, N., Schultz, A. R., Traer, E. A., Abel, M. L., et al. (2018) · Nature · doi:10.1038/s41586-018-0623-z · Cited by 1,359
A large-scale binding and functional map of human RNA-binding proteins
Van Nostrand, E. L., Freese, P., Pratt, G. A., Wang, X., Wei, X., Xiao, R., Blue, S. M., Chen, J.-Y., Cody, N. A. L., Dominguez, D., et al. (2020) · Nature · doi:10.1038/s41586-020-2077-3 · Cited by 1,317
A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis
Dillies, M., Rau, A., Aubert, J., Hennequet‐Antier, C., Jeanmougin, M., Servant, N., Keime, C., Marot, G., Castel, D., Estellé, J., et al. (2012) · Briefings in Bioinformatics · doi:10.1093/bib/bbs046 · Cited by 1,363
Lineage-specific and single-cell chromatin accessibility charts human hematopoiesis and leukemia evolution
Corces, M. R., Buenrostro, J. D., Wu, B., Greenside, P. G., Chan, S. M., Koenig, J. L., Snyder, M. P., Pritchard, J. K., Kundaje, A., Greenleaf, W. J., et al. (2016) · Nature Genetics · doi:10.1038/ng.3646 · Cited by 1,151
Count-based differential expression analysis of RNA sequencing data using R and Bioconductor
Anders, S., McCarthy, D. J., Chen, Y., Okoniewski, M. J., Smyth, G. K., Huber, W. K., & Robinson, M. D. (2013) · Nature Protocols · doi:10.1038/nprot.2013.099 · Cited by 1,170
Integrative functional genomic analyses implicate specific molecular pathways and circuits in autism
Parikshak, N. N., Luo, R., Zhang, A., Won, H., Lowe, J. K., Chandran, V., Horvath, S., & Geschwind, D. H. (2013) · Cell · doi:10.1016/j.cell.2013.10.031 · Cited by 1,039
Shared molecular neuropathology across major psychiatric disorders parallels polygenic overlap
Gandal, M. J., Haney, J. R., Parikshak, N. N., Leppa, V., Ramaswami, G., Hartl, C., Schork, A. J., Appadurai, V., Buil, A., Werge, T. M., et al. (2018) · Science · doi:10.1126/science.aad6469 · Cited by 1,095
Multi-stage Differentiation Defines Melanoma Subtypes with Differential Vulnerability to Drug-Induced Iron-Dependent Oxidative Stress
Tsoi, J., Robert, L., Paraiso, K., Galvan, C., Sheu, K. M., Lay, J., Wong, D. J. L., Atefi, M., Shirazi, R., Wang, X., et al. (2018) · Cancer Cell · doi:10.1016/j.ccell.2018.03.017 · Cited by 938
GC-Content Normalization for RNA-Seq Data
Risso, D., Schwartz, K., Sherlock, G., & Dudoit, S. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-480 · Cited by 867
edgeR v4: powerful differential analysis of sequencing data with expanded functionality and improved support for small counts and larger datasets
Chen, Y., Chen, L., Lun, A. T. L., Baldoni, P. L., & Smyth, G. K. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkaf018 · Cited by 951
Integrative functional genomic analysis of human brain development and neuropsychiatric risks
Li, M., Santpere, G., Imamura Kawasawa, Y., Evgrafov, O. V., Gulden, F. O., Pochareddy, S., Sunkin, S. M., Li, Z., Shin, Y., Zhu, Y., et al. (2018) · Science · doi:10.1126/science.aat7615 · Cited by 810
Chromosome conformation elucidates regulatory relationships in developing human brain
Won, H., de la Torre-Ubieta, L., Stein, J. L., Parikshak, N. N., Huang, J., Opland, C. K., Gandal, M. J., Sutton, G. J., Hormozdiari, F., Lu, D., et al. (2016) · Nature · doi:10.1038/nature19847 · Cited by 584
Large-scale deep multi-layer analysis of Alzheimer’s disease brain reveals strong proteomic disease-related changes not observed at the RNA level
Johnson, E. C. B., Carter, E. K., Dammer, E. B., Duong, D. M., Gerasimov, E. S., Liu, Y., Liu, J., Betarbet, R., Ping, L., Yin, L., et al. (2022) · Nature Neuroscience · doi:10.1038/s41593-021-00999-y · Cited by 561
Transcriptome analysis reveals dysregulation of innate immune response genes and neuronal activity-dependent genes in autism
Gupta, S., Ellis, S. E., Ashar, F. N., Moes, A., Bader, J. S., Zhan, J., West, A. B., & Arking, D. E. (2014) · Nature Communications · doi:10.1038/ncomms6748 · Cited by 504
“Subsequent to mapping, the gene count data was normalized for within and between lane biases (e.g. GC content) and sequencing depth by methods implemented in Conditional Quantile Normalization 45 (CQN) and Exploratory Data Analysis and Normalization for RNA-Seq (EDASeq) 46 , using the default settings for each method.”
Pancreatic islet enhancer clusters enriched in type 2 diabetes risk-associated variants
Pasquali, L., Gaulton, K. J., Rodríguez‐Seguí, S. A., Mularoni, L., Miguel-Escalada, I., Akerman, İ., Tena, J. J., Morán, I., Gómez-Marín, C., van de Bunt, M., et al. (2014) · Nature Genetics · doi:10.1038/ng.2870 · Cited by 521
Inheritable silencing of endogenous genes by hit-and-run targeted epigenetic editing
Amabile, A., Migliara, A., Capasso, P., Biffi, M., Cittaro, D., Naldini, L., & Lombardo, A. (2016) · Cell · doi:10.1016/j.cell.2016.09.006 · Cited by 516
A compendium of uniformly processed human gene expression and splicing quantitative trait loci
Kerimov, N., Hayhurst, J. D., Peikova, K., Manning, J. R., Walter, P., Kolberg, L., Samoviča, M., Sakthivel, M. P., Kuzmin, I., Trevanion, S. J., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00924-w · Cited by 440
Design and computational analysis of single-cell RNA-sequencing experiments
Bacher, R. L., & Kendziorski, C. M. (2016) · Genome biology · doi:10.1186/s13059-016-0927-y · Cited by 460
CMScaller: an R package for consensus molecular subtyping of colorectal cancer pre-clinical models
Eide, P. W., Bruun, J., Lothe, R. A., & Sveen, A. (2017) · Scientific Reports · doi:10.1038/s41598-017-16747-x · Cited by 439
RNA-Seq workflow: gene-level exploratory analysis and differential expression
Love, M. I., Anders, S., Kim, V., & Huber, W. K. (2015) · F1000Research · doi:10.12688/f1000research.7035.1 · Cited by 404
Integrative Analysis of Drug Response and Clinical Outcome in Acute Myeloid Leukemia
Bottomly, D., Long, N., Schultz, A. R., Kurtz, S. E., Tognon, C. E., Johnson, K., Abel, M., Agarwal, A., Avaylon, S., Benton, E., et al. (2022) · Cancer Cell · doi:10.1016/j.ccell.2022.07.002 · Cited by 403
The Dynamic Landscape of Open Chromatin during Human Cortical Neurogenesis
de la Torre-Ubieta, L., Stein, J. L., Won, H., Opland, C. K., Liang, D., Lu, D., & Geschwind, D. H. (2018) · Cell · doi:10.1016/j.cell.2017.12.014 · Cited by 353
Robustly detecting differential expression in RNA sequencing data using observation weights
Zhou, X., Lindsay, H., & Robinson, M. D. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku310 · Cited by 387
APOE4 exacerbates synapse loss and neurodegeneration in Alzheimer’s disease patient iPSC-derived cerebral organoids
Zhao, J., Fu, Y., Yamazaki, Y., Ren, Y., Davis, M. D., Liu, C.-C., Lu, W., Wang, X., Chen, K., Cherukuri, Y., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-19264-0 · Cited by 374
Transcriptomics in the RNA-seq era
McGettigan, P. (2013) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2012.12.008 · Cited by 383
Identifying and mitigating bias in next-generation sequencing methods for chromatin biology
Meyer, C. A., & Liu, X. S. (2014) · Nature Reviews Genetics · doi:10.1038/nrg3788 · Cited by 342
Shared genetic effects on chromatin and gene expression indicate a role for enhancer priming in immune response
HIPSCI Consortium, Alasoo, K., Rodrigues, J., Mukhopadhyay, S., Knights, A. J., Mann, A. L., Kundu, K., Hale, C., Dougan, G., & Gaffney, D. J. (2018) · Nature Genetics · doi:10.1038/s41588-018-0046-7 · Cited by 338
Gene expression in human brain implicates sexually dimorphic pathways in autism spectrum disorders
Werling, D. M., Parikshak, N. N., & Geschwind, D. H. (2016) · Nature Communications · doi:10.1038/ncomms10717 · Cited by 296
Tissue-resident macrophages in omentum promote metastatic spread of ovarian cancer
Etzerodt, A., Moulin, M., Doktor, T. K., Delfini, M., Mossadegh‐Keller, N., Bajénoff, M., Sieweke, M. H., Moestrup, S. K., Auphan‐Anezin, N., & Lawrence, T. (2020) · The Journal of Experimental Medicine · doi:10.1084/jem.20191869 · Cited by 321
PD-1 combination therapy with IL-2 modifies CD8+ T cell exhaustion program
Hashimoto, M., Araki, K., Cardenas, M. A., Li, P., Jadhav, R. R., Kissick, H. T., Hudson, W. H., McGuire, D. J., Obeng, R. C., Wieland, A., et al. (2022) · Nature · doi:10.1038/s41586-022-05257-0 · Cited by 334
Large eQTL meta-analysis reveals differing patterns between cerebral cortical and cerebellar brain regions
Sieberts, S. K., Perumal, T. M., Carrasquillo, M. M., Allen, M., Reddy, J. S., Hoffman, G. E., Dang, K. K., Calley, J., Ebert, P. J., Eddy, J., et al. (2020) · Scientific Data · doi:10.1038/s41597-020-00642-8 · Cited by 318
Polyester : simulating RNA-seq datasets with differential transcript expression
Frazee, A. C., Jaffe, A. E., Langmead, B., & Leek, J. T. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv272 · Cited by 313
Small intestinal microbial dysbiosis underlies symptoms associated with functional gastrointestinal disorders
Saffouri, G. B., Shields‐Cutler, R. R., Chen, J., Yang, Y., Lekatz, H. R., Hale, V. L., Cho, J. M., Battaglioli, E. J., Bhattarai, Y., Thompson, K. J., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-09964-7 · Cited by 318
Transcriptome and epigenome landscape of human cortical development modeled in organoids
Amiri, A., Coppola, G., Scuderi, S., Wu, F., Roychowdhury, T., Liu, F., Pochareddy, S., Shin, Y., Safi, A., Song, L., et al. (2018) · Science · doi:10.1126/science.aat6720 · Cited by 308
Poly(GR) impairs protein translation and stress granule dynamics in C9orf72-associated frontotemporal dementia and amyotrophic lateral sclerosis
Zhang, Y., Gendron, T. F., Ebbert, M., O’Raw, A. D., Yue, M., Jansen‐West, K. R., Zhang, X., Prudencio, M., Chew, J., Cook, C. N., et al. (2018) · Nature Medicine · doi:10.1038/s41591-018-0071-1 · Cited by 299
QoRTs: a comprehensive toolset for quality control and data processing of RNA-Seq experiments
Hartley, S. W., & Mullikin, J. C. (2015) · BMC Bioinformatics · doi:10.1186/s12859-015-0670-5 · Cited by 259
Precursors for Nonlymphoid-Tissue Treg Cells Reside in Secondary Lymphoid Organs and Are Programmed by the Transcription Factor BATF
Delacher, M., Imbusch, C. D., Hotz-Wagenblatt, A., Mallm, J.-P., Bauer, K., Simon, M., Riegel, D., Rendeiro, A. F., Bittner, S., Sanderink, L., et al. (2020) · Immunity · doi:10.1016/j.immuni.2019.12.002 · Cited by 261
Genetic Control of Expression and Splicing in Developing Human Brain Informs Disease Mechanisms
Walker, R. L., Ramaswami, G., Hartl, C., Mancuso, N., Gandal, M. J., de la Torre-Ubieta, L., Paşaniuc, B., Stein, J. L., & Geschwind, D. H. (2019) · Cell · doi:10.1016/j.cell.2019.09.021 · Cited by 262
Broad transcriptomic dysregulation occurs across the cerebral cortex in ASD
Gandal, M. J., Haney, J. R., Wamsley, B., Yap, C. X., Parhami, S., Emani, P. S., Chang, N., Chen, G. T., Hoftman, G. D., de Alba, D., et al. (2022) · Nature · doi:10.1038/s41586-022-05377-7 · Cited by 283
Data-based filtering for replicated high-throughput transcriptome sequencing experiments
Rau, A., Gallopin, M., Celeux, G., & Jaffrézic, F. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt350 · Cited by 262
RNA-Seq and human complex diseases: recent accomplishments and future perspectives
Costa, V., Aprile, M., Esposito, R., & Ciccodicola, A. (2012) · European Journal of Human Genetics · doi:10.1038/ejhg.2012.129 · Cited by 277
CytoNorm: A Normalization Algorithm for Cytometry Data
Van Gassen, S., Gaudilliere, B., Angst, M. S., Saeys, Y., & Aghaeepour, N. (2020) · Cytometry Part A · doi:10.1002/cyto.a.23904 · Cited by 234
Epigenomics of human CD8 T cell differentiation and aging
Moskowitz, D. M., Zhang, D. W., Hu, B., Le Saux, S., Yanes, R. E., Ye, Z., Buenrostro, J. D., Weyand, C. M., Greenleaf, W. J., & Goronzy, J. J. (2017) · Science Immunology · doi:10.1126/sciimmunol.aag0192 · Cited by 252
“We also compared our findings with those attained by using alternative normalization strategies designed to correct for variability across samples in overall levels of accessibility (36) or in guanine-cytosine (GC) and peak-length biases (37).”
Result
A new shrinkage estimator for dispersion improves differential expression detection in RNA-seq data
Wu, H., Wang, C., & Wu, Z. J. (2012) · Biostatistics · doi:10.1093/biostatistics/kxs033 · Cited by 248
Self-citation
Measuring differential gene expression with RNA-seq: challenges and strategies for data analysis
Finotello, F., & Camillo, B. D. (2014) · Briefings in Functional Genomics · doi:10.1093/bfgp/elu035 · Cited by 253
Identification of osteoclast-osteoblast coupling factors in humans reveals links between bone and energy metabolism
Weivoda, M. M., Chew, C. K., Monroe, D. G., Farr, J. N., Atkinson, E. J., Geske, J. R., Eckhardt, B., Thicke, B., Ruan, M., Tweed, A. J., et al. (2020) · Nature Communications · doi:10.1038/s41467-019-14003-6 · Cited by 230
CommonMind Consortium provides transcriptomic and epigenomic data for Schizophrenia and Bipolar Disorder
Hoffman, G. E., Bendl, J. M., Voloudakis, G., Montgomery, K. S., Sloofman, L. G., Wang, Y., Shah, H., Hauberg, M. E., Johnson, J., Girdhar, K., et al. (2019) · Scientific Data · doi:10.1038/s41597-019-0183-6 · Cited by 221
Molecular and functional variation in iPSC-derived sensory neurons
HIPSCI Consortium, Schwartzentruber, J., Foskolou, S., Kilpinen, H., Rodrigues, J., Alasoo, K., Knights, A. J., Patel, M., Goncalves, A., Ferreira, R., et al. (2018) · Nature Genetics · doi:10.1038/s41588-017-0005-8 · Cited by 223
Splicing factor 1 modulates dietary restriction and TORC1 pathway longevity in C. elegans
Heintz, C., Doktor, T. K., Lanjuin, A., Escoubas, C. C., Zhang, Y., Weir, H. J. M., Dutta, S., Silva-García, C. G., Bruun, G. H., Morantte, I. G., et al. (2016) · Nature · doi:10.1038/nature20789 · Cited by 217
Alzheimer’s Risk Factors Age, APOE Genotype, and Sex Drive Distinct Molecular Pathways
Zhao, N., Ren, Y., Yamazaki, Y., Qiao, W., Li, F., Felton, L. M., Mahmoudiandehkordi, S., Kueider-Paisley, A., Sonoustoun, B., Arnold, M., et al. (2020) · Neuron · doi:10.1016/j.neuron.2020.02.034 · Cited by 225
Detecting and correcting systematic variation in large-scale RNA sequencing data
Li, S., Łabaj, P. P., Zumbo, P., Sykacek, P., Shi, W., Shi, L., Phan, J., Wu, P.-Y., Wang, M., Wang, C., et al. (2014) · Nature Biotechnology · doi:10.1038/nbt.3000 · Cited by 175
Sequence- and structure-specific cytosine-5 mRNA methylation by NSUN6
Selmi, T., Hussain, S., Dietmann, S., Heiß, M., Borland, K. M., Flad, S., Carter, J.-M., Dennison, R. A., Huang, Y., Kellner, S. M., et al. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa1193 · Cited by 202
Comparison of normalization methods for the analysis of metagenomic gene abundance data
Pereira, M. B., Wallroth, M., Jönsson, V., & Kristiansson, E. (2018) · BMC Genomics · doi:10.1186/s12864-018-4637-6 · Cited by 197
Genome-wide, integrative analysis implicates microRNA dysregulation in autism spectrum disorder
Wu, Y. E., Parikshak, N. N., Belgard, T. G., & Geschwind, D. H. (2016) · Nature Neuroscience · doi:10.1038/nn.4373 · Cited by 195
Shared Molecular Neuropathology Across Major Psychiatric Disorders Parallels Polygenic Overlap
Gandal, M. J., Haney, J. R., Parikshak, N., Leppä, V., Ramaswami, G., Hartl, C., Schork, A. J., Appadurai, V., Buil, A., Werge, T. M., et al. (2019) · FOCUS The Journal of Lifelong Learning in Psychiatry · doi:10.1176/appi.focus.17103 · Cited by 212
Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimation
Love, M. I., Hogenesch, J. B., & Irizarry, R. A. (2016) · Nature Biotechnology · doi:10.1038/nbt.3682 · Cited by 186
Self-citation
Blood lipids influence DNA methylation in circulating cells
Dekkers, K. F., van Iterson, M., Slieker, R. C., Moed, M. H., Bonder, M. J., van Galen, M., Mei, H., Zhernakova, D. V., van den Berg, L. H., Deelen, J., et al. (2016) · Genome biology · doi:10.1186/s13059-016-1000-6 · Cited by 190
Neuronal defects in a human cellular model of 22q11.2 deletion syndrome
Khan, T. A., Revah, O., Gordon, A., Yoon, S.-J., Krawisz, A. K., Goold, C., Sun, Y., Kim, C. H., Tian, Y., Li, M.-Y., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-1043-9 · Cited by 186
Human 3D cellular model of hypoxic brain injury of prematurity
Pașca, A. M., Park, J.-Y., Shin, H.-W., Qi, Q., Revah, O., Krasnoff, R., O’Hara, R., Willsey, A. J., Palmer, T. D., & Pașca, S. P. (2019) · Nature Medicine · doi:10.1038/s41591-019-0436-0 · Cited by 174
A comprehensive analysis of breast cancer microbiota and host gene expression
Thompson, K. J., Ingle, J. N., Tang, X., Chia, N., Jeraldo, P. R., Walther-Antonio, M. R., Kandimalla, K. K., Johnson, S., Yao, J. Z., Harrington, S. C., et al. (2017) · PLOS ONE · doi:10.1371/journal.pone.0188873 · Cited by 183
Comprehensive multi-center assessment of small RNA-seq methods for quantitative miRNA profiling
Giraldez, M. D., Spengler, R. M., Etheridge, A., Godoy, P. M., Barczak, A. J., Srinivasan, S., De Hoff, P. L., Tanriverdi, K., Courtright, A., Lu, S., et al. (2018) · Nature Biotechnology · doi:10.1038/nbt.4183 · Cited by 177
Autism-like phenotype and risk gene mRNA deadenylation by CPEB4 mis-splicing
Parras, A., Anta, H., Santos‐Galindo, M., Swarup, V., Elorza, A., Nieto-González, J. L., Picó, S., Hernández, I. H., Díaz‐Hernandéz, J. I., Belloc, E., et al. (2018) · Nature · doi:10.1038/s41586-018-0423-5 · Cited by 175
Age-related accrual of methylomic variability is linked to fundamental ageing mechanisms
Slieker, R. C., van Iterson, M., Luijk, R., Beekman, M., Zhernakova, D. V., Moed, M. H., Mei, H., van Galen, M., Deelen, P., Bonder, M. J., et al. (2016) · Genome biology · doi:10.1186/s13059-016-1053-6 · Cited by 171
Peripheral apoE4 enhances Alzheimer’s pathology and impairs cognition by compromising cerebrovascular function
Liu, C.-C., Zhao, J., Fu, Y., Inoue, Y., Ren, Y., Chen, Y., Doss, S. V., Shue, F., Jeevaratnam, S., Bastea, L., et al. (2022) · Nature Neuroscience · doi:10.1038/s41593-022-01127-0 · Cited by 168
LAG3 limits regulatory T cell proliferation and function in autoimmune diabetes
Zhang, Q., Chikina, M. D., Szymczak-Workman, A. L., Horne, W. T., Kolls, J. K., Vignali, K. M., Normolle, D. P., Bettini, M., Workman, C. J., & Vignali, D. A. A. (2017) · Science Immunology · doi:10.1126/sciimmunol.aah4569 · Cited by 170
The Olfactory Transcriptomes of Mice
Ibarra-Soria, X., Levitin, M. O., Saraiva, L. R., & Logan, D. W. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004593 · Cited by 156
APOE4 exacerbates α-synuclein pathology and related toxicity independent of amyloid
Zhao, N., Attrebi, O. N., Ren, Y., Qiao, W., Sonustun, B., Martens, Y. A., Meneses, A. D., Li, F., Shue, F., Zheng, J., et al. (2020) · Science Translational Medicine · doi:10.1126/scitranslmed.aay1809 · Cited by 156
RNA Sequencing Data: Hitchhiker's Guide to Expression Analysis
den Berge, K. V., Hembach, K., Soneson, C., Tiberi, S., Clement, L., Love, M. I., Patro, R., & Robinson, M. D. (2019) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-072018-021255 · Cited by 153
Integrated multi-omic characterization of congenital heart disease
Hill, M. C., Kadow, Z. A., Long, H., Morikawa, Y., Martin, T. J., Birks, E. J., Campbell, K. S., Nerbonne, J. M., Lavine, K. J., Wadhwa, L., et al. (2022) · Nature · doi:10.1038/s41586-022-04989-3 · Cited by 150
Conserved brain myelination networks are altered in Alzheimer’s and other neurodegenerative diseases
Allen, M., Wang, X., Burgess, J. D., Watzlawik, J., Serie, D. J., Younkin, C. S., Nguyen, T., Malphrus, K. G., Lincoln, S., Carrasquillo, M. M., et al. (2018) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2017.09.012 · Cited by 143
An inferred functional impact map of genetic variants in rice
Zhao, H., Li, J., Yang, L., Qin, G., Xia, C., Xu, X., Su, Y., Liu, Y., Ming, L., Chen, L., et al. (2021) · Molecular Plant · doi:10.1016/j.molp.2021.06.025 · Cited by 145
3D Genome of macaque fetal brain reveals evolutionary innovations during primate corticogenesis
Luo, X., Liu, Y., Dang, D., Hu, T., Hou, Y., Meng, X., Zhang, F., Li, T., Wang, C., Li, M., et al. (2021) · Cell · doi:10.1016/j.cell.2021.01.001 · Cited by 140
Comparison of normalization methods for differential gene expression analysis in RNA-Seq experiments
Maza, É., Frasse, P., Senin, P., Bouzayen, M., & Zouine, M. (2013) · Communicative & Integrative Biology · doi:10.4161/cib.25849 · Cited by 145
Identification of evolutionarily conserved gene networks mediating neurodegenerative dementia
Swarup, V., Hinz, F. I., Rexach, J. E., Noguchi, K.-I., Toyoshiba, H., Oda, A., Hirai, K., Sarkar, A., Seyfried, N. T., Cheng, C., et al. (2019) · Nature Medicine · doi:10.1038/s41591-018-0223-3 · Cited by 136
qSVA framework for RNA quality correction in differential expression analysis
Jaffe, A. E., Tao, R., Norris, A. L., Kealhofer, M., Nellore, A., Shin, J. H., Kim, D., Jia, Y., Hyde, T. M., Kleinman, J. E., et al. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1617384114 · Cited by 114
Core transcription programs controlling injury-induced neurodegeneration of retinal ganglion cells
Tian, F., Cheng, Y., Zhou, S., Wang, Q., Monavarfeshani, A., Gao, K., Jiang, W., Kawaguchi, R., Wang, Q., Tang, M., et al. (2022) · Neuron · doi:10.1016/j.neuron.2022.06.003 · Cited by 127
Allele-specific open chromatin in human iPSC neurons elucidates functional disease variants
Zhang, S., Zhang, H., Zhou, Y., Qiao, M., Zhao, S., Kozlova, A., Shi, J., Sanders, A. R., Wang, G., Luo, K., et al. (2020) · Science · doi:10.1126/science.aay3983 · Cited by 127
Statistical methods for detecting differentially methylated loci and regions
Robinson, M. D., Kahraman, A., Law, C. W., Lindsay, H. A., Nowicka, M., Weber, L. M., & Zhou, X. (2014) · Frontiers in Genetics · doi:10.3389/fgene.2014.00324 · Cited by 128
Aging is associated with a systemic length-associated transcriptome imbalance
Stoeger, T., Grant, R. A., McQuattie‐Pimentel, A. C., Anekalla, K. R., Liu, S. S., Navarro, H. T., Singer, B. D., Abdala‐Valencia, H., Schwake, M., Tétreault, M., et al. (2022) · Nature Aging · doi:10.1038/s43587-022-00317-6 · Cited by 129
Integrated PPI- and WGCNA-Retrieval of Hub Gene Signatures Shared Between Barrett's Esophagus and Esophageal Adenocarcinoma
Nangraj, A. S., Selvaraj, G., Kaliamurthi, S., Kaushik, A. C., Cho, W. C. S., & Wei, D. (2020) · Frontiers in Pharmacology · doi:10.3389/fphar.2020.00881 · Cited by 116
Mouse Ovarian Cancer Models Recapitulate the Human Tumor Microenvironment and Patient Response to Treatment
Maniati, E., Berlato, C., Gopinathan, G., Heath, O. M., Kotantaki, P., Lakhani, A., McDermott, J. R., Pegrum, C., Delaine‐Smith, R. M., Pearce, O. M. T., et al. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.12.034 · Cited by 110
Talking the talk, but not walking the walk: RT-qPCR as a paradigm for the lack of reproducibility in molecular research
Bustin, S., & Nolan, T. (2017) · European Journal of Clinical Investigation · doi:10.1111/eci.12801 · Cited by 113
cAMP response element‐binding (CREB) recruitment following a specific CpG demethylation leads to the elevated expression of the matrix metalloproteinase 13 in human articular chondrocytes and osteoarthritis
Bui, C., Barter, M. J., Scott, J. L., Xu, Y., Galler, M., Reynard, L. N., Rowan, A. D., & Young, D. A. (2012) · The FASEB Journal · doi:10.1096/fj.12-206367 · Cited by 114
Integrative genomics identifies a convergent molecular subtype that links epigenomic with transcriptomic differences in autism
Ramaswami, G., Won, H., Gandal, M. J., Haney, J., Wang, J. C., Wong, C. C. Y., Sun, W., Prabhakar, S., Mill, J., & Geschwind, D. H. (2020) · Nature Communications · doi:10.1038/s41467-020-18526-1 · Cited by 113
ABI3 and PLCG2 missense variants as risk factors for neurodegenerative diseases in Caucasians and African Americans
Conway, O. J., Carrasquillo, M. M., Wang, X., Bredenberg, J. M., Reddy, J. S., Strickland, S. L., Younkin, C. S., Burgess, J. D., Allen, M., Lincoln, S. J., et al. (2018) · Molecular Neurodegeneration · doi:10.1186/s13024-018-0289-x · Cited by 112
Differences in microRNA detection levels are technology and sequence dependent
Leshkowitz, D., Horn‐Saban, S., Parmet, Y., & Feldmesser, E. (2013) · RNA · doi:10.1261/rna.036475.112 · Cited by 113
Optimization of an RNA-Seq Differential Gene Expression Analysis Depending on Biological Replicate Number and Library Size
Lamarre, S., Frasse, P., Zouine, M., Labourdette, D., Sainderichin, E., Hu, G., Berre, V. L., Bouzayen, M., & Maza, É. (2018) · Frontiers in Plant Science · doi:10.3389/fpls.2018.00108 · Cited by 106
Time-Restricted Feeding Shifts the Skin Circadian Clock and Alters UVB-Induced DNA Damage
Wang, H., Spyk, E. N. V., Liu, Q., Geyfman, M., Salmans, M., Kumar, V., Ihler, A., Li, N., Takahashi, J. S., & D, B. A. M. (2017) · Cell Reports · doi:10.1016/j.celrep.2017.07.022 · Cited by 105
Genetic control of the human brain proteome
Robins, C., Wingo, A. P., Fan, W., Duong, D. M., Meigs, J., Gerasimov, E. S., Dammer, E. B., Cutler, D. J., Jager, P. L. D., Bennett, D. A., et al. (2019) · doi:10.1101/816652 · Cited by 104
Whole-genome sequencing reveals new Alzheimer’s disease–associated rare variants in loci related to synaptic function and neuronal development
Prokopenko, D., Morgan, S. L., Mullin, K., Hofmann, O., Chapman, B., Kirchner, R., (ADNI), A. D. N. I., Amberkar, S., Wohlers, I., Lange, C., et al. (2021) · Alzheimer's & Dementia · doi:10.1002/alz.12319 · Cited by 105
Reduced Expression of Histone Methyltransferases KMT2C and KMT2D Correlates with Improved Outcome in Pancreatic Ductal Adenocarcinoma
Dawkins, J. B. N., Wang, J., Maniati, E., Heward, J. A., Koniali, L., Kocher, H. M., Martin, S. A., Chelala, C., Balkwill, F. R., Fitzgibbon, J., et al. (2016) · Cancer Research · doi:10.1158/0008-5472.can-16-0481 · Cited by 104
RNA-sequencing of a mouse-model of spinal muscular atrophy reveals tissue-wide changes in splicing of U12-dependent introns
Doktor, T. K., Hua, Y., Andersen, H. S., Brøner, S., Liu, Y. H., Wieckowska, A., Dembić, M., Bruun, G. H., Krainer, A. R., & Andresen, B. S. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw731 · Cited by 103
LSD1 inhibition sustains T cell invigoration with a durable response to PD-1 blockade
Liu, Y., Debo, B. M., Li, M., Shi, Z., Sheng, W., & Shi, Y. (2021) · Nature Communications · doi:10.1038/s41467-021-27179-7 · Cited by 109
Tau Pathology Drives Dementia Risk-Associated Gene Networks toward Chronic Inflammatory States and Immunosuppression
Rexach, J. E., Polioudakis, D., Yin, A., Swarup, V., Chang, T. S., Nguyen, T., Sarkar, A., Chen, L., Huang, J., Lin, L.-C., et al. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.108398 · Cited by 94

Authors: Hansen, K. D., Irizarry, R. A., & WU, Z.
Affiliations: Johns Hopkins University (US)
Brown University (US)
Corresponding author: WU, Z.
ORCID: Hansen, K. D.
Irizarry, R. A.
WU, Z.
Venue: Biostatistics
Publisher: Oxford University Press (OUP)
Published: 2012-01-27
Volume: 13
Issue: 2
Pages: 204-216
ISSN: 1465-4644 (ISSN-L)
1468-4357
DOI: 10.1093/biostatistics/kxr054
PMC: PMC3297825
OpenAlex: W2170219300
Cited by: 607
References: 38
Citations by year: 2026: 25 · 2025: 42 · 2024: 40 · 2023: 38 · 2022: 51 · 2021: 64 · 2020: 53 · 2019: 50 · 2018: 38 · 2017: 39 · 2016: 54 · 2015: 21 · 2014: 39 · 2013: 31 · 2012: 21
FWCI: 14.39
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by-nc
Locations: Biostatistics (publishedVersion, cc-by-nc)
Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
PubMed Central (publishedVersion, cc-by-nc)
PubMed (publishedVersion)
Collection of Biostatistics Research Archive (submittedVersion)
Funders: National Science Foundation (1054905)
University of Memphis
National Institutes of Health
National Human Genome Research Institute (R01HG004059, R01HG005220)
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, gene expression normalization, GC content bias, technical variability, false positives, quantile normalization, microarray comparison, data distortion
MeSH terms: Algorithms, Analysis of Variance, Base Composition, Humans, Sequence Analysis, RNA, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling, Databases, Nucleic Acid, Biostatistics, High-Throughput Nucleotide Sequencing

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

Removing technical variability in RNA-seq data using conditional quantile normalization

Abstract

Overview

Integrity signals

Figures

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks