Removing technical variability in RNA-seq data using conditional quantile normalization
Open accessAbstract
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
Cites
Statistics
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?
Conditional quantile normalization using the cqn Bioconductor package was applied to the RNAseq data, to reduce variability introduced by GC content, gene size and total gene counts per sample.
“Conditional quantile normalization using the cqn Bioconductor package48 was applied to the RNAseq data, to reduce variability introduced by GC content, gene size and total gene counts per sample.”
From Cellular senescence mediates fibrotic pulmonary disease (2017) · cites this paper as [48]
In large-scale RNA-seq studies, commonly used normalization methods (cqn, EDASeq, RUV2, sva, PEER) differ in how well they remove systematic biases, depending on sample complexity and initial data quality.
“commonly used methods fornormalization (cqn, EDASeq, RUV2, sva, PEER) varied in their ability to remove these systematic biases, depending on sample complexity and initial data quality.”
From Detecting and correcting systematic variation in large-scale RNA sequencing data (2014) · cites this paper as [14]
Applying GC bias correction tools (cqn, EDASeq) to individual sites or restricting read counts to 3′-UTRs did not reduce inter-site false positive DEGs and often increased them.
“Notably, neither applying GC bias correction tools (cqn14 and EDASeq15) to individual sites nor changing read counts to only use 3′-UTRs was effective at decreasing the number of inter-site false positive DEGs (Fig. 1b and Supplementary Fig. 3); in most cases, these methods actually increased the number of false positives.”
From Detecting and correcting systematic variation in large-scale RNA sequencing data (2014) · cites this paper as [14]
GC content bias correction packages cqn and EDASeq could not remove the false positives, likely because GC content bias is not the only source of bias in gene expression data.
“The application of GC content bias correction packages including cqn14 and EDASeq15 could not remove these false positives, likely because GC content bias is not the only source that contributes to bias in gene expression data.”
From Detecting and correcting systematic variation in large-scale RNA sequencing data (2014) · cites this paper as [14]
Autism is characterized by extreme phenotypic heterogeneity and involvement of hundreds of loci.
“This slow progress can be attributed, in part, to both the extreme phenotypic heterogeneity and involvement of hundreds of loci in the disorder45.”
From Transcriptome analysis reveals dysregulation of innate immune response genes and neuronal activity-dependent genes in autism (2014) · cites this paper as [45]
voom has the ability to respect normalization factors computed outside of limma by methods such as trimmed mean of M-values or conditional quantile normalization.
“Alternatively, voom has the ability to respect normalization factors computed outside of limma by methods such as trimmed mean of M-values (41) or conditional quantile normalization (42).”
From limma powers differential expression analyses for RNA-sequencing and microarray studies (2015) · cites this paper as [42]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Robinson, M. D., McCarthy, D. J., & Smyth, G. K. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btp616 · Cited by 41,364 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Bolstad, B. M., Irizarry, R. A., Åstrand, M., & Speed, T. P. (2003) · Bioinformatics · doi:10.1093/bioinformatics/19.2.185 · Cited by 8,185 · see citing works
Self-citationA 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 · see citing works
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 · see citing works
McCarthy, D. J., Chen, Y., & Smyth, G. K. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks042 · Cited by 5,195 · see citing works
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 · see citing works
MAQC Consortium, Shi, L., Shi, L., Reid, L. H., Jones, W. D., Shippy, R., Warrington, J. A., Baker, S. C., Collins, P. J., de Longueville, F., et al. (2006) · Nature Biotechnology · doi:10.1038/nbt1239 · Cited by 2,126 · see citing works
Improving RNA-Seq expression estimates by correcting for fragment bias
Roberts, A., Trapnell, C., Donaghey, J., Rinn, J. L., & Pachter, L. (2011) · Genome Biology · doi:10.1186/gb-2011-12-3-r22 · Cited by 1,725 · see citing works
A Model-Based Background Adjustment for Oligonucleotide Expression Arrays
Wu, Z. J., Irizarry, R. A., Gentleman, R. C., Martínez-Murillo, F., & Spencer, F. A. (2004) · Journal of the American Statistical Association · doi:10.1198/016214504000000683 · Cited by 1,717 · see citing works
Self-citationBullard, J. H., Purdom, E., Hansen, K. D., & Dudoit, S. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-94 · Cited by 1,680 · see citing works
Self-citationUnderstanding mechanisms underlying human gene expression variation with RNA sequencing
Pickrell, J. K., Marioni, J. C., Pai, A. A., Degner, J. F., Engelhardt, B. E., Nkadori, E., Veyrieras, J.-B., Stephens, M., Gilad, Y., & Pritchard, J. K. (2010) · Nature · doi:10.1038/nature08872 · Cited by 1,256 · see citing works
Analyzing and minimizing PCR amplification bias in Illumina sequencing libraries
Aird, D., Ross, M. G., Chen, W.-S., Danielsson, M., Fennell, T., Russ, C., Jaffe, D. B., Nusbaum, C., & Gnirke, A. (2011) · Genome Biology · doi:10.1186/gb-2011-12-2-r18 · Cited by 1,174 · see citing works
Substantial biases in ultra-short read data sets from high-throughput DNA sequencing
Dohm, J. C., Lottaz, C., Borodina, T., & Himmelbauer, H. (2008) · Nucleic Acids Research · doi:10.1093/nar/gkn425 · Cited by 1,088 · see citing works
Small-sample estimation of negative binomial dispersion, with applications to SAGE data
Robinson, M. D., & Smyth, G. K. (2007) · Biostatistics · doi:10.1093/biostatistics/kxm030 · Cited by 1,073 · see citing works
Moderated statistical tests for assessing differences in tag abundance
Robinson, M. D., & Smyth, G. K. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btm453 · Cited by 856 · see citing works
Transcriptome genetics using second generation sequencing in a Caucasian population
Montgomery, S. B., Sammeth, M., Gutierrez-Arcelus, M., Lach, R. P., Ingle, C., Nisbett, J., Guigo, R., & Dermitzakis, E. T. (2010) · Nature · doi:10.1038/nature08903 · Cited by 797 · see citing works
Biases in Illumina transcriptome sequencing caused by random hexamer priming
Hansen, K. D., Brenner, S. E., & Dudoit, S. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq224 · Cited by 713 · see citing works
Self-citationNannya, Y., Sanada, M., Nakazaki, K., Hosoya, N., Wang, L., Hangaishi, A., Kurokawa, M., Chiba, S., Bailey, D. K., Kennedy, G. C., et al. (2005) · Cancer Research · doi:10.1158/0008-5472.can-05-0465 · Cited by 639 · see citing works
A model of molecular interactions on short oligonucleotide microarrays
Zhang, L., Miles, M. F., & Aldape, K. (2003) · Nature Biotechnology · doi:10.1038/nbt836 · Cited by 320 · see citing works
Cloud-scale RNA-sequencing differential expression analysis with Myrna
Langmead, B., Hansen, K. D., & Leek, J. T. (2010) · Genome biology · doi:10.1186/gb-2010-11-8-r83 · Cited by 313 · see citing works
Self-citationExploration, normalization, and genotype calls of high-density oligonucleotide SNP array data
Carvalho, B. S., Bengtsson, H. L., Speed, T. P., & Irizarry, R. A. (2006) · Biostatistics · doi:10.1093/biostatistics/kxl042 · Cited by 250 · see citing works
Self-citationSequencing technology does not eliminate biological variability
Hansen, K. D., Wu, Z., Irizarry, R. A., & Leek, J. T. (2011) · Nature Biotechnology · doi:10.1038/nbt.1910 · Cited by 226 · see citing works
Self-citationEvaluating Gene Expression in C57BL/6J and DBA/2J Mouse Striatum Using RNA-Seq and Microarrays
Bottomly, D., Walter, N. A. R., Hunter, J. E., Darakjian, P., Kawane, S., Buck, K. J., Searles, R. P., Mooney, M. A., McWeeney, S. K., & HITZEMANN, R. J. (2011) · PLoS ONE · doi:10.1371/journal.pone.0017820 · Cited by 225 · see citing works
Pai, A. A., Bell, J. T., Marioni, J. C., Pritchard, J. K., & Gilad, Y. (2011) · PLoS Genetics · doi:10.1371/journal.pgen.1001316 · Cited by 209 · see citing works
Naef, F., & Magnasco, M. O. (2003) · Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics · doi:10.1103/physreve.68.011906 · Cited by 201 · see citing works
Modeling non-uniformity in short-read rates in RNA-Seq data
Li, J., Jiang, H., & Wong, W. H. (2010) · Genome Biology · doi:10.1186/gb-2010-11-5-r50 · Cited by 193 · see citing works
Polymorphic Cis- and Trans-Regulation of Human Gene Expression
Cheung, V. G., Nayak, R. R., Wang, I. X., Elwyn, S., Cousins, S. M., Morley, M. P., & Spielman, R. S. (2010) · PLoS Biology · doi:10.1371/journal.pbio.1000480 · Cited by 165 · see citing works
Digital Gene Expression Signatures for Maize Development
Eveland, A. L., Satoh‐Nagasawa, N., Goldshmidt, A., Meyer, S., Beatty, M. K., Sakai, H., Ware, D., & Jackson, D. P. (2010) · PLANT PHYSIOLOGY · doi:10.1104/pp.110.159673 · Cited by 87 · see citing works
Defining transcribed regions using RNA-seq
Wilhelm, B. T., Marguerat, S., Goodhead, I., & Bähler, J. (2010) · Nature Protocols · doi:10.1038/nprot.2009.229 · Cited by 79 · see citing works
Lefebvre, G. C., Desfarges, S., Uyttebroeck, F., Muñoz, M., Beerenwinkel, N., Rougemont, J., Telenti, A., & Ciuffi, A. (2011) · Journal of Virology · doi:10.1128/jvi.00252-11 · Cited by 69 · see citing works
Subset Quantile Normalization Using Negative Control Features
Wu, Z. J., & Aryee, M. J. (2010) · Journal of Computational Biology · doi:10.1089/cmb.2010.0049 · Cited by 53 · see citing works
Self-citationGene expression profiling of human breast tissue samples using SAGE-SeqPreprint
Wu, Z. J., Meyer, C. A., Choudhury, S. A., Shipitsin, M. V., Maruyama, R., Bessarabova, M., Nikolskaya, T., Sukumar, S. V., Schwartzman, A., Liu, J. S., et al. (2010) · Genome Research · doi:10.1101/gr.108217.110 · Cited by 41 · see citing works
Self-citationSh, L., & 陳章榮 (2006) · Cited by 14 · see citing works
Flagged first, then by citations
Research works printed but not linked: 2
- Flicek P Amode MR Barrell D Beal K Brent S Chen Y Clapham P Coates G Fairley S Fitzgerald S and others Ensembl 2011. Nucleic Acids Research 39 Suppl 2011 1) D800 PMC3013672 21045057
- Koenker R Quantile Regression 2005 New York Cambridge University Press
Cited by
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 · see citing works
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 · see citing works
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 · see citing works
McCarthy, D. J., Chen, Y., & Smyth, G. K. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks042 · Cited by 5,195 · see citing works
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 2,169 · see citing works
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 · see citing works
“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 1,330 · see citing works
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 · see citing works
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 · see citing works
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 1,281 · see citing works
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 1,129 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 898 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
“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 502 · see citing works
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 · see citing works
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 · see citing works
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 437 · see citing works
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 · see citing works
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 397 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
<i>Polyester</i> : 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 · see citing works
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 · see citing works
Transcriptomics in the RNA-seq era
McGettigan, P. (2013) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2012.12.008 · Cited by 360 · see citing works
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 329 · see citing works
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 · see citing works
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 · see citing works
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 320 · see citing works
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 323 · see citing works
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 · see citing works
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 309 · see citing works
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 302 · see citing works
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 307 · see citing works
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 · see citing works
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 · see citing works
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 259 · see citing works
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 281 · see citing works
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 · see citing works
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 266 · see citing works
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 · see citing works
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 · see citing works
“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).”
ResultA 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 241 · see citing works
Self-citationMeasuring 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 234 · see citing works
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 · see citing works
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 219 · see citing works
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 · see citing works
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 218 · see citing works
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 · see citing works
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 · see citing works
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 200 · see citing works
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 · see citing works
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 · see citing works
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 209 · see citing works
Love, M. I., Hogenesch, J. B., & Irizarry, R. A. (2016) · Nature Biotechnology · doi:10.1038/nbt.3682 · Cited by 186 · see citing works
Self-citationBlood lipids influence DNA methylation in circulating cells
Dekkers, K., van Iterson, M., Slieker, R., Moed, M., Bonder, M., van Galen, M. V., Mei, H., Zhernakova, D., van den Berg, L. H., Deelen, J., et al. (2016) · Genome Biology · doi:10.1186/s13059-016-1000-6 · Cited by 198 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 172 · see citing works
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 169 · see citing works
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 · see citing works
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 164 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
RNA Sequencing Data: Hitchhiker's Guide to Expression Analysis
den Berge, K. V., Hembach, K. M., Soneson, C., Tiberi, S., Clement, L., Love, M., Patro, R., & Robinson, M. D. (2018) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-072018-021255 · Cited by 119 · see citing works
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 145 · see citing works
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 · see citing works
An inferred functional impact map of genetic variants in rice.
Zhao, H., Li, J., Yang, L., Qin, G.-J., Xia, C.-J., Xu, X.-B., Su, Y.-W., Liu, Y., Ming, L., Chen, L.-L., et al. (2021) · Molecular Plant · doi:10.1016/j.molp.2021.06.025 · Cited by 121 · see citing works
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 137 · see citing works
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 136 · see citing works
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 · see citing works
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 125 · see citing works
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 · see citing works
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 124 · see citing works
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 120 · see citing works
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 119 · see citing works
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 115 · see citing works
Bustin, S., & Nolan, T. (2017) · European Journal of Clinical Investigation · doi:10.1111/eci.12801 · Cited by 113 · see citing works
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 113 · see citing works
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 · see citing works
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 · see citing works
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 112 · see citing works
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 107 · see citing works
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 104 · see citing works
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 · see citing works
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 107 · see citing works
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 · see citing works
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 · see citing works
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 103 · see citing works
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 · see citing works
- Authors
- Hansen, K. D., Irizarry, R. A., & WU, Z.
- Affiliations
- Johns Hopkins University (US)
- Brown University (US)
- Corresponding author
- 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
- 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
