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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.
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“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
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“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
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“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]
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“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]
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“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]
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“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]
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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
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Huang, J., Bai, L., Cui, B., Wu, L. L., Wang, L., An, Z., Ruan, S., Yu, Y., Zhang, X., & Chen, J. (2020) · Genome biology · doi:10.1186/s13059-020-02001-7 · Cited by 26
Temporal Dynamic Methods for Bulk RNA-Seq Time Series Data
Oh, S., & Li, R. W. (2021) · Genes · doi:10.3390/genes12030352 · Cited by 26
Analysis of single-cell RNA sequencing data based on autoencoders
Tangherloni, A., Ricciuti, F., Besozzi, D., Lió, P., & Cvejic, A. (2021) · BMC Bioinformatics · doi:10.1186/s12859-021-04150-3 · Cited by 30
Xiong, Z., Li, M., Ma, Y., Li, R., & Bào, Y. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2021.810985 · Cited by 25
Approaches in Gene Coexpression Analysis in Eukaryotes
Zogopoulos, V. L., Saxami, G., Malatras, A., Papadopoulos, K. A., Tsotra, I., Iconomidou, V. A., & Michalopoulos, I. (2022) · Biology · doi:10.3390/biology11071019 · Cited by 27
Walker, E. R., McGrane, M., Aplin, J. D., Brison, D. R., & Ruane, P. T. (2023) · Reproduction and Fertility · doi:10.1530/raf-22-0115 · Cited by 26
Transcriptomic Profiling Using Next Generation Sequencing - Advances, Advantages, and Challenges
Anamika, K., Verma, S. P., Jere, A., & Desai, A. (2016) · InTech eBooks · doi:10.5772/61789 · Cited by 32
Lnc-CC3 increases metastasis in cervical cancer by increasing Slug expression
Jiang, B., Sun, R., Fang, S., Qin, C., Xi, P., Peng, L., Li, Y., & Li, G. (2016) · Oncotarget · doi:10.18632/oncotarget.9519 · Cited by 26
Tong, M., Zheng, W., Li, H., Li, X., Ao, L., Shen, Y., Liang, Q., Li, J., Hong, G., Yan, H., et al. (2016) · Oncogenesis · doi:10.1038/oncsis.2016.51 · Cited by 26
Stewart, P., Richman, S. D., Maughan, T., Lawler, M., Dunne, P. D., & Salto‐Tellez, M. (2017) · Biochimica et Biophysica Acta (BBA) - Reviews on Cancer · doi:10.1016/j.bbcan.2017.05.005 · Cited by 24
Niu, M., Li, L., Su, Z., Wei, L., Pu, W., Zhao, C., Ding, Y., Wazir, J., Cao, W., Song, S., et al. (2021) · Cell Death and Disease · doi:10.1038/s41419-021-03932-0 · Cited by 28
Thinking points for effective batch correction on biomedical data
Hui, H. W. H., Kong, W., & Goh, W. W. B. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae515 · Cited by 25
Heterogeneous Large Datasets Integration Using Bayesian Factor Regression
Avalos‐Pacheco, A., Rossell, D., & Savage, R. S. (2020) · Bayesian Analysis · doi:10.1214/20-ba1240 · Cited by 30
A signature of circadian rhythm genes in driving anaplastic thyroid carcinoma malignant progression
Xu, T., Jin, T., Lu, X., Pan, Z., Tan, Z., Zheng, C., Liu, Y., Hu, X., Ba, L., Ren, H., et al. (2022) · Cellular Signalling · doi:10.1016/j.cellsig.2022.110332 · Cited by 25
Komljenović, A., Roux, J., Robinson‐Rechavi, M., & Bastian, F. (2016) · F1000Research · doi:10.12688/f1000research.9973.1 · Cited by 25
Sandve, G. K., & Greiff, V. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac612 · Cited by 27
Identification of copper-related biomarkers and potential molecule mechanism in diabetic nephropathy
Ming, J., Sana, S. R. G. L., & Deng, X. (2022) · Frontiers in Endocrinology · doi:10.3389/fendo.2022.978601 · Cited by 26
Genetic and Nongenetic Variation Revealed for the Principal Components of Human Gene Expression
Goldinger, A., Henders, A. K., McRae, A. F., Martin, N. G., Gibson, G. C., Montgomery, G. W., Visscher, P. M., & Powell, J. E. (2013) · Genetics · doi:10.1534/genetics.113.153221 · Cited by 25
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Menke, A. (2013) · International Review of Psychiatry · doi:10.3109/09540261.2013.825580 · Cited by 25
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Reon, B. J., & Dutta, A. (2016) · American Journal Of Pathology · doi:10.1016/j.ajpath.2015.10.033 · Cited by 29
Nystad, A. E., Lereim, R. R., Wergeland, S., Oveland, E., Myhr, K., Bø, L., & Torkildsen, Ø. (2019) · Journal of Neuroimmunology · doi:10.1016/j.jneuroim.2019.577091 · Cited by 25
Doxakis, E. (2020) · Ageing Research Reviews · doi:10.1016/j.arr.2020.101023 · Cited by 25
Integrative computational epigenomics to build data-driven gene regulation hypotheses
Chen, T., & Tyagi, S. (2020) · GigaScience · doi:10.1093/gigascience/giaa064 · Cited by 21
Accommodating site variation in neuroimaging data using normative and hierarchical Bayesian modelsPreprint
Bayer, J., Dinga, R., Kia, S. M., Kottaram, A., Wolfers, T., Lv, J., Zalesky, A., Schmaal, L., & Marquand, A. F. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.02.09.430363 · Cited by 22
Lin, Q., Ma, W., Xu, M., Xu, Z., Wang, J., Liang, Z., Zhu, L., Wu, M., Luo, J., Liu, H., et al. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e36898 · Cited by 24
On the Identification of Circulating Tumor Cells in Breast Cancer
Sfakianakis, S. G., Bei, E. S., Zervakis, M., Vassou, D., & Kafetzopoulos, D. (2014) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2013.2295262 · Cited by 24
Younkin, S. G., Scharpf, R. B., Schwender, H. R., Parker, M. M., Scott, A. F., Marazita, M. L., Beaty, T. H., & Ruczinski, I. (2014) · BMC Genetics · doi:10.1186/1471-2156-15-24 · Cited by 25
Self-citationLessons learned in the analysis of high-dimensional data in vaccinomics
Oberg, A. L., McKinney, B. A., Schaid, D. J., Pankratz, V. S., Kennedy, R. B., & Poland, G. A. (2015) · Vaccine · doi:10.1016/j.vaccine.2015.04.088 · Cited by 26
Alcohol and the methylome: Design and analysis considerations for research using human samples
Harlaar, N., & Hutchison, K. E. (2013) · Drug and Alcohol Dependence · doi:10.1016/j.drugalcdep.2013.07.026 · Cited by 24
Meta-Analysis in Gene Expression Studies
Waldron, L., & Riester, M. (2016) · Methods in molecular biology · doi:10.1007/978-1-4939-3578-9_8 · Cited by 24
Ao, L., Guo, Y., Song, X., Guan, Q., Zheng, W., Zhang, J., Huang, H., Zou, Y., Guo, Z., & Wang, X. (2017) · Liver International · doi:10.1111/liv.13467 · Cited by 24
Carnero‐Montoro, E., & Alarcón-Riquelme, M. E. (2018) · Clinical Immunology · doi:10.1016/j.clim.2018.03.014 · Cited by 26
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Fang, J. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty513 · Cited by 24
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Grau-Pérez, M., Agha, G., Pang, Y., Bermúdez, J. D., & Téllez-Plaza, M. (2019) · Current Environmental Health Reports · doi:10.1007/s40572-019-0226-3 · Cited by 24
Quantify and control reproducibility in high-throughput experiments
Zhao, Y., Sampson, M. G., & Wen, X. (2020) · Nature Methods · doi:10.1038/s41592-020-00978-4 · Cited by 22
A Bayesian non-parametric mixed-effects model of microbial growth curves
Tonner, P. D., Darnell, C. L., Bushell, F. M. L., Lund, P. A., Schmid, A., & Schmidler, S. C. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008366 · Cited by 24
BatchServer: A Web Server for Batch Effect Evaluation, Visualization, and Correction
Zhu, T., Sun, R., Zhang, F., Chen, G., Yi, X., Ruan, G., Yuan, C., Zhou, S., & Guo, T. (2020) · Journal of Proteome Research · doi:10.1021/acs.jproteome.0c00488 · Cited by 23
Verma, P., & Shakya, M. (2021) · Cognitive Neurodynamics · doi:10.1007/s11571-021-09724-8 · Cited by 24
Waitman, L. R., Song, X., Walpitage, D. L., Connolly, D. C., Patel, L. P., Liu, M., Schroeder, M. C., VanWormer, J. J., Mosa, A. S. M., Anye, E. T., et al. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocab269 · Cited by 24
MmCMS: mouse models’ consensus molecular subtypes of colorectal cancer
Amirkhah, R., Gilroy, K. L., Malla, S. B., Lannagan, T. R. M., Byrne, R. M., Fisher, N. C., Corry, S. M., Mohamed, N.-E., Naderi‐Meshkin, H., Mills, M. L., et al. (2023) · British Journal of Cancer · doi:10.1038/s41416-023-02157-6 · Cited by 24
Tong, M., Zheng, W., Lu, X., Ao, L., Li, X., Guan, Q., Cai, H., Li, M., Yan, H., Guo, Y., et al. (2015) · Oncotarget · doi:10.18632/oncotarget.5649 · Cited by 24
Systems biology approach to understanding post-traumatic stress disorder
Thakur, G., Daigle, B. J., Dean, K. R., Zhang, Y., Rodriguez-Fernández, M., Hammamieh, R., Yang, R., Jett, M., Palma, J., Petzold, L. R., et al. (2015) · Molecular BioSystems · doi:10.1039/c4mb00404c · Cited by 25
The propagation of perturbations in rewired bacterial gene networks
Baumstark, R., Hänzelmann, S., Tsuru, S., Schaerli, Y., Francesconi, M., Mancuso, F. M., Castelo, R., & Isalan, M. (2015) · Nature Communications · doi:10.1038/ncomms10105 · Cited by 22
Kim, Y., Giusti‐Rodríguez, P., Crowley, J. J., Bryois, J., Nonneman, R. J., Ryan, A. K., Quackenbush, C. R., Iglesias-Ussel, M. D., Lee, P. H., Sun, W., et al. (2017) · Molecular Psychiatry · doi:10.1038/mp.2017.111 · Cited by 25
Song, K., Su, W., Liu, Y., Zhang, J., Liang, Q., Li, N., Guan, Q., He, J., Bai, X., Zhao, W., et al. (2018) · Journal of Gastroenterology and Hepatology · doi:10.1111/jgh.14529 · Cited by 24
Neuroprotectants attenuate hypobaric hypoxia-induced brain injuries in cynomolgus monkeys
Pei, Z., Chen, J.-S., Li, Q., Sheng, L., Yi-Xing, G., Lu, B., Zhu, W., Xiao-Yu, Z., Yuan, L., Yuan, Z.-B., et al. (2020) · 动物学研究 · doi:10.24272/j.issn.2095-8137.2020.012 · Cited by 24
Lu, M., Lai, C., Liao, B., & Tsai, I. J. (2020) · Genome Biology and Evolution · doi:10.1093/gbe/evaa110 · Cited by 23
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Pesenson, M. Z., Suram, S. K., & Gregoire, J. M. (2014) · ACS Combinatorial Science · doi:10.1021/co5001458 · Cited by 24
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Hicks, S. C., & Irizarry, R. A. (2014) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/012203 · Cited by 25
Self-citationA unified evolution-driven deep learning framework for virus variation driver prediction
Nie, Z., Liu, X., Chen, J., Wang, Z., Liu, Y., Si, H.-R., Dong, T., Xu, F., Song, G., Wang, Y., et al. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-024-00966-9 · Cited by 25
The Impact and Role of Artificial Intelligence (AI) in Healthcare: Systematic Review
Singh, K., Prabhu, A. A., & Kaur, N. (2025) · Current Topics in Medicinal Chemistry · doi:10.2174/0115680266339394250225112747 · Cited by 26
Wang, R.-L., Bencic, D. C., García‐Reyero, N., Perkins, E. J., Villeneuve, D. L., Ankley, G. T., & Biales, A. D. (2014) · PLoS ONE · doi:10.1371/journal.pone.0114178 · Cited by 23
Cai, H., Li, X., Li, J., Ao, L., Yan, H., Tong, M., Guan, Q., Li, M., & Guo, Z. (2015) · Oncotarget · doi:10.18632/oncotarget.6260 · Cited by 23
Single-Cell RNA Sequencing of Human T Cells
Villani, A., & Shekhar, K. (2016) · Methods in molecular biology · doi:10.1007/978-1-4939-6548-9_16 · Cited by 25
Lee, J., Geiss, G. K., Demirkan, G., Vellano, C. P., Filanoski, B., Lu, Y., Ju, Z., Yu, S., Guo, H., Bogatzki, L. Y., et al. (2018) · Molecular & Cellular Proteomics · doi:10.1074/mcp.ra117.000291 · Cited by 23
Batch Effects Correction with Unknown Subtypes
Luo, X., & Wei, Y. (2018) · Journal of the American Statistical Association · doi:10.1080/01621459.2018.1497494 · Cited by 22
SNP genotype calling and quality control for multi-batch-based studies
Seo, S., Park, K., Lee, J. J., Choi, K. Y., Lee, K. H., & Won, S. (2019) · Genes & Genomics · doi:10.1007/s13258-019-00827-5 · Cited by 23
Single-center versus multi-center data sets for molecular prognostic modeling: a simulation study
Samaga, D., Hornung, R., Braselmann, H., Heß, J., Zitzelsberger, H. F., Belka, C., Boulesteix, A., & Unger, K. (2020) · Radiation Oncology · doi:10.1186/s13014-020-01543-1 · Cited by 23
Estimating and Accounting for Unobserved Covariates in High-Dimensional Correlated Data
McKennan, C. G., & Nicolae, D. L. (2020) · Journal of the American Statistical Association · doi:10.1080/01621459.2020.1769635 · Cited by 26
Replicated umbilical cord blood DNA methylation loci associated with gestational age at birth
York, T. P., Latendresse, S. J., Jackson‐Cook, C. K., Lapato, D. M., Moyer, S. W., Wolen, A. R., Roberson‐Nay, R., Do, E. K., Murphy, S. K., Hoyo, C., et al. (2020) · Epigenetics · doi:10.1080/15592294.2020.1767277 · Cited by 23
A simple, scalable approach to building a cross-platform transcriptome atlas
Angel, P. W., Rajab, N., Deng, Y., Pacheco, C. M., Chen, T., Cao, K. L., Choi, J., & Wells, C. A. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008219 · Cited by 23
Sinke, L., Cats, D., & Heijmans, B. T. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab159 · Cited by 23
Machine learning alternative to systems biology should not solely depend on data
Yeo, H. C., & Selvarajoo, K. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac436 · Cited by 23
Principles of phosphoproteomics and applications in cancer research
Higgins, L., Gerdes, H., & Cutillas, P. R. (2023) · Biochemical Journal · doi:10.1042/bcj20220220 · Cited by 24
Artificial intelligence in liver imaging: methods and applications
Zhang, P., Gao, C., Huang, Y., Chen, X., Pan, Z., Wang, L., Dong, D., Li, S., & Qi, X. (2024) · Hepatology International · doi:10.1007/s12072-023-10630-w · Cited by 23
Wang, B., Sun, F., & Luan, Y. (2024) · Scientific Reports · doi:10.1038/s41598-024-57670-2 · Cited by 26
Recent advances in endothelial colony-forming cells: from the transcriptomic perspective
Liu, Y., Lyons, C. J., Lagonda, C. A., & O’Brien, T. (2024) · Journal of Translational Medicine · doi:10.1186/s12967-024-05108-8 · Cited by 23
Designing, Performing, and Interpreting a Microarray-Based Gene Expression Study
Coppola, G. (2011) · Methods in molecular biology · doi:10.1007/978-1-61779-328-8_28 · Cited by 23
Bugs, Stool, and the Irritable Bowel Syndrome: Too Much Is as Bad as Too Little?
Talley, N. J., & Fodor, A. A. (2011) · Gastroenterology · doi:10.1053/j.gastro.2011.09.019 · Cited by 23
Intra- and Inter-Individual Variance of Gene Expression in Clinical Studies
Cheng, W., Shu, W., Li, C., Tsai, M., Chang, C.-W., Chen, C.-R., Cheng, H.-T., Wang, T., & Hsu, I. C. (2012) · PLoS ONE · doi:10.1371/journal.pone.0038650 · Cited by 25
Lee, Y.-S., Krishnan, A., Zhu, Q., & Troyanskaya, O. G. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt529 · Cited by 23
Mass Spectrometry Analysis Using MALDIquant
Gibb, S., & Strimmer, K. (2016) · Springer eBooks · doi:10.1007/978-3-319-45809-0_6 · Cited by 23
Detecting hidden batch factors through data-adaptive adjustment for biological effects
Yi, H., Raman, A. T., Zhang, H., Allen, G. I., & Liu, Z. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx635 · Cited by 23
Adjusting for Principal Components of Molecular Phenotypes Induces Replicating False Positives
Dahl, A. W., Guillemot, V., Mefford, J., Aschard, H., & Zaitlen, N. (2019) · Genetics · doi:10.1534/genetics.118.301768 · Cited by 24
Berglund, E., Saarenpää, S., Jemt, A., Gruselius, J., Larsson, L., Bergenstråhle, L., Lundeberg, J., & Giacomello, S. (2020) · BMC Genomics · doi:10.1186/s12864-020-6631-z · Cited by 23
Bridgeford, E., Wang, S., Yang, Z., Wang, Z., Xu, T., Craddock, C., Dey, J. K., Kiar, G., Gray-Roncal, W., Colantuoni, C., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/802629 · Cited by 22
Scalable Transcriptome Preparation for Massive Parallel Sequencing
Stranneheim, H., Solnestam, B. W., Sherwood, E., & Lundeberg, J. (2011) · PLoS ONE · doi:10.1371/journal.pone.0021910 · Cited by 22
Research Techniques Made Simple: Bioinformatics for Genome-Scale Biology
Foulkes, A., Watson, D., Griffiths, C. E. M., Warren, R. B., Huber, W. K., & Barnes, M. R. (2017) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2017.07.095 · Cited by 22
Essex, A., Pineda, J. J., Acharya, G., Xin, H., Evans, J., Reproducibility Project: Cancer Biology, Iorns, E. J., Tsui, R., Denis, A., Perfito, N., et al. (2019) · eLife · doi:10.7554/elife.45426 · Cited by 22
A General Approach to Sensitivity Analysis for Mendelian Randomization
Zhang, W., & Ghosh, D. (2020) · Statistics in Biosciences · doi:10.1007/s12561-020-09280-5 · Cited by 22
Guan, Y., Xu, F., Wang, Y., Tian, J., Wan, Z., Wang, Z., & Chong, T. (2020) · BMC Medical Genomics · doi:10.1186/s12920-020-00795-w · Cited by 22
Liu, H., Xing, K., Jiang, Y., Liu, Y., Wang, C., & Ding, X. (2022) · Journal of Agricultural and Food Chemistry · doi:10.1021/acs.jafc.2c03339 · Cited by 22
Reproducibility and Concordance of Differential DNA Methylation and Gene Expression in Cancer
Yao, C., Li, H., Shen, X., He, Z., He, L. C., & Guo, Z. (2012) · PLoS ONE · doi:10.1371/journal.pone.0029686 · Cited by 22
Rudnick, P. A., Wang, X., Yan, X., Sedransk, N., & Stein, S. E. (2014) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m113.030593 · Cited by 22
Albrecht, M., Stichel, D., Müller, B., Merkle, R., Sticht, C., Gretz, N., Klingmüller, U., Breuhahn, K., & Matthäus, F. (2017) · BMC Bioinformatics · doi:10.1186/s12859-016-1440-8 · Cited by 22
Matrix Denoising for Weighted Loss Functions and Heterogeneous Signals
Leeb, W. (2021) · SIAM Journal on Mathematics of Data Science · doi:10.1137/20m1319577 · Cited by 22
Improving the prediction accuracy of protein abundance in Escherichia coli using mRNA accessibility
Terai, G., & Asai, K. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa481 · Cited by 22
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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
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