The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
Tackling the widespread and critical impact of batch effects in high-throughput data
Open accessAbstract
High-throughput technologies are widely used, for example to assay genetic variants, gene and protein expression, and epigenetic modifications. One often overlooked complication with such studies is batch effects, which occur because measurements are affected by laboratory conditions, reagent lots and personnel differences. This becomes a major problem when batch effects are correlated with an outcome of interest and lead to incorrect conclusions. Using both published studies and our own analyses, we argue that batch effects (as well as other technical and biological artefacts) are widespread and critical to address. We review experimental and computational approaches for doing so.
Overview
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
Verification
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?
The cited source says what the claim attributes to it.
In the citing paper
“These batch effects make it difficult to draw conclusions from data across experimental batches (Leek et al., 2010; Parker and Leek, 2012; Soneson et al., 2014; Nygaard et al., 2016; Caicedo et al., 2017).”
From WILDS: A Benchmark of in-the-Wild Distribution Shifts (2020) · cites this paper as Leek et al., 2010
The cited source says what the claim attributes to it.
In the citing paper
“Several methods have been suggested to adjust for batch effects, including recommendations for experimental designs and statistical approaches (see for example Leek et al., 2010).”
From The applied implications of epigenetics in anxiety, affective and stress-related disorders - A review and synthesis on psychosocial stress, psychotherapy and prevention (2020) · cites this paper as Leek et al., 2010
The cited source says what the claim attributes to it.
In the citing paper
“Despite attempts to carefully control experimental variables such as temperature, humidity, and reagent concentration, measurements from these screens are confounded by technical artifacts that arise from differences in the execution of each batch.”
From WILDS: A Benchmark of in-the-Wild Distribution Shifts (2020) · cites this paper as Leek et al., 2010
In the citing paper
“Batch effects and technical variation due to unknown (unmodeled) confounders are common in both scRNA-seq and bulk RNA-seq.”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [45]
In the citing paper
“Notably, edges are susceptible to ascertainment biases52,214,215 and confounding factors that can induce spurious relationships178 (FIG. 2b).”
From Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders (2015) · cites this paper as [178]
In the citing paper
“Optimal methodological choices and study designs ensure that the biological signals from the main factors are not confounded by variation from unwanted factors178.”
From Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders (2015) · cites this paper as [178]
In the citing paper
“there are no batch normalization approaches for microbiome data, unlike in transcriptional or genetic meta-analyses26.”
From Assessment of variation in microbial community amplicon sequencing by the Microbiome Quality Control (MBQC) project consortium (2017) · cites this paper as [26]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
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
Self-citationAdjusting batch effects in microarray expression data using empirical Bayes methods
Johnson, W. E., Li, C., & Rabinovic, A. (2007) · Biostatistics · doi:10.1093/biostatistics/kxj037 · Cited by 8,658
Self-citationBolstad, B. M., Irizarry, R. A., Åstrand, M., & Speed, T. P. (2003) · Bioinformatics · doi:10.1093/bioinformatics/19.2.185 · Cited by 8,328
Self-citationComprehensive genomic characterization defines human glioblastoma genes and core pathways
The Cancer Genome Atlas Research Network (2008) · Nature · doi:10.1038/nature07385 · Cited by 7,735
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
Use of proteomic patterns in serum to identify ovarian cancer
Petricoin, E. F., Ardekani, A. M., Hitt, B. A., Levine, P. J., Fusaro, V. A., Steinberg, S. M., Mills, G. B., Simone, C., Fishman, D. A., Kohn, E. C., et al. (2002) · The Lancet · doi:10.1016/s0140-6736(02)07746-2 · Cited by 3,236
Principles of Numerical Taxonomy
Eysenck, H. J., & Eysenck, S. B. G. (2013) · Routledge eBooks · doi:10.4324/9780203766804-11 · Cited by 3,001
Singular value decomposition for genome-wide expression data processing and modeling
Alter, O., Brown, P. O., & Botstein, D. (2000) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.97.18.10101 · Cited by 1,989
Capturing Heterogeneity in Gene Expression Studies by Surrogate Variable Analysis
Leek, J. T., & Storey, J. D. (2007) · PLoS Genetics · doi:10.1371/journal.pgen.0030161 · Cited by 1,930
Self-citationMicroarray data analysis: from disarray to consolidation and consensus
Allison, D. B., Cui, X., Page, G. P., & Sabripour, M. (2006) · Nature Reviews Genetics · doi:10.1038/nrg1749 · Cited by 1,339
Flagged first, then by citations
Cited by
Sorted by citations
MultiQC: summarize analysis results for multiple tools and samples in a single report
Ewels, P., Magnusson, M., Lundin, S., & Käller, M. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw354 · Cited by 10,143
Predicting effective microRNA target sites in mammalian mRNAs
Agarwal, V., Bell, G. W., Nam, J.-W., & Bartel, D. P. (2015) · eLife · doi:10.7554/elife.05005 · Cited by 7,318
Leek, J. T., Johnson, W. E., Parker, H. S., Jaffe, A. E., & Storey, J. D. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts034 · Cited by 6,231
Self-citationAryee, M. J., Jaffe, A. E., Bravo, H. C., Ladd‐Acosta, C., Feinberg, A. P., Hansen, K. D., & Irizarry, R. A. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu049 · Cited by 4,389
Self-citationMetsalu, T., & Vilo, J. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv468 · Cited by 3,770
Revolutionizing healthcare: the role of artificial intelligence in clinical practice
Alowais, S. A., Alghamdi, S. S., Alsuhebany, N., Alqahtani, T., Alshaya, A. I., Almohareb, S. N., Aldairem, A., Alrashed, M., Bin Saleh, K., Badreldin, H. A., et al. (2023) · BMC Medical Education · doi:10.1186/s12909-023-04698-z · Cited by 3,146
The prognostic landscape of genes and infiltrating immune cells across human cancers
Gentles, A. J., Newman, A. M., Liu, C. L., Bratman, S. V., Feng, W., Kim, D., Nair, V. S., Xu, Y., Khuong, A., Hoang, C. D., et al. (2015) · Nature Medicine · doi:10.1038/nm.3909 · Cited by 3,163
Methods for interpreting and understanding deep neural networks
Montavon, G., Samek, W., & Müller, K.-R. (2018) · Digital Signal Processing · doi:10.1016/j.dsp.2017.10.011 · Cited by 2,665
Reducing the Effects of PCR Amplification and Sequencing Artifacts on 16S rRNA-Based Studies
Schloss, P. D., Gevers, D., & Westcott, S. L. (2011) · PLoS ONE · doi:10.1371/journal.pone.0027310 · Cited by 2,112
McCarthy, D. J., Campbell, K. R., Lun, A. T. L., & Wills, Q. F. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btw777 · Cited by 1,852
ComBat-seq: batch effect adjustment for RNA-seq count data
Zhang, Y., Parmigiani, G., & Johnson, W. E. (2020) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqaa078 · Cited by 1,566
Self-citationMicroRNA profiling: approaches and considerations
Pritchard, C. C., Cheng, H. H., & Tewari, M. (2012) · Nature Reviews Genetics · doi:10.1038/nrg3198 · Cited by 1,765
Harmonization of cortical thickness measurements across scanners and sites
Fortin, J.-P., Cullen, N., Sheline, Y. I., Taylor, W. D., Aselcioglu, I., Cook, P. A., Adams, P., Cooper, C., Fava, M., McGrath, P. J., et al. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2017.11.024 · Cited by 1,580
Eleven grand challenges in single-cell data science
Lähnemann, D., Köster, J., Szczurek, E., McCarthy, D. J., Hicks, S. C., Robinson, M. D., Vallejos, C. A., Campbell, K. R., Beerenwinkel, N., Mahfouz, A., et al. (2020) · Genome Biology · doi:10.1186/s13059-020-1926-6 · Cited by 1,327
Robust decomposition of cell type mixtures in spatial transcriptomics
Cable, D. M., Murray, E., Zou, L. S., Goeva, A., Macosko, E. Z., Chen, F., & Irizarry, R. A. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-00830-w · Cited by 1,487
Self-citationCount-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
Increased methylation variation in epigenetic domains across cancer types
Hansen, K. D., Timp, W., Bravo, H. C., Sabunciyan, S., Langmead, B., McDonald, O. G., Wen, B., Wu, H., Liu, Y., Diep, D. H., et al. (2011) · Nature Genetics · doi:10.1038/ng.865 · Cited by 1,062
Self-citationThe role of regulatory variation in complex traits and disease
Albert, F. W., & Kruglyak, L. (2015) · Nature Reviews Genetics · doi:10.1038/nrg3891 · Cited by 1,047
Liu, Y., Aryee, M. J., Padyukov, L., Fallin, M. D., Hesselberg, E., Rúnarsson, Á. R., Reinius, L. E., Acevedo, N., Taub, M. A., Ronninger, M., et al. (2013) · Nature Biotechnology · doi:10.1038/nbt.2487 · Cited by 997
Orchestrating single-cell analysis with Bioconductor
Amezquita, R. A., Lun, A. T. L., Becht, E., Carey, V. J., Carpp, L. N., Geistlinger, L., Marini, F., Rue-Albrecht, K., Risso, D., Soneson, C., et al. (2020) · Nature Methods · doi:10.1038/s41592-019-0654-x · Cited by 945
Deep learning in cancer diagnosis, prognosis and treatment selection
Tran, K. A., Kondrashova, O., Bradley, A., Williams, E. D., Pearson, J. V., & Waddell, N. (2021) · Genome Medicine · doi:10.1186/s13073-021-00968-x · Cited by 932
Single-Cell RNA-Seq Technologies and Related Computational Data Analysis
Chen, G., Ning, B., & Shi, T. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00317 · Cited by 966
MRIQC: Advancing the automatic prediction of image quality in MRI from unseen sites
Esteban, O., Birman, D., Schaer, M., Koyejo, O. O., Poldrack, R. A., & Gorgolewski, K. J. (2017) · PLOS ONE · doi:10.1371/journal.pone.0184661 · Cited by 920
Functional normalization of 450k methylation array data improves replication in large cancer studies
Fortin, J.-P., Labbe, A., Lemire, M., Zanke, B. W., Hudson, T. J., Fertig, E. J., Greenwood, C. M. T., & Hansen, K. D. (2014) · Genome biology · doi:10.1186/s13059-014-0503-2 · Cited by 893
Data-analysis strategies for image-based cell profiling
Caicedo, J. C., Cooper, S., Heigwer, F., Warchal, S., Qiu, P., Molnar, C., Vasilevich, A. S., Barry, J. D., Bansal, H. S., Kraus, O., et al. (2017) · Nature Methods · doi:10.1038/nmeth.4397 · Cited by 739
A Single-Cell Atlas of the Tumor and Immune Ecosystem of Human Breast Cancer
Wagner, J., Rapsomaniki, M., Chevrier, S., Anzeneder, T., Langwieder, C., Dykgers, A., Rees, M., Ramaswamy, A., Muenst, S. E., Soysal, S. D., et al. (2019) · Cell · doi:10.1016/j.cell.2019.03.005 · Cited by 807
Metagenome-wide analysis of antibiotic resistance genes in a large cohort of human gut microbiota
Hu, Y., Yang, X., Qin, J., Lü, N., Cheng, G., Wu, N., Pan, Y., Li, J., Zhu, L., Wang, X., et al. (2013) · Nature Communications · doi:10.1038/ncomms3151 · Cited by 796
Revealing the vectors of cellular identity with single-cell genomics
Wagner, A., Regev, A., & Yosef, N. (2016) · Nature Biotechnology · doi:10.1038/nbt.3711 · Cited by 632
Bump hunting to identify differentially methylated regions in epigenetic epidemiology studies
Jaffe, A. E., Murakami, P. N., Lee, H.-J., Leek, J. T., Fallin, M. D., Feinberg, A. P., & Irizarry, R. A. (2012) · International Journal of Epidemiology · doi:10.1093/ije/dyr238 · Cited by 700
Self-citationLibrary construction for next-generation sequencing: Overviews and challenges
Head, S. R., Komori, H. K., LaMere, S. A., Whisenant, T. C., Nieuwerburgh, F. V., Salomon, D. R., & Ordoukhanian, P. T. (2014) · BioTechniques · doi:10.2144/000114133 · Cited by 716
Erho, N., Crisan, A., Vergara, I. A., Mitra, A. P., Ghadessi, M., Buerki, C., Bergstralh, E. J., Kollmeyer, T. M., Fink, S. R., Haddad, Z., et al. (2013) · PLoS ONE · doi:10.1371/journal.pone.0066855 · Cited by 646
Oligotyping: differentiating between closely related microbial taxa using 16S rRNA gene data
Eren, A. M., Maignien, L., Sul, W. J., Murphy, L. G., Grim, S. L., Morrison, H. G., & Sogin, M. L. (2013) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.12114 · Cited by 639
The Microbiome Quality Control Project Consortium, Sinha, R., Abu-Ali, G., Vogtmann, E., Fodor, A. A., Ren, B., Amir, A., Schwager, E., Crabtree, J., Ma, S., et al. (2017) · Nature Biotechnology · doi:10.1038/nbt.3981 · Cited by 587
Evaluation of the Infinium Methylation 450K technology
Dedeurwaerder, S., Defrance, M., Calonne, E., Denis, H., Sotiriou, C., & Fuks, F. (2011) · Epigenomics · doi:10.2217/epi.11.105 · Cited by 569
Comprehensive analysis of DNA methylation data with RnBeads
Assenov, Y., Müller, F., Lutsik, P., Walter, J., Lengauer, T., & Bock, C. (2014) · Nature Methods · doi:10.1038/nmeth.3115 · Cited by 602
Missing data and technical variability in single-cell RNA-sequencing experiments
Hicks, S. C., Townes, F. W., Teng, M., & Irizarry, R. A. (2018) · Biostatistics · doi:10.1093/biostatistics/kxx053 · Cited by 635
Self-citationAnalysing and interpreting DNA methylation data
Bock, C. (2012) · Nature Reviews Genetics · doi:10.1038/nrg3273 · Cited by 600
svaseq: removing batch effects and other unwanted noise from sequencing data
Leek, J. T. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku864 · Cited by 551
Self-citationA Review of Domain Adaptation without Target Labels
Kouw, W. M., & Loog, M. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2945942 · Cited by 613
SeSAMe: reducing artifactual detection of DNA methylation by Infinium BeadChips in genomic deletions
Zhou, W., Triche, T. J., Laird, P. W., & Shen, H. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky691 · Cited by 536
Big Data Analytics in Healthcare
Belle, A., Thiagarajan, R., Soroushmehr, S. M. R., Navidi, F., Beard, D. A., & Najarian, K. (2015) · BioMed Research International · doi:10.1155/2015/370194 · Cited by 580
Genome-scale analysis of DNA methylation in lung adenocarcinoma and integration with mRNA expressionPreprint
Selamat, S. A., Chung, B. S. I., Girard, L., Zhang, W., Zhang, Y., Campan, M., Siegmund, K. D., Koss, M. N., Hagen, J. A., Lam, W. L., et al. (2012) · Genome Research · doi:10.1101/gr.132662.111 · Cited by 529
the BIOS Consortium, van Iterson, M., van Zwet, E. W., & Heijmans, B. T. (2017) · Genome biology · doi:10.1186/s13059-016-1131-9 · Cited by 421
Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders
Parikshak, N. N., Gandal, M. J., & Geschwind, D. H. (2015) · Nature Reviews Genetics · doi:10.1038/nrg3934 · Cited by 430
Using control genes to correct for unwanted variation in microarray data
Gagnon-Bartsch, J. A., & Speed, T. P. (2012) · Biostatistics · doi:10.1093/biostatistics/kxr034 · Cited by 442
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
Salgado, H., Peralta-Gil, M., Gama‐Castro, S., Santos-Zavaleta, A., Muñiz-Rascado, L., García-Sotelo, J. S., Weiss, V., Solano-Lira, H., Martínez‐Flores, I., Medina-Rivera, A., et al. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks1201 · Cited by 425
Single-cell RNA sequencing: Technical advancements and biological applications
Hedlund, E., & Deng, Q. (2017) · Molecular Aspects of Medicine · doi:10.1016/j.mam.2017.07.003 · Cited by 410
Why Batch Effects Matter in Omics Data, and How to Avoid Them
Goh, W. W. B., Wang, W., & Wong, L. (2017) · Trends in biotechnology · doi:10.1016/j.tibtech.2017.02.012 · Cited by 419
Pinto, D., Darvishi, K., Shi, X. M., Rajan, D., Rigler, D., Fitzgerald, T. W., Lionel, A. C., Thiruvahindrapuram, B., MacDonald, J. R., Mills, R. E., et al. (2011) · Nature Biotechnology · doi:10.1038/nbt.1852 · Cited by 415
Sagawa, S., Koh, P. W., Hashimoto, T., & Liang, P. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.08731 · Cited by 1,178
Dimension reduction techniques for the integrative analysis of multi-omics data
Meng, C., Zeleznik, O. A., Thallinger, G., Küster, B., Gholami, A. M., & Culhane, A. C. (2016) · Briefings in Bioinformatics · doi:10.1093/bib/bbv108 · Cited by 384
Nygaard, V., Rødland, E. A., & Hovig, E. J. (2015) · Biostatistics · doi:10.1093/biostatistics/kxv027 · Cited by 377
Recommendations for the design and analysis of epigenome-wide association studies
Michels, K. B., Binder, A. M., Dedeurwaerder, S., Epstein, C. B., Greally, J. M., Gut, I., Houseman, E. A., Izzi, B., Kelsey, K. T., Meissner, A., et al. (2013) · Nature Methods · doi:10.1038/nmeth.2632 · Cited by 378
Self-citationWGCNA Application to Proteomic and Metabolomic Data Analysis
Pei, G., Chen, L., & Zhang, W. (2016) · Methods in enzymology on CD-ROM/Methods in enzymology · doi:10.1016/bs.mie.2016.09.016 · Cited by 374
Quantitative analysis of fitness and genetic interactions in yeast on a genome scale
Baryshnikova, A., Costanzo, M., Kim, Y., Ding, H., Koh, J., Toufighi, K., Youn, J.-Y., Ou, J., San Luis, B.-J., Bandyopadhyay, S., et al. (2010) · Nature Methods · doi:10.1038/nmeth.1534 · Cited by 364
Heritability and genomics of gene expression in peripheral blood
Wright, F. A., Sullivan, P. F., Brooks, A. I., Zou, F., Sun, W., Xia, K., Madar, V., Jansen, R., Chung, W., Zhou, Y.-H., et al. (2014) · Nature Genetics · doi:10.1038/ng.2951 · Cited by 362
Evolution of Translational Omics: Lessons Learned and the Path Forward
Micheel, C., Nass, S. J., & Omenn, G. S. (2013) · Cited by 365
Differential immune system DNA methylation and cytokine regulation in post-traumatic stress disorder
Smith, A. K., Conneely, K. N., Kilaru, V., Mercer, K. B., Weiss, T. E., Bradley, B., Tang, Y., Gillespie, C. F., Cubells, J. F., & Ressler, K. J. (2011) · American Journal of Medical Genetics Part B: Neuropsychiatric Genetics · doi:10.1002/ajmg.b.31212 · Cited by 355
From promises to practical strategies in epigenetic epidemiology
Mill, J. S., & Heijmans, B. T. (2013) · Nature Reviews Genetics · doi:10.1038/nrg3405 · Cited by 356
Li, B., Cui, Y., Diehn, M., & Li, R. (2017) · JAMA Oncology · doi:10.1001/jamaoncol.2017.1609 · Cited by 514
Computational solutions for omics data
Berger, B., Peng, J., & Singh, M. (2013) · Nature Reviews Genetics · doi:10.1038/nrg3433 · Cited by 353
He, X., Sanders, S. J., Liu, L., De Rubeis, S., Lim, E. T., Sutcliffe, J. S., Schellenberg, G. D., Gibbs, R. A., Daly, M. J., Buxbaum, J. D., et al. (2013) · PLoS Genetics · doi:10.1371/journal.pgen.1003671 · Cited by 304
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
Big Data in Public Health: Terminology, Machine Learning, and Privacy
Mooney, S. J., & Pejaver, V. (2018) · Annual Review of Public Health · doi:10.1146/annurev-publhealth-040617-014208 · Cited by 325
Inflammatory Bowel Disease as a Model for Translating the Microbiome
Huttenhower, C., Kostic, A. D., & Xavier, R. J. (2014) · Immunity · doi:10.1016/j.immuni.2014.05.013 · Cited by 332
Reconstructing A/B compartments as revealed by Hi-C using long-range correlations in epigenetic data
Fortin, J.-P., & Hansen, K. D. (2015) · Genome Biology · doi:10.1186/s13059-015-0741-y · Cited by 333
Batch effect removal methods for microarray gene expression data integration: a survey
Lazar, C., Meganck, S., Taminau, J., Steenhoff, D., Coletta, A., Molter, C., Weiss-Solis, D. Y., Duque, R., Bersini, H., & Nowe, A. (2013) · Briefings in Bioinformatics · doi:10.1093/bib/bbs037 · Cited by 333
Transcriptomic and metabolomic data integration
Cavill, R., Jennen, D. G. J., Kleinjans, J. C. S., & Briedé, J. J. (2015) · Briefings in Bioinformatics · doi:10.1093/bib/bbv090 · Cited by 341
Cloud computing for genomic data analysis and collaboration
Langmead, B., & Nellore, A. (2018) · Nature Reviews Genetics · doi:10.1038/nrg.2017.113 · Cited by 310
“That is, researchers can use public data to boost the power available to analyse a locally generated data set, a paradigm that already prevails in micro-array data analysis 8,9,12,92 .”
BackgroundSelf-citationMeffil: efficient normalization and analysis of very large DNA methylation datasets
Min, J. L., Hemani, G., Smith, G. D., Relton, C. L., & Suderman, M. J. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty476 · Cited by 322
Age-associated epigenetic drift: implications, and a case of epigenetic thrift?
Teschendorff, A. E., West, J., & Beck, S. (2013) · Human Molecular Genetics · doi:10.1093/hmg/ddt375 · Cited by 330
–Omic and Electronic Health Record Big Data Analytics for Precision Medicine
Wu, P.-Y., Cheng, C., Kaddi, C. D., Venugopalan, J., Hoffman, R., & Wang, M. D. (2016) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2016.2573285 · Cited by 317
Associating cellular epigenetic models with human phenotypes
Lappalainen, T., & Greally, J. M. (2017) · Nature Reviews Genetics · doi:10.1038/nrg.2017.32 · Cited by 282
The role of replicates for error mitigation in next-generation sequencing
Robasky, K., Lewis, N. E., & Church, G. M. (2013) · Nature Reviews Genetics · doi:10.1038/nrg3655 · Cited by 316
Common Disease Is More Complex Than Implied by the Core Gene Omnigenic Model
Wray, N. R., Wijmenga, C., Sullivan, P. F., Yang, J., & Visscher, P. M. (2018) · Cell · doi:10.1016/j.cell.2018.05.051 · Cited by 309
Use and abuse of correlation analyses in microbial ecology
Carr, A. V., Diener, C., Baliga, N. S., & Gibbons, S. M. (2019) · The ISME Journal · doi:10.1038/s41396-019-0459-z · Cited by 299
Resolving host–pathogen interactions by dual RNA-seq
Westermann, A. J., Barquist, L., & Vogel, J. (2017) · PLoS Pathogens · doi:10.1371/journal.ppat.1006033 · Cited by 301
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
Schloss, P. D. (2018) · mBio · doi:10.1128/mbio.00525-18 · Cited by 298
WILDS: A Benchmark of in-the-Wild Distribution Shifts
Koh, P. W., Sagawa, S., Marklund, H., Xie, S. M., Zhang, M., Balsubramani, A., Hu, W., Yasunaga, M., Phillips, R. L., Gao, I., et al. (2020) · arXiv · doi:10.48550/arxiv.2012.07421 · Cited by 518
“These batch effects make it difficult to draw conclusions from data across experimental batches (Leek et al., 2010; Parker & Leek, 2012; Soneson et al., 2014; Nygaard et al., 2016; Caicedo et al., 2017).”
Navigating the pitfalls of applying machine learning in genomics
Whalen, S., Schreiber, J. M., Noble, W. S., & Pollard, K. S. (2021) · Nature Reviews Genetics · doi:10.1038/s41576-021-00434-9 · Cited by 291
Kyttälä, A., Moraghebi, R., Valensisi, C., Kettunen, J., Andrus, C., Pasumarthy, K. K., Nakanishi, M., Nishimura, K., Ohtaka, M., Weltner, J., et al. (2016) · Stem Cell Reports · doi:10.1016/j.stemcr.2015.12.009 · Cited by 257
Normalization of circulating microRNA expression data obtained by quantitative real-time RT-PCR
Marabita, F., de Candia, P., Torri, A., Tegner, J. N., Abrignani, S., & Rossi, R. L. (2015) · Briefings in Bioinformatics · doi:10.1093/bib/bbv056 · Cited by 276
DrugComb: an integrative cancer drug combination data portal
Zagidullin, B., Aldahdooh, J., Zheng, S., Wang, W., Wang, Y., Saad, J. J., Malyutina, A., Jafari, M., Tanoli, Z., Pessia, A., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz337 · Cited by 270
Biomarker Discovery and Validation: Statistical Considerations
Ou, F., Michiels, S., Shyr, Y., Adjei, A. A., & Oberg, A. L. (2021) · Journal of Thoracic Oncology · doi:10.1016/j.jtho.2021.01.1616 · Cited by 289
A Statistical Framework for Joint eQTL Analysis in Multiple Tissues
Flutre, T., Wen, X., Pritchard, J. K., & Stephens, M. (2013) · PLoS Genetics · doi:10.1371/journal.pgen.1003486 · Cited by 259
recount3: summaries and queries for large-scale RNA-seq expression and splicing
Wilks, C., Zheng, S., Chen, F. Y., Charles, R., Solomon, B., Ling, J. P., Imada, E. L., Zhang, D., Joseph, L., Leek, J. T., et al. (2021) · Genome biology · doi:10.1186/s13059-021-02533-6 · Cited by 269
Self-citationBig data in basic and translational cancer research
Jiang, P., Sinha, S., Aldape, K., Hannenhalli, S., Sahinalp, C., & Ruppin, E. (2022) · Nature Reviews Cancer · doi:10.1038/s41568-022-00502-0 · Cited by 254
Obayashi, T., Aoki, Y., Tadaka, S., Kagaya, Y., & Kinoshita, K. (2017) · Plant and Cell Physiology · doi:10.1093/pcp/pcx191 · Cited by 250
Artificial intelligence for precision medicine in neurodevelopmental disorders
Uddin, M. J., Wang, Y., & Woodbury‐Smith, M. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0191-0 · Cited by 261
Heath, A. P., Ferretti, V., Agrawal, S., An, M., Angelakos, J. C., Arya, R., Bajari, R., Baqar, B., Barnowski, J. H. B., Burt, J., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00791-5 · Cited by 248
Hong, X., Hao, K., Ladd‐Acosta, C., Hansen, K. D., Tsai, H., Liu, X., Xu, X. P., Thornton, T. A., Caruso, D., Keet, C. A., et al. (2015) · Nature Communications · doi:10.1038/ncomms7304 · Cited by 252
Criteria for the use of omics-based predictors in clinical trials
McShane, L. M., Cavenagh, M. M., Lively, T., Eberhard, D. A., Bigbee, W. L., Williams, P. M., Mesirov, J. P., Polley, M. C., Kim, K. Y., Tricoli, J. V., et al. (2013) · Nature · doi:10.1038/nature12564 · Cited by 250
A single-sample microarray normalization method to facilitate personalized-medicine workflows
Piccolo, S., Sun, Y., Campbell, J. D., Lenburg, M. E., Bild, A. H., & Johnson, W. E. (2012) · Genomics · doi:10.1016/j.ygeno.2012.08.003 · Cited by 244
Self-citationGut microbiome heritability is nearly universal but environmentally contingent
Grieneisen, L., Dasari, M., Gould, T. J., Björk, J. R., Grenier, J.-C., Yotova, V., Jansen, D., Gottel, N., Gordon, J. B., Learn, N. H., et al. (2021) · Science · doi:10.1126/science.aba5483 · Cited by 243
A novel multi-tissue RNA diagnostic of healthy ageing relates to cognitive health status
Sood, S., Gallagher, I. J., Lunnon, K., Rullman, E., Keohane, A., Crossland, H., Phillips, B. E., Cederholm, T., Jensen, T., van Loon, L. J. C., et al. (2015) · Genome biology · doi:10.1186/s13059-015-0750-x · Cited by 240
Rapid neurogenesis through transcriptional activation in human stem cells
Busskamp, V., Lewis, N. E., Guye, P., Ng, A. H. M., Shipman, S. L., Byrne, S. M., Sanjana, N. E., Murn, J., Li, Y., Li, S., et al. (2014) · Molecular Systems Biology · doi:10.15252/msb.20145508 · Cited by 222
Regier, A., Farjoun, Y., Larson, D. E., Krasheninina, O. A., Kang, H. M., Howrigan, D. P., Chen, B.-J., Kher, M., Banks, E., Ames, D. C., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-06159-4 · Cited by 224
Metadata
- Authors
- Leek, J. T., Scharpf, R. B., Bravo, H. C., Simcha, D., Langmead, B., Johnson, W. E., Geman, D., Baggerly, K., & Irizarry, R. A.
- Affiliations
- Johns Hopkins University (US)
- Brigham Young University (US)
- The University of Texas MD Anderson Cancer Center (US)
- Corresponding author
- Irizarry, Rafael A.
- Venue
- Nature Reviews Genetics
- Published
- 2010-09-14
- Volume
- 11
- Issue
- 10
- Pages
- 733-739
- ISSN
- 1471-0056 (ISSN-L)
- 1471-0064
- PMC
- PMC3880143
- OpenAlex
- W2096192437
- Cited by
- 2,196
- References
- 30
- Citations by year
- 2026: 235 · 2025: 166 · 2024: 159 · 2023: 125 · 2022: 178 · 2021: 178 · 2020: 167 · 2019: 138 · 2018: 152 · 2017: 130 · 2016: 133 · 2015: 107 · 2014: 100 · 2013: 95 · 2012: 70
- FWCI
- 6.29
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Reviews Genetics (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Juvenile Diabetes Research Foundation International
- Wellcome Trust
- National Institute for Health and Care Research
- National Institutes of Health (5u41hg004059-15)
- Cambridge Institute for Medical Research, University of Cambridge (079895)
- NIHR Cambridge Biomedical Research Centre
- National Human Genome Research Institute (HG005220, U41-HG004059, HG004059, R00HG005015, R01HG005220, P41HG004059, U24HG004059)
- National Institute of General Medical Sciences (GM0083084, R01GM083084)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Keywords
- batch effects, high-throughput omics data, confounding, gene expression, protein expression, epigenetic modifications, laboratory conditions
- MeSH terms
- Biotechnology, Periodicals as Topic, Research Design, Sequence Analysis, DNA, Computational Biology, Oligonucleotide Array Sequence Analysis, Genomics
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
