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
Cited by
Sorted by citations
Items 201–300 of 1,004
Host-dependent nitrogen recycling as a mechanism of symbiont control in Aiptasia
Cui, G., Liew, Y. J., Li, Y., Kharbatia, N. M., Zahran, N., Emwas, A.-H. M., Eguı́luz, V. M., & Aranda, M. (2019) · PLoS Genetics · doi:10.1371/journal.pgen.1008189 · Cited by 122
From bench to bedside: Single-cell analysis for cancer immunotherapy
Davis-Marcisak, E. F., Deshpande, A. M., Stein-O’Brien, G., Ho, W. J., Laheru, D. A., Jaffee, E. M., Fertig, E. J., & Kagohara, L. T. (2021) · Cancer Cell · doi:10.1016/j.ccell.2021.07.004 · Cited by 126
Using the R Package crlmm for Genotyping and Copy Number Estimation.
Scharpf, R. B., Irizarry, R. A., Ritchie, M. E., Carvalho, B. S., & Ruczinski, I. (2011) · PubMed · Cited by 119
Self-citationConfounder adjustment in multiple hypothesis testing
Wang, J., Zhao, Q., Hastie, T. J., & Owen, A. B. (2017) · The Annals of Statistics · doi:10.1214/16-aos1511 · Cited by 117
Zhuang, J. J., Widschwendter, M., & Teschendorff, A. E. (2012) · BMC Bioinformatics · doi:10.1186/1471-2105-13-59 · Cited by 123
Peixoto, L., Risso, D., Poplawski, S. G., Wimmer, M. E., Speed, T. P., Wood, M. A., & Abel, T. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv736 · Cited by 105
Qi, L., Chen, L., Li, Y., Qin, Y., Pan, R., Zhao, W., Gu, Y., Wang, H., Wang, R., Chen, X., et al. (2015) · Briefings in Bioinformatics · doi:10.1093/bib/bbv064 · Cited by 118
Crompton, J. G., Narayanan, M., Cuddapah, S., Roychoudhuri, R., Ji, Y., Yang, W., Patel, S. J., Sukumar, M., Palmer, D. C., Peng, W., et al. (2015) · Cellular and Molecular Immunology · doi:10.1038/cmi.2015.32 · Cited by 123
Multi-perspective quality control of Illumina RNA sequencing data analysis
Sheng, Q., Vickers, K. C., Zhao, S., Wang, J., Samuels, D. C., Koues, O. I., Shyr, Y., & Guo, Y. P. (2016) · Briefings in Functional Genomics · doi:10.1093/bfgp/elw035 · Cited by 123
Warnat-Herresthal, S., Perrakis, K., Taschler, B., Becker, M., Baßler, K., Beyer, M., Günther, P., Schulte-Schrepping, J., Seep, L., Klee, K., et al. (2020) · iScience · doi:10.1016/j.isci.2019.100780 · Cited by 117
on behalf of the GUSTO study group, Lin, X., Lim, I. Y., Wu, Y., Teh, A. L., Chen, L., Aris, I. M., Soh, S., Tint, M. T., MacIsaac, J. L., et al. (2017) · BMC Medicine · doi:10.1186/s12916-017-0800-1 · Cited by 120
Shinohara, R. T., Oh, J., Nair, G., Calabresi, P. A., Davatzikos, C. A., Doshi, J. J., Henry, R. G., Kim, G., Linn, K. A., Papinutto, N., et al. (2017) · American Journal of Neuroradiology · doi:10.3174/ajnr.a5254 · Cited by 116
Ghiassian, S., Greiner, R., Jin, P., & Brown, M. R. G. (2016) · PLOS ONE · doi:10.1371/journal.pone.0166934 · Cited by 116
UMI-count modeling and differential expression analysis for single-cell RNA sequencing
Chen, W., Li, Y., Easton, J., Finkelstein, D., Wu, G., & Chen, X. (2018) · Genome Biology · doi:10.1186/s13059-018-1438-9 · Cited by 113
Adjusting for Batch Effects in DNA Methylation Microarray Data, a Lesson Learned
Price, E. M., & Robinson, W. P. (2018) · Frontiers in Genetics · doi:10.3389/fgene.2018.00083 · Cited by 117
Impact of the gut microbiota on the m6A epitranscriptome of mouse cecum and liver
Jabs, S., Biton, A., Bécavin, C., Nahori, M., Ghozlane, A., Pagliuso, A., Spanò, G., Guérineau, V., Touboul, D., Gianetto, Q. G., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-15126-x · Cited by 120
Cytokine Patterns in Maternal Serum From First Trimester to Term and Beyond
Jarmund, A. H., Giskeødegård, G. F., Ryssdal, M., Steinkjer, B., Stokkeland, L. M. T., Madssen, T. S., Stafne, S. N., Stridsklev, S., Moholdt, T. T., Heimstad, R., et al. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.752660 · Cited by 126
Epigenetics in eating disorders: a systematic review
Hübel, C., Marzi, S. J., Breen, G., & Bulik, C. M. (2019) · Molecular Psychiatry · doi:10.1038/s41380-018-0254-7 · Cited by 116
The Impact of Normalization Methods on RNA-Seq Data Analysis
Zyprych-Walczak, J., Szabelska‐Beręsewicz, A., Handschuh, L., Górczak, K., Klamecka, K., Figlerowicz, M., & Siatkowski, I. (2015) · BioMed Research International · doi:10.1155/2015/621690 · Cited by 125
Bandyopadhyay, S., Mallik, S., & Mukhopadhyay, A. (2014) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2013.147 · Cited by 112
Yu, Y., Zhang, N., Mai, Y., Ren, L., Chen, Q., Cao, Z., Chen, Q., Liu, Y., Hou, W., Yang, J., et al. (2023) · Genome biology · doi:10.1186/s13059-023-03047-z · Cited by 123
Pathway-level information extractor (PLIER) for gene expression data
Mao, W., Zaslavsky, E., Hartmann, B., Sealfon, S. C., & Chikina, M. D. (2019) · Nature Methods · doi:10.1038/s41592-019-0456-1 · Cited by 107
Epigenetic epidemiology: Promises for public health research
Bakulski, K. M., & Fallin, M. D. (2014) · Environmental and Molecular Mutagenesis · doi:10.1002/em.21850 · Cited by 113
quantro: a data-driven approach to guide the choice of an appropriate normalization method
Hicks, S. C., & Irizarry, R. A. (2015) · Genome biology · doi:10.1186/s13059-015-0679-0 · Cited by 117
Self-citationGenerally applicable transcriptome-wide analysis of translation using anota2seq
Oertlin, C., Lorent, J., Murie, C., Furic, L., Topisirovic, I., & Larsson, O. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz223 · Cited by 107
Metagenomic mining for microbiologists
Delmont, T. O., Malandain, C., Prestat, E., Larose, C., Monier, J.-M., Simonet, P., & Vogel, T. M. (2011) · The ISME Journal · doi:10.1038/ismej.2011.61 · Cited by 109
Liu, Y., Li, X., Aryee, M. J., Ekström, T. J., Padyukov, L., Klareskog, L., Vandiver, A. R., Moore, A. Z., Tanaka, T., Ferrucci, L., et al. (2014) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2014.02.011 · Cited by 107
Peng, L. J., Bian, X. W., Li, D. K., Xu, C. S., Wang, G., Xia, Q. Y., & Xiong, Q. (2015) · Scientific Reports · doi:10.1038/srep13413 · Cited by 116
Kong, Y., Xu, C., Cui, C., Yang, W., Yang, S., Chi, Z., Sheng, X., Si, L., Xie, Y., Yu, R., et al. (2021) · npj Genomic Medicine · doi:10.1038/s41525-021-00169-w · Cited by 119
Systems biology approaches to new vaccine development
Oberg, A. L., Kennedy, R. B., Li, P., Ovsyannikova, I. G., & Poland, G. A. (2011) · Current Opinion in Immunology · doi:10.1016/j.coi.2011.04.005 · Cited by 111
Alternative empirical Bayes models for adjusting for batch effects in genomic studies
Zhang, Y., Jenkins, D. F., Manimaran, S., & Johnson, W. E. (2018) · BMC Bioinformatics · doi:10.1186/s12859-018-2263-6 · Cited by 107
Self-citationmiQC: An adaptive probabilistic framework for quality control of single-cell RNA-sequencing data
Hippen, A. A., Falco, M. M., Weber, L. M., Erkan, E. P., Zhang, K., Doherty, J. A., Vähärautio, A., Greene, C. S., & Hicks, S. C. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009290 · Cited by 114
Multi-omics strategies for biomarker discovery and application in personalized oncology
Jiang, Z., Zhang, H., Gao, Y., & Sun, Y. (2025) · Molecular Biomedicine · doi:10.1186/s43556-025-00340-0 · Cited by 143
Batch effect correction for genome-wide methylation data with Illumina Infinium platform
Sun, Z., Chai, H. S., Wu, Y., White, W. M., Donkena, K. V., Klein, C. J., Garovic, V. D., Therneau, T. M., & Kocher, J.-P. A. (2011) · BMC Medical Genomics · doi:10.1186/1755-8794-4-84 · Cited by 108
shinyMethyl: interactive quality control of Illumina 450k DNA methylation arrays in R
Fortin, J.-P., Fertig, E. J., & Hansen, K. D. (2014) · F1000Research · doi:10.12688/f1000research.4680.2 · Cited by 108
Rahnenführer, J., Bin, R. D., Benner, A., Ambrogi, F., Lusa, L., Boulesteix, A., Migliavacca, E., Binder, H., Michiels, S., Sauerbrei, W., et al. (2023) · BMC Medicine · doi:10.1186/s12916-023-02858-y · Cited by 132
Minimizing Batch Effects in Mass Cytometry Data
Schuyler, R. P., Jackson, C. L., Garcia‐Perez, J. E., Baxter, R. M., Ogolla, S., Rochford, R. A., Ghosh, D., Rudra, P., & Hsieh, E. W. Y. (2019) · Frontiers in Immunology · doi:10.3389/fimmu.2019.02367 · Cited by 110
Ly, C. O., Unnikrishnan, B., Tadic, T., Patel, T., Duhamel, J., Kandel, S., Moayedi, Y., Brudno, M., Hope, A. J., Ross, H. J., et al. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01118-4 · Cited by 123
Graphical Models via Univariate Exponential Family Distributions.
Yang, E., Ravikumar, P., Allen, G. I., & Liu, Z. (2015) · PubMed · Cited by 105
Immune cell profiling in cancer: molecular approaches to cell-specific identification
Lyons, Y. A., Wu, S. Y., Overwijk, W. W., Baggerly, K., & Sood, A. K. (2017) · npj Precision Oncology · doi:10.1038/s41698-017-0031-0 · Cited by 106
Self-citationBuschmann, D., Haberberger, A., Kirchner, B., Spornraft, M., Riedmaier, I., Schelling, G., & Pfaffl, M. W. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw545 · Cited by 102
Wang, S., Kuo, C., & Tseng, Y. J. (2012) · Analytical Chemistry · doi:10.1021/ac302877x · Cited by 101
Addressing confounding artifacts in reconstruction of gene co-expression networks
Parsana, P., Ruberman, C., Jaffe, A. E., Schatz, M. C., Battle, A. J., & Leek, J. T. (2019) · Genome biology · doi:10.1186/s13059-019-1700-9 · Cited by 103
Self-citationIdentifying and removing the cell-cycle effect from single-cell RNA-Sequencing data
Barron, M., & Li, J. (2016) · Scientific Reports · doi:10.1038/srep33892 · Cited by 105
Okano, M., Oshi, M., Butash, A. L., Katsuta, E., Tachibana, K., Saito, K., Okayama, H., Peng, X., Yan, L., Kono, K., et al. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20174197 · Cited by 101
Transcriptional effects of copy number alterations in a large set of human cancers
Bhattacharya, A., Bense, R. D., Urzúa-Traslaviña, C. G., de Vries, E. G. E., van Vugt, M. A. T. M., & Fehrmann, R. S. N. (2020) · Nature Communications · doi:10.1038/s41467-020-14605-5 · Cited by 102
Benchmarking clustering, alignment, and integration methods for spatial transcriptomics
Hu, Y., Li, Y., Xie, M., Rao, M., Shen, W., Luo, C., Qin, H., Baek, J., & Zhou, X. (2024) · Genome biology · doi:10.1186/s13059-024-03361-0 · Cited by 106
Age and gender effects on DNA strand break repair in peripheral blood mononuclear cells
Garm, C., Moreno‐Villanueva, M., Bürkle, A., Petersen, I., Bohr, V. A., Christensen, K., & Stevnsner, T. (2012) · Aging Cell · doi:10.1111/acel.12019 · Cited by 105
Conserved senescence associated genes and pathways in primary human fibroblasts detected by RNA-seq
Marthandan, S., Baumgart, M., Priebe, S., Groth, M., Schaer, J., Kaether, C., Guthke, R., Cellerino, A., Platzer, M., Diekmann, S., et al. (2016) · PLOS ONE · doi:10.1371/journal.pone.0154531 · Cited by 91
“Sample clustering using multidimensional scaling (MDS) plots indicated that the expression of one late PD IMR-90 fibroblast sample (one of the three vials) showed increased variance (probably due to batch effects [80]) compared to the other two vials (S3 Fig); this outlying sample was removed from further analysis [81…”
ResultDevelopment and validation of an immune-related gene pairs signature in colorectal cancer
Wu, J., Zhao, Y., Zhang, J., Wu, Q., & Wang, W. (2019) · OncoImmunology · doi:10.1080/2162402x.2019.1596715 · Cited by 101
Enhancing Reproducibility in Cancer Drug Screening: How Do We Move Forward?
Hatzis, C., Bedard, P. L., Birkbak, N. J., Beck, A. H., Aerts, H. J. W. L., Stern, D. F., Shi, L. M., Clarke, R. C., Quackenbush, J. F., & Haibe‐Kains, B. (2014) · Cancer Research · doi:10.1158/0008-5472.can-14-0725 · Cited by 96
Determining Microbial Products and Identifying Molecular Targets in the Human Microbiome
Cordy, R. J., Yasuda, K., Shafquat, A., Morgan, X. C., & Huttenhower, C. (2014) · Cell Metabolism · doi:10.1016/j.cmet.2014.10.003 · Cited by 102
Removing batch effects from purified plasma cell gene expression microarrays with modified ComBat
Stein, C. K., Qu, P., Epstein, J., Buros, A. F., Rosenthal, A., Crowley, J. J., Morgan, G. J., & Barlogie, B. (2015) · BMC Bioinformatics · doi:10.1186/s12859-015-0478-3 · Cited by 107
Significance analysis and statistical dissection of variably methylated regions
Jaffe, A. E., Feinberg, A. P., Irizarry, R. A., & Leek, J. T. (2011) · Biostatistics · doi:10.1093/biostatistics/kxr013 · Cited by 100
Self-citationEvaluating and minimizing batch effects in metabolomics
Han, W., & Li, L. (2022) · Mass Spectrometry Reviews · doi:10.1002/mas.21672 · Cited by 105
Xiong, L., Tian, K., Li, Y., Ning, W., Gao, X., & Zhang, Q. C. (2022) · Nature Communications · doi:10.1038/s41467-022-33758-z · Cited by 106
Overcoming bias and systematic errors in next generation sequencing data
Taub, M. A., Bravo, H. C., & Irizarry, R. A. (2010) · Genome Medicine · doi:10.1186/gm208 · Cited by 104
Self-citationReview of software tools for design and analysis of large scale MRM proteomic datasets
Colangelo, C. M., Chung, L. M., Bruce, C., & Cheung, K. (2013) · Methods · doi:10.1016/j.ymeth.2013.05.004 · Cited by 99
Machine learning-integrated omics for the risk and safety assessment of nanomaterials
Ahmad, F., Mahmood, A., & Muhmood, T. (2020) · Biomaterials Science · doi:10.1039/d0bm01672a · Cited by 102
Multiplatform single-sample estimates of transcriptional activation
Piccolo, S., Withers, M. R., Francis, O. E., Bild, A. H., & Johnson, W. E. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1305823110 · Cited by 97
Self-citationFernandes, M. C., Dillon, L. A. L., Belew, A. T., Bravo, H. C., Mosser, D. M., & El-Sayed, N. M. (2016) · mBio · doi:10.1128/mbio.00027-16 · Cited by 104
Self-citationLaursen, M. F., Dalgaard, M., & Bahl, M. I. (2017) · Frontiers in Microbiology · doi:10.3389/fmicb.2017.01934 · Cited by 96
Design and application of single-cell RNA sequencing to study kidney immune cells in lupus nephritis
Rao, D. A., Arazi, A., Wofsy, D., & Diamond, B. (2019) · Nature Reviews Nephrology · doi:10.1038/s41581-019-0232-6 · Cited by 96
Fast and robust adjustment of cell mixtures in epigenome-wide association studies with SmartSVA
Chen, J., Behnam, E., Huang, J., Moffatt, M. F., Schaid, D. J., Liang, L., & Lin, X. (2017) · BMC Genomics · doi:10.1186/s12864-017-3808-1 · Cited by 92
Cross-study validation for the assessment of prediction algorithms
Bernau, C., Riester, M., Boulesteix, A., Parmigiani, G., Huttenhower, C., Waldron, L., & Trippa, L. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu279 · Cited by 94
Fredlund, E., Staaf, J., Rantala, J., Kallioniemi, O., Borg, Å., & Ringnér, M. (2012) · Breast Cancer Research · doi:10.1186/bcr3236 · Cited by 93
Oytam, Y., Sobhanmanesh, F., Duesing, K., Bowden, J. C., Osmond-McLeod, M. J., & Ross, J. P. (2016) · BMC Bioinformatics · doi:10.1186/s12859-016-1212-5 · Cited by 94
Future Direction for Using Artificial Intelligence to Predict and Manage Hypertension
Krittanawong, C., Bomback, A. S., Baber, U., Bangalore, S., Messerli, F. H., & Tang, W. H. W. (2018) · Current Hypertension Reports · doi:10.1007/s11906-018-0875-x · Cited by 96
Poli‐Neto, O. B., Meola, J., Silva, J. C. R. E., & Tiezzi, D. G. (2020) · Scientific Reports · doi:10.1038/s41598-019-57207-y · Cited by 102
Proteomic Biomarker Discovery in 1000 Human Plasma Samples with Mass Spectrometry
Cominetti, O., Galindo, A. N., Corthésy, J., Moreno, S. O., Irincheeva, I., Valsesia, A., Astrup, A. V., Saris, W. H. M., Hager, J., Kussmann, M., et al. (2015) · Journal of Proteome Research · doi:10.1021/acs.jproteome.5b00901 · Cited by 94
Statistical Inference Enables Bad Science; Statistical Thinking Enables Good Science
Tong, C. (2019) · The American Statistician · doi:10.1080/00031305.2018.1518264 · Cited by 103
Maksimovic, J., Gagnon-Bartsch, J. A., Speed, T. P., & Oshlack, A. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv526 · Cited by 90
Tucker, S. L., Gharpure, K. M., Herbrich, S. M., Unruh, A. K., Nick, A. M., Crane, E. K., Coleman, R. L., Guenthoer, J., Dalton, H. J., Wu, S. Y., et al. (2014) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-14-0445 · Cited by 91
Self-citationStatistical analysis in metabolic phenotyping
Blaise, B. J., Correia, G. D. S., Haggart, G. A., Surowiec, I., Sands, C. J., Lewis, M. R., Pearce, J. T. M., Trygg, J., Nicholson, J. K., Holmes, E. C., et al. (2021) · Nature Protocols · doi:10.1038/s41596-021-00579-1 · Cited by 93
Illimoottil, M., & Ginat, D. T. (2023) · Cancers · doi:10.3390/cancers15133267 · Cited by 99
Predictors and Consequences of Global DNA Methylation in Cord Blood and at Three Years
Herbstman, J. B., Wang, S., Perera, F. P., Lederman, S. A., Vishnevetsky, J., Rundle, A., Hoepner, L., Qu, L., & Tang, D. L. (2013) · PLoS ONE · doi:10.1371/journal.pone.0072824 · Cited by 93
Evaluating batch correction methods for image-based cell profiling
Arevalo, J., Su, E., van Dijk, R., Carpenter, A. E., & Singh, S. (2023) · doi:10.1101/2023.09.15.558001 · Cited by 89
Dillon, L. A. L., Suresh, R., Okrah, K., Bravo, H. C., Mosser, D. M., & El-Sayed, N. M. (2015) · BMC Genomics · doi:10.1186/s12864-015-2237-2 · Cited by 88
Self-citationEvaluating batch correction methods for image-based cell profiling
Arévalo, J., Su, E., Ewald, J., van Dijk, R., Carpenter, A. E., & Singh, S. (2024) · Nature Communications · doi:10.1038/s41467-024-50613-5 · Cited by 100
A Local Poisson Graphical Model for Inferring Networks From Sequencing Data
Allen, G. I., & Liu, Z. (2013) · IEEE Transactions on NanoBioscience · doi:10.1109/tnb.2013.2263838 · Cited by 88
Zindler, T., Frieling, H., Neyazi, A., Bleich, S., & Friedel, E. (2020) · BMC Bioinformatics · doi:10.1186/s12859-020-03559-6 · Cited by 92
Glycosyltransferase Gene Expression Profiles Classify Cancer Types and Propose Prognostic Subtypes
Ashkani, J., & Naidoo, K. J. (2016) · Scientific Reports · doi:10.1038/srep26451 · Cited by 89
Meta‐analysis of genome‐wide DNA methylation and integrative omics of age in human skeletal muscle
Voisin, S. A. C., Jacques, M., Landen, S., Harvey, N. R., Haupt, L. M., Griffiths, L. R., Gancheva, S., Ouni, M., Jähnert, M., Ashton, K. J., et al. (2021) · Journal of Cachexia Sarcopenia and Muscle · doi:10.1002/jcsm.12741 · Cited by 93
Removing unwanted variation from large-scale RNA sequencing data with PRPS
Molania, R., Foroutan, M., Gagnon-Bartsch, J. A., Gandolfo, L. C., Jain, A., Sinha, A., Olshansky, G., Dobrovic, A., Papenfuss, A. T., & Speed, T. P. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01440-w · Cited by 92
Bose, M., Wu, C., Pankow, J. S., Demerath, E. W., Bressler, J., Fornage, M., Grove, M. L., Mosley, T. H., Hicks, C., North, K., et al. (2014) · BMC Bioinformatics · doi:10.1186/1471-2105-15-312 · Cited by 86
Leder, E. H., McCairns, R. J. S., Leinonen, T., Cano, J. M., Viitaniemi, H. M., Nikinmaa, M., Primmer, C. R., & Merilä, J. (2014) · Molecular Biology and Evolution · doi:10.1093/molbev/msu328 · Cited by 85
DNA methylation in human sperm: a systematic review
Åsenius, F., Danson, A. F., & Marzi, S. J. (2020) · Human Reproduction Update · doi:10.1093/humupd/dmaa025 · Cited by 86
Dugué, P., English, D. R., MacInnis, R. J., Jung, C., Bassett, J. K., FitzGerald, L. M., Wong, E. M., Joo, J. E., Hopper, J. L., Southey, M. C., et al. (2016) · Scientific Reports · doi:10.1038/srep30317 · Cited by 83
Stem cells: Aging and transcriptional fingerprints
Keyes, B. E., & Fuchs, E. V. (2017) · The Journal of Cell Biology · doi:10.1083/jcb.201708099 · Cited by 84
Detecting copy number variation in next generation sequencing data from diagnostic gene panels
Singh, A., Olsen, M. F., Lavik, L. A. S., Vold, T., Drabløs, F., & Sjursen, W. (2021) · BMC Medical Genomics · doi:10.1186/s12920-021-01059-x · Cited by 85
SIMBA: single-cell embedding along with features
Chen, H., Ryu, J., Vinyard, M. E., Lerer, A. K., & Pinello, L. (2023) · Nature Methods · doi:10.1038/s41592-023-01899-8 · Cited by 83
Dynamic linear models guide design and analysis of microbiota studies within artificial human guts
Silverman, J. D., Durand, H. K., Bloom, R. J., Mukherjee, S., & David, L. A. (2018) · Microbiome · doi:10.1186/s40168-018-0584-3 · Cited by 84
Baron, D., Ramstein, G., Chesneau, M., Echasseriau, Y., Pallier, A., Paul, C., Degauque, N., Hernández-Fuentes, M. P., Fueyo, A. S., Newell, K. A., et al. (2015) · Kidney International · doi:10.1038/ki.2014.395 · Cited by 84
Removing batch effects for prediction problems with frozen surrogate variable analysis
Parker, H. S., Bravo, H. C., & Leek, J. T. (2014) · PeerJ · doi:10.7717/peerj.561 · Cited by 85
Self-citationLempiäinen, H., Müller, A., Brasa, S., Teo, S.-S., Roloff, T. C., Morawiec, L., Zamurovic, N., Vicart, A., Funhoff, E. G., Couttet, P., et al. (2011) · PLoS ONE · doi:10.1371/journal.pone.0018216 · Cited by 81
Mega‐analysis methods in ENIGMA : The experience of the generalized anxiety disorder working group
Zugman, A., Harrewijn, A., Cardinale, E. M., Zwiebel, H., Freitag, G. F., Werwath, K. E., Bas‐Hoogendam, J. M., Groenewold, N. A., Aghajani, M., Hilbert, K., et al. (2020) · Human Brain Mapping · doi:10.1002/hbm.25096 · Cited by 83
Explainable AI: A review of applications to neuroimaging data
Farahani, F. V., Fiok, K., Lahijanian, B., Karwowski, W., & Douglas, P. K. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.906290 · Cited by 87
Sample processing obscures cancer-specific alterations in leukemic transcriptomes
Dvinge, H., Ries, R. E., Ilagan, J. O., Stirewalt, D. L., Meshinchi, S., & Bradley, R. K. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1413374111 · Cited by 80
Chu, S. H., Huang, M., Kelly, R. S., Benedetti, E., Siddiqui, J., Zeleznik, O. A., Pereira, A., Herrington, D. M., Wheelock, C. E., Krumsiek, J., et al. (2019) · Metabolites · doi:10.3390/metabo9060117 · Cited by 83
Integrative clustering methods of multi‐omics data for molecule‐based cancer classifications
Wang, D., & Gu, J. (2016) · Quantitative Biology · doi:10.1007/s40484-016-0063-4 · Cited by 81
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
