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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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Sáez, C., & Garcia-Gomez, J. M. (2018) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2018.09.015 · Cited by 33
Continuity of transcriptomes among colorectal cancer subtypes based on meta-analysis
Ma, S., Ogino, S., Parsana, P., Nishihara, R., Qian, Z., Shen, J., Mima, K., Masugi, Y., Cao, Y., Nowak, J. A., et al. (2018) · Genome biology · doi:10.1186/s13059-018-1511-4 · Cited by 33
Considerations when processing and interpreting genomics data of the placenta
Konwar, C., Gobbo, G. F. D., Yuan, V., & Robinson, W. P. (2019) · Placenta · doi:10.1016/j.placenta.2019.01.006 · Cited by 33
Wu, Y., Lin, X., Lim, I. Y., Chen, L., Teh, A. L., MacIsaac, J. L., Tan, K. H., Kobor, M. S., Chong, Y. S., Gluckman, P. D., et al. (2019) · Clinical Epigenetics · doi:10.1186/s13148-018-0599-4 · Cited by 33
A Functional Landscape of CKD Entities From Public Transcriptomic Data
Tajti, F., Kuppe, C., Antoranz, A., Ibrahim, M. M., Kim, H., Ceccarelli, F., Holland, C. H., Olauson, H., Floege, J., Alexopoulos, L. G., et al. (2019) · Kidney International Reports · doi:10.1016/j.ekir.2019.11.005 · Cited by 31
A pooling-based approach to mapping genetic variants associated with DNA methylationPreprint
Kaplow, I. M., MacIsaac, J. L., Mah, S. M., McEwen, L. M., Kobor, M. S., & Fraser, H. B. (2015) · Genome Research · doi:10.1101/gr.183749.114 · Cited by 38
Faria, J. P., Overbeek, R., Xia, F., Rocha, M., Rocha, I., & Henry, C. S. (2013) · Briefings in Bioinformatics · doi:10.1093/bib/bbs071 · Cited by 37
Yang, X. H., Tang, F., Shin, J., & Cunningham, J. M. (2017) · Scientific Reports · doi:10.1038/s41598-017-00122-x · Cited by 32
An improved and explicit surrogate variable analysis procedure by coefficient adjustment
Lee, S., Sun, W., Wright, F. A., & Zou, F. (2017) · Biometrika · doi:10.1093/biomet/asx018 · Cited by 33
Arnold, W. K., Savage, C. R., Lethbridge, K. G., Smith, T. C., Brissette, C. A., Seshu, J., & Stevenson, B. (2018) · PLoS ONE · doi:10.1371/journal.pone.0203286 · Cited by 32
microRNA expression profiles and personal monitoring of exposure to particulate matter
Mancini, F. R., Laine, J. E., Tarallo, S., Vlaanderen, J. J., Vermeulen, R. C. H., van Nunen, E. J. H. M., Hoek, G., Probst-Hensch, N. M., Imboden, M., Jeong, A., et al. (2020) · Environmental Pollution · doi:10.1016/j.envpol.2020.114392 · Cited by 32
Song, F., Chan, G. M. A., & Wei, Y. (2020) · Nature Communications · doi:10.1038/s41467-020-16905-2 · Cited by 31
Furlong, M., Paul, K. C., Yan, Q. J., Chuang, Y., Cockburn, M., Bronstein, J. M., Horvath, S., & Ritz, B. R. (2020) · International Journal of Hygiene and Environmental Health · doi:10.1016/j.ijheh.2020.113569 · Cited by 31
EHRtemporalVariability: delineating temporal data-set shifts in electronic health records
Sáez, C., Gutiérrez‐Sacristán, A., Kohane, I. S., Garcia-Gomez, J. M., & Avillach, P. (2020) · GigaScience · doi:10.1093/gigascience/giaa079 · Cited by 34
Epigenetics and Development of Food Allergy (FA) in Early Childhood
Hong, X., & Wang, X. (2014) · Current Allergy and Asthma Reports · doi:10.1007/s11882-014-0460-6 · Cited by 31
SIGNATURE: A workbench for gene expression signature analysis
Chang, J. T., Gatza, M. L., Lucas, J. E., Barry, W. T., Vaughn, P., & Nevins, J. R. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-443 · Cited by 32
Lagani, V., Karozou, A. D., Gómez-Cabrero, D., Silberberg, G., & Tsamardinos, I. (2016) · BMC Bioinformatics · doi:10.1186/s12859-016-1038-1 · Cited by 32
The multi-omic landscape of transcription factor inactivation in cancer
Teschendorff, A. E., Zheng, S., Feber, A., Yang, Z., Beck, S., & Widschwendter, M. (2016) · Genome Medicine · doi:10.1186/s13073-016-0342-8 · Cited by 30
Goh, W. W. B., & Wong, L. (2017) · BMC Genomics · doi:10.1186/s12864-017-3490-3 · Cited by 33
Representing high throughput expression profiles via perturbation barcodes reveals compound targets
Filzen, T. M., Kutchukian, P. S., Hermes, J. D., Li, J., & Tudor, M. (2017) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1005335 · Cited by 31
Model-based clustering with data correction for removing artifacts in gene expression data
Young, W. C., Raftery, A. E., & Yeung, K. Y. (2017) · The Annals of Applied Statistics · doi:10.1214/17-aoas1051 · Cited by 32
An immune-related gene pairs signature predicts overall survival in serous ovarian carcinoma
Zhang, L., Zhu, P., Tong, Y., Wang, Y. Z., Ma, H., Xia, X., Zhou, Y., Zhang, X., Gao, F., & Shu, P. (2019) · OncoTargets and Therapy · doi:10.2147/ott.s200191 · Cited by 31
Explaining a Deep Reinforcement Learning (DRL)-Based Automated Driving Agent in Highway Simulations
Bellotti, F., Lazzaroni, L., Capello, A., Cossu, M., Gloria, A. D., & Berta, R. (2023) · IEEE Access · doi:10.1109/access.2023.3259544 · Cited by 33
Design, execution, and interpretation of plant RNA-seq analyses
Upton, R. N., Correr, F. H., Lile, J., Reynolds, G. L., Falaschi, K., Cook, J. P., & Lachowiec, J. (2023) · Frontiers in Plant Science · doi:10.3389/fpls.2023.1135455 · Cited by 33
Malerba, S., Vladimirov, M., Goyal, A., Dulskas, A., Baušys, A., Cwalinski, T., Girnyi, S., Skokowski, J., Duka, R., Молчанов, Р., et al. (2026) · Journal of Clinical Medicine · doi:10.3390/jcm15062208 · Cited by 33
Afshar, A. S., Xu, J., & Goutsias, J. I. (2014) · PLoS ONE · doi:10.1371/journal.pone.0100806 · Cited by 31
Huang, H.-C. R., Niu, Y., & Qin, L. (2015) · Cancer Informatics · doi:10.4137/cin.s21631 · Cited by 31
Lind, U., Järvå, M. A., Rosenblad, M. A., Pingitore, P., Karlsson, E., Wrange, A., Kamdal, E., Sundell, K. S., André, C., Jonsson, P. R., et al. (2017) · PLoS ONE · doi:10.1371/journal.pone.0181192 · Cited by 31
Pyrkov, T. V., Sokolov, I. S., & Fedichev, P. O. (2021) · Aging · doi:10.18632/aging.202816 · Cited by 31
Advances in single-cell sequencing: insights from organ transplantation
Wang, Y., Wang, J., Schnieke, A., & Fischer, K. (2021) · Military Medical Research · doi:10.1186/s40779-021-00336-1 · Cited by 34
Ren, L., Duan, X., Dong, L., Zhang, R., Yang, J., Gao, Y., Peng, R., Hou, W., Liu, Y., Li, J., et al. (2023) · Genome biology · doi:10.1186/s13059-023-03109-2 · Cited by 27
Bioinformatic identification of genes suppressing genome instability
Putnam, C. D., Allen-Soltero, S., Martinez, S. L., Chan, J. E., Hayes, T. K., & Kolodner, R. D. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1216733109 · Cited by 32
Li, H., Hong, G., Lin, M., Shi, Y., Wang, L., Jiang, F., Zhang, F., Wang, Y., & Guo, Z. (2017) · Scientific Reports · doi:10.1038/s41598-017-13700-w · Cited by 31
RNA-Seq optimization with eQTL gold standards
Ellis, S. E., Gupta, S., Ashar, F. N., Bader, J. S., West, A. B., & Arking, D. E. (2013) · BMC Genomics · doi:10.1186/1471-2164-14-892 · Cited by 30
A Field Guide to Genomics Research
Bild, A. H., Chang, J. T., Johnson, W. E., & Piccolo, S. (2014) · PLoS Biology · doi:10.1371/journal.pbio.1001744 · Cited by 30
Self-citationDetecting Copy Number Variation via Next Generation Technology
Mason‐Suares, H., Landry, L. G., & Lebo, M. S. (2016) · Current Genetic Medicine Reports · doi:10.1007/s40142-016-0091-4 · Cited by 29
Kras, K. A., Langlais, P. R., Hoffman, N., Roust, L. R., Benjamin, T. R., Filippis, E. A. D., Dinu, V., & Katsanos, C. S. (2018) · Metabolism · doi:10.1016/j.metabol.2018.09.006 · Cited by 30
Cinelli, C., LaPierre, N., Hill, B. L., Sankararaman, S., & Eskin, E. (2022) · Nature Communications · doi:10.1038/s41467-022-28553-9 · Cited by 30
A single-cell atlas of the rat amygdala reveals molecular signatures of cocaine addiction
Zhou, J., de Guglielmo, G., Ho, A. J., Kallupi, M., Pokhrel, N. P., Li, H.-R., Chitre, A. S., Munro, D. B., Mohammadi, P., Carrette, L. L. G., et al. (2023) · Nature Neuroscience · doi:10.1038/s41593-023-01454-w · Cited by 33
Kuligowski, J., Pérez-Guaita, D., Lliso, I., Escobar, J., León, Z., Gombau, L., Solberg, R., Saugstad, O. D., Vento, M., & Quintás‐Cardama, A. (2014) · Talanta · doi:10.1016/j.talanta.2014.07.031 · Cited by 30
Soriano‐Tárraga, C., Jiménez-Conde, J., Steinhauer, E. G., Ois, Á., Rodríguez-Campello, A., Cuadrado‐Godia, E., Fernández‐Cadenas, I., Montaner, J., Lucas, G., Elosúa, R., et al. (2013) · PLoS ONE · doi:10.1371/journal.pone.0060750 · Cited by 30
Confounded by sequencing depth in association studies of rare alleles
Garner, C. P. (2011) · Genetic Epidemiology · doi:10.1002/gepi.20574 · Cited by 31
Extensive up-regulation of gene expression in cancer: the normalised use of microarray data
Wang, D., Cheng, L., Zhang, Y., Wu, R., Wang, M., Gu, Y., Zhao, W., Li, P., Li, B., Zhang, Y., et al. (2012) · Molecular BioSystems · doi:10.1039/c2mb05466c · Cited by 30
Zhang, S., Chen, Y., He, X., Du, J., Zhang, R., Ma, Y., Hu, X., Zhang, Z., Chen, Q., & Wan, X. (2020) · Journal of Agricultural and Food Chemistry · doi:10.1021/acs.jafc.9b06730 · Cited by 30
The practical effect of batch on genomic prediction
Parker, H. S., & Leek, J. T. (2012) · Statistical Applications in Genetics and Molecular Biology · doi:10.1515/1544-6115.1766 · Cited by 34
Self-citationWang, H., Cai, H., Ao, L., Yan, H., Zhao, W., Qi, L., Gu, Y., & Guo, Z. (2015) · Briefings in Bioinformatics · doi:10.1093/bib/bbv030 · Cited by 30
Komljenović, A., Roux, J., Wollbrett, J., Robinson‐Rechavi, M., & Bastian, F. (2018) · F1000Research · doi:10.12688/f1000research.9973.2 · Cited by 30
Stanley, P. D., Ng’oma, E., O’Day, S., & King, E. G. (2017) · Genetics · doi:10.1534/genetics.116.197780 · Cited by 30
Comparison of pre-processing methods for Infinium HumanMethylation450 BeadChip array
Shiah, Y.-J., Fraser, M. E., Bristow, R. G., & Boutros, P. C. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx372 · Cited by 30
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Michels, K. B., & Binder, A. M. (2017) · Methods in molecular biology · doi:10.1007/978-1-4939-7481-8_2 · Cited by 28
The effect of tissue composition on gene co-expression
Zhang, Y., Cuerdo, J., Halushka, M. K., & McCall, M. N. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bbz135 · Cited by 28
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Ibing, S., Michels, B. E., Mosdzien, M., Meyer, H. R., Feuerbach, L., & Körner, C. (2021) · NAR Cancer · doi:10.1093/narcan/zcab007 · Cited by 25
Making the most of high‐dimensional cytometry data
Marsh‐Wakefield, F., Mitchell, A. J., Norton, S. E., Ashhurst, T. M., Leman, J. K. H., Roberts, J., Harte, J., McGuire, H. M., & Kemp, R. A. (2021) · Immunology and Cell Biology · doi:10.1111/imcb.12456 · Cited by 29
Li, T., Zhang, Y., Patil, P., & Johnson, W. E. (2021) · Biostatistics · doi:10.1093/biostatistics/kxab039 · Cited by 30
Self-citationMeta-analysis of epigenome-wide association studies of major depressive disorder
Li, Q. S., Morrison, R. L., Turecki, G., & Drevets, W. C. (2022) · Scientific Reports · doi:10.1038/s41598-022-22744-6 · Cited by 37
Dancik, G. M., Ru, Y., Owens, C. R., & Theodorescu, D. (2011) · Cancer Research · doi:10.1158/0008-5472.can-11-2427 · Cited by 29
Measuring epigenetics as the mediator of gene/environment interactions in DOHaD
Ong, M., Lin, X., & Holbrook, J. D. (2014) · Journal of Developmental Origins of Health and Disease · doi:10.1017/s2040174414000506 · Cited by 30
Banks, C. A. S., Thornton, J. L., Kempf, C. G., Adams, M. K., Miah, S., Boanca, G., Liu, X., Katt, M. L., Parmely, T. J., Florens, L. A., et al. (2018) · Molecular & Cellular Proteomics · doi:10.1074/mcp.tir118.000661 · Cited by 29
Zhu, H., Li, G., & Lock, E. F. (2018) · Biostatistics · doi:10.1093/biostatistics/kxy052 · Cited by 29
Wang, K., Song, K., Ma, Z., Yao, Y., Liu, C., Yang, J., Xiao, H., Zhang, J., Zhang, Y., & Zhao, W. (2020) · British Journal of Cancer · doi:10.1038/s41416-020-0902-y · Cited by 30
Gallo, V., Egger, M., McCormack, V., Farmer, P. B., Ioannidis, J. P. A., Kirsch‐Volders, M., Matullo, G., Phillips, D. H., Schoket, B., Strömberg, U., et al. (2011) · Journal of Epidemiology & Community Health · doi:10.1136/jech-2011-200318 · Cited by 29
Variation-preserving normalization unveils blind spots in gene expression profiling
Roca, C. P., Gomes, S. I. L., Amorim, M. J. B., & Scott-Fordsmand, J. J. (2017) · Scientific Reports · doi:10.1038/srep42460 · Cited by 30
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Segni, A. D., Braun, T., BenShoshan, M., Barhom, S. F., Saar, E. G., Cesarkas, K., Squires, J. E., Keller, N., & Haberman, Y. (2018) · Journal of Visualized Experiments · doi:10.3791/56845 · Cited by 53
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Espín-Pérez, A., Portier, C. J., Chadeau‐Hyam, M., van Veldhoven, K., Kleinjans, J. C. S., & de Kok, T. M. C. M. (2018) · PLoS ONE · doi:10.1371/journal.pone.0202947 · Cited by 28
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Lee, Y.-S., Krishnan, A., Oughtred, R., Rust, J., Chang, C., Ryu, J., Kristensen, V. N., Dolinski, K. J., Theesfeld, C. L., & Troyanskaya, O. G. (2019) · Cell Systems · doi:10.1016/j.cels.2018.12.010 · Cited by 28
McKennan, C. G., & Nicolae, D. L. (2019) · Biometrika · doi:10.1093/biomet/asz037 · Cited by 29
MetaOmGraph: a workbench for interactive exploratory data analysis of large expression datasets
Singh, U., Hur, M., Dorman, K. S., & Wurtele, E. S. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz1209 · Cited by 26
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Miao, W., Hu, W., Ogburn, E. L., & Zhou, X. (2022) · Journal of the American Statistical Association · doi:10.1080/01621459.2021.2023551 · Cited by 35
Zhao, N., Ruan, M., Koestler, D. C., Lu, J., Marsit, C. J., Kelsey, K. T., Platz, E. A., & Michaud, D. S. (2021) · Epigenetics · doi:10.1080/15592294.2021.1923615 · Cited by 28
A pan-cancer survey of cell line tumor similarity by feature-weighted molecular profiles
Sinha, R., Luna, A., Schultz, N. D., & Sander, C. (2021) · Cell Reports Methods · doi:10.1016/j.crmeth.2021.100039 · Cited by 28
Flimma: a federated and privacy-aware tool for differential gene expression analysis
Zolotareva, O. I., Nasirigerdeh, R., Matschinske, J., Torkzadehmahani, R., Bakhtiari, M., Frisch, T., Späth, J., Blumenthal, D. B., Abbasinejad, A., Tieri, P., et al. (2021) · Genome biology · doi:10.1186/s13059-021-02553-2 · Cited by 31
Extent, impact, and mitigation of batch effects in tumor biomarker studies using tissue microarrays
Stopsack, K. H., Tyekucheva, S., Wang, M., Gerke, T. A., Vaselkiv, J. B., Penney, K. L., Kantoff, P. W., Finn, S. P., Fiorentino, M., Loda, M. F., et al. (2021) · eLife · doi:10.7554/elife.71265 · Cited by 28
Brown, R. F., Sala, O. E., Sinsabaugh, R. L., & Collins, S. L. (2022) · Journal of Geophysical Research Biogeosciences · doi:10.1029/2022jg006938 · Cited by 31
ResPAN: a powerful batch correction model for scRNA-seq data through residual adversarial networks
Wang, Y., Liu, T., & Zhao, H. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac427 · Cited by 25
Rotation‐based multiple testing in the multivariate linear model
Solari, A., Finos, L., & Goeman, J. J. (2014) · Biometrics · doi:10.1111/biom.12238 · Cited by 28
GFS: fuzzy preprocessing for effective gene expression analysis
Belorkar, A., & Wong, L. (2016) · BMC Bioinformatics · doi:10.1186/s12859-016-1327-8 · Cited by 26
Statistical contributions to bioinformatics: Design, modelling, structure learning and integration
Morris, J. S., & Baladandayuthapani, V. (2017) · Statistical Modelling · doi:10.1177/1471082x17698255 · Cited by 30
Cai, H., Li, X., Li, J., Liang, Q., Zheng, W., Guan, Q., Guo, Z., & Wang, X. (2018) · International Journal of Biological Sciences · doi:10.7150/ijbs.24548 · Cited by 28
Lu, X., Vano, Y. A., Su, X., Helleux, A., Lindner, V., Mouawad, R., Spano, J., Morgan Rouprêt, A., Compérat, É., Verkarre, V., et al. (2023) · Cell Reports Medicine · doi:10.1016/j.xcrm.2023.101287 · Cited by 31
Shao, Y., Lv, X.-Y., Ying, S., & Guo, Q. (2024) · Frontiers in Bioscience-Landmark · doi:10.31083/j.fbl2912404 · Cited by 30
Gut microbiota and tuberculosis
Liu, Y., Yang, L., Meskini, M., Goel, A., Opperman, M., Shyamal, S. S., Manaithiya, A., Xiao, M., Ni, R., An, Y., et al. (2025) · iMeta · doi:10.1002/imt2.70054 · Cited by 36
Hormetic effect of rotenone in primary human fibroblasts
Marthandan, S., Priebe, S., Groth, M., Guthke, R., Platzer, M., Hemmerich, P. H., & Diekmann, S. (2015) · Immunity & Ageing · doi:10.1186/s12979-015-0038-8 · Cited by 26
Distinct Functional Patterns of Gene Promoter Hypomethylation and Hypermethylation in Cancer Genomes
Shen, X., He, Z., Li, H., Yao, C., Zhang, Y., He, L. C., Li, S., Huang, J. H., & Guo, Z. (2012) · PLoS ONE · doi:10.1371/journal.pone.0044822 · Cited by 27
An update on epigenetics and childhood respiratory diseases
Gruzieva, O., Merid, S. K., & Melén, E. (2014) · Paediatric Respiratory Reviews · doi:10.1016/j.prrv.2014.07.003 · Cited by 27
Identification of differentially methylated loci using wavelet-based functional mixed models
Lee, W., & Morris, J. S. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv659 · Cited by 27
SMART: Statistical Metabolomics Analysis—An R Tool
Liang, Y.-J., Lin, Y., Chen, C., Lin, C., Chao, K., Pan, W., & Yang, H. (2016) · Analytical Chemistry · doi:10.1021/acs.analchem.6b00603 · Cited by 27
Ten simple rules for providing effective bioinformatics research support
Kumuthini, J., Chimenti, M. S., Nahnsen, S., Peltzer, A., Meraba, R. L., McFadyen, R., Wells, G., Taylor, D. M., Maienschein‐Cline, M., Li, J., et al. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007531 · Cited by 27
Zhao, H., Gu, S., Bao, S., Yan, C., Zhang, Z., Hou, P. K., Zhou, M., & Sun, J. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa069 · Cited by 27
Lemas, D. J., Loop, M. S., Duong, M., Schleffer, A., Collins, C., Bowden, J. A., Du, X., Patel, K., Ciesielski, A. L., Ridge, Z., et al. (2020) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2020.143963 · Cited by 27
Moossavi, S., Fehr, K. B., Khafipour, E., & Azad, M. B. (2021) · Microbiome · doi:10.1186/s40168-020-00998-4 · Cited by 27
Shen, S., Wang, X., Zhu, X., Rasam, S. S., Ma, M., Huo, S., Qian, S., Zhang, M., Qu, M., Hu, C., et al. (2022) · Nature Protocols · doi:10.1038/s41596-022-00780-w · Cited by 26
Storey, A. J., Wang, H.-P., Protacio, R. U., Davidson, M. K., Tackett, A. J., & Wahls, W. P. (2018) · Epigenetics & Chromatin · doi:10.1186/s13072-018-0233-x · Cited by 26
Kim, J., Afshari, A., Sengupta, R., Sebastiano, V., Gupta, A. A., Kim, Y. H., Reproducibility Project: Cancer Biology, Iorns, E. J., Tsui, R., Denis, A., et al. (2018) · eLife · doi:10.7554/elife.39944 · Cited by 27
Extensive increase of microarray signals in cancers calls for novel normalization assumptions
Wang, D., Cheng, L., Wang, M., Wu, R., Li, P., Li, B., Zhang, Y., Gu, Y., Zhao, W., Wang, C., et al. (2011) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2011.04.006 · Cited by 27
Auerbach, B. J., Hu, J., Reilly, M. P., & Li, M. (2021) · Genome Research · doi:10.1101/gr.275430.121 · Cited by 28
Papiez, A., Marczyk, M., Polanska, J., & Polanski, A. (2019) · Bioinformatics · doi:10.1093/bioinformatics/bty900 · Cited by 26
Increased Methylation Variation in Epigenetic Domains across Cancer Types
Bravo, H. C. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-29627-7_4 · Cited by 26
Self-citationKazemian, M., Ren, M., Lin, J., Liao, W., Spolski, R. J., & Leonard, W. J. (2015) · Journal of Virology · doi:10.1128/jvi.00822-15 · Cited by 27
In Pursuit of Greater Reproducibility and Credibility of Early Clinical Biomarker Research
McShane, L. (2017) · Clinical and Translational Science · doi:10.1111/cts.12449 · Cited by 28
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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